Value network and firm performance: the role of knowledge distance and environmental uncertainty

Purpose: This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance. Design/methodology/approach: The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic. Findings: The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP. Research limitations/implications: This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications: The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance. Originality/value: The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics. © 2023, Emerald Publishing Limited.

Multi-tier analysis of the medical equipment supply chain network: empirical analysis and simulation of a major rupture

Purpose: While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance. Design/methodology/approach: Using the mined data from five tiers of the backward supply chain of medical equipment, the authors constructed a large supply chain network consisting of close to 160,000 dyadic connections. The authors used various network centrality and clustering algorithms to measure the influence of each firm across the supply chain structure. Furthermore, the authors ran a scenario to simulate the elimination of Chinese firms from the global supply chain and recalculated all centralities. Regression analysis was used to measure the effect of supply chain network centralities on firms' performance across the supply chain with and without Chinese firms. Findings: The complexity of global purchasing across global tiers of supply networks had been recognized as a source of uncertainty before the COVID-19 pandemic. This pandemic was the black swan that the medical supply chain professionals had noted its threat in recent years. While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance. Research limitations/implications: This research has three main implications. First, it provides a benchmark for manufacturing firms and distributors around the world operating in the post-COVID-19 business environment to better understand the relationship of their supply chain strategy and firms' financial performance. Second, investors and asset managers can evaluate their portfolios in light of the changing relationship as a result of possible protectionism initiatives. Finally, policymakers can apply the research methodology of this work in various industries while reevaluating post-COVID-19 international relations and trades policies at the firm, industry and country levels. Practical implications: Policymakers working on global connection can utilize the outcome of this research to explore the consequences of local and global policies on trade patterns, organizational performance as well as individuals' movements. Another implication of this study for policymakers is that it provides a powerful simulation and analytical tool to launch or combat the global ruptures, including trade wars and natural disasters stemming from natural events (e.g. climate change) and human-made events (e.g. wars, supply-chain interruptions, sanctions). Originality/value: To the best of our knowledge, this is the first large-scale empirical study that measures the effect of supply chain structure across multiple (five) tiers of the global supply chain on firms' performance. The present study uses the original supply chain network data mined by the authors from financial publications. © 2022, Emerald Publishing Limited.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan.

This article presents a method that detects tweet communities with similar topics and ranks the communities by importance measures. By identifying the tweet communities that have high importance measures, it is possible for users to easily find important information about the coronavirus disease (COVID-19). Specifically, we first construct a community network, whose nodes are tweet communities obtained by applying a community detection method to a tweet network. The community network is constructed based on textual similarities between tweet communities and sizes of tweet communities. Second, we apply algorithms for calculating centrality to the community network. Because the obtained centrality is based on tweet community sizes as well, we call it the importance measure in distinction to conventional centrality. The importance measure can simultaneously evaluate the importance of topics in the entire data set and occupancy (or dominance) of tweet communities in the network structure. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to May 15, 2020. The results show that the proposed method is able to extract keywords that have a high correlation with the number of people infected with COVID-19 in Japan. Because users can browse the keywords from a small number of central tweet communities, quick and easy understanding of important information becomes feasible.

The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms

There are diverging results in the literature on whether engaging in ESG related activities increases or decreases the financial and systemic risks of firms. In this study, we explore whether maintaining higher ESG ratings reduces the systemic risks of firms in a stock market context. For this purpose we analyse the systemic risk indicators of the constituent stocks of S&P Europe 350 for the period of January 2016–September 2020, which also partly covers the COVID-19 period. We apply a VAR-MGARCH model to extract the volatilities and correlations of the return shocks of these stocks. Then, we obtain the systemic risk indicators by applying a principle components approach to the estimated volatilities and correlations. Our focus is on the impact of ESG ratings on systemic risk indicators, while we consider network centralities, volatilities and financial performance ratios as control variables. We use fixed effects and OLS methods for our regressions. Our results indicate that (1) the volatility of a stock’s returns and its centrality measures in the stock network are the main sources contributing to the systemic risk measure, (2) firms with higher ESG ratings face up to 7.3% less systemic risk contribution and exposure compared to firms with lower ESG ratings and (3) COVID-19 augmented the partial effects of volatility, centrality measures and some financial performance ratios. When considering only the COVID-19 period, we find that social and governance factors have statistically significant impacts on systemic risk. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Multi-tier analysis of the medical equipment supply chain network: empirical analysis and simulation of a major rupture

Purpose: While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance. Design/methodology/approach: Using the mined data from five tiers of the backward supply chain of medical equipment, the authors constructed a large supply chain network consisting of close to 160,000 dyadic connections. The authors used various network centrality and clustering algorithms to measure the influence of each firm across the supply chain structure. Furthermore, the authors ran a scenario to simulate the elimination of Chinese firms from the global supply chain and recalculated all centralities. Regression analysis was used to measure the effect of supply chain network centralities on firms' performance across the supply chain with and without Chinese firms. Findings: The complexity of global purchasing across global tiers of supply networks had been recognized as a source of uncertainty before the COVID-19 pandemic. This pandemic was the black swan that the medical supply chain professionals had noted its threat in recent years. While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance. Research limitations/implications: This research has three main implications. First, it provides a benchmark for manufacturing firms and distributors around the world operating in the post-COVID-19 business environment to better understand the relationship of their supply chain strategy and firms' financial performance. Second, investors and asset managers can evaluate their portfolios in light of the changing relationship as a result of possible protectionism initiatives. Finally, policymakers can apply the research methodology of this work in various industries while reevaluating post-COVID-19 international relations and trades policies at the firm, industry and country levels. Practical implications: Policymakers working on global connection can utilize the outcome of this research to explore the consequences of local and global policies on trade patterns, organizational performance as well as individuals' movements. Another implication of this study for policymakers is that it provides a powerful simulation and analytical tool to launch or combat the global ruptures, including trade wars and natural disasters stemming from natural events (e.g. climate change) and human-made events (e.g. wars, supply-chain interruptions, sanctions). Originality/value: To the best of our knowledge, this is the first large-scale empirical study that measures the effect of supply chain structure across multiple (five) tiers of the global supply chain on firms' performance. The present study uses the original supply chain network data mined by the authors from financial publications. © 2022, Emerald Publishing Limited.

COVID-19 and the Economic Importance of In-Person K-12 Schooling.

The extent to which elementary and secondary (K-12) schools should remain open is at the forefront of discussions on long-term pandemic management. In this context, little mention has been made of the immediate importance of K-12 schooling for the rest of the economy. Eliminating in-person schooling reduces the amount of time parents of school-aged children have available to work and therefore reduces income to those workers and the economy as a whole. We discuss two measures of economic importance and how they can be modified to better reflect the vital role played by K-12 education. The first is its size, as captured by the fraction of gross domestic product produced by that sector. The second is its centrality, reflecting how essential the sector is to the network of economic activity. Using data from Canada's Census of Population and Symmetric Input-Output Tables, we show how accounting for this role dramatically increases the importance of K-12 schooling.

La mesure dans laquelle il conviendrait de garder ouverts les établissements d'enseignement de la maternelle à la 12e année est au premier plan des discussions liées à la gestion à long terme de la pandémie. Dans ce contexte, l'importance immédiate de l'éducation de la maternelle à la 12e année pour le reste de l'économie n'a été que timidement évoquée. La suppression de l'enseignement en classe réduit le temps dont disposent les parents d'enfants d'âge scolaire pour travailler, ce qui a pour effet de réduire le revenu versé à ces travailleurs et d'affaiblir l'économie dans son ensemble. Nous traitons de deux indicateurs de cette importance économique et de la façon dont ces indicateurs peuvent être modifiés de manière à mieux refléter le rôle déterminant que joue l'éducation de la maternelle à la 12e année. Le premier indicateur est la taille du secteur, représentée par la fraction du produit intérieur brut qu'il engendre. Le second est la centralité du secteur, soit la mesure dans laquelle il est essentiel au réseau d'activité économique. À l'aide de données tirées du recensement de la population du Canada et des tableaux d'entrées-sorties symétriques, nous démontrons que la prise en compte de ce rôle crucial accroît considérablement l'importance de l'éducation de la maternelle à la 12e année.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.

COVID-19 (Coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the pathophysiology of this deadly virus is complex and largely unknown, we employ a network biology-fueled approach and integrate multiomics data pertaining to lung epithelial cells-specific co-expression network and human interactome to generate Calu-3-specific human-SARS-CoV-2 Interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 target central nodes of host-viral network that participate in core functional pathways. Network centrality analyses discover 28 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host modifying responses and cytokine storm. Overall, our network centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into SARS-CoV-2 pathogenicity that may foster effective therapeutic design. Funding: This work was supported by the National Science Foundation (IOS-1557796) to M.S.M., and U54 ES 030246 from NIH/NIEHS to M. A. Conflict of Interest: The authors declare no competing interests. The authors also declare no financial interests.