Over recent years, the issue of corruption in the public construction sector has attracted increasing attention from both practitioners and researchers worldwide. However, limited efforts are available for investigating the underlying factors of corruption in this sector. Thus, this study attempted to bridge this knowledge gap by exploring the underlying factors of corruption in the public construction sector of China. To achieve this goal, a total of 14 structured interviews were first carried out, and a questionnaire survey was then administered to 188 professionals in China. Two iterations of multivariate analysis approaches, namely, stepwise multiple regression analysis and partial least squares structural equation modeling were successively utilized to analyze the collected data. In addition, a case study was also conducted to triangulate the findings obtained from the statistical analysis. The results generated from these three research methods achieve the same conclusion: the most influential underlying factor leading to corruption was immorality, followed by opacity, unfairness, procedural violation, and contractual violation. This study has contributed to the body of knowledge by exploring the properties of corruption in the public construction sector. The findings from this study are also valuable to the construction authorities as they can assist in developing more effective anti-corruption strategies.
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http://dx.doi.org/10.1007/s11948-016-9865-z | DOI Listing |
Sci Data
December 2024
University of Turin, Computer Science Department, Turin, 10149, Italy.
Governments procure large amounts of goods and services to help them implement policies and deliver public services; in Italy, this is an essential sector, corresponding to about 12% of the gross domestic product. Data are increasingly recorded in public repositories, although they are often divided into multiple sources and not immediately available for consultation. This paper provides a description and analysis of an effort to collect and arrange a legal public administration database.
View Article and Find Full Text PDFSoc Sci Med
December 2024
Department of Kinesiology and Health Education, University of Texas at Austin, United States.
Climate-related disasters pose significant risks to mental health and well-being globally. Individuals from disaster-prone regions, such as Puerto Rico, are at even greater risk. The devastating effects of recurrent hurricanes, compounded with pre-existing structural disparities (e.
View Article and Find Full Text PDFFront Res Metr Anal
December 2024
Law School, University of Wisconsin-Madison, Madison, WI, United States.
This article examines the landscape of Science, Technology, and Innovation policies in Central America, focusing on Nicaragua, Guatemala, Honduras, and El Salvador. These nations face significant challenges in leveraging STI for sustainable development, including financial constraints and limited resources. Additionally, Central America struggles with systemic issues such as corruption, violence, and high levels of emigration, further complicating efforts to advance STI.
View Article and Find Full Text PDFInt J Health Plann Manage
December 2024
The University of Bamenda, Bamenda, Cameroon.
The study investigates the role of governance quality on the effect of health expenditure on health outcomes captured by life expectancy at birth, infant mortality, under-five mortality, crude mortality and maternal mortality rates in West African Countries. Although these countries have made significant efforts to increase health expenditure over the years, health outcomes have only responded marginally in West African Countries, raising concerns about the importance of health expenditure in improving health outcomes. This study analyses the relationship between the role of governance and health expenditure and health outcomes using the feasible generalised least squares estimation techniques.
View Article and Find Full Text PDFBMC Res Notes
December 2024
Information Theory and Coding (ITC) Laboratory, University of Tehran, Tehran, Iran.
Objectives: Image file fragment classification is a critical area of study in digital forensics. However, many publicly available datasets in this field are derived from a single source, often lacking consideration of the diversity in image settings and content. To demonstrate the effectiveness of a given methodology, it is essential to evaluate it using datasets that are sampled from varied data sources.
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