The role of resource orchestration in humanitarian operations: a COVID-19 case in the US healthcare.

This paper investigates the role of resource allocation in alleviating the impact on from disruptions in healthcare operations. We draw on resource orchestration theory and analyse data stemming from US healthcare to discuss how the US healthcare system structured, bundled and reconfigured resources (i.e. number of hospital beds, and vaccines) during the COVID-19 pandemic. Following a comprehensive and robust econometric analysis of two key resources (i.e. hospital beds and vaccines), we discuss its effect on the outcomes of the pandemic measured in terms of confirmed cases and deaths, and draw insights on how the learning curve effect and other factors might influence in the efficient and effective control of the pandemic outcomes through the resource usage. Our contribution lies in revealing how different resources are orchestrated ('structured', 'bundled', and 'leveraged') to help planning responses to and dealing with the disruptions to create resilient humanitarian operations. Managerial implications, limitations and future research directions are also discussed.

On identifying risk-adjusted efficiency gains or losses of prospective mergers and acquisitions.

We propose a new approach to evaluate and compare ex-ante the risk-adjusted efficiency gains or losses of potential mergers and acquisitions (M &A). We test our methodology in the banking sector by estimating a latent class stochastic frontier model to account for the unobserved heterogeneity. We show that post-prospective M &A financial institutions can be better equipped to withstand potential adverse economic conditions. We highlight that similarities in strategic characteristics are vital in the creation of post-consolidation cost efficiency surplus. Our results are consistent after various robustness tests. Our findings have important policy implications in light of the challenges the traditional banking business model faces in the current digitalisation era.

FDI, economic growth, and carbon emissions of the Chinese steel industry: new evidence from a 3SLS model.

Determine the main factors affecting carbon emissions of the Chinese steel industry is indispensable commitments to achieve the sustainable development of China. Hereby, based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIPRAT) model, this paper combines the economic growth function, carbon emission production function, and the FDI function of the Chinese steel industry, and uses the three-stage least square equation model (3SLS) to analyze the relationship between China's economic growth, carbon emissions in the steel industry, and FDI (foreign direct investment) inflows. The results document a complete two-way causal relationship of three variables in the whole country and the Western region, while the relationship in the Eastern region and the Central region is not complete. Moreover, there are no bidirectional causal relationship between carbon emissions and FDI in the Eastern region, while only bidirectional causality between carbon emissions and FDI in the Central region. These findings are of great significance for the Chinese steel industry to formulate effective emission reduction policies.

The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice.

Scholars have highlighted the role of Digital Technologies (DT) in enhancing productivity and performance in Small and Medium Enterprises (SMEs). However, there is limited evidence on the use of DT for dealing with the consequences of extreme events, such as COVID-19. We discuss this gap by (i) outlining potential research avenues and (ii) reflecting on the managerial implications of using DT within SMEs to deal with the repercussions of COVID-19 and securing business continuity.