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BISE 65(1) - February 2023

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  • Zeitschriftenartikel
    Mini-App Ecosystems
    (Business & Information Systems Engineering: Vol. 65, No. 1, 2023) Schreieck, Maximilian; Ou, Ange; Krcmar, Helmut
  • Zeitschriftenartikel
    Towards an Interdisciplinary Development of IoT-Enhanced Business Processes
    (Business & Information Systems Engineering: Vol. 65, No. 1, 2023) Valderas, Pedro; Torres, Victoria; Serral, Estefanía
    IoT-enhanced Business Processes make use of sensors and actuators to carry out the process tasks and achieve a specific goal. One of the most important difficulties in the development of IoT-enhanced BPs is the interdisciplinarity that is demanded by this type of project. Defining an interdisciplinary tool-supported development approach that facilitates the collaboration of different professionals, with a special focus on three main facets: business process requirements, interoperability between IoT devices and BPs, and low-level data processing. The study followed a Design Science Research methodology for information systems that consists of a 6-step process: (1) problem identification and motivation; (2) define the objectives for a solution; (3) design and development; (4) demonstration; (5) evaluation; and (6) communication. The paper presents an interdisciplinary development process to support the creation of IoT-enhanced BPs by applying the Separation of Concerns principle. A collaborative development environment is built to provide each professional with the tools required to accomplish her/his development responsibilities. The approach is successfully validated through a case-study evaluation. The evaluation allows to conclude that the proposed development process and the supporting development environment are effective to face the interdisciplinary nature of IoT-enhanced BPs.
  • Zeitschriftenartikel
    The Impact of High-Frequency Trading on Modern Securities Markets
    (Business & Information Systems Engineering: Vol. 65, No. 1, 2023) Clapham, Benjamin; Haferkorn, Martin; Zimmermann, Kai
    High-frequency traders account for a significant part of overall price formation and liquidity provision in modern securities markets. In order to react within microseconds, high-frequency traders depend on specialized low latency infrastructure and fast connections to exchanges, which require significant IT investments. The paper investigates a technical failure of this infrastructure at a major exchange that prevents high-frequency traders from trading at low latency. This event provides a unique opportunity to analyze the impact of high-frequency trading on securities markets. The analysis clearly shows that although the impact on trading volume and the number of trades is marginal, the effects on liquidity and to a lesser extent on price volatility are substantial when high-frequency trading is interrupted. Thus, investments in high-frequency trading technology provide positive economic spillovers to the overall market since they reduce transaction costs not only for those who invest in this technology but for all market participants by enhancing the quality of securities markets.
  • Zeitschriftenartikel
    Sustainable Systems Engineering
    (Business & Information Systems Engineering: Vol. 65, No. 1, 2023) Aalst, Wil M. P.; Hinz, Oliver; Weinhardt, Christof
  • Zeitschriftenartikel
    Reuse, Reduce, Support: Design Principles for Green Data Mining
    (Business & Information Systems Engineering: Vol. 65, No. 1, 2023) Schneider, Johannes; Seidel, Stefan; Basalla, Marcus; Brocke, Jan
    This paper reports on a design science research (DSR) study that develops design principles for “green” – more environmentally sustainable – data mining processes. Grounded in the Cross Industry Standard Process for Data Mining (CRISP-DM) and on a review of relevant literature on data mining methods, Green IT, and Green IS, the study identifies eight design principles that fall into the three categories of reuse, reduce, and support. The paper develops an evaluation strategy and provides empirical evidence for the principles’ utility. It suggests that the results can inform the development of a more general approach towards Green Data Science and provide a suitable lens to study sustainable computing.
  • Zeitschriftenartikel
    Predictive End-to-End Enterprise Process Network Monitoring
    (Business & Information Systems Engineering: Vol. 65, No. 1, 2023) Oberdorf, Felix; Schaschek, Myriam; Weinzierl, Sven; Stein, Nikolai; Matzner, Martin; Flath, Christoph M.
    Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method’s utility for organizations.