Value-Driven IT Project Portfolio Management: Tool-Based Scoring, Selection, and Scheduling

Managing IT project portfolios is a challenge because of IT projects\u27 complexity, dynamics, and uncertainty. Many IT projects exceed resources or time frames and do not reach their value-driven goals. A continuous scoring, selection, and scheduling of IT project proposals is thus essential to build an optimal portfolio. It has a significant impact on value contribution, strategic direction, goal achievement, and competitive advantages. We quantify an IT project\u27s urgency, strategy, efficiency, risk, and complexity as important evaluation and scoring criteria. To support top management decision makers in the IT project portfolio management process, we outline a combination of an evaluation approach with an optimization model. We develop a prototype decision support system to automate and simplify this process and demonstrate its applicability. Our recommendations address both theory and practice, improve IT project portfolio management, support value creation, and goal achievement

Value-driven IT Project Portfolio Management: Process Model, Evaluation Framework, and Decision Support

Companies must optimize their information technology (IT) project portfolio to achieve goals. However, IT projects often exceed resources and do not create their promised value, for example, because of missing structured processes and evaluation methods. Continuous IT portfolio management is thus of importance and a critical business activity to reach value-driven goals. Guided by Design Science Research with literature reviews and expert interviews, we develop, evaluate, and adjust an IT project portfolio management process model, a holistic IT project evaluation framework, and implement a decision support system prototype. Our results and findings synthesize and extend previous research and expert opinions and guide decision-makers to make more informed and objective IT project portfolio management decisions aligned with optimal value creation. Furthermore, we deduce new research opportunities for IT project portfolio management process models, decision tools, and evaluation frameworks

A Decision Support Tool for the Risk Management of Offshore Wind Energy Projects

This paper provides a decision support tool (DST) to analyze and evaluate the project value of offshore wind energy projects within the framework of project finance. The DST is based on a discounted cash-flow model in combination with a Monte Carlo simulation (MCS) to measure project risks and manage these risks. To consider the special requirements of debt capital providers in this context, key figures like the debt service cover ratio (DSCR) are calculated. The DST is realized in Excel/VBA with the Excel Add-In Oracle Crystal Ball. An offshore wind park example in the German North Sea is simulated to validate the underlying simulation model and the DST

Why Are Electronic Invoice Processes Risky? - Empirical Analysis and Discussion of Risk Factors

Electronic invoice processes are characterized by various software solutions, legal uncertainty, heter-ogeneous demands, lack of know how, and information system infrastructure incompatibilities. Due to this complexity and the uncertainty that companies face, a holistic map of risk factors of e-invoice processes is required. Companies must be conscious not only about potential opportunities but also about potential risks before they change their business processes and their information systems’ archi-tecture. Potential risk factors are identified theoretically and empirically evaluated with a quantitative expert survey that investigates risk probabilities and potential losses associated with these factors. The empirical analysis reveals that the investigated factors are valid and reliable. After conducting an ex-plorative factor analysis, 37 statistically significant risk factors are grouped into ten risk dimensions: process organization, standard, environment, project management, strategy, acceptance, system, pro-cess execution, security, and change management

Mobile Applications and Access to Personal Information: A Discussion of Users\u27 Privacy Concerns

Mobile applications (apps) have become highly popular and are creating new economic opportunities for app providers, developers, software companies, and advertisers. Due to the access to personal information, mobile apps may pose a threat to users’ privacy, which can incite users not to install or to uninstall mobile apps. In the last twenty years, concerns for information privacy (CFIP) have been investigated by several studies, which adapted CFIP to an online and to a mobile context. Our extended approach for mobile users’ information privacy concerns (MUIPC) analyzes four dimensions of access to personal information, i.e., personal identity, location, device content, and system and network settings. By conducting an online survey with 474 participants, we test the influence of these dimensions on MUIPC with a structural equation model (SEM). Three dimensions are found to be significantly influential. The results are discussed and implications for research and practice are given

A Decision Support System for the Optimization of Electric Car Sharing Stations

Electric car sharing is a mobility alternative addressing the world’s growing need for sustainability and allowing to reduce pollution, traffic congestion, and shortage of parking in cities. The positioning and sizing of car sharing stations are critical success factors for reaching many potential users. This represents a multi-dimensional challenge that requires decision makers to address the conflicting goals of fulfilling demands and maximizing profit. To provide decision support in anticipating optimal locations and to further achieve profitability, an optimization model in accordance to design science research principles is developed. The integration of the model into a decision support system (DSS) enables easy operability by providing a graphical user interface that helps the user import, edit, export, and visualize data. Solutions are illustrated, discussed, and evaluated using San Francisco as an application example. Results demonstrate the applicability of the DSS and indicate that profitable operation of electric car sharing is possible

Successful Mobile Application Development: Towards a Taxonomy of Domain-Specific Process Models and Methodologies

Mobile applications and mobile application development issues receive an increasing attention for practitioners and academics. The development of mobile applications is connected with a number of domain-specific issues and challenges (e.g., fulfilment of customer requirements or the prevention of high development costs). Consequently, the decision of the most effective process model to develop a mobile application plays a crucial role for software and mobile application development teams. With the help of a structured taxonomy-building methodology, we contribute to the extant literature by creating and presenting a taxonomy for process models and methodologies in software engineering and the mobile application development domain. The taxonomy enrich the existing knowledge base and can help mobile application developers to choose the most suitable process model or methodology. Based on our examination, our results indicate new directions for mobile application research and implications for mobile application development

Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation

Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research

ENHANCING LITERATURE REVIEW METHODS - TOWARDS MORE EFFICIENT LITERATURE RESEARCH WITH LATENT SEMANTIC INDEXING

Nowadays, the facilitated access to increasing amounts of information and scientific resources means that more and more effort is required to conduct comprehensive literature reviews. Literature search, as a fundamental, complex, and time-consuming step in every literature research process, is part of many established scientific methods. However, it is still predominantly supported by search techniqus based on conventional term-matching methods. We address the lack of semantic approaches in this context by proposing an enhancement of established literature review methods. For this purpose, we followed design science research (DSR) principles in order to develop artifacts and implement a prototype of our Tool for Semantic Indexing and Similarity Quries (TSISQ) based on the core concepts of latent semantic indexing (LSI). Its applicability is demonstrated and evaluated in a case study. Results indicate that the presented approach can help save valuable time in finding basic literature in a desired research field or increasing the comprehensiveness of a review by efficiently identifying sources that otherwise would not have been taken into account. The target audience for our findings includes researchers who need to efficiently gain an overview of a specific research field, deepen their knowledge or refine the theoretical foundations of their research