Alan Belasen, Ariel Belasen and Zhilan Feng
Prior studies have shown that physician-led hospitals have several advantages over non-physician-led hospitals. This study seeks to test whether these advantages also extend to…
Abstract
Purpose
Prior studies have shown that physician-led hospitals have several advantages over non-physician-led hospitals. This study seeks to test whether these advantages also extend to periods of extreme disruptions such as the COVID-19 pandemic, which affect bed availability and hospital utilization.
Design/methodology/approach
The authors utilize a bounded Tobit estimation to identify differences in patient satisfaction rates and in-hospital utilization rates of top-rated hospitals in the United States.
Findings
Among top-rated US hospitals, those that are physician-led achieve higher patient satisfaction ratings and are more likely to have higher utilization rates.
Research limitations/implications
While the COVID-19 pandemic generated greater demand for inpatient beds, physician-led hospitals improved their hospitals’ capacity utilization as compared with those led by non-physician leaders. A longitudinal study to show the change over the years and whether physician Chief Executive Officers (CEOs) are more likely to improve their hospitals’ ratings than non-physician CEOs is highly recommended.
Practical implications
Recruiting and retaining physicians to lead hospitals, especially during disruptions, improve hospital’s operating efficiency and enhance patient satisfaction.
Originality/value
The paper reviews prior research on physician leadership and adds further insights into the crisis leadership literature. The authors provide evidence based on quantitative data analysis that during the COVID-19 pandemic, physician-led top-rated US hospitals experienced an improvement in operating efficiency.
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Keywords
Chris Ratcliffe and Bill Dimovski
The purpose of this paper is to use Australian Real Estate Investment Trust (A‐REIT) data to empirically examine potential influencing factors on A‐REITs becoming a bidder or a…
Abstract
Purpose
The purpose of this paper is to use Australian Real Estate Investment Trust (A‐REIT) data to empirically examine potential influencing factors on A‐REITs becoming a bidder or a target in the mergers and acquisitions (M&A) area.
Design/methodology/approach
This study uses logistic regression analysis to investigate the odds of publically traded A‐REITs being either a bidder or a target as a function of a number of financial and corporate governance variables.
Findings
Prior research in the US REIT M&A area has shown that target size is inversely related to takeover likelihood; in contrast, the authors' Australian results show that size has a positive impact. Prior research on share price and asset performance has shown that underperformance increases the odds of an entity becoming a target, but this paper's results further support these findings and provide confirmation of the inefficient management hypothesis. For acquirers it was found that leverage, cash balances, management structure, the level of shares held by related parties and the global financial crisis have an important impact on bidder likelihood.
Practical implications
Given that the literature suggests that investors can earn significant positive abnormal returns by owning targets, but incur significant abnormal losses by owning bidders, at announcement, this study will be useful to fund managers and other investors in A‐REITs by investigating the characteristics of those firms that become targets and bidders.
Originality/value
This paper adds to the recent US REIT M&A literature by examining the second biggest REIT market in the world and reporting a number of factors that might influence A‐REITs to become targets or bidders.
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Maria Barbarosou, Ioannis Paraskevas and Amr Ahmed
– This paper aims to present a system framework for classifying different models of military aircrafts, which is based on the sound they produce.
Abstract
Purpose
This paper aims to present a system framework for classifying different models of military aircrafts, which is based on the sound they produce.
Design/methodology/approach
The technique is based on extracting a compact feature set, of only two features, extracted from the frequency domain of the aircrafts’ sound signals produced by their engines, namely, the spectral centroid and the signal bandwidth. These features are then introduced to an artificial neural network to classify the aircraft signals.
Findings
The current system identifies the aircraft type among four military aircrafts: Mirage 2000, F-16 Fighting Falcon, F-4 Phantom II and F-104 Starfighter. The experimental results show that the aforementioned types of aircrafts can be accurately classified up to 96.2 per cent via the proposed method.
Practical implications
The proposed system can be used as a low-cost assistive tool to the already existing radar systems to avoid cases of missed detection or false alarm. More importantly, the same method can be used for aircrafts that use stealth technology that cannot be detected using radar devices.
Originality/value
The proposed method constitutes a novel approach to classifying military aircrafts based on their sound signature. It utilizes only two spectral features extracted from the sound of the aircraft engine; these features are then introduced to a neural network classifier.