Business analytics competencies in stabilizing firms’ agility and digital innovation amid COVID-19

Digital innovation is not a technology in itself but businesses’ ability to exploit digital technology to resolve outmoded problems. Digital innovation is leveraging improvements or innovating technology to reimagine business practice. The Coronavirus disease 2019 exerted enormous effects on people's physical and psychological health. In addition, this pandemic adversely affected the global economy, from sole proprietors to multinational firms. However, such an effect did not hinder versatile products, services, and upgraded versions of technologies. Modern firms rely heavily on available data sets to make decisions through analytics. Manufacturing is one of the most dynamic industries due to market pressures and continually changing customer demands. This study examines the relationship between business analytics competencies and digital innovation and explores the mediating role of absorptive capacity and firm agility. Data are collected from 493 managers of manufacturing firms and analyzed by using structural equation modeling through smart-PLS. Results reveal a positive relationship between business analytics competence and digital innovation mediated by absorptive capacity and firm agility. With its theoretical contributions, this study can initiate practical research outcomes in manufacturing firms.

Genome sequencing and analysis of genomic diversity in the locally transmitted SARS-CoV-2 in Pakistan.

Surveillance of genetic diversity of the SARS-CoV-2 is extremely important to detect the emergence of more infectious and deadly strains of the virus. In this study, we evaluated mutational events in the SARS-CoV-2 genomes through whole genome sequencing. The samples were collected from COVID-19 patients in different major cities of Pakistan during the four waves of the pandemic (May 2020 to July 2021) and subjected to whole genome sequencing. Using in silico and machine learning tools, the viral mutational events were analyzed, and variants of concern and of interest were identified during each of the four waves. The overall mutation frequency (mutations per genome) increased during the course of the pandemic from 12.19 to 23.63, 31.03, and 41.22 in the first, second, third, and fourth waves, respectively. We determined that the viral strains rose to higher frequencies in local transmission. The first wave had three most common strains B.1.36, B.1.160, and B.1.255, the second wave comprised B.1.36 and B.1.247 strains, the third wave had B.1.1.7 (Alpha variant) and B.1.36 strains, and the fourth waves comprised B.1.617.2 (Delta). Intriguingly, the B.1.36 variants were found in all the waves of the infection indicating their survival fitness. Through phylogenetic analysis, the probable routes of transmission of various strains in the country were determined. Collectively, our study provided an insight into the evolution of SARS-CoV-2 lineages in the spatiotemporal local transmission during different waves of the pandemic, which aided the state institutions in implementing adequate preventive measures.

Modeling the enablers of online consumer engagement and platform preference in online food delivery platforms during COVID-19

Due to the COVID-19 outbreak globally during 2020, the usage and virtues of food delivery apps (FDA) have increased immensely, facilitating the consumer to access the food and food providers to keep functioning. However, this study aims to investigate the enablers of online consumer engagement (OCE) and platform preference in the foodservice industry, keeping in view the moderating role of peer pressure by following uses and gratifications theory (UGT). The data were collected from 322 FDA's user in China during the COVID-19 lockdown and analyzed employing partial least-square structural equation modeling (PLS-SEM). PLS-SEM results revealed that consumer’s self-concept and platform interactivity affect OCE and platform preference. Further, OCE mediates the effect of the relationship between platform interactivity, self-concept, and platform preference. Furthermore, peer pressure significantly moderates the relationship between OCE and platform preference. This research contributes to the prevailing body of literature in a novel way by employing UGT on consumer behavior in the FDA. The study has value for online food businesses and implications for consumers, retailers, and practitioners to formulate and implement value-added strategies in a consumption-oriented emerging economy.

In silico screening, SAR and kinetic studies of naturally occurring flavonoids against SARS CoV-2 main protease.

The Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) pandemic has become a global challenge based on its replication within the host cells that relies on non-structural proteins, protease (Mpro). Flavonoids, an important class of naturally occurring compounds with medicinal importance, are frequently available within fruits and vegetables. Herein, we report the in silico studies on naturally occurring flavonoids consisting of molecular docking studies and evaluation of theoretical kinetics. In this study, we prepared a library of nine different classes of naturally occurring flavonoids and screened them on Autodock and Autodockvina. The pharmacokinetic properties of most promising compounds have been predicted through ADMET SAR, inhibition constants, ligand efficiency and ligand fit quality have been worked out theoretically. The results revealed that naturally occurring flavonoids could fit well in the receptor's catalytic pocket, interact with essential amino acid residues and could be useful for future drug candidates through in vitro and in vivo studies. Moreover, MD simulation studies were conducted for two most promising flavonoids and the protein-ligand complexes were found quite stable. The selected natural flavonoids are free from any toxic effects and can be consumed as a preventive measure against SARS CoV-2.

Phenolic and flavonoid contents in Malva sylvestris and exploration of active drugs as antioxidant and anti-COVID19 by quantum chemical and molecular docking studies

The exploration of natural sources of antioxidant phytochemicals for human use with little toxicity gained worldwide attention. The preliminary screening of Malva sylvestrisextracts revealed that its phytochemicals such as polyphenols, flavonoids and tannins, have high therapeutic potential. The total phenolic/flavonoids compounds ofMalva sylvestris were extracted and isolated using bioassay guidelines, as well as in-silico studies. The extracts radical scavenging activity was further investigated using 1-diphenyl-2-picrylhydrazyl (DPPH) and nitric oxide (NO) radical bioassays. The antioxidant potentials of various fractions were compared to standard antioxidants such as ascorbic acid and quercetin. The dichloromethane extracts of Malva sylvestris exhibited the antiradical activity against DPPH and NO with radical scavenging activities (RSA) of 88.52 and 91.05 % with IC50 values 22.11 and 19.01 µg/mL respectively. Bio guided isolation form the dichloromethane sub fractions that afforded twelve phytochemicals. Furthermore, the frontier molecular orbitals (FMO), several molecular descriptors, electron affinity, ionization potential and molecular electrostatic potential (MEP) have been discussed to probe the active sites of various phytochemicals. A systematic study of isolated drugs wasconducted, as well as docking, frontier molecular orbitals energies, active sites and molecular descriptors were compared with drugs currently used against COVID19 namely, dexamethasone, hydroxychloroquine, favipiravir and remdesivir. For the first time, through molecular docking approach, the inhibitions of these plant phytochemicals with NADPH were recorded to show antioxidant behavior and to explore anti-SARS-CoV-2 using core protease (6LU7) protein.