scholarly journals The relationship between a physician incentive plan and departmental performance in a Taiwan hospital

2013 ◽  
Vol 2 (3) ◽  
pp. 79 ◽  
Author(s):  
Nai-Yng Liu ◽  
Hsuan-Lien Chu ◽  
Chiu-Chuo Liao
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Measurement ◽  
Multiple Regression ◽  
Regression Models ◽  
Data Envelopment ◽  
For Profit ◽  
Multiple Regression Models ◽  
Incentive Plan ◽  
The Impact ◽  
The Relationship

The objective of this study is to investigate the influence of a physician incentive plan based upon treatment of patients in a large private non-for-profit hospital in Taiwan. We examine the relationship between physicians’ bonuses and departmental performance to assess the impact of the physician incentive plan in the case hospital. The multiple regression models are used to examine the relationship between physicians’ bonuses and departmental profitability. In addition, we use Data Envelopment Analysis (DEA) model to measure the operational efficiency of each department in the case hospital. Then, a multi-factor tobit model is used to examine the relationship between physicians’ bonuses and departmental efficiency. The results indicate that physicians’ bonuses in the case hospital are negatively correlated with departmental profitability and efficiency. That is, the performance measurement of current incentive plan may not be appropriate and it does not induce physicians to increase departmental profitability and improve efficiency. Our results suggest that the incentive plan is flawed and might fail to hold physicians accountable for improving departmental performance in the case hospital.  

DOES GOVERNMENT OWNERSHIP MATTER? COMPARATIVE STUDY BETWEEN GLCS AND NGLCS IN MALAYSIA

2015 ◽  
Vol 60 (02) ◽  
pp. 1550019 ◽  
Author(s):  
IRENE WEI KIONG TING ◽  
HOOI HOOI LEAN
Keyword(s):  
Firm Performance ◽  
Panel Data ◽  
Comparative Study ◽  
Multiple Regression ◽  
Regression Models ◽  
Public Perception ◽  
Negative Relationship ◽  
Government Ownership ◽  
Multiple Regression Models ◽  
The Impact

This study investigates whether government participation in firm ownership leads to better firm performance of publicly listed companies in Malaysia. The sample covers 257 companies listed on the Bursa Malaysia from 1997 to 2009. Multiple regression models with balanced panel data are used to examine the impact of government ownership (GOVN) on firm performance. We find a negative relationship between GOVN and firm performance, a finding that supports the negative public perception of government-linked companies (GLCs) in Malaysia. We conclude that government ownership is not an effective tool for improvement of firm performance in Malaysia.

Impact of individual and organizational factors on job satisfaction: A comparison of multilevel models and multiple regression models using different data arrangements

2013 ◽  
Vol 19 (1) ◽  
pp. 44-59 ◽  
Author(s):  
Jun Yi Hsieh
Keyword(s):  
Job Satisfaction ◽  
Multiple Regression ◽  
Regression Models ◽  
Multilevel Models ◽  
Organizational Factors ◽  
Ordinary Least Squares ◽  
Organizational Level ◽  
Multiple Regression Models ◽  
Research Questions ◽  
The Impact

AbstractTypically most studies of individual employees perceptions of the work place adopt multiple regression models (ordinary least squares [OLS]) which ignore inherent clustering in their data. However, such an approach does not supply unbiased and accurate answers to research questions. This study intends to simulate three data alternatives – weighted, disaggregated (individual level), and aggregated (organizational level) using the OLS and multilevel models to compare the results of different research designs. To answer the research questions, the current study investigates the impact of individual and organizational factors on job satisfaction, using a 2000 USA National Partnership for Reinventing Government survey. This study presents the methodological misuse and measurement errors of the previous research and presents guidelines for future research.

scholarly journals Economic Liberalization in Nepal: Determinants, Structure, and Trends of FDI

The Winners ◽  
2017 ◽  
Vol 18 (1) ◽  
pp. 33
Author(s):  
Raghu Bir Bista
Keyword(s):  
Direct Investment ◽  
Regression Models ◽  
Positive Relationship ◽  
Econometric Model ◽  
Production Model ◽  
Model Based ◽  
Multiple Regression Models ◽  
The Impact ◽  
Fdi Determinants ◽  
The Relationship

This research examined the relationship between FDI (Foreign Direct Investment) and GDP (Gross Domestic Product) along with the impact of FDI determinants on FDI inflow in Nepal. This research used literature review by doing multiple regression models. This research used an econometric model based on Cobb Douglas Production Model and a theoretical growth model based on Solow Growth. The result indicates the positive relationship between GDP and FDI. Furthermore, liberalization and privatization policies are positive, but insecurity is disturbing.

scholarly journals Anxiety level in context of chosen pro-health attitudes of male students during COVID-19 pandemic

2021 ◽  
pp. 1
Keyword(s):  
Social Support ◽  
Multiple Regression ◽  
Trait Anxiety ◽  
State Anxiety ◽  
Regression Models ◽  
Eating Behaviors ◽  
Anxiety Level ◽  
Male Students ◽  
Multiple Regression Models ◽  
The Impact

Background and objective: Assessing the impact of different factors on anxiety level is a complex and challenging problem, especially during pandemic or similar life threatening situations. Stress can affect dietary and eating behaviors. The aim of this study is to extend knowledge concerning the relation between increased anxiety level during pandemic and attitude towards dietary and eating behaviors in the context of social situation and support of relatives and friends. This study was conducted in 2020 during COVID-19 pandemic. Methods: State-Trait Anxiety Inventory alongside auxiliary questions about social relations and eating habits were asked to the male students from different universities and courses in Eastern Europe. To assess differences and dynamics of anxiety level, multiple regression models were used. Results: Multiple regression models between state anxiety level with context to the following factors: paying particular attention to one's diet, namely, the way of nutrition during severe anxiety felt during the pandemic and factors such as strong social support and type of university course was R = 0.41 (p = 0.00). For trait-anxiety the same model returned R = 0.39 (p = 0.00). Analysis of variance revealed that support of relatives is significant factor for state anxiety level, whereas this factor is insignificant for trait anxiety level. Conclusions: Models obtained from this study indicated that there are significant relations between anxiety level of male students and social support, which is expressed in the form of proper eating, therefore pro-health habits are revealed during elevated prolonged stress state such as COVID-19 pandemic.

Incarcerated for a Sex Offense: In-Prison Experiences and Concerns about Reentry

Sexual Abuse ◽  
2019 ◽  
pp. 107906321988458
Author(s):  
Thomas Baker ◽  
Kristen Zgoba ◽  
Jill A. Gordon
Keyword(s):  
Regression Models ◽  
Social Bonds ◽  
Policy Implications ◽  
Fear Of Victimization ◽  
Incarcerated Men ◽  
Multiple Regression Models ◽  
Sex Offense ◽  
The Impact ◽  
The Relationship ◽  
Relational Difficulties

Using a sample of 3,041 incarcerated men and women, this study examines the impact of being incarcerated for a sex offense and individuals’ in-prison experiences on their concerns about reentry into society. Bivariate analyses suggest that being incarcerated for a sex offense is related to greater concerns about housing and fewer concerns about recidivism compared with individuals incarcerated for nonsex offenses. A sex offense conviction is also associated with weaker social bonds, greater relational difficulties, and greater fear of victimization while incarcerated. Multiple regression models indicate that, after controlling for in-prison experiences, being incarcerated for a sex offense increases reentry concerns related to employment and housing, though may reduce them for recidivism. In-prison experiences mediate the relationship between being incarcerated for a sex offense and reentry concerns. Policy implications of these findings are discussed.

scholarly journals Multivariate Multiple Regression Models for a Big Data-Empowered SON Framework in Mobile Wireless Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yoonsu Shin ◽  
Chan-Byoung Chae ◽  
Songkuk Kim
Keyword(s):  
Wireless Networks ◽  
Big Data ◽  
Gaussian Process ◽  
Multiple Regression ◽  
Regression Models ◽  
Mobile Wireless Networks ◽  
Mobile Wireless ◽  
Multivariate Multiple Regression ◽  
Multiple Regression Models ◽  
The Relationship

In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON) are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs) and related network parameters (NPs) using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.

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