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The purpose of this paper is to explore the relationship between civil engineers’ demographics (e.g. age, marital status, education, work experience) and their personal values. The objective was to predict civil engineers’ personal values based on their demographics.

A questionnaire survey was administered to civil engineers to collect data on their demographics and their personal values. Statistical analysis was performed to verify whether a significant statistical relationship exists between civil engineers’ demographics and their personal values.

The most important and the least important personal values were identified for civil engineers. Statistical analysis indicated that civil engineers’ values do vary based on their demographics.

The results of this study cannot be generalized, because individuals’ personal values and demographics are, by definition, local. Location and culture may affect the personal values of civil engineers.

Team leaders normally have access to information about the demographics of the engineers they employ; based on the results of this study, they should be able to predict their personal values, and to make more informed decisions when appointing them to particular positions on project teams.

The research presented in this paper, establishes for the first time, that a linkage exists between civil engineers’ personal values and their demographics, and makes it easier for team leaders to make assignment decisions.

The purpose of this paper is to assess the impacts of implementing different combinations of various organizational changes on the economics of rebar supply chains in the special conditions of a project environment, where on‐site fabrication of rebar is considered to be more economical than off‐site fabrication practice.

A range of recently published works (2005‐2006) seem to confirm that on‐site fabrication of rebar results in less cost to the contractor compared with off‐site fabrication in a special project environment. However, those analyses did not take into account two main cost components (i.e. storage cost and waiting cost) owing to the lack of such information, and they were based on a number of assumptions regarding current managerial capabilities, which may likely be enhanced through implementing various organizational changes. This study overcame these shortcomings of the recent studies through restoring the formerly developed simulation model to mimic the materials management system actually used by a contractor, and running this system by plugging in realistic input values associated with both those cost components and organizational changes.

The paper reveals that while fabricating rebar off site is more economical than fabricating it on site when the storage and waiting costs were taken into account in the current state of the construction industry, and the lowest total cost of rebar can be achieved by implementing organizational changes in the on‐site fabrication practice.

This paper is a very useful source of information for practitioners and researchers because it indicates that off‐site fabrication practice is only beneficial to builders if they have the ability to exploit it, and if the cultural and business environment enables that exploitation.

Construction companies typically allocate limited resources, i.e. human resources and funds, for marketing activities; so, they commonly face the problem of deciding in which marketing activities they should primarily invest and how much resource they should allocate for the selected marketing activities. Indeed, the problem of selecting the best set of marketing activities should be treated as a multi‐objective optimisation problem with multiple obligatory and flexible goals with different priorities, several interdependencies, and multiple constraints on resources. The main objective of this paper is to solve the problem of optimal allocation of scarce marketing resources using the combination of analytic network process (ANP) and zero‐one‐goal‐programming (ZOGP) models.

The research methodology of this paper mainly involves: reviewing the relevant literature on marketing and construction marketing; identifying the marketing activity alternatives in which construction companies may consider to invest; constructing an ANP model in order to calculate the importance weights of the different marketing activities; formulating a ZOGP model, which uses the weights obtained from the ANP model and considers the obligatory and flexible goals with different priorities, interdependencies and constraints on resources, that enables construction companies to optimize their scarce marketing resources; and conducting two case studies in order to illustrate how the proposed methodology works.

This paper provides construction companies with an integrated decision‐support methodology, which will assist them in allocating their limited marketing resources for different marketing activity alternatives in a more efficient way. The proposed methodology does not only consider the constraints on marketing resources and priorities of the goals that need to be satisfied but also show the deviations from the desired goals.

This paper is of benefit to construction companies as it offers an efficient and convenient tool that allows construction companies to optimise scarce marketing resources.

The main objective of this paper is to show how fuzzy logic and multiple regression analysis (MRA) techniques can be used by construction companies for determining the size of contingency that will be included in bid prices for international construction projects in a more systematic way and to compare their modelling performances.

The steps followed in the execution of this study mainly consists of: conducting a literature review on international construction in order to identify the factors that may affect contingency amounts that will be included in bid prices for international construction projects; developing the general framework of the proposed contingency estimation model; designing a questionnaire based on the information gathered from the literature review, delivering these questionnaires to construction experts, and obtaining the actual data of 36 international construction projects; developing a fuzzy logic model based on expert judgments and three multiple regression analysis models (MRAM) using the collected data; and comparing the performances of these approaches.

In this study, a fuzzy logic model and three MRAM were developed. Their modelling performances were compared using actual data obtained from 36 international construction projects that had been completed by 20 large-scale Turkish construction companies in 14 different countries. It is found that the developed fuzzy logic model outperforms the MRAM built for the studied projects.

This study shows that fuzzy logic and MRA techniques can be successfully used by construction companies, which predominantly do business in foreign countries, for estimating the size of cost contingency that will be included in bid prices for international construction projects. The modelling performances of fuzzy logic and MRAM are also compared.

The purpose of this paper is to provide construction companies with an analytic model, namely a four‐level analytic network process (ANP) model, to assist them in prioritising and thereby selecting marketing activities which offer maximum return on investment, for which they should primarily allocate their limited resources.

The research methodology of this study mainly involves: reviewing the literature on marketing and construction marketing in order to identify the marketing activity selection criteria and their constituent sub‐criteria; determining the marketing activity alternatives in which construction companies may consider to invest; constructing an analytic model, namely a four‐level ANP model – to assist them in selecting the most viable marketing activities for which they should primarily allocate their limited resources; and conducting two case studies, one in a bidding contractor and the other in a developer, in order to illustrate how the model works.

This paper illustrates how to empirically prioritise marketing activities by using a four‐level marketing activity selection model. Two case studies are also demonstrated in order to illustrate how the model works. The case studies indicate that the rankings of the marketing activities for these companies are rather different due to the type of the construction company (i.e. bidding contractors or building developers) and its client profile (i.e. public or private owners). The findings of the case studies concurred with the expected direction in these companies.

This model is of benefit to construction companies and researchers. The proposed ANP model offers an efficient, convenient and simple tool that allows construction companies to select the marketing activities for which they should primarily allocate their limited resources. It also allows researchers to see the potential use of ANP in the marketing activity selection problem.

In this study, the aim is to address the void in the comparative literature of criminology and criminal justice by investigating public attitudes toward law enforcement in a rapidly developing country, Turkey.

Three different models of satisfaction with law enforcement, the demographic model, the quality of life model and the experiential model are subjected to empirical scrutiny in the context of policing in Turkey. In line with extant research on satisfaction with law enforcement in the West, all three models significantly explain the variation of satisfaction with law enforcement across a random sample of 6,713 individuals living in urban and rural parts of Turkey. Specifically, the quality of life model and the experiential model had considerable impact on public satisfaction with law enforcement and in general, all three models produced results in the predicted direction.

Overall, the findings suggest the robust nature of the integrated demographic, quality of life, and experiential models in explaining public satisfaction with law enforcement. In the demographic model, however, income and education had significant negative impact on global satisfaction with law enforcement. Considering the fact that more educated and well off citizens are likely to value freedom more and that law enforcement may represent an oppressive part of a democratic government, this might account for the reaction of higher socioeconomic classes to the power distance between the state, the police in particular, and civil society.

In terms of policy recommendations, to the extent that community policing is regarded as a set of strategies for improving the quality of police‐citizen encounters and reforming police organizations, these findings lend support for the potential of community policing in Turkey.

This study aims to measure economic uncertainty in Turkey by a novel economic uncertainty index (EUI) employing principal component analysis (PCA). We assess the impact of Covid-19 pandemic in Turkey with our constructed uncertainty index.

In order to obtain the EUI, this study employs a dimension reduction method of PCA using 14 macroeconomic indicators that spans from January 2011 to July 2020. The first principal component is picked as a proxy for the economic uncertainty in Turkey which explains 52% of total variation in entire sample. In the second part of our analysis, with our constructed EUI we conduct a structural vector autoregressions (SVAR) analysis simulating the Covid-19-induced uncertainty shock to the real economy.

Our EUI sensitively detects important economic/political events in Turkey as well as Covid-19-induced uncertainty rising to extremely high levels during the outbreak. Our SVAR results imply a significant decline in economic activity and in the sub-indices as well. Namely, industrial production drops immediately by 8.2% and cumulative loss over 8 months will be 15% on average. The losses in the capital and intermediate goods are estimated to be 18 and 25% respectively. Forecast error variance decomposition results imply that uncertainty shocks preserve its explanatory power in the long run, and intermediate goods production is more vulnerable to uncertainty shocks than overall industrial production and capital goods production.

The results indicate that monetary and fiscal policy should aim to decrease uncertainty during Covid-19. Moreover, since investment expenditures are affected severely during the outbreak, policymakers should impose investment subsidies.

This is the first study constructing a novel EUI which sensitively captures the critical economic/political events in Turkey. Moreover, we assess the impact of Covid-19-driven uncertainty on Turkish Economy with a SVAR model.

This study proposes a framework based on salience theory and shows that focusing on one type of risk (idiosyncratic or systemic) can explain overpricing of securities ex ante, and resales at low prices during crisis periods.

The author consider an overlapping generations (OLG) model where each generation lives for two periods and there is no population growth. Agents (investors) start their lives with an endowment W > 0 and have mean-variance utility. They invest their endowment when young and consume when old. Each period, the young investors optimally choose their portfolio from different risky assets acquired from the old generation, all assumed to be in fixed supply.

The author show that investor salience bias can explain excess volatility of asset prices and the resulting fire-sales in periods of financial turmoil. A change in salience – from one component (idiosyncratic) to the other (systemic) – will generate excess volatility. Interestingly, higher risk aversion generally exacerbates the excess volatility of prices. Moreover, the model predicts that if a big systemic shock hits the financial system, due to salience bias the price of systemic assets falls sharply. This relates to the observed fire-sales of assets during the global financial crisis.

The proposed model and results suggest that there may be a scope for intervention in financial markets during turbulences. In terms of ex ante policies the study suggests that investors and regulator should use better risk assessment technologies.

This is the first study constructing a tractable model based on the argument that investor salience may exacerbate the excess volatility of prices during financial downturns. The author relate salience to two types of risk; idiosyncratic and systemic and assume that investors' risk perception is biased towards the type of risk that is currently salient based on prior beliefs or past data. The author show that the diversification fallacy of the precrisis period, where seemingly safe assets were overpriced, can be explained by agents overweighing idiosyncratic risk and ignoring systemic risk.