Teaching Predictive Model Management in MIS Classrooms: A Tutorial

Analytics has become a key element of the business decision process over the last decade. In today’s competitive business world, organizations have found out that their data and how they use it can make them much more competitive. According to many research institutions (e.g., Gartner and McKinsey), the worldwide market for business analytics solutions in practice, research, and education is growing exponentially. As the use of analytics become widespread, business school graduates need to gain the necessary knowledge and skill sets to use these assets effectively. In the spirit of analytical thinking, we developed a practice-oriented business case that uses a sample scenario, managerial dashboards, betting templates, model repository and model performance management metrics that teaches predictive analytics concepts and decision making with incomplete information intended for MIS courses. Through exercises and interactions, students gain the skills, knowledge and experience necessary to be become effective decision makers through applying analytical thinking. Digital copies of workshop lesson plans with dashboard and data entry templates can be downloaded free of charge from the Teradata University Network

I’m Going Mobile: Teaching Freshmen Business Students Mobile Application Development

IS enrollment has been declining in recent years. In an attempt to introduce key Information Systems concepts to freshmen business students in a fun and engaging way, we introduced a mobile application design project that recently allowed them to develop a live application on real smartphones. While going through this process, students learned some of the basic tenets of the discipline of Information Systems while simultaneously realizing the relevance and applicability to their future lives as 21st Century professionals. In this paper, we outline the core course progression for a typical Information Systems department, detail the process through which we engaged the students, and confirm our assertions through textual analysis of self-reported comments on their experience with this mobile application project

Going Mobile: Teaching First-Year Business Students Mobile Application Design

Information systems (IS) enrollment has been declining in recent years. In an attempt to introduce key IS concepts to freshmen business students in a more engaging way, we introduced a semester-long mobile application-design project and a separate tutorial assignment involving real smartphones. Through this process, students learned basic tenets of IS while simultaneously recognizing the relevance and applicability of the field to their future lives as 21st century business professionals. In this paper, we outline the core course progression for a typical IS department, detail the process through which we engaged the students, and confirm our assertions through textual analysis of self-reported comments about their experience with this mobile application project

PREDICTIVE MODEL MARKETS: DESIGN PRINCIPLES FOR MANAGING ENTERPRISE-LEVEL ADVANCED ANALYTICS

As advanced analytics penetrate a wide range of business applications, companies face the challenge of managing analytics-based assets, such as predictive models. Tasks ahead include model selection, scoring and deployment planning. One way to optimize model selection is to tap the combined knowledge of company staff through a “prediction market,” a virtual market designed to reveal participants’ aggregate wisdom by seeing where people “invest” their money. In the context of predictive-model selection, this paper refers to such devices as predictive-model markets. This paper examines design possibilities for building experimental markets that can ultimately be used to test whether predictive-model markets will improve model selection and deployment. The researchers test two types of incentives for participation: economic and social. Study results indicate that such markets can effectively work using either; a surprising finding is that social incentives did not improve effectiveness when added to economic incentives

Trends in U.S. Consumers’ Use of E-Health Services: Fine-Grained Results from a Longitudinal, Demographic Survey

Although growth in U.S. consumers’ overall use of e-health is strong, it is being driven by only a portion of the e-health services that are offered through online health portals. Fine-grained, longitudinal analysis of three representative e-health services shows that, while online communication with medical personnel has grown consistently between 2003 and 2012, the purchase of health supplies online plateaued by 2007, and participation in online support groups has been flat since 2003. Socio-economic factors of income and education level continue to have an impact on consumers’ use of e-health; however, differences based on age, sex, and race/ethnicity are trending lower during this period. The findings caution against the common practice of studying e-health adoption principally at the level of online health portals, which can mask substantial variation in adoption trends among the underlying e-health services, and suggest that it is important to update trend studies on a regular basis to maintain currency

Social insurance programs and compensating wage differentials in the United States

This dissertation brings together empirical analyses of the impact of social insurance programs on compensating wage differentials under different institutional frameworks. I study three periods: the late nineteenth century prior to the introduction of Unemployment Insurance, the Great Depression when Unemployment Insurance is introduced, and then the recent period, in which UI has been long established. Initially, late nineteenth century labor markets with no social programs for workers were investigated. Three different data sets were analyzed from two different states, Maine and Kansas, to examine the precautionary saving behavior of workers and the wage premium they received for the expected unemployment prevalent in their industry. Results showed that workers were receiving statistically and economically significant wage premiums in two of the three samples. Also, in two of the three samples, households were able to save against expected unemployment using family resources. In the second chapter, after reviewing the historical backgrounds of social insurance programs, namely Workers' Compensation, Compensation for Occupational Diseases, and Unemployment Insurance (UI), the empirical literature about the impacts of these programs on wages is reviewed. Later in the chapter, hours and earnings data for various manufacturing industries across forty-eight states for the years 1933-1939 are brought together with the state UI, Workers' Compensation, and Compensation for Occupational Diseases provisions to test the impact of these laws on wage rates. The economic history and origins of UI have not been elaborated before and no previous study has analyzed the simultaneous impacts of different social insurance programs. Results showed that higher accident rates, limited working hours and the higher regional cost of living had a positive impact on wages. Workers' Compensation continued to have a negative impact on wages. During its infancy, UI benefits did not have a statistically significant effect on wages. The last chapter analyzes the impact of UI and the unemployment rate for the labor market of the worker on wage rates using micro level modern data. Results from the analysis of the National Longitudinal Survey of Youth suggest that expected UI benefits have a negative and statistically significant impact on wages, holding worker and labor market characteristics constant. However, the unemployment rate of the labor market did not have a statistically significant impact on wages

Adaptive Advertisement Recommender Systems for Digital Signage

With the incorporation of new technologies, digital signages can adopt their content in real time to the audience demographic and temporal features. This research proposes an adaptive advertisement recommender system for digital signage. Our objective is to create a quantitative method for targeted advertising. After analyzing digital signage advertisement viewing data collected over the course of two months, our results show that learning-to-rank approach using Stochastic Gradient-Boosted Trees (SGBT) yields the best adaptive advertisement recommender system. Our system can identify the best sequence of advertisements to attract the most viewing. More importantly, we can use the same method for different business objectives like attracting the longest time of viewing or targeting a certain age groups or genders. _x000D_ _x000D_ _x000D

Current Trends in Patients’ Adoption of Advanced E-Health Services

This paper presents a fine-grained, longitudinal analysis of demographic factors contributing to adoption by patients of advanced e-health services in the areas of transaction, communication, and personal support. The research uses Health Information National Trends Survey (HINTS), conducted in 2003, 2005, and 2007 by the U.S. National Cancer Institute. The findings show that while use of advanced e-health services is increasing overall, adoption trends vary substantially by service and by patients’ demographic characteristics

Demographic Trends in Consumer E-Health Adoption: Analysis of NCI HINTS 2003 and 2005 National Surveys

It has become common for healthcare providers to offer e-health services to patients and other consumers. Experts suggest these services are desired by users, and this has been confirmed generally through empirical research. However, most empirical studies of e-health adoption have focused on demographically homogeneous populations and have been implemented through cross-sectional designs. This study applies data from two administrations of the Health Information National Trends Survey (HINTS) conducted by the U.S. National Cancer Institute to develop an analysis of adoption trends that crosses time (2003-2005) and also addresses effects of gender, age, socio-economic status, and race/ethnicity on e-health use. The analysis is further developed to distinguish differences in adoption of informational e-health services vs. transactional e-health services. Key findings of the analysis are that e-health use is increasing but usage is much higher for informational than for transactional uses. Informational e-health use is found to be significantly associated with gender, age, and race/ethnicity demographics. Transactional e-health use is significantly associated only with race/ethnicity and gender measures