8 research outputs found

    Early History of Survey and Core Courses with Implications for Information Systems Education

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    Recent proposals for a model MIS curriculum recommend that all business students be exposed to an introductory course. It is assumed that this course is a survey course, covering the spectrum of hardware, software, personnel, data, and information. In taking for granted that readers know the nature and purpose of a survey course, curriculum authors are tapping into ideas and experiences so common that no one feels the need to examine them. This article focuses on the history and philosophy of the survey course. Survey courses have played an integral role in the business curriculum since the advent of collegiate business schools. However, we shall show that the development of such a course and the way it plays in the MIS curriculum is an unsettled issue. We see that it is far from settled how to develop such a course and the role it plays in the broader curriculum. Two ideas concerning the survey course emerge and are in continual tension: the survey as the introduction to the field for future specialists and the survey as transmitter of fundamental ideas and skills to all business students. The broader implications for information systems education are discussed

    Networking Skills and Hiring Managers

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    Providing career opportunities is the major goal of many a college and university. Institutions which offer these programsrequire an efficient and timely method for determining which skills to teach in their programs. In addition, the job searchprocess itself requires additional skills such as interviewing and networking. This research postulates that the skill set andmethods of looking for new employees differ for the hiring manager versus the non-hiring manager. Specifically, hiringmanagers may place more emphasis on networking as a way to find employees and may require a different set ofcommunication and technical skills. A research design is discussed

    IT Project Crisis and Escalation

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    Crises caused by IT failures of one sort or another are in the news. The Queensland Health payroll implementation (Glass 2013) and the United States Office of Personnel Management’s Retirement Systems Modernization program (Fahrenthold 2014) are examples of failures in IT project management. Some failures are perceived as a crisis. For example, the rollout of the Healtcare.gov website was extremely public and was understood at the time as a threat to the universal health insurance agenda of the United States President (Brill 2015). \ \ Three threads of research are relevant to IT projects in enough trouble to constitute a crisis. One is that of crisis frameworks which describe the development of a crisis in terms of recognizable (and predictable) phases (e.g., Pearson and Mitroff 1993). Second there is research into adapting or developing tools for assisting in crisis management, such as social media (see Vaast, et al 2017 for a case study investigating microblogging during the 2010 Gulf of Mexico oil spill). Third, there is research into the role of de-escalation in resolving IT crises which resulted in a four-phase model (see Montealegre and Keal 2000 for a case study of the IT project behind the baggage handling system Denver International Airport during the 1990s). \ \ Open for investigation are the actions taken to recover from an IT crisis. In particular, what are the procedures and techniques used to resolve a crisis? Do crises follow the paths laid out by the crisis frameworks and does the resolution of the crisis follow any of the existing escalation (or de-escalation) phase models? Answers to these questions may provide useful tools which can be incorporated into best practices for project managers. \ \ For this work, we have chosen to conduct case research and to start with a single case. We have chosen to study the Healthcare.gov website rollout and rescue of 2013. The advantage of studying this event is that it is a well-known event involving a successful recovery after a disastrous start. There is an existing public record, documents are available through FOI (Freedom of Information) requests and the major players are still active

    A semi-empirical, low-latitude ionospheric model /

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    Since current empirical models specifying low-latitude electron density profiles severely underestimate the daytime plasma density scale-height and total electron content (TEC), a Semi-empirical, Low-latitude, Ionospheric Model (SLIM) was developed which is not only more realistic but is also computationally fast. Electron density profiles (180 to 1800 km) are theoretically calculated as a function of latitude (every 2 deg between 24 N and 24 S dip latitude) and local time (every half-hour over 24 hours LT) by solving the time-dependent plasma continuity equation. Assuming a Chapman-like profile, sets coefficients are then generated which reproduce these individual profiles. The coefficients themselves are easily stored, quickly retrieved and form the basis for a fast, portable, semi-empirical computer code. This report describes briefly the input parameters used to theoretically calculate profiles and the procedures used to generate the coefficients. The SLIM profiles are compared with the Chiu and Bent empirical models for Equinox, solar maximum conditions. Finally electron densities, the coefficients, TEC and 6300 A airglow intensities are listed in tabular form for three seasons (Equinox, June solstice, and December solstice) and two solar cycle periods (solar maximum and solar minimum)."Ionospheric Physics Division Project 2310.""10 October 1985."Distributed to depository libraries in microfiche.Cover title.Includes bibliographical references (pages 101-102).Scientific. Interim.Since current empirical models specifying low-latitude electron density profiles severely underestimate the daytime plasma density scale-height and total electron content (TEC), a Semi-empirical, Low-latitude, Ionospheric Model (SLIM) was developed which is not only more realistic but is also computationally fast. Electron density profiles (180 to 1800 km) are theoretically calculated as a function of latitude (every 2 deg between 24 N and 24 S dip latitude) and local time (every half-hour over 24 hours LT) by solving the time-dependent plasma continuity equation. Assuming a Chapman-like profile, sets coefficients are then generated which reproduce these individual profiles. The coefficients themselves are easily stored, quickly retrieved and form the basis for a fast, portable, semi-empirical computer code. This report describes briefly the input parameters used to theoretically calculate profiles and the procedures used to generate the coefficients. The SLIM profiles are compared with the Chiu and Bent empirical models for Equinox, solar maximum conditions. Finally electron densities, the coefficients, TEC and 6300 A airglow intensities are listed in tabular form for three seasons (Equinox, June solstice, and December solstice) and two solar cycle periods (solar maximum and solar minimum).Mode of access: Internet

    A Dynamic Changepoint Model for New Product Sales Forecasting

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    At the heart of a new product sales-forecasting model for consumer packaged goods is a multiple-event timing process. Even after controlling for the effects of time-varying marketing mix covariates, this timing process is not a stationary one, which means the standard interpurchase time models developed within the marketing literature are not suitable for new products. In this paper, we develop a dynamic changepoint model that captures the underlying evolution of the buying behavior associated with the new product. This extends the basic changepoint framework, as used by a number of statisticians, by allowing the changepoint process itself to evolve over time. Additionally, this model nests a number of the standard multiple-event timing models considered in the marketing literature. In our empirical analysis, we show that the dynamic changepoint model accurately tracks (and forecasts) the total sales curve as well as its trial and repeat components and other managerial diagnostics (e.g., percent of triers repeating).sales forecasting, trial/repeat, new product research, duration models, nonstationarity, changepoint models
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