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With the advent of artificial intelligence (AI), individuals are increasingly teaming up with AI-based systems to enhance their creative collaborative performance. When working with AI-based systems, several aspects of team dynamics need to be considered, which raises the question how humans’ approach and perceive their new teammates. In an experimental setting, we investigate the influence of social presence in a group ideation process with an AI-based teammate and examine its effects on the motivation to contribute. Our results show a multi-mediation model in which social presence indirectly influences whether human team members are motivated to contribute to a team with AI-based teammates, which is mediated by willingness to depend and team-oriented commitment.

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To face the challenge of digital transformation as well as to implement digital innovation many incumbent companies have set up digital innovation units (DIUs). Despite a steadily growing body of knowledge, there is a rather static picture of DIUs in the literature to date, and we have little knowledge of how these units evolve over time to continuously contribute to digital transformation and innovation. To lay the foundation for an understanding of DIUs as dynamically evolving entities, we conduct a multiple-case study with DIUs of five manufacturing companies and identify DIU evolution as a process driven by an interplay of life-cycle and dialectic motor of change. In the course of this, we also outline specific triggers, sequences, and the nature of change. We generalize our findings with a conceptual process model of DIU evolution and three propositions on their current and future development to inform the existing and forthcoming literature.

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While information systems development (ISD) projects play a pivotal role in maintaining a competitive advantage, ISD project distress evolves dramatically. Given the complex and dynamic nature of ISD projects, they are prone to Escalation of Commitment (EoC), the irrational tendency to persist with failing courses of action. While EoC has been studied to a great extent in management and psychology literature, research on its role in the context of ISD project distress is fragmented, making it challenging to develop de-escalation strategies. To address this gap, we conduct a literature review on EoC in the context of ISD project distress. The proposed nomological net including triggering factors, consequences, mediators, and moderators, as well as a set of developed de-escalation strategies can serve as an inspiration and foundation for future IS researchers. By presenting this review we hope to inform future IS research to acknowledge the role of EoC in ISD projects.

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Today formal education like higher education relies on digital learning content like learning videos or quizzes. Using such online learning material enables students to learn independently from time and place. While improvements have been made, there are still many issues as the two-year long crisis in 2020 has revealed. Many offerings do not consider the learners’ needs and can result in unsuccessful learning. One way to address these short comings is to actively include learners in the creation process of learning content. However, co-creation oftentimes relies on face to face and or group settings that may not be possible for all students at all times. Therefore, we undertake a long-term action design research project to investigate the novel concept of conversational co-creation of learning material using a conversational agent and persuasive design to engage and motivate learners. In this article we present an early-stage prototype and concept of conversational co-creation.

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Crises become the norm for organizations, as recent years have shown. Especially the automotive industry is still facing disruptive changes such as e-mobility, connected cars or autonomous driving. Disrupted supply chains, related production downtimes and associated financial losses are consequences. Procurement departments are the interface between internal and external stakeholders in supply chains, and therefore, the central authority for managing crises. In such situations, effective decision-making is essential. Positive effects of data analytics on decision-making were part of numerous research endeavors, as well as related data analytics competencies. We conducted semi-structured interviews with experienced experts about relevant data analytics competencies in procurement departments. We present an overview specifically for procurement departments and derive implications of these competencies on decision-making. As a result, we apply our findings to existing research from a theoretical perspective and support procurement leaders and their departments in facing current and future challenges from a practical perspective.

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Although conversational agents (CA) are increasingly used for providing purchase recommendations, important design questions remain. Across two experiments we examine with a novel fluency mechanism how recommendation modality (speech vs. text) shapes recommendation evaluation (persuasiveness and risk), the intention to follow the recommendation, and how modality interacts with the style of recommendation explanation (verbal vs. numerical). Findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. They show that numerical explanations increase processing fluency and purchase intention of both recommendation modalities. The results underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal explanation style. For practice, we offer actionable implications on how to make effective sales agents out of CAs.

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The centrality of information systems (IS) customization to match companies' needs with software systems available in the market has been researched extensively. The distinctive characteristics of Artificial Intelligence (AI) systems compared to other types of IS suggest that customization needs a new conceptualization in this context. We draw on evidence from expert interviews to conceptualize customization of AI systems as composed of four layers: data, models, algorithms, infrastructures. We identify a continuum of levels of customization, from no to complete customization. Since companies customize AI systems in response to business needs, we develop a theoretical model with six antecedents of AI systems' customization choices. In so doing, we contribute to both AI management research, by introducing the IS customization perspective in the field, and IS customization literature, by introducing AI systems as a novel class of systems and enlarging the understanding of customization for a specific class of software systems.

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Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.

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The advantages offered by natural language processing (NLP) and machine learning enable students to receive automated feedback on their argumentation skills, independent of educator, time, and location. Although there is a growing amount of literature on formative argumentation feedback, empirical evidence on the effects of adaptive feedback mechanisms and novel NLP approaches to enhance argumentative writing remains scarce. To help fill this gap, the aim of the present study is to investigate whether automated feedback and social comparison nudging enable students to internalize and improve logical argumentation writing abilities in an undergraduate business course. We conducted a mixed-methods study to investigate the impact of argumentative writing on 71 students in a field experiment. Students in treatment group 1 completed their assignment while receiving automated feedback, whereas students in treatment group 2 completed the same assignment while receiving automated feedback with a social comparison nudge that indicated how other students performed on the same assignment. Students in the control group received generalized feedback based on rules of syntax. We found that participants who received automated argumentation feedback with a social comparison nudge wrote more convincing texts with higher-quality argumentation compared to the two benchmark groups (p < 0.05). The measured self-efficacy, perceived ease of use, and qualitative data provide valuable insights that help explain this effect. The results suggest that embedding automated feedback in combination with social comparison nudges enables students to increase their argumentative writing skills by triggering psychological processes. Receiving only automated feedback in the form of in-text argumentative highlighting without any further guidance appears not to significantly influence students’ writing abilities when compared to syntactic feedback.

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Recent advantages from computational linguists can be leveraged to nudge students with adaptive self evaluation based on their argumentation skill level. To investigate how individual argumentation self evaluation will help students write more convincing texts, we designed an intelligent argumentation writing support system called ArgumentFeedback based on nudging theory and evaluated it in a series of three qualitative and quaxntitative studies with a total of 83 students. We found that students who received a self-evaluation nudge wrote more convincing texts with a better quality of formal and perceived argumentation compared to the control group. The measured self-efficacy and the technology acceptance provide promising results for embedding adaptive argumentation writing support tools in combination with digital nudging in traditional learning settings to foster self-regulated learning. Our results indicate that the design of nudging-based learning applications for self-regulated learning combined with computational methods for argumentation self-evaluation has a beneficial use to foster better writing skills of students.

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Anhaltende und massive Veränderungen im regulatorischen und technischen Kontext führen zu neuen bzw. veränderten Anforderungen an das Datenmanagement grosser Banken. Daraus resultieren Chancen und Herausforderungen im Datenmanagement, für die es hinsichtlich des Vorgehens und der Konsequenzen oft (noch) keine „Best Practices“ gibt. Hierzu haben wir entsprechende Erkenntnisse aus den Strategien und Umsetzungen der Partnerunternehmen der Data Management und Analytics Community (DMAC) der letzten zehn Jahre analysiert. Wir fassen den Status Quo des Datenmanagements zusammen und skizzieren wesentliche Entwicklungsrichtungen bzw. strategische Initiativen. Dabei fällt auf, dass europäische Grossbanken neben den generellen Entwicklungen in Richtung Cloud und agiler Strukturen / Prozesse auch ein zunehmend dezentrales Datenmanagement als geeigneten Weg zur datengetriebenen Organisation ansehen.

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Novel technologies such as smart personal assistants integrate digital services into everyday life. These services use personal data to offer personalized services. While they are subject to special data protection regulations at the time of development, there are few guidelines describing the transition from legal requirements to implementation. To reduce risks, services depend on external legal assessments. With developers and legal experts often missing either legal or technical knowledge, the challenge lies in bridging this gap. We observe that design patterns support both developers and legal experts, and we present an approach in which design patterns are leveraged to provide twofold value for both developers and legal experts when dealing with novel technologies. We conducted a revelatory case study for smart personal assistants and scaffolded the case interpretation through cognitive fit theory. On the basis of the findings, we develop a theoretical model to explain and predict the twofold value of design patterns to develop and assess lawful technologies.

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The case describes the history of Bosch and its business model. Since its founding in 1886, Bosch has been a product technology company through and through. Serving primarily B2B customers in the automotive industry but growing significantly in scope and size, the German company has developed into a diversified conglomerate. Case A takes students back to the board meeting in 2009 when, in the midst of the financial crisis, the Bosch board of management was considering entering the electric bicycle market, a completely new market for Bosch with uncertain future prospects. The case asks whether Bosch should enter the electric bike market at all and how this opportunity should be pursued strategically and from a business model point of view. Case B looks at further developments in the market for electric bicycles and asks what opportunities and threats lie ahead for Bosch in the light of growing competition.

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The case describes the history of Bosch and its business model. Since its founding in 1886, Bosch has been a product technology company through and through. Serving primarily B2B customers in the automotive industry but growing significantly in scope and size, the German company has developed into a diversified conglomerate. Case A takes students back to the board meeting in 2009 when, in the midst of the financial crisis, the Bosch board of management was considering entering the electric bicycle market, a completely new market for Bosch with uncertain future prospects. The case asks whether Bosch should enter the electric bike market at all and how this opportunity should be pursued strategically and from a business model point of view. Case B looks at further developments in the market for electric bicycles and asks what opportunities and threats lie ahead for Bosch in the light of growing competition.

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Online training to improve problem-solving skills has become increasingly important in management learning. In online environments, learners take a more active role which can lead to stressful situations and decreased motivation. Gamification can be applied to support learner motivation and emotionally boost engagement by using game-like elements in a non-game context. However, using gamification does not necessarily result in supporting positive learning outcomes. Our analysis sheds light on these aspects and evaluates the effects of points and badges on engagement and problem-solving outcomes. We used an experimental approach with a fully randomized pre-test/post-test design of a gamified online management training program with 68 participants. The results demonstrate that points and badges do not directly improve problem-solving skills but are mediated by emotional engagement to positively influence problem-solving skills. Additionally, satisfaction with the gamification learning process positively relates to emotional engagement. Thus, when creating online training programs, it is essential to consider how to engage students and to think about the design of the learning environment. By identifying the limitations of gamification elements, the study’s results can provide educators with information about the design implications of online training programs for management learning.

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Design Science Research (DSR) is a pragmatic, utility-oriented, scientific approach to solving relevant Information Technology (IT) and Information Systems (IS) related organisational problems. It represents one of two essential paradigms in IS research, and its research output is not descriptive but prescriptive. It instructs about how gen-eral problems of the same class can be solved. Research projects in DSR (i.e., design research projects) involve many stakeholders from research and practice. They are iterative, lengthy, and complex, combining the roams of theory (ensuring rigour by using existing knowledge) and practice (ensuring relevance by actively integrating the stakeholders of the problem in the research process). At the same time, such re-search contributes both to research (through the identification of prescriptive means-end relationships) and to practice (providing instructions to solve practical organisa-tional problems). This very brief summary of DSR already makes one thing evident: Design research project communication is essential for this kind of research. Poor communication leads to inefficient exchange with practice, rejected research articles, slow accumula-tion of knowledge, or low practical impact of IS research. Another aspect that is re-vealed is that these projects are likely to be complicated to communicate (causes in-clude, e.g., lengthiness, multi-stakeholder involvement, practitioner and academic audiences, addressing problem classes rather than problem instances). This problem has been recognised in various instances (e.g., writing of design research articles for academic journals), but existing support on how to communicate is ineffective, as many perceive the communication of design research projects to be a problem. This dissertation addresses that. Employing DSR as the overarching research methodology, the presented research in this dissertation provides a solution that guides design researchers in general commu-nication of their projects (DSR communication framework), in writing design re-search articles (a process with prescriptive instructions for each step), and in present-ing DSR research designs (a checklist for effective DSR research design presentation, e.g., in the context of a research methods course). These artefacts (DSR contribu-tions) are both built and evaluated based on empirical studies. This research thus of-fers a solution to the research problem. It furthermore puts a new topic of research on the map: DSR communication.

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Kognitive Automation geht über die regelbasierte Geschäftsprozessautomation hinaus und zielt auf kognitive Wissens- und Dienstleistungsarbeit ab. Dies ermöglicht die Automatisierung von Aufgaben und Prozessen, die noch vor einem Jahrzehnt unvorstellbar schien. Sie hat somit das Potenzial, Front- und Backoffices in ähnlicher Weise zu beeinflussen wie physische Roboter bei Produktionsanlagen. Kognitive Automatisierung stellt Unternehmen jedoch vor neue Herausforderungen bei der Entscheidung über das Automatisierungspotenzial von Anwendungsfällen, was zu einer geringen Akzeptanz und hohen Misserfolgsraten entsprechender Projekte führt. Hinzu kommt der trans- und interdisziplinäre Charakter des Phänomens der kognitiven Automation, der dazu führt, dass es in Forschung und Praxis an einem gemeinsamen Verständnis und einer einheitlichen Terminologie fehlt, um dieses Feld voranzutreiben. Vor diesem Hintergrund verfolgt diese Dissertation das Ziel, Organisationen in die Lage zu versetzen, strukturierter und fundierter zu entscheiden, ob eine Aufgabe oder ein Prozess für kognitive Automation geeignet ist und wie diese Erkenntnisse in entsprechende Projektanforderungen übersetzt werden können. Um dieses Ziel zu erreichen, folgt die Dissertation einem qualitativen, sozialkonstruktivistischen Paradigma, das auf systematischen Literaturrecherchen, Interviews, Fokusgruppen, Fallstudien, Aktionsforschung und gestaltungsorientierter Forschung basiert. Zunächst konzeptualisiere ich in meiner Dissertation die unterschiedlichen Perspektiven der kognitiven Automation, um ein repräsentatives Bild des Phänomens und seiner unterstützenden Technologien zu zeichnen. Die ganzheitliche Konzeptualisierung dient als Grundlage, auf der zukünftige Forschung aufbauen kann und ebnet den Weg für eine tiefgreifendere konzeptionelle Konvergenz in diesem Feld. Zweitens entwickle und teste ich ein Modell zur Bewertung von Anwendungsfällen kognitiver Automation. Das Modell soll Unternehmen helfen, fundiertere Entscheidungen bei der Auswahl von Anwendungsfällen für kognitive Automation und der Planung dieser Initiativen zu treffen. Aus Forschungssicht werden die identifizierten Determinanten unser Verständnis von kognitiver Automation und von Künstlicher Intelligenz als deren treibende Kraft vertiefen. Drittens bette ich das Modell in eine Methode ein, um es in die Praxis zu übertragen. Dabei erweitere ich die Frage, «welche» Faktoren bei der Bewertung der Eignung von Anwendungsfällen zu berücksichtigen sind, um die Frage, «wie» diese Bewertung im Sinne reproduzierbarer Managementpraktiken durchgeführt werden soll. Ich ergänze die Methode um eine Reihe allgemeiner Projektmanagement-Praktiken für Künstliche Intelligenz, die helfen nach der Bewertung von Anwendungsfällen Projektimplikationen für kognitive Automatisierungsprojekte abzuleiten.

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Todays organizations have to continuously transform themselves to survive in the rapidly changing environment. In particular, continuous, rapid developments of digital technologies are key drivers of these changes. They leave hardly any organization, even organizations with long traditions, unaffected. The resulting urge to progress with digital transformation has motivated many organizations to set up large-scale, enterprise-wide programs. However, successfully realizing these programs is far from an easy task. While program management as a discipline has significantly matured over the previous years, increasing the understanding of and improving program governance to ensure direction, coordination, and control of joint objectives in programs, has been neglected in both theory and practice. This dissertation studies governance in digital transformation programs. Firstly, it investigates the importance of program governance as a means of ensuring program success. Secondly, it studies the current shortcomings of program governance in the context of the concurrence of opposing agile and traditional management approaches within programs and in the context of the concurrence of opposing local and global interests of involved stakeholders. Thirdly, it provides insights into possible improvements and further developments of program governance based on tension theory. To gain rich empirical insights, all papers of this dissertation are based on a qualitative research approach. The dissertation is of value for researchers and practitioners: The findings of the papers constituting this dissertation contribute to a better understanding of why governance is important to successfully progress with digital transformation endeavors and how it can be improved through ensuring both context- and tension-awareness. This dissertation lays the foundation to further investigate and develop governance practice in temporary organizations set up to progress with enterprise-wide digital transformation.

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Die Fähigkeit, überzeugende Argumente zu formulieren, ist nicht nur von grundlegender Bedeutung, um ein Publikum von neuen Ideen zu überzeugen, sondern spielt auch eine wichtige Rolle bei strategischen Entscheidungsfindungen, bei Verhandlungen und im allgemeinen politischen Diskurs. Menschen haben jedoch oft Schwierigkeiten, Argumentationsfähigkeiten zu entwickeln. Ein Grund dafür ist der Mangel an individuellem und formativem Feedback im Lernprozess von Studierenden oder in der Aus-und Weiterbildung. Eine Rückmeldung über den Stand der individuellen Argumentationsfähigkeiten von Lernenden ist zeitaufwändig und für Lehrkräfte nicht skalierbar. Neuartige adaptive Argu-mentationslernsysteme haben das Potential, Studierende zu unterstützen selbstständig und unabhängig von Lehrkräften, Zeit und Ort zu lernen. Obwohl der Einsatz von Künstlicher Intelligenz (KI) ein vielversprechender Ansatz zu sein scheint, fehlt es in der aktuellen Literatur (1) an Erkenntnissen über die theoriebasierten und lernerzentrierten Anforderungen an Lernen von Argumentationsfähigkeiten mit adaptiven Lerntools, (2) an Wissen darüber wie KI-basierte adaptive Argumentationslernsysteme konzipiert und gebaut werden können, um die individuellen Lernpfade und die Nutzung der Studierenden zu verbessern und (3) an Erkenntnissen über den Einfluss adaptiver Argumentationslernsysteme auf die eigentlichen Argumentationsfähigkeiten der Studierenden.Diese Dissertation greift diese drei Forschungslücken auf und untersucht das Potential von adaptivem Lernen von Argumentationsfähigkeiten mit Hilfe von KI. Dazu werden neue technologiegestützte pädagogische Konzepte entworfen, implementiert und evaluiert, die Studierende aktiv unterstützen, strukturiert, logisch und reflektiert zu argumentieren. Auf Basis eines designwissenschaftlichen Forschungsansatzes entwickle ich neue studierenden-zentrierte pädagogische Szenarien mit empirisch evaluierten Designprinzipien, linguistischen Korpora, ML-Algorithmen und innovativen Lernwerkzeugen. Ich stelle dazu zwei neuartige Klassen von IT-basierten Lernwerkzeugen für das Argumentieren vor: (1) KI-basierte Systeme zur Unterstützung des Schreibens von Argumenten und (2) dialog-basierte Argumentationslernsysteme. Meine Ergebnisse zeigen, dass die beiden neuen Systemklassen den Studierenden helfen bessere Argumentationsfähigkeiten in verschiedenen pädagogischen Bereichen zu entwickeln. Dabei überbrücke ich erstmals die Grenzen von Argumentationslernen und Argumentation Mining, indem ich neue pädagogische Szenarien für adaptives Argumentationslernen aus einer studierendenzentrierten Perspektive untersuche. Daher trägt diese Arbeit nicht nur mit neuen, reichhaltigen Argumentationsannotationsschemata, Argumentationskorpora und neuartigen ML-Modellen in deutscher Sprache bei, sondern vor allem mit Einblicken in die allgemeine sozio-technische Einbettung, das Design und die Auswirkungen von KI-basierten Argumentationslernsysteme, um Lernenden zu helfen besser zu argumentieren.

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Moving toward sustainable energy systems to address climate change is one of the key challenges of our generation. To that end, investments in renewable energy and balancing renewable supply and energy demand on the larger scale are crucial. One mechanism to create price signals for demand balancing, as well as for consumer engagement, is to establish trading platforms (or peer-to-peer (P2P) markets) through which households can directly buy and sell renewable energy. However, residential consumers are typically lay users with little or no previous exposure to the complexity and the dynamics involved in energy markets. More so, empirical research on consumer engagement in the energy sector indicates that individuals tend to act against their stated proenvironmental intentions and to lose interest in energy management systems particularly quickly—calling into question regulatory efforts to foster P2P markets to push the transition to renewable energy. We have implemented the first empirical study worldwide that analyzes bidding behavior in a real-world P2P energy market, in which users bid for solar energy via an auction mechanism. For the duration of an entire year, users could interact with the market using a web app. The prices settled on the P2P market directly impacted participants’ electricity bills. We provide unique empirical evidence showing that (1) participants were willing to engage in energy trading and that (2) they understood the market mechanism surprisingly well and exhibited learning effects. Still, bidding behavior did not reflect their stated intention of paying a price premium for local solar energy. The market outcomes reveal that P2P energy markets can indeed have a positive impact on balancing demand and supply, thereby addressing the fundamental challenge of distributed renewable energy systems

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This paper employs a longitudinal perspective to examine continued system use (CSU) by individuals in utilitarian, volitional contexts when alternative systems are present . We focus on two key behavioural antecedents of CSU – habit and continuance intention – and theorise how the relationships between CSU and these antecedents evolve over time. In addition, we hypothesise how the interaction effect of habit and intention on CSU evolves temporally. Our theorising differs from extant literature in two important respects: 1) In contrast to the widespread acceptance of the diminishing effect of continuance intention on CSU in the information systems (IS) literature, we hypothesise that in our context, its impact increases with time; and 2) In contrast to the negative moderation effect of habit on the relationship between intention and CSU proposed in the literature, we posit a positive interaction effect. We collect longitudinal survey data on the use of a higher education IS from students in a European university. Our results suggest that the impact of continuance intention on CSU as well as the interaction effect between habit and intention are increasing over time. We further introduce a methodological innovation – the permutation approach to conduct the multi-group analysis with repeated measures – to the literature.

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Datengetriebene Plattform Ökosysteme bilden eine Formalisierung und Automatisierung des Datenaustausches zwischen Unternehmen. Als solche stellen sie eine Technologie dar, durch dessen Verwendung Vorteile gegenüber eines herkömmlichen Datenaustausches (z. B. über Excel-Sheets und Emails) erzielt werden können. Jedoch sind Studien, die aus einer Ökosystem-Perspektive beleuchten, wie solche Plattformen mehrwert-optimiert gestaltet werden können, bislang unterrepräsentiert. Diese Forschungslücke soll innerhalb der vorliegenden Studie durch eine Datenerhebung in der Praxis geschlossen werden. In einer Interviewstudie mit 11 Vertretern aus deutschsprachigen, international tätigen Unternehmen wurden Organisationsteilnehmer des mittleren und gehobenen Managements zu ihrer Partizipation in datengetriebenen Plattformen befragt. Dabei liefert dieses Paper im Wesentlichen 3 Beiträge: 1. Designfaktoren zur Wertmaximierung der Plattform-Teilnehmer wurden aus einer Ökosystem-Perspektive herausgearbeitet. Zusätzlich wurde ein Framework zur Auflistung und Kategorisierung wichtiger Designfaktoren angefertigt. 2. Auf Grundlage der Literaturanalyse und Praxisdaten wurden zwei Thesen entwickelt, die spezifische Design-Anforderungen beschreiben, welchen im Rahmen der Datensammlung und -Auswertung besondere Beachtung zugesprochen wurde. 3. Das Verständnis von Wertbildung im digitalen Raum wurde auf Datenaustausch-Plattformen erweitert. Die ersten beiden Beiträge dieses Papers adressieren Praktiker. Unternehmen sollen ermächtigt werden, zielgerichtete und informierte Entscheidungen im Bezug zur wertmaximierenden Gestaltung inter-organisationalen Datenaustauschs treffen zu können. Gleichzeitig soll mittels der zwei Thesen zu datengetriebenen Austauschplattformen eine Diskussion über ihre Rolle in der Praxis angestoßen werden. Der dritte Beitrag dieser Studie widmet sich dem akademischen Diskurs, da über die Erweiterung des Verständnisses von Wertbildung im digitalen Raum auf Datenaustausch-Plattformen eine Grundlage für die weitergehende Erforschung dieses Plattform-Typs geschaffen werden soll.

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Innovation is one of the most important antecedents of a company's competitive advantage and long-term survival. Prior research has alluded to teamwork being a primary driver of a firm's innovation capacity. Still, many firms struggle with providing an environment that supports innovation teams in working efficiently together. Thereby, a team's failure can be attributed to several factors, such as inefficient working methods or a lack of internal communication that leads to so-called innovation blockages. There are a number of approaches that are targeted at supporting teams to overcome innovation blockages, but they mainly focus on the collaboration process and rarely consider the needs and potentials of individual team members. In this paper, we argue that Conversational Agents (CAs) can efficiently support teams in overcoming innovation blockages by enhancing collaborative work practices and, specifically, by facilitating the contribution of each individual team member. To that end, we design a CA as a team facilitator that provides nudges to reduce innovation blocking actions according to requirements we systematically derived from scientific literature and practice. Based on a rigorous evaluation, we demonstrate the potential of CAs to reduce the frequency of innovation blockages. The research implications for the development and deployment of CAs as team facilitators are explored.

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Voice assistants’ (VAs) increasingly nuanced and natural communication opens up new opportunities for the experience of users, providing task assistance and automation possibilities, and also offer an easy interface to digital services and ecosystems. However, VAs face various problems, such as low adoption and satisfaction rates as well as other negative reactions from users. Companies, therefore, need to consider how individuals utilize VAs and what contributes to user satisfaction. Key for the design of VAs are their unique affordances and their agentic nature that distinguish these IT artifacts from non-agentic IS. A configurative and dynamic approach enables to shed light on the complex causalities underlying user outcomes with these novel systems. Consequently, we examine in this study how individuals actualize the affordances of VAs during the initial adoption stage. For this purpose, we draw on a diary study research design that examines affordance actualization processes with new VA users. We examine with a configurational approach, how the actualization of VA affordances contributes to the outcomes of VAs, i.e., in our case user satisfaction. The results of our diary study show distinct patterns of functional affordance configurations. In addition, we show that affordances unfold and evolve over time. The derived implications provide a configurative theoretical understanding for the role of VAs affordances for user satisfaction that provides practitioners useful guidance to actualize the potential of VAs.

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Agile work organization has experienced a significant increase in acceptance in recent years. However, literature falls short in explaining the transformation process that results from the implementation of agile work organization as a means of achieving increased adaptability, rate of speed and flexibility. We apply a process ontology to agile work organization by following three multinational firms that apply agile work organization, utilizing a case study approach over three years. At the macro level, our theory describes the transformation process set in motion by agile work organization as a three-phase process. At the micro level, we show that this transformation process is driven by specific design decisions on individual elements. Thus, our process theory contributes to a better understanding of agile work organization as a means to achieve organizational agility and to STS theory by showing that the emergence and constitution of STS are mainly driven by micro-level processes.

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get_appRoman Rietsche, Christian Dremel, Samuel Bosch, Léa Steinacker, Miriam Meckel, Jan Marco Leimeister
Wissenschaftlicher Artikel
Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.

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Viele der wertvollsten Unternehmen der Welt betreiben ihr Geschäft auf Basis einer digitalen Plattform samt umgebendem Ökosystem. Während es in der Theorie zahlreiche Erklärungs-und Gestaltungsansätze für die erfolgreiche Umsetzung gibt, gelten diese wirtschaftlich attraktiven Geschäftsmodelle in der Praxis nach wie vor als herausfordernd. Auf der empirischen Grundlage von sieben Plattform-Innovationsprojekten und mit Methoden der Fallstudienforschung untersucht der vorliegende Artikel, welche Rolle digitale Plattformen in der Praxis spielen und wie diese Artefakte entwickelt werden können. Mit den Ergebnissen in Form von vier Einsatz- (Platform-as-a-Core, Platform-as-an-Evolution, Platform-as-an-Enabler, Platform-as-an-Add-On) und vier Entwicklungsmodellen (Methodic Problem Solvers, Methodic Strategists, Methodic Leaders, Ad-Hoc Developers) kann gefolgert werden: Digitale Plattformen können in der Praxis vielfältige Rollen einnehmen und deren Entwicklung kann mit unterschiedlicher Methodikintensität erfolgen. Für die Praxis profitieren Fach- und Führungskräfte von industrienahen Einblicken und abgeleiteten Handlungsempfehlungen. Für die Forschung wird der Wissensfundus im Bereich des Designs und der Entwicklung digitaler Plattformen erweitert.

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Artificial Intelligence (AI) shows great potential to tackle environmental sustainability issues that are critical to the survival of Humanity and Planet Earth. However, the development and use of AI causes indirect emissions leading to detrimental effects on the environment. Therefore, it is important for organizations, researchers, and practitioners in the Information Systems (IS) domain to understand both the positive and negative effects of AI on the environment. This article contributes to this topic by performing a theoretical review of the literature at the intersection of AI and Sustainability to determine the current research streams. Further, this article adopts the affordance theory as a theoretical lens with the goal to identify the affordances of Sustainable AI – a field that encompasses the research areas ‘AI for Sustainability as well as ‘Sustainability of AI’ – in the Green IS community. The identified affordances would enable researchers and practitioners to design and use Sustainable AI systems.

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Background: Investigating ways to improve well-being in everyday situations as a means of fostering mental health has gained substantial interest in recent years. For many people, the daily commute by car is a particularly straining situation of the day, and thus researchers have already designed various in-vehicle well-being interventions for a better commuting experience. Current research has validated such interventions but is limited to isolating effects in controlled experiments that are generally not representative of real-world driving conditions. Objective: The aim of the study is to identify cause--effect relationships between driving behavior and well-being in a real-world setting. This knowledge should contribute to a better understanding of when to trigger interventions. Methods: We conducted a field study in which we provided a demographically diverse sample of 10 commuters with a car for daily driving over a period of 4 months. Before and after each trip, the drivers had to fill out a questionnaire about their state of well-being, which was operationalized as arousal and valence. We equipped the cars with sensors that recorded driving behavior, such as sudden braking. We also captured trip-dependent factors, such as the length of the drive, and predetermined factors, such as the weather. We conducted a causal analysis based on a causal directed acyclic graph (DAG) to examine cause--effect relationships from the observational data and to isolate the causal chains between the examined variables. We did so by applying the backdoor criterion to the data-based graphical model. The hereby compiled adjustment set was used in a multiple regression to estimate the causal effects between the variables. Results: The causal analysis showed that a higher level of arousal before driving influences driving behavior. Higher arousal reduced the frequency of sudden events (P=.04) as well as the average speed (P=.001), while fostering active steering (P<.001). In turn, more frequent braking (P<.001) increased arousal after the drive, while a longer trip (P<.001) with a higher average speed (P<.001) reduced arousal. The prevalence of sunshine (P<.001) increased arousal and of occupants (P<.001) increased valence (P<.001) before and after driving. Conclusions: The examination of cause--effect relationships unveiled significant interactions between well-being and driving. A low level of predriving arousal impairs driving behavior, which manifests itself in more frequent sudden events and less anticipatory driving. Driving has a stronger effect on arousal than on valence. In particular, monotonous driving situations at high speeds with low cognitive demand increase the risk of the driver becoming tired (low arousal), thus impairing driving behavior. By combining the identified causal chains, states of vulnerability can be inferred that may form the basis for timely delivered interventions to improve well-being while driving.

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Residential heating is a major source of carbon emissions and, at the same time, represents a significant cost factor for households. Thus, reducing heating costs through sustainable heating behaviors is of great individual and societal interest. However, the consequences of heating behaviors are complex and delayed, so most people are unaware of them. To address this problem, we designed two loss aversion nudges, using (i) a cost salience and (ii) a health risk framing to induce more sustainable heating and ventilation behaviors. We evaluated them against a no-intervention control group in a field experiment at a major German real estate company. While the cost salience nudge was found to improve heating behaviors and became more effective over time, the health risk nudge did not show an effect. Finally, our findings have implications for research on nudging and loss aversion and for practitioners, namely housing providers and more generalized entities aiming to nudge for pro-environmental behaviors.

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Technical debt (TD) is a technical compromise wherein the ability to maintain information technology (IT) applications over the long term is sacrificed for short-term goals. TD occurs when software development teams undergo constant pressure to release applications swiftly, on a tight schedule. The accumulation of TD, which often leads to a significant cost surplus, presents a ubiquitous challenge in technology-driven organisations. To keep TD levels under control, many organisations implement top-down mechanisms that impose enterprise-wide principles on software development teams. This clinical research presents a complementary but distinct approach to managing TD. A digital nudge was introduced at Credit Suisse, a global financial services company, to help raise awareness and understanding, and stimulate actions related to TD decision-making in software development teams. This paper reports on the nudge’s clinical design, implementation, impact, and evaluation. As the nudge was effective in reducing TD in IT applications after one year of use, we demonstrate that digital nudges are viable means for guiding collective decisions in complex decision environments like that of TD management. Our findings have several implications for research and practice.

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Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of the existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their performance on the detection of HR does not guarantee a reliable computation of the HR variability (HRV) measure, which is a more applicable metric for inferring mental and physiological status. The accurate computation HRV measure is based on the precise measurement of the beat-to-beat interval, which can only be accomplished by medical-grade devices that attach electrodes to the body. Considering these existing challenges, we proposed a facial expression-based HRV estimation solution. The rationale is to establish a link between facial expression and heartbeat since both are controlled by the autonomic nervous system. To solve this problem, we developed a tree-based probabilistic fusion neural network approach, which significantly improved HRV estimation performance compared to conventional random forest or neural network methods and the measurements from smartwatches. The proposed solution relies only on commodity camera with a lightweighted algorithm, facilitating its ubiquitous deployment in current and future vehicles. Our experiments are based on 3400 km of driving data from nine drivers collected in a naturalistic field study.

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IT carve-out projects are complex and cost-intensive components of M&A transactions. Existing research sheds little light on the determinants of IT carve-out project complexity and/or its effects on divestor performance. Instead, research has focused on the post-acquisition IT integration project and acquirer performance. This paper presents the first divestor-centric model of IT transactions from the divestor to the acquirer when a Business Unit in a Multi-Business Organization (MBO) is carved out and integrated into another MBO. The model explains how divestor business and IT alignment pre-conditions contribute to increased IT carve-out project complexity. Such complexity increases IT carve-out project time to physical IT separation and creates IT stranded assets, which decrease post-divestment business, IT alignment and divestor performance. The current recommended strategy of adopting transitional service agreements (TSAs) to handle IT carve-out complexity is compared with two new proactive strategies derived from the model. TSA-based strategies restrict the divestor from both decommissioning IT stranded assets and reconfiguring its IT assets to support its new post-divestment business strategy. The two new strategies address IT carve-out complexity without incurring the negative effects from adopting TSAs

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The potentials of artificial intelligence (AI) are manifold and their discussion has gained momentum in research and practice. In the same realm, AI also raises fears among employees of being replaced by AI technologies and, therefore, shying away from using it. Generally, employees want to be empowered to do their job and seek both more responsibility, as well as make their own decisions. In this paper, we conduct a systematic literature review that investigates the current state of the literature on the potential empowering effects of AI-based human-machine behavior. We thus sorted the literature into three behavioral categories: humans shape machine behavior, machines shape human behavior, human-machine co-behavior and crossed them with psychological empowerment dimensions of significance, competence, self-determination, and influence. Our results show corresponding literature streams and provide future research directions in a field that is likely to disrupt the way we work in the future.

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Investors increasingly use machine learning (ML) algorithms to support their early stage investment decisions. However, it remains unclear if algorithms can make better investment decisions and if so, why. Building on behavioral decision theory, our study compares the investment returns of an algorithm with those of 255 business angels (BAs) investing via an angel investment platform. We explore the influence of human biases and experience on BAs’ returns and find that investors only outperformed the algorithm when they had extensive investment experience and managed to suppress their cognitive biases. These results offer novel insights into the role of cognitive limitations, experience, and the use of algorithms in early stage investing.

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Designing for system trustworthiness promises to address challenges of opaqueness and uncertainty introduced through Machine Learning (ML)-based systems by allowing users to understand and interpret systems’ underlying working mechanisms. However, empirical exploration of trustworthiness measures and their effectiveness is scarce and inconclusive. We investigated how varying model confidence (70% versus 90%) and making confidence levels transparent to the user (explanatory statement versus no explanatory statement) may influence perceptions of trust and performance in an information retrieval task assisted by a conversational system. In a field experiment with 104 users, our findings indicate that neither model confidence nor transparency seem to impact trust in the conversational system. However, users’ task performance is positively influenced by both transparency and trust in the system. While this study considers the complex interplay of system trustworthiness, trust, and subsequent behavioral outcomes, our results call into question the relation between system trustworthiness and user trust.

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get_appNaim Zierau, Christian Alexander Hildebrand, Anouk Samantha Bergner, Francesc Busquet I Segui, Anuschka Schmitt, Jan Marco Leimeister
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Voice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. The current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). Moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). The findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. These findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers' service experiences and the theory-driven ''conversational design'' of voice-based interfaces.

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Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.

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Many organizations struggle to measure, control, and manage agility in a manner of continuous improvement. Therefore, we draw on Design Science Research to develop and test a tool for Continuously Assessing and Improving Agile Practices (CAIAP). CAIAP helps agile practitioners to monitor the alignment of “as is” agile practices on individual, team levels with the overall agile strategy of the organization. To develop CAIAP, we first empirically gather requirements, draw on the ICAP framework to base the tool development on a solid conceptual and theoretical basis. CAIAP helps agile practitioners to constantly monitor their agile practices on individual and team levels and to identify areas for improvement to gain greater organizational agility. To researchers, CAIAP helps to make the unit of analysis of agile work explainable, predictable and helps researchers to guide their own empirical research as well as serve as a basis for designing further tool support.

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To develop and implement digital innovations many incumbent companies have set up digital innovation units (DIUs) primarily as part of their digital transformation journey. Despite a steadily growing body of knowledge, however, extant literature is in a nascent stage to explain the unfolding of digital innovations in such units. Drawing on empirical data from a multiple case study we adopt a temporal perspective to contribute to a better understanding of digital innovation in DIUs. To do so, we studied the DIUs of five manufacturing companies and were able to identify several other temporal aspects besides speed that influence or result from DIU’s digital innovation activities. We generalize our findings in the form of five propositions that depict the special role of time to inform extant literature.

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In today's hyper-dynamic business environment, the capability to foster innovation is critical. Many organizations recognize their employees as an unresolved source for innovation during digital transformation. Consequently, intrapreneurship has become of strategic importance, and initiatives, such as digital intrapreneurship platforms, arise. However, many initiatives do not provide aspired outcomes due to the lack of organizational readiness. We follow the action design research method to design a multi-dimensional framework that measures organizational readiness for digital intrapreneurship. Hitherto, we identify 27 factors that contribute to an organization's readiness for the successful implementation and usage of digital intrapreneurship platforms. Ultimately, we strive to provide a digital intrapreneurship readiness tool that helps innovation managers to detect and remove hindering factors before implementing solutions.

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get_appVera Lehmann, Thomas Züger, Martin Maritsch, Michael Notter, Simon Schallmoser, Caterina Bérubé, Caroline Albrecht, Mathias Kraus, Stefan Feuerriegel, Elgar Fleisch, Tobias Kowatsch, Sophie N. Lagger, Markus Laimer, Felix Wortmann, Christoph Stettler
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Aim: To develop a non-invasive machine learning (ML) approach to detect hypoglycemia during real car driving based on driving (CAN) , and eye and head motion (EHM) data. Methods: We logged CAN and EHM data in 21 subjects with type 1 diabetes (18 male, 41 ± yrs, A1c 6.8 ± 0.7 % [51 ± 7 mmol/mol]) during driving in eu- (EU) and hypoglycemia (< 3.0 mmol/L, HYPO) . Participants drove in a car (Volkswagen Touran) supervised by a driving instructor on a closed test-track. Using CAN and EHM data, we built ML models to predict the probability of the driver being in HYPO. To make our approach applicable to different generations of cars, we present 3 ML models: first, a model combining CAN+EHM, representing the modern car with integrated camera. Second, a CAN model using driving data only, since modern cars are not generally equipped with EHM tracking. Third, anticipating that autonomous driving will limit the role of CAN data in the future, we tested a model solely based on EHM. Results: Mean BG in EU and HYPO was 6.3 ± 0.8 mmol/L and 2.5 ± 0.5 mmol/L (p< 0.001) , respectively. The model CAN+EHM achieved an area under the receiver operating characteristic curve of 0.88 ± 0.05, sensitivity of 0.70 ± 0.30, and specificity of 0.83 ± 0.in detecting HYPO. Further results are in Fig. 1. Conclusion: We propose ML-based approaches to non-invasively detect HYPO from driver behavior, applicable to contemporary cars and anticipating developments in automotive technology.

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The Design Science Research (DSR) paradigm is highly relevant to the Information Systems (IS) discipline because DSR aims to improve the state of practice and contribute design knowledge through the systematic construction of useful artefacts. Since study designs can be understood as useful artefacts, DSR can also contribute to improving conceptualizing a research project. This study developed a taxonomy with relevant dimensions and characteristics for DSR research. Such a taxonomy is useful for analyzing existing DSR study designs and successful DSR study design patterns. In addition, the taxonomy is valuable for identifying DSR study design principles (dependencies among characteristics) and subsequently for systematically designing DSR studies. We constructed the DSR study taxonomy through a classification process following the taxonomy development approach of Nickerson et al.

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get_appSimon Föll, Adrian Lison, Martin Maritsch, Karsten Klingberg, Vera Lehmann, Thomas Züger, David Srivastava, Sabrina Jegerlehner, Stefan Feuerriegel, Elgar Fleisch, Aristomenis Exadaktylos, Felix Wortmann
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Background: To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards. Objective: In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches). Methods: Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. Results: First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation. Conclusions: Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system. Trial Registration: Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834

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In-company training is facing new challenges in preparing employees for the demands of digitalized and automated manufacturing. New training concepts like microlearning are necessary to support work-process-related learning. To handle the limitations of microlearning, we develop a 360-degree learning system to demonstrate a realistic work environment. Nonetheless, there is a lack of design knowledge supporting the motivation and performance of employees using the system. Based on a systematic literature review and semi-structured interviews, we have developed design requirements for interactive 360-degree learning environments. We used a workshop-based mixed-method approach with interviews, concept maps, and video analysis to evaluate the motivation and performance of precision mechanics within a prototypical work-process-oriented learning environment in an inter-company vocational training center. The results show a positive effect on learning outcomes and motivation. In addition, the ease of use and sense of presence while using the learning environment are rated as high. We contribute to theory by shedding new light on learners' motivation and performance within work-process-oriented interactive 360-degree learning environments. Furthermore, we offer guidelines for developing such interactive 360-degree learning environments.

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Digital Transformation challenges companies in all industries. Ongoing improvement of their successful existing business is not enough, they must simultaneously build new digital business models to compensate for revenue sources that may already be disappearing. In new strategic ventures (exploration), the concept of organizational ambidexterity reflects the capabilities of companies to simultaneously rely on their core competencies from their existing business (exploitation) to achieve competitive advantage. This paper presents a catalog of criteria that describe the ability of organizations to implement organizational ambidexterity specifically in strategic initiatives of Digital Transformation. The catalog serves as a management tool for assessing the current state of these capabilities, and for setting respective objectives. This initial management instrument can be further developed into a maturity model. Both IT expertise, as well as the interaction of the Chief Information Officer (CIO) and the more recent role of the Chief Digital Officer (CDO) are of central importance here, and hence constitute a difference from the general organizational ambidexterity concept. The catalog has been developed in an iterative Design Science Research approach with two design and evaluation cycles. It contains ready-to-use questions for a survey instrument regarding 46 design factors in seven dimensions with a total of 99 criteria. Several sources served as conceptual foundations. In particular, literature-based knowledge on role requirements for CIO and CDO, relevant subsets of criteria from maturity models, and assessments on general Digital Transformation, intrapreneurship, and innovation culture for digital solutions. The naming as CDO- CIO Do-it Kit is derived from the first letters of the seven dimensions: CDO-CIO collaboration, Digital Transformation strategy, organization, innovation, transformation management, culture & competence, and IT & IT competence.

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