Today’s cities face numerous challenges due to climate change and urbanization. The concept of a smart city aims to help cities to address these challenges by adapting modern information and communication technology. Smart mobility and transportation form one important aspect of smart cities. Inefficient mobility in cities can lead to problems such as traffic congestion, which results in frustration for residents and a decrease in the quality of life. Against the backdrop of global warming, cities also strive to reduce CO2 emissions, an attempt which requires sustainable and novel mobility concepts. Blockchain is a current technology, said to have huge potential, that is being investigated for application in many facets of smart cities. In the context of smart mobility, blockchain can be used for transactions relating to ridesharing and electric charging, handling of interactions of platoon members, or serving as a foundation for communication between vehicles. Although initial research about this topic exists, it is distributed among different use-cases and applications. This article conducts a systematic literature review to analyze blockchain’s role in mobility and transportation in smart cities, and its potential to increase efficiency in these areas. With this review, we aim to consolidate and summarize the current knowledge about this topic. As a first result, we present the findings from our literature review, which can be divided into five categories of use-cases. We also present a platform for further research about this emerging topic by identifying promising future research avenues. For this purpose, we derive a future research agenda based on our findings.
Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.
The amount of data and the speed at which it increases grows rapidly. Companies and public institutions try to manage this increasing flood of data effectively and in a manner that adds value. Besides, the companies and public institutions also join corporate networks or platforms to increase their value by sharing their data. The evolution of traditional business intelligence into business analytics, including real-time analysis, increases the high demand for qualitative data. Data governance tries to create a framework to manage these issues. This interdisciplinary research field has now been in existence for nearly two decades. With this contribution, we attempt to provide the research field with a blueprint. This paper aims to explore the past to understand the present and shape the future of data governance. We give an overview of how the research field changed from 2005 to 2020, commenting on its development and pointing out future research paths based on our findings. We, therefore, conducted a bibliometric analysis to describe the research field’s bibliometric and intellectual structure. The findings show that for years the research field concentrated on a few topics, which currently undergoes change and has led to an opening up of the research field. Finally, the results are discussed and future research strands are highlighted
After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.
An important economic sector influenced by the development of platforms is e-commerce. The most successful companies in e commerce employ platform business models and strive to provide other companies with application services. Despite growing economic importance and rising research interest, thus far, no attempts were made to structure existing research into platforms in e-commerce. Hence, a quantitative bibliometric analysis of 7,463 platform-related papers in the context of e commerce was conducted. The papers were published in major conferences, journals, and books from 1993 to 2021. The authors identified a continuous development of platform research in e commerce, with the continuous development characterized by three major periods of research. Furthermore, four clusters in platform research are outlined, i.e., business models, social commerce, infrastructure, and socio-technical characteristics. These clusters can serve as a foundation for future research. The conducted bibliometric analysis contributes to scientific research by offering an objective and systematic overview of platform research in e-commerce
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.