PurposeGiven the growing importance of blockchain technology (BT), the authors use the unified theory of acceptance and use of technology (UTAUT), which posits that BT adoption intention depends on the complementarity between UTAUT and blockchain transparency (BTRAN) and examine it in a new setting: the boundary condition of perceived helpfulness.Design/methodology/approachThe authors review the major conceptual literature on both UTAUT and BT to identify their principal common factors. They examine the complementarity between UTAUT and BTRAN and further test the moderating effect of perceived helpfulness. The authors used the PLS technique for data analysis because this technique can test the direct and interaction effects.FindingsThe complementarity between UTAUT and BTRAN strongly affects BT adoption intention. The authors further show that perceived helpfulness moderates the relationship between adoption intention and usage behavior. At high levels of perceived helpfulness, usage behavior increases rapidly with adoption intention.Originality/valueThe results indicate that UTAUT is a valuable theory to identify the determinants of adoption intention, confirming its robustness in blockchain-enabled supply chain management. The combination of UTAUT and BTRAN can contribute a plausible approach to the strategy literature: the complementarity effect might create more benefits than adopting a single practice.
Viruses are the most numerous biological entity, existing in all environments and infecting all cellular organisms. Compared with cellular life, the evolution and origin of viruses are poorly understood; viruses are enormously diverse, and most lack sequence similarity to cellular genes. To uncover viral sequences without relying on either reference viral sequences from databases or marker genes that characterize specific viral taxa, we developed an analysis pipeline for virus inference based on clustered regularly interspaced short palindromic repeats (CRISPR). CRISPR is a prokaryotic nucleic acid restriction system that stores the memory of previous exposure. Our protocol can infer CRISPR-targeted sequences, including viruses, plasmids, and previously uncharacterized elements, and predict their hosts using unassembled short-read metagenomic sequencing data. By analyzing human gut metagenomic data, we extracted 11,391 terminally redundant CRISPR-targeted sequences, which are likely complete circular genomes. The sequences included 2,154 tailed-phage genomes, together with 257 complete crAssphage genomes, 11 genomes larger than 200 kilobases, 766 genomes of Microviridae species, 56 genomes of Inoviridae species, and 95 previously uncharacterized circular small genomes that have no reliably predicted protein-coding gene. We predicted the host(s) of approximately 70% of the discovered genomes at the taxonomic level of phylum by linking protospacers to taxonomically assigned CRISPR direct repeats. These results demonstrate that our protocol is efficient for de novo inference of CRISPR-targeted sequences and their host prediction.
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.