Toward Architecture-Driven Interdisciplinary Research Learnings from a Case Study of COVID-19 Contact Tracing Apps

This paper explores the use of an architectural perspective to study complex data ecosystems and to facilitate a normative discourse on such ecosystems. It argues that an architectural perspective is helpful to bridging discursive and methodological gaps between information systems (IS) research and legal studies. Combining architectural and normative perspectives is a novel interdisciplinary research approach that provides a framework for analyzing techno-legal contexts. The merits and challenges of this approach are demonstrated and discussed in this paper using the example of COVID-19 contact tracing apps. We conceptualize our results on three levels of knowledge: the first is the actual knowledge of the exemplary contact tracing app we studied and its ecosystem;the second is knowledge of the architectural meta-model that we used, its benefits and its shortcomings;and the third is knowledge of the interdisciplinary research process of acquiring common knowledge shared by IS scholars and legal experts. © 2022 Owner/Author.

The challenges of data in future pandemics.

The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.

Evolutionary Game Analysis of Co-Opetition Strategy in Energy Big Data Ecosystem under Government Intervention

This study discusses how to facilitate the barrier-free circulation of energy big data among multiple entities and how to balance the energy big data ecosystem under government supervision using dynamic game theory. First, we define the related concepts and summarize the recent studies and developments of energy big data. Second, evolutionary game theory is applied to examine the interaction mechanism of complex behaviors between power grid enterprises and third-party enterprises in the energy big data ecosystem, with and without the supervision of government. Finally, a sensitivity analysis is conducted on the main factors affecting co-opetition, such as the initial participation willingness, distribution of benefits, free-riding behavior, government funding, and punitive liquidated damages. The results show that both government supervision measures and the participants’ own will have an impact on the stable evolution of the energy big data ecosystem in the dynamic evolution process, and the effect of parameter changes on the evolution is more significant under the state of no government supervision. In addition, the effectiveness of the developed model in this work is verified by simulated analysis. The present model can provide an important reference for overall planning and efficient operation of the energy big data ecosystem.

Growing Process of Communities on Data Platforms: Case Analysis of a COVID-19 Dataset

In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value. © 2021 IEEE.