BackgroundThe World Health Organization (WHO) reported that cardiovascular diseases (CVDs) are the leading cause of death worldwide. CVDs are chronic, with complex progression patterns involving episodes of comorbidities and multimorbidities. When dealing with chronic diseases, physicians often adopt a “watchful waiting” strategy, and actions are postponed until information is available. Population-level transition probabilities and progression patterns can be revealed by applying time-variant stochastic modeling methods to longitudinal patient data from cohort studies. Inputs from CVD practitioners indicate that tools to generate and visualize cohort transition patterns have many impactful clinical applications. The resultant computational model can be embedded in digital decision support tools for clinicians. However, to date, no study has attempted to accomplish this for CVDs. ObjectiveThis study aims to apply advanced stochastic modeling methods to uncover the transition probabilities and progression patterns from longitudinal episodic data of patient cohorts with CVD and thereafter use the computational model to build a digital clinical cohort analytics artifact demonstrating the actionability of such models. MethodsOur data were sourced from 9 epidemiological cohort studies by the National Heart Lung and Blood Institute and comprised chronological records of 1274 patients associated with 4839 CVD episodes across 16 years. We then used the continuous-time Markov chain method to develop our model, which offers a robust approach to time-variant transitions between disease states in chronic diseases. ResultsOur study presents time-variant transition probabilities of CVD state changes, revealing patterns of CVD progression against time. We found that the transition from myocardial infarction (MI) to stroke has the fastest transition rate (mean transition time 3, SD 0 days, because only 1 patient had a MI-to-stroke transition in the dataset), and the transition from MI to angina is the slowest (mean transition time 1457, SD 1449 days). Congestive heart failure is the most probable first episode (371/840, 44.2%), followed by stroke (216/840, 25.7%). The resultant artifact is actionable as it can act as an eHealth cohort analytics tool, helping physicians gain insights into treatment and intervention strategies. Through expert panel interviews and surveys, we found 9 application use cases of our model. ConclusionsPast research does not provide actionable cohort-level decision support tools based on a comprehensive, 10-state, continuous-time Markov chain model to unveil complex CVD progression patterns from real-world patient data and support clinical decision-making. This paper aims to address this crucial limitation. Our stochastic model–embedded artifact can help clinicians in efficient disease monitoring and intervention decisions, guided by objective data-driven insights from real patient data. Furthermore, the proposed model can unveil progression patterns of any chronic disease of interest by inputting only 3 data elements: a synthetic patient identifier, episode name, and episode time in days from a baseline date.

This paper investigates the alignment of the demand side of business analytics (based on advertised job positions) with the supply side (based on the university curricula of U.S. business analytics programs). We text-mined job advertisements and core and elective course descriptions to identify the competencies demanded by business analytics–related jobs and those being taught in graduate analytics programs that include a business component. A comparative analysis of the competencies reveals that, although some of the competencies required by the jobs are taught adequately at the universities, a few key concepts and topics are not covered at sufficient depth as needed by the jobs. The research also shows that many traditional analytics topics are being over-taught at the universities when compared with the demand, whereas some key soft skills are not addressed at a sufficient level in many programs. The study provides a basis for future comparison of data-science positions and programs with the jobs and programs in business analytics.

It is imperative to foster data acumen in our university student population in order to respond to an increased attention to statistics in society and in the workforce, as well as to contribute to improved career preparation for students. This article discusses 13 learning outcomes that represent achievement of undergraduate data acumen for university level students across different disciplines.

The explosive growth of business analytics has created a high demand for individuals who can help organizations gain competitive advantage by extracting business knowledge from data. What types of jobs satisfy this demand and what types of skills should individuals possess to satisfy this huge and growing demand? The authors perform a content analysis of 958 job advertisements posted during 2014-2015 for four types of positions: business analyst, data analyst, data scientist, and data analytics manager. They use a text mining approach to identify the skills needed for these job types and identify six distinct broad competencies. They also identify the competencies unique to a particular type of job and those common to all job types. Their job type categorization provides a framework that organizations can use to inventory their existing workforce competencies in order to identify critical future human resources. It can also guide individual professionals with their career planning as well as academic institutions in assessing and advancing their business analytics curricula.

Hypoxia is a fetal stressor that leads to the production of endothelin-1 (ET-1). Previous work has shown that ET-1 treatment leads to the premature terminal differentiation of fetal cardiomyocytes. However, the precise mechanism is unknown. We tested the hypothesis that the fetal cardiomyocyte proteome will be greatly altered due to ET-1-treatment, which reveals a potential molecular mechanism of ET-1-induced terminal differentiation. Over a thousand proteins were detected in the fetal cardiomyocytes and among them 75 proteins were significantly altered due to ET-1 treatment. Using IPA pathway analysis, the merged network depicted several key proteins that appeared to be involved in regulating proliferation, including: EED, UBC, ERK1/2, MAPK, Akt, and EGFR. EED protein, which is associated with regulating proliferation via epigenetic mechanisms, is of particular interest. Herein we propose a model of the molecular mechanism by which ET-1 induced cardiomyocyte terminal differentiation occurs.

Most organizations use large and complex spreadsheets that are embedded in their mission-critical processes and are used for decision-making. Identification of the various types of errors that can be present in these spreadsheets is, therefore, an important first step to creating controls that organizations can use to govern their spreadsheets. While a considerable amount of research on quantitative error taxonomies exists, there is comparatively little research concerning qualitative error taxonomies. In this paper, we propose a taxonomy for categorizing qualitative errors in spreadsheet models that offers an exploratory framework for evaluating the quality of a spreadsheet model before it is released for use by others in the organization. The classification was developed based on types of qualitative errors identified in the literature and errors committed by end-users in developing a spreadsheet model for Panko's (1996) “Wall Problem.” A principal component analysis of the errors reveals four logical groupings thereby creating four categories of qualitative errors. The usability and limitations of the proposed taxonomy and areas for future research are discussed.

Mobile Payment is considered to be a potentially disruptive agent for payment mechanisms currently existing in various markets. With the integration of NFC technologies in new smart phones, that possibility is on the verge of becoming reality. However creating widespread adoption of mobile payments is still a huge challenge because of the complexities involved in delivering compelling values beyond those already offered by existing payment systems. We present a framework to systematically investigate the values and the barriers associated with mobile payment systems and validate it with examples from our field research in the US, South Korea, Japan and China.

Our chapter introduces an integrated way to look into the world of mobile offerings, referred often as m-commerce,- through a cross-country analysis of multiple applications in various countries in Europe and Asia. We investigated the role of four dominant factors - technology, government, financial incentives and culture - that influence mobile value offerings and connected them to Methlie and Pederson’s (2005) model of extrinsic and intrinsic attributes in mobile space. We found that there are many applications and services available that provide added value to customers, but an ill-defined business model prevents their successful realization. An unclear working relationship among the value players and their relative power position in the market also hinder the roll out of many services. The key findings from our research consist of-: i. the role of the four factors in development and deployment of m-commerce across multiple countries; ii. The level of influence of each of the four factors has on various applications; iii. the importance of value delivery, not merely the promotion of the “bells and whistles” of new applications, with a clear focus on a viable business model; iv. the role of socio-cultural differences in influencing mobile value applications and acceptance. In addition, we found that competition in the early phase of market growth is counter-productive and cooperative relationship between all value players is absolutely crucial for healthy growth of the mobile market and ultimately leads to a win-win situation for everyone.

Do students learn to model OR/MS problems better by using computer-based interactive tutorials and, if so, does increased interactivity in the tutorials lead to better learning? In order to determine the effect of different levels of interactivity on student learning, we used screen capture technology to design interactive support materials for modeling and solving the transportation problem in a spreadsheet. A controlled experiment was carried out and the results indicate a general support for the effectiveness of interactive tutorials in enhancing students' learning of modeling concepts. However, the study also found that excessive interactivity increased the cognitive load for the students and hindered their learning by making it difficult for them to consolidate concepts, integrate previous knowledge, and create meaningful mental models of the process.

In the past ten years, many executive education programs have been developed at universities to assist leaders in learning new skills and proficiencies. However, there is little published literature that examines specifically how CEOs have learned needed new skills in the past or on the learning preferences of CEOs. This research examines how the CEOs of thirty-three real estate companies in the United States like to learn needed new skills and proficiencies and presents its findings as twelve CEO learning preference themes. It further compares these themes to the relevant literature distinguishing adult learning preferences (e.g., executives) from university undergraduate education, and reports on education techniques and methods favored by these CEOs compared to other adult learners.

Kolb's experiential theory of learning, later modified by McCarthy to develop the 4MAT model, shows that active experimentation is a large part of learning for all types of learners. We use the 4MAT model as the theoretical underpinning to explore and develop some illustrative interactive tutorials to support the teaching of OR/MS spreadsheet modeling. Due to a much shallower learning curve on the new generation of screen capture technology, the design and creation of such spreadsheet support modules can now realistically be done by individual faculty in a reasonable amount of time. Three levels of interactivity are used in the modules to match the learning stages of the 4MAT model. We discuss implementation issues with current screen capture software and the benefits and limitations of this approach for supporting the teaching of spreadsheet modeling in OR/MS.

Teaching probability can be challenging because the mathematical formulas often are too abstract and complex for the students to fully grasp the underlying meaning and effect of the concepts. Games can provide a way to address this issue. For example, the game of roulette can be an exciting application for teaching probability concepts. In this paper, we implement a model of roulette in a spreadsheet that can simulate outcomes of various betting strategies. The simulations can be analyzed to gain better insights into the corresponding probability structures. We use the model to simulate a particular betting strategy known as the bet-doubling, or Martingale, strategy. This strategy is quite popular and is often erroneously perceived as a winning strategy even though the probability analysis shows that such a perception is incorrect. The simulation allows us to present the true implications of such a strategy for a player with a limited betting budget and relate the results to the underlying theoretical probability structure. The overall validation of the model, its use for teaching, including its application to analyze other types of betting strategies are discussed.

This paper presents a framework to plan organizational incentives for aligning usage behavior with organizational objectives across various IT adoption stages. Our framework is motivated by the introduction of incentive alignment as another dimension in Information Systems design (Ba et al., 2001) and is based on technology diffusion models presented in the literature. We focus in particular on the usage of technology that is discretionary in nature. In addition, the user group is limited to internal users of the technology at the operational level. The framework integrates key issues on motivation and incentives in order to understand how organizations can induce desired user behaviors congruent with the goals of the organization. We conclude with the use of an illustrative example to show how the framework can be used in an academic environment. This framework will help researchers and practitioners understand how to better manage and align the incentive structures for internal users across IT adoption stages.

We tried several methods to improve pedagogy in a graduate introductory OR/MS course. We developed digital video instruction modules, animations, computer-based tutorials, and a course Web site and used Web-based feedback, virtual classrooms, and collaborative learning methods to support students' learning. We learned that the course Web site, Web-based feedback, virtual classrooms, and some collaborative learning methods are easy to develop and implement and provide immediate returns. Others, such as digital video instructions, animations, and real-time collaborative computing, need more time but may provide better pedagogic benefits in the long run. The benefits from all the efforts accumulate over time. Individual instructors will have to decide whether the potential benefits provide enough payback, depending upon the reward structure of their institutions.

Discusses the use of intranet technology within business and academic institutions. Presents the CLASIC model, a layered application model to plan, design, develop and implement systems intended to support collaborative activities in an organization using intranet capabilities. Discusses how the model can be adapted to one type of institution in an academic context. Provides a framework to assist planners as they contemplate the use of the intranet and identifies the issues faced when attempting to implement the CLASIC model. Suggests strategies and provides a basis to plan for changes in organizations.

This paper describes the implementation of the traditional PERT/CPM algorithm for finding the critical path in a project network in a spreadsheet. The problem is of importance due to the recent shift of attention to using the spreadsheet environment as a vehicle for delivering MS/OR techniques to end-users.