A Survey of the Application of Machine Learning in Decision Support Systems

Machine learning is a useful technology for decision support systems and assumes greater importance in research and practice. Whilst much of the work focuses technical implementations and the adaption of machine learning algorithms to application domains, the factors of machine learning design affecting the usefulness of decision support are still understudied. To enhance the understanding of machine learning and its use in decision support systems, we report the results of our content analysis of design-oriented research published between 1994 and 2013 in major Information Systems outlets. The findings suggest that the usefulness of machine learning for supporting decision-makers is dependent on the task, the phase of decision-making, and the applied technologies. We also report about the advantages and limitations of prior research, the applied evaluation methods and implications for future decision support research. Our findings suggest that future decision support research should shed more light on organizational and people-related evaluation criteria

Characteristics of Contemporary Artificial Intelligence Technologies and Implications for IS Research

Artificial Intelligence (AI) is often presented as a new phenomenon that is primarily driven by advances in contemporary machine learning technologies. Despite the steep rise, conceptualizations of contemporary AI technologies tend to be vague in many studies. This is problematic not only for positioning and focusing such research, but also for theorizing on the pervasive AI phenomenon. This paper presents a systematic literature review to understand and synthesize distinctive characteristics of contemporary AI technologies. In the course of our ongoing research, the preliminary findings encompass the changing role of data, feature extraction, adaptivity, transparency, and biases. With our future research, we seek to provide guidance on the conceptualizations of AI in IS research and to facilitate a more nuanced and focused theorization of AI in future IS studies

Impact of Business Intelligence Solutions on Export Performance of Software Firms in Emerging Economies

The article is written with the aim of understanding how well software firms in emerging economies perform when exporting their goods. Focusing on Paraguay as a representative context, a multiple-case-study research design was adopted using different sources of evidence, including 15 in-depth interviews with founders, shareholders, and CEOs. The data were analyzed using grounded theory in order to develop patterns and categories, and to understand differences and regularities. The revised Uppsala internationalization process model was used as a theoretical framework. This article highlights the experts’ views of the impact of business intelligence on the export performance of software firms in Paraguay. Although only a few of the interviewees currently use business intelligence solutions to support international strategic decision-making processes, most of them reveal a desire to use them because they expect it will have a positive impact on export performance and international competitiveness. The main factors for selecting a business intelligence solution are transparency of cost and benefits, excellent client service, and an attractive pricing model. The study results apply to all stakeholders who support the impact of business intelligence systems on the export performance of software firms in emerging economies. The article fulfils an identified need and call for research to study the use and impact of business intelligence on the way an emerging country’s exportation of goods actually performs, and the ability of its software firms to globalize successfully

Artificial intelligence in information systems research: A systematic literature review and research agenda

AI has received increased attention from the information systems (IS) research community in recent years. There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously. This study addresses this concern, by conducting a systematic literature review of AI research in IS between 2005 and 2020. The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented. In doing so, this study makes important contributions, namely (i) an identification of the current reported business value and contributions of AI, (ii) research and practical implications on the use of AI and (iii) opportunities for future AI research in the form of a research agenda

Machine Learning in Application-Based Case Management: A study on using machine learning to predict decision making in case management processes

This thesis studies the possibility of using machine learning to predict the outcome of applications processed by the Regional Committees for Medical and Health Research Ethics (REK) in Norway. More specifically, the purpose is to predict rejections of medical research applications. Four supervised prediction methods are used to achieve this: Logistic regression, Naive Bayes, Random Forest, and XGBoost. Before training the models, a Latent Dirichlet Allocation topic model is implemented to extract structured features from the textual project description data, making it suitable for the supervised prediction models. The prediction models are evaluated and compared using metrics derived from the confusion matrix, namely Accuracy, ROC AUC, and Cohen’s Kappa. The results show that the methods are suitable for predicting application outcomes, and XGBoost proves to have the best overall performance based on the selected metrics. Moreover, the topic variables from the LDA model prove to be influential to the predictions. Based on the results, the thesis discusses some use cases of the XGBoost methodology, investigating the possibility of flagging applications predicted by the model to be rejected. Such an implementation aims to help case officers quickly identify applications that likely should be rejected, simplifying the work related to the initial assessment. The thesis finds this feasible but discusses some challenges of implementation. Subsequently, a discussion is made regarding the possibility of using the methodology to reject applications automatically. This is a more radical intervention in the case management system, and further clarification with REK is essential before real-world implementation. Furthermore, the thesis looks at the weaknesses of the results. A discussion is made regarding the model’s ineffectiveness in adapting to rapid changes in the environment, which is an inevitable issue when it comes to predicting the future based on historical data. In addition, the thesis examines which variables are ethically sound to include as predictors in predicting application rejections, and reflecting upon this issue before real-world implementation is advised.nhhma

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem

Interactive optimization for supporting multicriteria decisions in urban and energy system planning

Climate change and growing urban populations are increasingly putting pressure on cities to reduce their carbon emissions and transition towards efficient and renewable energy systems. This challenges in particular urban planners, who are expected to integrate technical energy aspects and balance them with the conflicting and often elusive needs of other urban actors. This thesis explores how multicriteria decision analysis, and in particular multiobjective optimization techniques, can support this task. While multiobjective optimization is particularly suited for generating efficient and original alternatives, it presents two shortcomings when targeted at large, intractable problems. First, the problem size prevents a complete identification of all solutions. Second, the preferences required to narrow the problem size are difficult to know and formulate precisely before seeing the possible alternatives. Interactive optimization addresses both of these gaps by involving the human decision-maker in the calculation process, incorporating their preferences at the same time as the generated alternatives enrich their understanding of acceptable tradeoffs and important criteria. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Accordingly, the main objective of this thesis is: To develop a decision support methodology which enables the integration of energy issues in the early stages of urban planning. The proposed response and main contribution is SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), an interactive multiobjective optimization decision support methodology, which addresses the practical and technical shortcomings above. Its innovative aspect resides in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the alternative-generation process, (ii) a quasi-random sampling technique to efficiently explore the solution space in areas specified by the decision maker, and (iii) the integration of multiattribute decision analysis, cluster analysis and linked data visualization techniques to facilitate the interpretation of the Pareto front in real-time. Developed in collaboration with urban and energy planning practitioners, the methodology was applied to two Swiss urban planning case-studies: one greenfield project, in which all buildings and energy technologies are conceived ex nihilo, and one brownfield project, in which an existing urban neighborhood is redeveloped. These applications led to the progressive development of computational methods based on mathematical programming and data modeling (in the context of another thesis) which, applied with SAGESSE, form the planning support system URBio. Results indicate that the methodology is effective in exploring hundreds of plans and revealing tradeoffs and synergies between multiple objectives. The concrete outcomes of the calculations provide inputs for specifying political targets and deriving urban master plans

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band III

Übersicht der Teilkonferenzen Band III • Service Systems Engineering • Sicherheit, Compliance und Verfügbarkeit von Geschäftsprozessen • Smart Services: Kundeninduzierte Kombination komplexer Dienstleistungen • Strategisches IT-Management • Student Track • Telekommunikations- und Internetwirtschaft • Unternehmenssoftware – quo vadis? • Von der Digitalen Fabrik zu Industrie 4.0 – Methoden und Werkzeuge für die Planung und Steuerung von intelligenten Produktions- und Logistiksystemen • Wissensmanagemen