Following the massive impact of the Covid-19 pandemic on the global economy and on small and medium-sized enterprises (SMEs) in particular, the concept of resilience has experienced a renaissance. As an organizational concept, business model resilience describes the extent to which an organization can maintain or quickly recover its value proposition despite unexpected current or future disruptions (Palzkill-Vorbeck 2018). Although research has been conducted in this area for decades, there is still a lack of a unified framework that brings together the findings from research and links them to organizational practice. The paper addresses this gap by developing a framework for business model resilience and demonstrating its practical relevance for organizational performance during the Covid-19 pandemic in 2020. The framework includes 11 factors that characterize the resilience of an organization’s business model. For managers and decision-makers, the framework is an opportunity to assess and improve the resilience of their organizations. For researchers, the framework is an important foundation for transferring the concept of business model resilience into organizational practice.

Background: The rapidly growing quantity of health data presents researchers with ample opportunity for innovation. At the same time, exploitation of the value of Big Data poses various ethical challenges that must be addressed in order to fulfil the requirements of responsible research and innovation (Gerke et al. 2020; Howe III and Elenberg 2020). Data sovereignty and its principles of self-determination and informed consent are central goals in this endeavor. However, their consistent implementation has enormous consequences for the collection and processing of data in practice, especially given the complexity and growth of data in healthcare, which implies that artificial intelligence (AI) will increasingly be applied in the field due to its potential to unlock relevant, but previously hidden, information from the growing number of data (Jiang et al. 2017). Consequently, there is a need for ethically sound guidelines to help determine how data sovereignty and informed consent can be implemented in clinical research. Methods: Using the method of a narrative literature review combined with a design thinking approach, this paper aims to contribute to the literature by answering the following research question: What are the practical requirements for the thorough implementation of data sovereignty and informed consent in healthcare? Results: We show that privacy-preserving technologies, human-centered usability and interaction design, explainable and trustworthy AI, user acceptance and trust, patient involvement, and effective legislation are key requirements for data sovereignty and self-determination in clinical research. We outline the implications for the development of IT solutions in the German healthcare system.

Due to increased digitalisation and dematerialisation, the traditional value chain concept appears to be outdated. Instead, the term value network has emerged to describe how organisations co-create value in today’s economy. However, it remains unclear what contributes to success or failure of collaborative business models in value networks. The article closes this research gap. The authors identify relevant papers through a systematic literature review process and review them using qualitative content analysis. From the 45 papers analysed, 21 critical factors contributing to success or failure of business models in value networks were extracted. They can be structured along 6 dimensions. From a theoretical perspective, the article deepens the understanding of business models in value networks and provides clear perspectives for future research. From a practical perspective, managers can use the results as target variables for strategic management to ensure success of their value network.

Background: The healthcare sector is currently undergoing a significant transformation, driven by an increased utilization of data. In this evolving landscape, surveys are of pivotal importance to the comprehension of patient needs and preferences. Moreover, the digital affinity of patients and physicians within the healthcare system is reforming the manner in which healthcare services are accessed and delivered. The utilization and donation of data are influencing the future of medical research and treatment, while artificial intelligence (AI) is empowering patients and physicians with knowledge and improving healthcare delivery. Methods: In order to evaluate the opinions of patients and physicians regarding the management of personal health data and the functionality of upcoming data management devices in the context of healthcare digitization, we conducted an exploratory study and designed a survey. The survey focused on a number of key areas, including demographics, experience with digitization, data handling, the identification of needs for upcoming digitization, and AI in healthcare. Results: A total of 40 patients and 15 physicians participated in the survey. The results indicate that data security, timesaving/administrative support, and digital communication are aspects that patients associate with patient-friendly digitization. Based on the responses provided by physicians, it might be concluded that future digital platforms should prioritize usability, time efficacy, data security, and interoperability. Conclusions: In terms of expectations for future digital platforms, there is a notable overlap between the needs expressed by patients and those identified by physicians, particularly in relation to usability, time management, data security, and digital communication. This suggests that the requirements of different stakeholders can be combined in a future system, although individual issues may still require attention.