Search results

Megaprojects are large-scale and long-term investments. Three pillars of sustainable construction objectives, namely, social, environmental and economic, should be integrated into megaproject risk management to ensure long-term success. A risk assessment method, Risk Assessment Method for Sustainable Construction Objectives in Megaprojects (RAMSCOM), was developed for this purpose.

RAMSCOM was developed based on the latest concepts and identifying relevant and critical sustainability objectives and risks through an extensive literature review. Analytical Hierarchy Process (AHP) and Cross-Impact Analysis (CIA) were used to determine and quantify the threats regarding the importance of the sustainability objectives. The applicability of RAMSCOM was demonstrated on a real megaproject.

The findings revealed that sustainability risk assessment requires integration of economic, environmental, social objectives and analysis of cross-impacts of risk factors. Visualization of interrelated threats, vulnerabilities and objectives has a potential to support risk mitigation decisions to achieve sustainability goals.

The method has been developed based on the findings from a detailed literature survey on sustainability objectives and risks. RAMSCOM was tested on a single project with the assistance of three experts' views. Findings from the case project cannot be directly generalised for various megaprojects considering the unique nature of megaprojects.

Decision-makers can use RAMSCOM to assess sustainability risks in megaprojects and develop risk management plans for the most vulnerable and important sustainable objectives in a visual and quantified approach to ensure megaproject's sustainability in the long-term.

The theoretical contribution is a novel risk assessment method that integrates all dimensions of sustainability and quantifies the vulnerability of sustainability objectives considering their priorities, interrelations and risks. Sustainability dimensions, objectives and risks used in RAMSCOM can be useful for other researchers aiming to develop similar methods.

The purpose of this paper is to address the limitations of conventional risk matrix based tools such that both positive and negative connotation of uncertainty could be captured within a unified framework that is capable of modeling the direction and strength of causal relationships across uncertainties and prioritizing project uncertainties as both threats and opportunities.

Theoretically grounded in the frameworks of Bayesian belief networks (BBNs) and interpretive structural modeling (ISM), this paper develops a structured process for assessing uncertainties in projects. The proposed process is demonstrated by a real application in the construction industry.

Project uncertainties must be prioritized on the basis of their network-wide propagation impact within a network setting of interacting threats and opportunities. Prioritization schemes neglecting interdependencies across project uncertainties might result in selecting sub-optimal strategies. Selection of strategies should focus on both identifying common cause uncertainty triggers and establishing the strength of interdependency between interconnected uncertainties.

This paper introduces a novel approach that integrates both facets of project uncertainties within a project uncertainty network so that decision makers can prioritize uncertainty factors considering the trade-off between threats and opportunities as well as their interactions. The ISM based development of the network structure helps in identifying common cause uncertainty triggers whereas the modeling of a BBN makes it possible to visualize the propagation impact of uncertainties within a network setting. Further, the proposed approach utilizes risk matrix data for project managers to be able to adopt this approach in practice. The proposed process can be used by practitioners while developing uncertainty management strategies, preparing risk management plans and formulating their contract strategy.

Megaprojects are known as complex projects that involve high levels of uncertainty. This interpretive study explores and portrays perceived complexity in mega construction projects by lived experiences of project managers.

This study utilises a ground theory approach to analyse data gathered from semi-structured interviews with 18 professionals involved in 11 megaprojects.

Complexity in mega construction projects is defined as a project property that stems from the interaction of project features, uncertain variables/conditions, and managerial actions forming a pattern, which emerges over time, based on the reflections of construction practitioners.

This study defines complexity based on the reflections of the practitioners in the construction industry and uniquely identifies complexity patterns that may have implications for project management, particularly risk management.

In the global construction industry, experience gained in various countries is a major source of competitive advantage. By transferring the knowledge gained in previous projects using an effective knowledge management strategy, they can increase their competitiveness by adopting best practices and by not repeating the same mistakes under similar conditions. The purpose of this paper is to demonstrate how similar countries can be clustered to facilitate learning in global markets.

Initially, country factors that can affect success in international projects and can be used to assess the similarity of markets were identified by an extensive literature review and prioritised by a web-based questionnaire. A country evaluation form was prepared to collect country-specific data to be used in the cluster analysis of 39 countries where the Turkish contractors have been frequently working since the last 45 years. Cluster analysis was performed with SPSS 23.0.

Three country clusters were obtained and validated. Ultimately, how obtained country clusters may be used to facilitate learning from international construction projects was demonstrated by using an illustrative example.

The findings depend on the experience and perspective of Turkish contractors doing business abroad. Thus, the identified clusters are not generic. Moreover, country clusters were not identified considering only the country factors such as economy, culture, politics, etc.; thus, countries in the same cluster do not necessarily represent “similar” countries in terms of macro-factors. Clusters were identified so that the lessons learned can be effectively transferred within the same cluster considering construction and project-related factors as well as country-related factors. Although the findings cannot be generalised and clusters are not static, it is believed that the methodology used in this research is repeatable for different countries considering different timeframes.

Theoretical contributions include the identification of factors that can be used for similarity assessment of countries for transferring lessons learned as well as a methodology for clustering. Findings may also have a practical value for the Turkish contractors.

The purpose of this paper is to identify and prioritize key performance indicators (KPIs) that can be used for stage-based performance assessment of build-operate-transfer (BOT) projects.

This research was conducted through focus group discussions and face-to-face questionnaires. Firstly, stage-level KPIs for BOT projects were identified by conducting a literature survey. The list of KPIs that can be used for measuring performance at different stages of a BOT project was finalized by conducting focus group discussions with 12 participants. The data related to the importance of identified KPIs were collected via a face-to-face questionnaire in which 30 high-level managers participated. Based on these data, KPIs were prioritized considering eight different stages of a BOT project by using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The research findings reveal that 63 stage-level KPIs can be used for measuring the performance of BOT projects at eight different stages, which are “feasibility study and preliminary plan,” “announcement and submission of application,” “evaluation and selection,” “negotiation and signing of concession agreement,” “design,” “construction,” “operation” and “transfer.” The most important KPIs were determined as “comprehensiveness of project technical feasibility,” “detailed tendering procedure,” “effectiveness of concessionaires' technical knowledge/capability evaluation,” “good relationships between government and concessionaire,” “technology transfer,” “effectiveness of quality control,” “effectiveness of facility management” and “effectiveness of hand-back management” for each stage. The findings can be used by companies to evaluate performance at each stage of a BOT project and, if necessary, take the necessary actions for performance improvement at the stage level.

The main limitation is the size of the sample, which represents the perspectives of 30 Turkish high-level managers on KPIs in BOT projects. Besides, the selected method, namely, TOPSIS, does not provide quality measures related to the outputs; therefore, it is difficult to see the inconsistencies among the experts.

The study findings will help in devising appropriate performance evaluation practices for BOT projects to overcome the shortfalls of the existing practices and systems proposed in the literature and help in achieving the superior performance while developing infrastructure through the BOT route.

This paper proposes a process-based approach for measuring the performance of a BOT project considering eight different stages. It fills a research gap in the public–private partnership literature by focusing on stages rather than phases. The results can be used by practitioners to establish stage-level performance management systems for BOT projects.

The purpose of this paper is to develop a new conceptual life cycle performance measurement system (PMS) based on stage level key performance indicators (KPIs) to measure the performance of build–operate–transfer (BOT) projects.

This study uses a literature review to reveal the deficiencies of existing PMSs for public–private partnership (PPP) projects. Based on these deficiencies, four recommendations were proposed for developing a PMS. The validation of these recommendations was performed via focus group discussion sessions conducted with 12 experts. Then, a conceptual framework was developed based on the validated recommendations and the additional recommendations emerged during focus group discussions. Finally, the recommendations proposed by the focus group were tested by a questionnaire survey, the findings of the Friedman test and descriptive analysis validated these recommendations.

Findings of the focus group discussion, Friedman test and descriptive analysis indicated that the PMS should have four crucial features to develop an efficient, effective and comprehensible PMS for the BOT project. Firstly, non-financial and financial KPIs should be integrated. Secondly, the PMS should be developed as a process-based system with stage level KPIs. The performance of the BOT projects should be monitored and reviewed continuously, however the most important KPIs should be used for continuous performance measurement to increase the applicability of the system and allocate the resources more efficiently.

This survey was conducted on experts who have experience with BOT projects located in Turkey, therefore, judgments of experts might be affected by external factors specific to Turkey such as geopolitical situations, investment environment. However, due to the nature of BOT projects, the experience of experts can also be utilized at the international level.

This is the first time a PMS has been developed to measure the performance of a BOT project. In addition, this system has unique features when compared with the PMS proposed in the literature. Especially, the stage level KPIs and continuous performance measurement with the most important KPIs throughout each stage has never been used. This research provides both public sector and private entities with an insight into effectively measure, control and manage their BOT infrastructure projects' performances throughout their life cycles.

The positive effect of differentiation strategy on achieving competitive advantage among construction companies is widely acknowledged in the literature. However, there exists only a limited number of studies in the construction management literature that investigate the drivers and modes of differentiation. The purpose of this study is to determine appropriate modes and drivers of differentiation strategy in the Turkish construction industry.

In this study, based on the results of an extensive literature survey, a set of modes and drivers are defined as well as a conceptual framework to investigate their interrelations. Structural equation modelling (SEM) is proposed as an effective tool to investigate the relations between various factors of differentiation and data regarding the Turkish contractors have been used to demonstrate its applicability.

The results demonstrate that construction companies can differentiate on two modes of differentiation, either on “quality and image” or “product variety and speed”. Among the identified drivers, project management capabilities influence “product variety and speed related differentiation” and “quality and image related differentiation” directly; corporate management affects “quality and image related differentiation” directly, whereas corporate management influences “product variety and speed differentiation” indirectly. “Resources” has a strong direct effect on project and corporate management, in addition “resources” influences “product variety and speed related differentiation” and “quality and image related differentiation” modes indirectly through corporate and project management.

Research findings may help companies to choose an appropriate differentiation mode considering their strengths and weaknesses related with the drivers. Although findings reflect the competitive environment prevailing in the Turkish construction industry, it is believed that the parameters identified in this study can further be used as a benchmark to conduct similar studies in other countries.

The paper seeks to identify the determinants of business failure in construction and to predict the failure likelihood of construction companies by assessing their current situation based on both company‐specific and external factors.

A conceptual model is designed based on an extensive literature survey. The analytical network process together with the Delphi method is utilised to compute the importance weights of variables on business failure through interviews and discussions with experts. The applicability of the proposed model is tested on five companies to estimate their failure likelihood by using the findings derived from the analysis.

The results suggest the importance of organisational and managerial factors, including the efficiency of the value chain at the corporate level, the appropriateness of organisational decisions, and the availability of intangible resources for the survival of construction companies.

The findings of the analysis are limited to the experiences of three professionals in the Turkish construction industry. The performance of the model is only tested in five companies. The accuracy of the model may be improved by using the diverse experiences of a larger group of experts.

The proposed tool may act as an early warning system for construction companies by estimating the level of their failure likelihood. Companies may benefit from the findings of the model to assess their current situations and take necessary action to avoid possible business failures.

The knowledge and experiences of experts are used to obtain a complete model that accommodates both external and company‐specific variables, and more importantly the inter‐relations among them. Similar models may also be developed for companies in other industries to diagnose their bankruptcy or failure likelihood.

Almost all projects in the world are delayed, and sometimes even lead to the full bankruptcy of their beneficiaries. These delays can be calculated using techniques, but most importantly, there must be a fair and realistic division of delays between project beneficiaries. The most valid delay calculation techniques belong to the SCL Global Protocol, but they also have significant drawbacks, such as these: (1) They do not have the capability to prevent project delays (Delay Risk Management); (2) The protocol identifies and introduces any delays in activities with a ratio of one to one as a delay (Effective Delay); (3) It also does not offer the capability to share delays between stakeholders, which is a huge weakness. Floating in the base schedule activities is one of the cost control tools of projects, but it can hide project delays. In this paper, the researchers believe that the floating ownership belongs to the project and not belong to the stakeholders. This is the main tool for analyzing and sharing delays in this research.

The research methodology adopted included an extensive literature review, expert interviews, use of questionnaire and designing three innovative linked together models by researchers.

In this research, an integrated technique is introduced which has the following capabilities; delay risk control, result-based delay analysis and stakeholders delay sharing. This technique with an incursive and defensive approach implements claims management principles and calculates, respectively, non-attributable and attributable delays for each beneficiary.

This creativity led to the introduction of the Incursive and Defensive (In-De) technique; in the SCL protocol techniques, none of these capabilities exist.

This study provides an integrated risk-based cost and time estimation approach for deep excavation projects. The purpose is to identify the best practices in recent advances of excavation risk analysis (RA) and integrate them with traditional cost and time estimation methods.

The implemented best practices in this research are as follows: (1) fault-tree analysis (FTA) for risk identification (RI); (2) Bayesian belief networks (BBNs), fuzzy comprehensive analysis and Monte Carlo simulation (MCS) for risk analysis; and (3) sensitivity analysis and root-cause analysis (RCA) for risk response planning (RRP). The proposed approach is applied in an actual deep excavation project in Tehran, Iran.

The results show that the framework proposes a practical approach for integrating the risk management (RM) best practices in the domain of excavation projects with traditional cost and time estimation approaches. The proposed approach can consider the interrelationships between risk events and identify their root causes. Further, the approach engages different stakeholders in the process of RM, which is beneficial for determining risk owners and responsibilities.

This research contributes to the project management body of knowledge by integrating recent RM best practices in deep excavation projects for probabilistic estimation of project time and cost.