Purpose The purpose of this paper is to explore and address the possible reasons for the hesitancy in accepting cryptocurrency as an asset class by the world governments and central banks. The behaviour of delaying the acceptance or using cryptocurrency has been termed as crypto-hesitancy. Design/methodology/approach To establish the conceptual understanding of crypto-hesitancy, the bibliometric analysis was performed through Bibliometrix and VOSviewer. Through keyword search technique this study has located 507 useful studies in Scopus database, which were used for the bibliometric analysis. Findings The findings of the study reveal that the government of developed and developing nations and central banks hesitate to regulate and accept cryptocurrency due to the following reasons: cryptocurrency’s ties to illegal activity, speculation and cryptocurrency’s capacity to circumvent government-imposed capital controls. The findings of this study can be used as platform to develop the construct – crypto-hesitancy – further and explore the empirical insights of it. Originality/value To the best of the authors’ knowledge, the construct crypto-hesitancy has not been evolved yet, which makes this study the first attempt to theoretically understand the concept and its evolution.
In this paper, the patterns of differences of some key economic variables among the four major U.S. regions are investigated. These four regions include: Northeast, Midwest, South and West. The grouping of individual U.S. states into these four regions is in accordance with the U.S. Census Bureau and the U.S. Department of Commerce classification. Two main points are explored in this study. The first one examined and summarized the descriptive statistics of the variables for states in these four regions. These variables and data were culled from the databases of U.S. Census Bureau, the Bureau of Economic Analysis (BEA), the U.S. Department of Commerce, the Bureau of Labor Statistics, World Population Review and the U.S Department of Agriculture. The second one covered the identification of the four regions' similarities and dissimilarities for the identified variables. One-way analysis of variance (ANOVA) is the statistical tool for the testing hypothesis of four regions' means equality. To ascertain whether the regions that ranked the highest actually differ from another and from the remaining regions, the researcher made use of multiple comparison procedures, specifically the Scheffe test.
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