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Auflistung BISE 64(3) - June 2022 nach Erscheinungsdatum
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- ZeitschriftenartikelEsport(Business & Information Systems Engineering: Vol. 64, No. 3, 2021) Werder, Karl
- ZeitschriftenartikelConcepts for Modeling Smart Cities(Business & Information Systems Engineering: Vol. 64, No. 3, 2021) Bastidas, Viviana; Reychav, Iris; Ofir, Alon; Bezbradica, Marija; Helfert, MarkusThe rapid increase and adoption of new Information Technologies (IT) in Smart Cities make the provision of public services more efficient. However, various municipalities and cities deal with challenges to transform and digitize city services. Smart Cities have a high degree of complexity where offered city services must respond to the concerns and goals of multiple stakeholders. These city services must also involve diverse data sources, multi-domain applications, and heterogeneous systems and technologies. Enterprise Architecture (EA) is an instrument to deal with complexity in both private and public organizations. The paper defines the concepts for modeling Smart Cities in ArchiMate, guided by a design-oriented research approach. Particularly, the focus of this paper is on the concepts for modeling city services and underlying information systems which are added to the EA metamodel. The metamodel is demonstrated in a real-world case and validated by Smart City domain experts. The findings suggest that these concepts are essential to achieve the Smart City strategy (e.g., city goals and objectives), as well as to meet the needs of different city stakeholders. Furthermore, an extension mechanism allows addressing the alignment of business and IT in complex environments such as Smart Cities, by adjusting EA metamodels and notations. This can help cities to design, visualize, and communicate architecture decisions when managing the transformation and digitalization of public services.
- ZeitschriftenartikelIn Stars We Trust – A Note on Reputation Portability Between Digital Platforms(Business & Information Systems Engineering: Vol. 64, No. 3, 2021) Hesse, Maik; Teubner, Timm; Adam, Marc T. P.Complementors accumulate reputation on an ever-increasing number of online platforms. While the effects of reputation within individual platforms are well-understood, its potential effectiveness across platform boundaries has received much less attention. This research note considers complementors’ ability to increase their trustworthiness in the eyes of prospective consumers by importing reputational data from another platform. The study evaluates this potential lever by means of an online experiment, during which specific combinations of on-site and imported rating scores are tested. Results reveal that importing reputation can be advantageous – but also detrimental, depending on ratings’ values. Implications for complementors, platform operators, and regulatory bodies concerned with online reputation are considered.
- ZeitschriftenartikelArchetypes of Digital Twins(Business & Information Systems Engineering: Vol. 64, No. 3, 2021) van der Valk, Hendrik; Haße, Hendrik; Möller, Frederik; Otto, BorisCurrently, Digital Twins receive considerable attention from practitioners and in research. A Digital Twin describes a concept that connects physical and virtual objects through a data linkage. However, Digital Twins are highly dependent on their individual use case, which leads to a plethora of Digital Twin configurations. Based on a thorough literature analysis and two interview series with experts from various electrical and mechanical engineering companies, this paper proposes a set of archetypes of Digital Twins for individual use cases. It delimits the Digital Twins from related concepts, e.g., Digital Threads. The paper delivers profound insights into the domain of Digital Twins and, thus, helps the reader to identify the different archetypical patterns.
- ZeitschriftenartikelRadiologists’ Usage of Diagnostic AI Systems(Business & Information Systems Engineering: Vol. 64, No. 3, 2021) Jussupow, Ekaterina; Spohrer, Kai; Heinzl, ArminWhile diagnostic AI systems are implemented in medical practice, it is still unclear how physicians embed them in diagnostic decision making. This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists’ own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. In deriving six propositions, the account of sensemaking and usage of diagnostic AI systems in medical practice paves the way for future research.
- ZeitschriftenartikelThe Influence of Situational Involvement on Employees’ Intrinsic Involvement During IS Development(Business & Information Systems Engineering: Vol. 64, No. 3, 2022) Leso, Bernardo Henrique; Cortimiglia, Marcelo Nogueira; Caten, Carla SchwengberThe accelerated pace of digital technology development and adoption and the ensuing digital disruption challenge established business models at many levels, particularly by invalidating traditional value proposition logics. Therefore, processes of technology and information system (IS) adoption and implementation are crucial to organizations striving to survive in complex digitalized environments. In these circumstances, organizations should be aware of and minimize the possibilities of not using IS. The user involvement perspective may help organizations face this issue. Involving users in IS implementation through activities, agreements, and behavior during system development activities (what the literature refers to as situational involvement) may be an effective way to increase user psychological identification with the system, achieving what the literature describes as intrinsic involvement, a state that ultimately helps to increase the adoption rate. Nevertheless, it is still necessary to understand the influence of situational involvement on intrinsic involvement. Thus, the paper explores how situational involvement and intrinsic involvement relate through a fractional factorial experiment with engineering undergraduate students. The resulting model explains 57.79% of intrinsic involvement and supports the importance of the theoretical premise that including users in activities that nurture a sense of responsibility contributes toward system implementation success. To practitioners, the authors suggest that convenient and low-cost hands-on activities may contribute significantly to IS implementation success in organizations. The study also contributes to adoption and diffusion theory by exploring the concept of user involvement, usually recognized as necessary for an IS adoption but not entirely contemplated in the key adoption and diffusion models.
- ZeitschriftenartikelInterview with Peter Mertens and Wolfgang König: “From Reasonable Automation to (Sustainable) Autonomous Systems"?(Business & Information Systems Engineering: Vol. 64, No. 3, 2022) Beck, Roman; Dibbern, Jens; Wiener, Martin
- ZeitschriftenartikelA Multi-Perspective Framework for Research on (Sustainable) Autonomous Systems(Business & Information Systems Engineering: Vol. 64, No. 3, 2022) Beck, Roman; Dibbern, Jens; Wiener, Martin
- ZeitschriftenartikelWhen Self-Humanization Leads to Algorithm Aversion(Business & Information Systems Engineering: Vol. 64, No. 3, 2022) Heßler, Pascal Oliver; Pfeiffer, Jella; Hafenbrädl, SebastianDecision support systems are increasingly being adopted by various digital platforms. However, prior research has shown that certain contexts can induce algorithm aversion, leading people to reject their decision support. This paper investigates how and why the context in which users are making decisions (for-profit versus prosocial microlending decisions) affects their degree of algorithm aversion and ultimately their preference for more human-like (versus computer-like) decision support systems. The study proposes that contexts vary in their affordances for self-humanization. Specifically, people perceive prosocial decisions as more relevant to self-humanization than for-profit contexts, and, in consequence, they ascribe more importance to empathy and autonomy while making decisions in prosocial contexts. This increased importance of empathy and autonomy leads to a higher degree of algorithm aversion. At the same time, it also leads to a stronger preference for human-like decision support, which could therefore serve as a remedy for an algorithm aversion induced by the need for self-humanization. The results from an online experiment support the theorizing. The paper discusses both theoretical and design implications, especially for the potential of anthropomorphized conversational agents on platforms for prosocial decision-making.
- ZeitschriftenartikelThe Cost of Fairness in AI: Evidence from E-Commerce(Business & Information Systems Engineering: Vol. 64, No. 3, 2022) Zahn, Moritz; Feuerriegel, Stefan; Kuehl, NiklasContemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness�? have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.