Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics used in business analysis. ROI analysis (when applied correctly) is a powerful tool for evaluating existing information systems and making informed decisions on software acquisitions and other projects. Decades ago, ROI was conceived as a financial term and defined as a concept based on a rigorous and quantifiable analysis of financial returns and costs. At present, ROI has been widely recognized and accepted in business and financial management in the private and public sectors. Wide proliferation of the ROI method, though, has lead to the situation today where ROI is often experienced as a non-rigorous, amorphous bundle of mixed approaches, prone to the risks of inaccuracy and biased judgement. The main contribution of this study is in presenting a systematic view of ROI by identifying its key attributes and classifying ROI types by these attributes. ROI taxonomy has been developed and discussed, including traditional ROI extensions, virtualizations, and imitations. All ROI types are described through simple real life examples and business cases. Inherent limitations of ROI have been identified and advice is provided to keep ROI-based recommendations useful and meaningful. The paper is intended for researchers in information systems, technology solutions, and business management, and also for information specialists, project managers, program managers, technology directors, and information systems evaluators.

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Complexity is an inherent attribute of any project. The purpose of defining and documenting complexity is to have an early warning tool allowing a project team to focus on certain areas and aspects of the project in order to prevent and alleviate future risks and issues caused by this complexity.The main contribution of this paper 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", based on a survey of the existing complexity literature, is developed and discussed including the product, project, and external environment dimensions.We show how complexity types are described through simple real life examples and business cases. Then we develop a framework (tool) for applying the notion of complexity as an early warning tool for a project manager in order to timely foresee future risks and problems.The paper 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 (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The