The purpose of this study is to conduct a bibliometric analysis to examine the most influential journals, institutions, and countries in social media (SM) publications related to knowledge management (KM). Moreover, various research themes in SM KM publications are also explored. VOSviewer was employed to process 234 SM KM publications retrieved from Web of Science (WoS) in the time period 2009-2019. Different methodologies were used according to the nature of bibliometric analysis and explained in each section. Journal of Knowledge Management was the most influential journal in SM KM publications. USA and England ranked first and second respectively, while the Tampere University of Technology was the most productive institute in SM KM research. Four emerged themes indicated an explicit contribution of SM users in KM through big data, knowledge sharing, innovation, Enterprise 2.0, and social capital. This is the first bibliometric study that explores the overall contribution of SM publications in the KM field.

In literature, there is a shortage of comprehensive documents that can provide proper details about Twitter in research community. This study conducted a first descriptive bibliometric analysis to examine the most influential journals, institutions, and countries on Twitter. Similarly, bibliometric mapping analysis is carried out to explore different research themes in Twitter publications. VOSviewer was employed to process the 11,006 Twitter publications retrieved from the Web of Science (WoS) from 2009 to 2018. Obtained results suggest that USA and China received the highest number of publications on Twitter research, while the University of Illinois was the most productive institute. Furthermore, the five major themes have emerged in Twitter publications, and its remarkable role has been found in event detection, sentiment analysis, education, health, politics, and crisis as well as risk management. The authors believe that this study will open new doors for researchers to use online Twitter social networking communities in beauty salons, consulting companies, banks, and airlines.

Generally, comprehensive documents are needed to provide the research community with relevant details of any research direction. This study conducted the first descriptive bibliometric analysis to examine the most influential journals, institutions, and countries in the field of artificial intelligence in textiles. Furthermore, bibliometric mapping analysis was also used to examine diverse research topics of artificial intelligence in textiles. VOSviewer was used to process 996 articles retrieved from Web of Science—Core Collection from 2007 to 2020. The results show that China and the United States have the largest number of publications, while Donghua University and Jiangnan University have the highest output. These three themes have also appeared in textile artificial intelligence publications and played a significant role in the textile structure, textile inspection, and textile clothing production. The authors believe that this research will unfold new research domains for researchers in computer science, electronics, material science, imaging science, and optics and will benefit academic and industrial circles.

Knowledge management has been the subject of much research in buyer-supplier relationship literature. Many of these studies outline the benefits attributed to buying and supplying firms as a result of knowledge sharing between its managers. However, it is important to note managers across firms can also deliberately hide knowledge from each other during their business interaction. This study fills this important gap. The analysis of this study is based on a pilot study comprising six semi-structured interviews with the managers of buying and supplying firms. The findings suggest that managers of buying and supplying firms experience five different types of antecedents during their business interaction. This paper provides theoretical contribution to supply chain knowledge management literature by proposing five antecedents of knowledge hiding in a buyer-supplier relationship. The findings of this study also have practical implications. Specifically, firms can use initial findings from this study to understand what compels their management to engage in knowledge hiding.

Background: This study enriched our understanding by systematically reviewing knowledge management twitter health (KMTH) articles extracted from Web of Science (WoS) using cartography analysis through VOSviewer–for the last 11 years. Methods: A total of 798 KMTH articles were found from 2009 to 2019, analyzed based on the most co-occurrence keywords of KMTH articles. Results: Three clusters emerged through cartography analysis; Cluster 1: Twitter as health education and health promotion platform; Cluster 2: Twitter as public health promotion platform and Cluster 3: Twitter as health sentiment platform through big data and machine learning. Conclusion: This study opened new avenues for all health care providers to utilize Twitter as a KM platform to promote health care. This is the first bibliometric analysis of KMTH publications according to our best knowledge.