The consciousness of safety risk factors and the emergence of WHO guidelines for the preparedness of health care workers have pushed the health care systems to take proactive decisions to maintain a safe and productive working environment during the COVID-19 outbreak. In order to provide this working environment, detailed identification, and analysis of safety risk factors are required. In this context, we proposed a hybrid fuzzy-based decision-making framework to rank the Indian hospitals based on the prevalence of safety risk factors among the health care workers. First, fifteen relevant safety risk factors are identified with the help of the Fuzzy Delphi Method (FDM). Second, the weights of categories and their respective factors are computed and are ranked based on their criticality by the Fuzzy Analytic Hierarchy Process (FAHP). Finally, Indian Hospitals are ranked based on these factors using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS).

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PurposeThis study aims to review and organize the research articles which focused on the impact of COVID-19 pandemic in the supply chain (SC) domain through a bibliometric and network analysis.Design/methodology/approachInitially, a total of 772 research articles with selected keywords were retrieved from the Scopus database for the year 2020 (with the commencement of COVID-19 outbreak). After the filtration and refinement, 484 research articles were found relevant and unique. Further, this study systematically reviews and evaluates the 484 research articles including influential authors, keys journals, influential research work, and collaboration among the countries and institutes with the help of bibliometric analysis tool. The emergent research clusters are identified and established.FindingsThe findings reveal that the total number of related publications are steadily growing with the United States leading the way. European countries have made notable accomplishments as well. In addition, both the most cited publications and the keyword distribution provide research guidance for future research.Practical implicationsThis study focuses on the need and advancement of the literature on the impacts of the COVID-19 pandemic on SCs to frame a research agenda for researchers and practitioners.Originality/valueThe present study offers future research directions in the area of SC under the pandemic situation.

PurposeThis study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.Design/methodology/approachA fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.FindingsThe effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.Research limitations/implicationsThe outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.Originality/valueBig data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.