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When the factors affecting health expenditures are examined in the literature, it is seen that one of the most important factors is income. In this context, the purpose of this study is to investigate the relationship between out-of-pocket health expenditures and disposable personal income and revealing the income elasticity of health expenditures.

Therefore, short/long-term coefficients were obtained by analyzing Westerlund (2007) co-integration analysis and pooled mean group (PMG) regression methods for 22 European Union (EU) member states during the period 2003–2017. In addition, a comparison of the long-term coefficients for each country was obtained with augmented mean group (AMG) estimator.

The results of the AMG and PMG tests show that the long-term coefficients between disposable personal income and health expenditures are 0.83 and 0.97, respectively. These results imply that there is a significant relationship between the variables, and that health care should be categorized in the group of normal goods. However, the fact that the long-term coefficient is very close to 1, despite being classified in the category of necessity goods, requires more care to be taken in evaluating whether health services are luxury goods or necessity goods.

The use of second generation econometric tests on both cross-sectional dependence and heterogeneity demonstrates the value of the study. On the other hand, obtaining similar results by investigating the relationship between variables using different appropriate econometric models reveals the importance of the methodology used in this study. It reveals important details in terms of the literature regarding the long-term and short-term results obtained in this study.

Delays occur mostly in the construction process of many projects, which can have a consequent effect on the overall performance of the project in areas such as profitability, efficiency and safety. This study aims to suggest a structure that can be applied to manage construction projects effectively and, thus, to reduce delays. The integrated balanced scorecard (BSC) and quality function deployment (QFD) framework proposed in the present study enabled the identification and ranking of the objectives of the financial perspective and the enablers of construction delay mitigation. This will help construction industry professionals prioritize the enabling factors that influence the financial perspective, thereby helping them focus on the achievement of the most important ones which subsequently results in efficiency. Consequently, more tasks are accomplished with the use of less time and resources as the actions tend to be more narrowly focused on the achievement of the most important factors such as client and contractor-related factors, as opposed to the low-value adding factors.

A literature review was conducted to determine the essential factors that would help resolve or reduce delays. A total of 41 mitigation factors (seven financial objectives and 36 enabler objectives) were identified and categorized into four BSC perspectives: financial, client, contractor and project management team, and innovation and learning. Two management tools, the BSC and QFD, were used to develop the system based on the mitigation factors defined.

The results of this study show that the most significant factors affecting the achievement of the financial objectives of the project are mainly customer-related factors, accompanied by factors related to contractors and project management teams. With the fishbone diagram and cause and effect analysis, the proposed BSC and QFD system provides a long-term approach for all stakeholders to help professionals in the construction industry prioritize and reduce delays more effectively. Moreover, the findings of the present study highlight the utility of the integrated BSC and QFD framework in quantifying the strengths of association of different objectives of the financial perspective and the enablers of construction delay mitigation.

The contribution of this paper to the body of knowledge is the proposed integrated structure for BSC and QFD that can serve as a comprehensive and structural approach to rating the essential enabling delay mitigation factors based on the magnitude of their effects on the financial performance of the project. The proposed framework can be considered a novel tool since this is the first integrated BSC and QFD framework for construction delay mitigation. Finally, the proposed BSC and QFD framework, along with the fishbone diagram and cause and effect analysis, provides a long-term strategy for all stakeholders to mitigate delays. Thus, the proposed integrated BSC and QFD framework can serve as a systematic and structural approach for measuring the strength of influence of the enablers of delay mitigation against the financial perspective.

This paper aims to present the development of a holistic campus facility management (CFM) performance assessment framework that incorporates a fuzzy logic approach and integrates a comprehensive set of key factors for successful management of campus facilities. The devised framework aims to cater to the needs of campus facilities management firms and departments for the purpose of gauging and assessing their performance across different management domains. Through this approach, facility management organizations can detect potential areas of enhancement and adopt preemptive steps to evade issues, foster progress and ensure success.

After a comprehensive analysis of the literature, conducting in-depth interviews with industry experts and employing the Delphi technique in two rounds, a total of 45 indicators critical to CFM success were identified and subsequently sorted into seven distinct groups. Through an online questionnaire, 402 subject-matter experts proficiently assessed the significance of the critical success indicators and their groups. A fuzzy logic framework was developed to evaluate and quantify a firm's compliance with the critical success indicators and groups of indicators. The framework was subsequently weighted using computations of the relative importance index (RII) based on the responses received from the questionnaire participants. The initial section of the framework involved a comprehensive analysis of the firm's performance vis-à-vis the indicators, while the latter part sought to evaluate the impact of the indicators groups on the overall firm's performance.

The utilization of fuzzy logic has uncovered the significant effects each effective CFM key indicator on indicators groups, as well as the distinct effects of each CFM indicators group on the overall performance of CFM. The results reveal that financial management, communications management, sustainability and environment management and workforce management are the most impactful indicators groups on the CFM performance. This suggests that it is imperative for management to allocate increased attention to these specific areas.

This study contributes to the advancement of current knowledge by revealing vital indicators of effective CFM and utilizing them to construct a thorough fuzzy logic framework that can assist in evaluating the effectiveness of CFM firms worldwide. This has the potential to provide crucial assistance to facility management organizations, facility managers and policymakers in their quest for informed decision-making.

This paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.

The data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.

The contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

The construction sector has a global reach, and construction professionals worldwide often encounter challenges in delivering a project on time and within the assigned budget. Hence, this paper aims to investigate the preproject factors that most affect the performance of construction projects.

A literature review was conducted to identify these factors from previous research, after which a questionnaire was developed and distributed to construction industry professionals worldwide. The response data were collected and analyzed using several methods, including Cronbach’s alpha, Relative Importance Index (RII), Kruskal–Wallis test, and Spearman’s and Pearson correlations.

The results highlight four categories of significance, namely design, stakeholder, engineering, and procurement, with 31 factors being assigned to these categories. The relationships between each factor based on the categories established in the survey are then presented. With the help of data analysis, focusing on these significant preproject factors will help management teams to evaluate and improve the preconstruction process to achieve a higher project success rate.

This study differs from other studies in the literature by gathering all relevant preconstruction success factors by an extensive literature review. Finally, highly ranked factors are studied in detail for a better understanding of the impact of preconstruction factors on project performance. This study is supported by powerful tests such as Kruskal–Wallis test and Spearman’s correlation to study the perception of different groups on preconstruction factors. Furthermore, the data analysis will help in identifying and avoiding the failure part of the previous projects and will improve the planning and/or forecasting of the new projects.

Pavement is one of the main elements of the roads network. It is extremely essential to study and understand the factors affecting its performance and highlight the most important ones for decision-makers and pavement experts to consider during the design, construction and maintenance stages. The purpose of this paper was to identify the factors affecting pavement performance and rank them according to their importance using Analytic Hierarchy Process (AHP) for decision-makers and pavement experts to consider during the design, construction and maintenance stages.

A survey was developed considering 29 factors found in the literature that affect pavement performance. The survey was sent to pavement professionals in Qatar to rate their perception of factors affecting pavement performance to enhance roads' sustainability. 205 responses were collected and analyzed using AHP.

The findings indicate that the factor “unconsidered heavy vehicles volume” is the most critical factor that affects pavement performance. The second most critical factor affecting the pavement performance is the “low asphalt content” due to escalating binder aging, reducing fatigue life of the pavement and decreasing the durability of roads. The third and fourth factors are “poor mechanical and thermal properties” and “unexpected high traffic volume,” respectively. These two factors are strongly attached to the first and second factors since the traffic volume affects the pavement performance less but similar to the heavy vehicles and a mix with poor mechanical and thermal properties is related indirectly to the asphalt content in the mix.

The research provides help for decision-makers in the construction industry to improve the performance of pavements using a multi-criteria decision-making tool. This paper's outcome would help the pavement management professionals in the construction industry to improve pavement performance and management, increase the pavement's life cycle and reduce maintenance costs.

The aim of this paper is to study the effect of strategic and project related potential risks on project delivery in Qatar. Two objectives have been defined. The first is to identify potential risk indicators (manifest variables) and categorize them (constructs/latent variables) based on a literature review, while the second is to examine and rank the relationships between the indicators and constructs by developing a structural equation model.

Twenty-five indicators were identified from the literature review and categorized into five groups. To collect the data, an online questionnaire was distributed in Qatar, and 116 responses were obtained. Structural equation modeling (SEM) was used to examine the model. The model that was developed based on the research hypothesis met goodness-of-fit, reliability and validity requirements.

The results showed that all constructs contributed well to the model and that the project parties (PPs) have the highest contribution with an effect weight of 0.209 followed by economic and legal (EL) conditions with an effect weight of 0.205. Site and safety (SS) conditions were third with an effect weight of 0.200 while environmental, natural and technological (ENT) conditions were fourth with an effect weight of 0.1989. The last ranked construct is political and social (PS) conditions with an effect weight of 0.186. Based on the outcome of the SEM, recommendations were provided to industry professionals in Qatar about mitigating the impact of potential risks on construction project.

To the authors' best knowledge, this is the first study to quantify the effects of strategic and project related risks on a construction project using SEM, considering the risk management indicators of SS, EL, ENT, PS in Qatar. The study's practical implications are to enlarge the project's risk management plan by considering the strategic and project related risks to enhance the project performance for the cost overrun and delay. The study is intended for construction projects in Qatar, but it can easily be adapted to other parts of the world given the local circumstances.

Bid/no-bid decision is a significant and strategic decision, which must be finalized at an early stage of the bidding process. Such decision-making may have significant impact on the performance of the contractors. Using Chi-square Automatic Interaction Detector (CHAID) and Classification and Regression (CRT) decision tree algorithms, this paper aims to develop bid/no-bid models for design-bid-build projects for contractors.

The models in this study have been developed using CHAID and CRT algorithms. Thirty-four bid/no-bid key factors were collected via extensive research. The bid/no-bid factors were listed based on their importance index as a result of a questionnaire distributed among the construction professionals. These factors were divided into five main risk categories – owner, project, bidding situation, contract and contractor – which were taken as inputs for the models. Split-sample validation was applied for testing and measuring the accuracy of the CHAID and CRT models. Moreover, Spearman's rank correlation and Analysis of Variance (ANOVA) tests were employed to identify the statistical features of the received 169 responses.

The key bid/no-bid factors in construction industry were categorized in five related groups and ranked based on the relative importance index. It was found that the top 6 ranked bid/no-bid factors were (1) current workload, (2) need for work, (3) previous experience with employer; (4) timely payment by the employer; (5) availability of other projects for bidding (6) reputation of employer in the industry. Matrix comparison between all bid/no-bid groups was performed using Spearman's correlation to measure the relationship between each of the two paired groups. It was concluded that all the relationships were positive.

Existing bidding models require many inputs and advanced understanding of mathematics and software to run the model. Contractors tend to use easy, fast and available support methods. Excluding a great number of the bid/no-bid factors may affect the final decision. This paper proposes a bid/no-bid decision tree models for contractors of different sizes. It is the first study in the literature, to the best of authors' knowledge, to study bid/no-bid decision with the proposed decision tree algorithm. The proposed models in this study overcome the shortfalls of most previous models such as avoiding the complexity and difficulties of applying the concept. The proposed model will provide the contractors with a bid/no-bid decision based on the input for the defined bid factor groups. The proposed models display the soft spots and hot spots between the independent and dependent variables, which leads to a better decision. The proposed models display the result effectively in visual terms, easy to understand and easy to apply. The proposed models are a form of multiple effect (or variable) analysis which allows the companies to explain, describe, predict or classify an outcome.

Along with accounting scandals in the past, academics, researchers, and legislators have focused on fraud. The purpose of this study is to examine postgraduate and doctoral studies, articles, and books about forensic accounting and fraud audit published between the years 2008 and 2018 in Turkey. For this purpose, a total of 96 studies have been examined and 35 of these are master’s theses, 10 of them are PhD theses, 45 of them are articles, and six of them are books. These studies were presented in tables as classified. The studies examined in our research are summarized as year they were published, the author, and the scope of the topic and in terms of results. The conclusions of this study can be summarized as follows: (a) the majority of thesis published about forensic accounting and fraud audit are in 2011 and following years. In addition, most of the theses are focused on forensic accounting review rather than fraud audit. (b) Results in the articles reviewed are in the same direction with theses. (c) There are very few books about fraud audit and forensic accounting. One of them is related to fraud audit, while the rest of them are related to forensic accounting and forensic accounting profession. We suggest extending the scope of the study and making to other countries.

The purpose of this paper is to systematically develop a delay risk terminology and taxonomy. This research also explores two external and internal dimensions of the taxonomy to determine how much the taxonomy as a whole or combinations of its elements are generalisable.

Using mixed methods research, this systematic literature review incorporated data from 46 articles to establish delay risk terminology and taxonomy. Qualitative data of the top 10 delay risks identified in each article were coded based on the grounded theory and constant comparative analysis using a three-stage coding approach. Word frequency analysis and cross-tabulation were used to develop the terminology and taxonomy. Association rules within the taxonomy were also explored to define risk paths and to unmask associations among the risks.

In total, 26 delay risks were identified and grouped into ten categories to form the risk breakdown structure. The universal delay risks and other delay risks that are more or less depending on the project location were determined. Also, it is realized that delays connected to equipment, sub-contractors and design drawings are highly connected to project planning, finance and owner slow decision making, respectively.

The established terminology and taxonomy may be used in manual or automated risk management systems as a baseline for delay risk identification, management and communication. In addition, the association rules assist the risk management process by enabling mitigation of a combination of risks together.