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Article
Publication date: 3 March 2023

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…

314

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.

Details

Journal of Health Organization and Management, vol. 37 no. 3
Type: Research Article
ISSN: 1477-7266

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Article
Publication date: 2 August 2013

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…

823

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.

Details

Journal of Property Investment & Finance, vol. 31 no. 5
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 4 January 2016

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.

590

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.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 88 no. 1
Type: Research Article
ISSN: 0002-2667

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