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The integration of external data offers enormous potential for new and expanded value propositions for companies. Despite this potential, organizations often hesitate to share data due to the anticipated negative consequences. This study delves into the decision-making process of organizations regarding data sharing within an ecosystem and explores strategies to motivate other organizations to share data. By combining privacy calculus and ecosystems theory, we employ a qualitative case study approach to examine the German orthopedic market. Our findings reveal three risks (weakening one's position in the ecosystem, IT alignment investments, and penalties for data protection violations) and three benefits (increased value creation for the ecosystem, competitive advantage over other ecosystems, and gaining additional transactions) that organizations perceive when contemplating data sharing. Moreover, we identify three strategies for obtaining data from other organizations: mitigating the risks, emphasizing the benefits, and bypassing the calculus. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has the potential to be a powerful tool for the future of medical professionals. However, we recognize a tension between AI independence (automation) and human involvement (augmentation) in medical decision-making. To resolve this tension, we conducted a systematic literature review to gain an overview of the current knowledge of AI in healthcare. In this systematic literature review, we search and analyze the leading journals in management and information systems for a period from 2012 to 2022. We find that several tasks related to diagnosis, treatment, or administrative work can be effectively automated or augmented with AI. We contribute to information systems research by highlighting possible scenarios for the effective use of AI in medical decision-making. Understanding and mitigating these tensions are particularly important for medical decision-makers, regulators, and managers who are struggling with guidelines for the appropriate use of AI and certification of accountability. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) has emerged as a promising technology for healthcare. As a result, incumbents and startups entering the healthcare market with AIenabled products need to explore the user requirements of medical professionals to enable reliable and satisfactory solutions. To overcome barriers and channel AI for value creation, we examine how startups and incumbents in the healthcare industry are innovating their business models to create value with AI. We conducted a qualitative interview study in which we investigate medical professionals from a provider perspective and representatives of medical companies. In the first round of data collection, we developed a holistic view of the specific requirements for AI-enabled medical devices in radiology (organizational, regulatory, product, communicative, and financial). In the second round, we plan to explore how companies can respond to the previously identified requirements through business model innovation and identified risks. We contribute to research and practice. [ABSTRACT FROM AUTHOR]

Interorganizational data sharing (IODS) grows increasingly complex in the context of business ecosystems. As the literature on IODS finds that organizations only share data if they perceive benefits from it, this study explores value appropriation strategies (VAS) through an ongoing inductive case study of the German orthopedic ecosystem, exploiting interview and secondary data from executives of various treatment facilities and a leading manufacturer of health products. Our preliminary grounded theorizing indicates that VAS for IODS involve four elements (partner selection, data tailoring, reciprocal design, and control enforcement) that each are realized by specific VAS activities. Further, we find that, different from other contexts, ensuring VAS for IODS is not about controlling the value creation process but is grounded in upstream strategic decisions. This understanding of VAS for IODS indicates that having strategies for value appropriation in place before diving into IODS is crucial for succeeding in data ecosystems. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a powerful tool for complex decision-making. However, little is known about how AI-based systems should be integrated into clinical practice to improve patient and provider outcomes. We argue that AI-based systems are no longer passive tools and can assume certain responsibilities for tasks. A novel aspect of our study is the exploration of the power relationship between AI and physicians, which can significantly impact medical care. Therefore, we aim to shed light on how configurations of power in AI interactions influence technostress and work outcomes. We outline a lab-in-the-field experiment on diagnosis and reporting by physicians using AI. Our study aims to uncover (1) the differential effects of power configurations in AI on physicians’ positive and negative technostress levels, and (2) their psychological and behavioral outcomes. We aim to contribute to information systems research and practice by highlighting the power implications of AI. [ABSTRACT FROM AUTHOR]

AI as a Service (AIaaS) is a promising path to leverage AI capabilities from the cloud. However, there is no one-size-fits-all service, but heterogenous service options since interdependent AIaaS characteristics require trade-offs. We lack knowledge on these tradeoffs and how they result from conflicting characteristics. Therefore, we interviewed 39 AIaaS providers, customers, and consultants to provide rich descriptions of interdependent characteristics and uncover resulting trade-offs and their consequences. This study contributes to a better understanding of the inner functioning and interplay of AIaaS characteristics and discusses how this complex nature of service offerings impacts providers’ design and customers’ selection decisions. [ABSTRACT FROM AUTHOR]