ABSTRACT
PurposeDrawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant. Design/methodology/approachA two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry. FindingsThe empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants. Practical implicationsThis research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology. Originality/valueThis study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.
ABSTRACT
Objective: To explore the main active components, key targets and signaling pathways of Qingfei Dayuan Granules in treating of COVID-19 based on network pharmacology and molecular docking. Methods: TCMSP, ETCM and YATCM databases were used to search the chemical constituents of Qingfei Dayuan Granules, and the threshold values of OB ≥ 30% and DL ≥0.18 were used to screen the potential active compounds. SIB and STITCH databases were used to query the targets corresponding to the active compounds, and the PPI network and network topology parameters were obtained by using STRING database. Cytoscape 3.6.0 was used to screen the hub targets. The key targets were analyzed by Gene Ontology (GO), the Kyoto Encyclopedia of genes and genomes (KEGG) pathway enrichment and tissue enrichment using DAVID 6.8 software. The molecular docking was performed by AutoDock Tools 1.5.6 software. Results: A total of 251 active compounds and 1 037 targets were obtained, 107 key targets and 185 corresponding compounds were screened. The key targets involved ESR1, AR, EGFR, KDR, MMP2, and 52 genes were coexpressed with ACE2. The results of GO function enrichment analysis showed that Qingfei Dayuan Granules mainly regulated the biological processes of cell surface signaling transduction, molecular function, phosphorylation and transcription. KEGG pathway enrichment mainly involved chemokine signaling pathway, T cell receptor signaling pathway, B cell receptor signaling pathway, natural killer cell mediated cytotoxicity and Toll like receptor signaling pathway. The results of tissue enrichment showed that the key gene expression sites were mainly in lung and epithelial cells, involving a variety of immune cells, such as T cells, B cells, lymphocytes, etc. Molecular docking showed that the compounds with good binding power to SARS-CoV-2-RBD-ACE2 complex in Qingfei Dayuan granules were mainly come from Bupleuri Radix, Codonopsis Radix, Anemarrhenae Rhizoma, and Glycyrrhizae Radix et Rhizoma. Saikosaponin, glycyrrhizic acid, anemarsaponin had good binding power with SARS-CoV-2-S-RBD-ACE2, which may be potential active components against SARS-CoV-2. Conclusion: Qingfei Dayuan Granules has the characteristics of multi-components, multi-targets and multi-pathway regulation. Saikosaponin, glycyrrhizic acid, and anemarsaponin may be the potential active components against SARS-CoV-2. The mechanisms of its treatment against COVID-19 may be related to the regulation of the co-expressed genes with ACE2, inhibition of inflammation and immune related signaling pathways, and the destruction of the complex structure of SARS-CoV-2-S-RBD-ACE2.