Complexity in the Context of Information Systems Project Management

Complexity is an inherent attribute of any project. The purpose of defining and documenting complexity is to enable a project team to foresee resulting challenges in a timely manner, and take steps to alleviate them.The main contribution of this article is to present a systematic view of complexity in project management by identifying its key attributes and classifying complexity by these attributes. A “complexity taxonomy” is developed and discussed within three levels: the product, the project and the external environment.Complexity types are described through simple real-life examples. Then a framework (tool) is developed for applying the notion of complexity as an early warning tool.The article is intended for researchers in complexity, project management, information systems, technology solutions and business management, and also for information specialists, project managers, program managers, financial staff and technology directors

Return on Investment: A Placebo for the Chief Financial Officer… And Other Paradoxes

Background: Return on investment (ROI) is one of the most popular evaluation metrics. ROI analysis (when applied correctly) is a powerful tool of evaluating existing information systems and making informed decisions on the acquisitions. However, practical use of the ROI is complicated by a number of uncertainties and controversies. The article reveals some of these controversies in an engaging and thought-provocative manner. Purpose: The intent of this note is to highlight several of the ROI paradoxes in a format of an opinion or a viewpoint with a hope that drawing attention of the ROI practitioners and researchers to these issues will contribute to more transparent and responsible application of the ROI evaluation. Setting:  Not applicable. Intervention: Not applicable. Research Design: Not applicable. Data Collection and Analysis: Review of current practice. Findings:  The article reveals three weaknesses of the ROI evaluations, which in the absence of the commonly accepted ROI standard, can make results of the ROI evaluations uncertain or questionable.  Keywords:  return on investment; ROI; paradox; evaluatio

A Method of Rapid Evaluation of a Multipurpose Customer Relationship Management System

Evaluation and selection of a Customer Relationship Management (CRM) system for an organization is a complex and time-consuming process, which has all the difficulties and risks of selecting any type of enterprise-level software. Additional evaluation complexities arise for the organizations, where a versatile CRM will be used by many departments with disparate functions and a variety of the customer service processes. This paper presents a method that allows a newly formed CRM evaluation project team to perform a quick evaluation of the commercially available CRM platforms. The method makes use of reliable sources of information readily available on the internet, often without a charge. Minimal spreadsheet skills required to process information in a consistent and verifiable way, and present it in a visually compelling and clear format for further discussions. The method directs a project team to a focused area of vendors/solutions, which have the highest likelihood of being relevant to the goals of implementing a general purpose CRM for a diverse multi-unit organization

An Introduction to Return on Investment for Information Systems

Return on Investment (ROI) is an important financial metric used to evaluate existing information systems and justify investment decisions on new acquisitions. This presentation provides an introduction to the ROI. It helps to understand the concept, methods of gathering data and calculating assessments. Several types of ROI are described and compared

Toward Development of a New Health Economic Evaluation Definition

Abstract. Economic evaluation (EE) is a dynamically advancing knowledge area of health economics. It has been conceived to provide evidence for allocating scarce resources to gain the best value for money. The problem of efficiency of investments becomes even more crucial with advances in modern medicine and public health which bring about both improved patient outcomes and higher costs. Despite the abundance of literature on the EE concepts, some key notions including the definition of the health economic evaluation remain open for discussion. Academic literature offers a large number and growing variety of EE definitions. It testifies to the fact that existing definitions do not meet economists’ requirements. The aim of this study was to examineexisting definitions and reveal their common features.Keywords. Economic evaluation, Health care, Definition, Evidence-informed policy, Health technology assessment, Health economics.JEL. D61, I18, I10, I11, O30

Assessing Excel VBA Suitability for Monte Carlo Simulation

Monte Carlo (MC) simulation includes a wide range of stochastic techniques used to quantitatively evaluate the behavior of complex systems or processes. Microsoft Excel spreadsheets with Visual Basic for Applications (VBA) software is, arguably, the most commonly employed general purpose tool for MC simulation. Despite the popularity of the Excel in many industries and educational institutions, it has been repeatedly criticized for its flaws and often described as questionable, if not completely unsuitable, for statistical problems. The purpose of this study is to assess suitability of the Excel (specifically its 2010 and 2013 versions) with VBA programming as a tool for MC simulation. The results of the study indicate that Microsoft Excel (versions 2010 and 2013) is a strong Monte Carlo simulation application offering a solid framework of core simulation components including spreadsheets for data input and output, VBA development environment and summary statistics functions. This framework should be complemented with an external high-quality pseudo-random number generator added as a VBA module. A large and diverse category of Excel’s incidental simulation components that includes statistical distributions, linear and non-linear regression and other statistical, engineering and business functions require execution of due diligence to determine their suitability for a specific MC project. Editorial note Although the present article is not directly concerned with “spreadsheets in education”, the decision to publish it was made on more general grounds. Microsoft Excel has received plenty of bad press concerning its statistical functions over a period of many years, and Microsoft has been slow to address many of these issues. However, the situation appears to have improved significantly since the early publications of McCullough & Wilson in the journal Computational Statistics & Data Analysis (references [72] and [73] herein), in which problems with Excel’s statistical functions were highlighted. We have chosen to publish the present article so readers have some more up-to-date information on which to base their decisions. As a final comment, readers should also be aware of the existence of RExcel, a port of the statistical package R to Excel as an add-in. It is a free download and is the work of one of our own editorial board members, Emeritus Professor Erich Neuwirth, University of Vienna. Thus, one may have one’s cake and eat it too: the acknowledged accuracy and respectability of R, along with the friendly, well-known interface of Excel