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Article
Publication date: 23 January 2023

Amir Naser Ghanbaripour, Craig Langston, Roksana Jahan Tumpa and Greg Skulmoski

Despite considerable research on the subject, there is still some misunderstanding about what characterizes successful project delivery in construction projects. Evaluating…

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Abstract

Purpose

Despite considerable research on the subject, there is still some misunderstanding about what characterizes successful project delivery in construction projects. Evaluating project delivery success is crucial for organizations since it enables them to prepare for future growth through more effective project management mechanisms and rank the organization's projects for continuous improvement. There is considerable disagreement over a set of success criteria that can be applied to all kinds of projects when evaluating project delivery success, making it a complicated procedure for practitioners and scholars. This research seeks to alleviate the problem by validating and testing a systematic project delivery success model (3D integration model) in the Australian construction industry. The aim is to establish a dependable approach built upon prior research and reliable in evaluating delivery success for any project type.

Design/methodology/approach

Based on a novel project delivery success model, this research applies a case study methodology to analyse 40 construction projects undertaken by a single Australian project management consultancy. The research utilizes a mixed-method research approach and triangulates three sets of data. First, the project delivery success (PDS) scores of the projects are calculated by the model. Second, a qualitative analysis targeting the performance of the same projects using a different system called the performance assessment review (PAR) scores was obtained. These culminate in two sets of ranking. The third step seeks validation of results from the head of the partnering organization that has undertaken the projects.

Findings

The findings of this study indicate that the 3D integration model is accurate and reliable in measuring the success of project delivery in construction projects of various sizes, locations and durations. While the model uses six key performance indicators (KPIs) to measure delivery success, it is evident that three of these may significantly improve the likelihood of PDS: value, speed and impact. Project managers should focus on these priority aspects of performance to generate better results.

Research limitations/implications

Restrictions inherent to the case study approach are identified for this mixed-method multiple-case study research. There is a limitation on the sample size in this study. Despite the researcher's best efforts, no other firm was willing to share such essential data; therefore, only 40 case studies could be analysed. Nonetheless, the number of case studies met the literature's requirements for adequate units for multiple-case research. This research only looked at Australian construction projects. Thus, the conclusions may not seem applicable to other countries or industries. The authors investigated testing the PDS in the construction sector. It can assist in improving efficiency and resource optimization in this area. Nonetheless, the same technique may be used to analyse and rank the success of non-construction projects.

Originality/value

Despite the research conducted previously on the PDS of construction projects, there is still confusion among researchers and practitioners about what constitutes a successful project delivery. Although several studies have attempted to address this confusion, no consensus on consistent performance metrics or a practical project success model has been formed. More importantly, (1) the ability to measure success across multiple project types, (2) the use of triple bottom line (TBL) to incorporate sustainability in evaluating delivery success and (3) the use of a complexity measurement tool to adjust delivery success scores set the 3D integration model apart from others.

Details

Smart and Sustainable Built Environment, vol. 13 no. 3
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 7 February 2023

Muath Abu Arqoub, Amir Naser Ghanbaripour, Craig Langston and Greg Skulmoski

This research aims to develop and test a model for measuring end-user satisfaction (EUS) in a practical manner and then statistically verify its reliability and validity.

Abstract

Purpose

This research aims to develop and test a model for measuring end-user satisfaction (EUS) in a practical manner and then statistically verify its reliability and validity.

Design/methodology/approach

A comprehensive list of attributes using extensive literature review, focus group and brainstorming meetings were used to create a set of attributes for the model. These attributes were then used in a survey among the end-users (N = 687) of seven case studies with different characteristics (type, size and location) to assess the reliability and validity of the model. The statistical methods included reliability tests (Cronbach's alpha), confirmatory factor analysis (CFA) and correlation analysis), canonical discriminant analysis (CDA), item response theory (IRT) and model specification tests.

Findings

EUS must be quantified before determining a project's overall performance. The analysis of repeatability and consistency (reliability and validity) performed on case studies (data collected from the end-users) strongly suggests that the EUS model is robust for a range of project types.

Originality/value

Although many studies have proposed customer satisfaction models in the project success context, research on quantitative measurement tools is scarce. The paper departs from past research and develops and validates a new EUS model independent of project characteristics (while the study's limitations are acknowledged).

Details

Built Environment Project and Asset Management, vol. 13 no. 4
Type: Research Article
ISSN: 2044-124X

Keywords

Article
Publication date: 28 July 2022

Xiaoyan Jiang, Sai Wang, Yong Liu, Bo Xia, Martin Skitmore, Madhav Nepal and Amir Naser Ghanbaripour

With the increasing complexity of public–private partnership (PPP) projects, the amount of data generated during the construction process is massive. This paper aims to develop a…

Abstract

Purpose

With the increasing complexity of public–private partnership (PPP) projects, the amount of data generated during the construction process is massive. This paper aims to develop a new information management method to cope with the risk problems involved in dealing with such data, based on domain ontologies of the construction industry, to help manage PPP risks, share and reuse risk knowledge.

Design/methodology/approach

Risk knowledge concepts are acquired and summarized through PPP failure cases and an extensive literature review to establish a domain framework for risk knowledge using ontology technology to help manage PPP risks.

Findings

The results indicate that the risk ontology is capable of capturing key concepts and relationships involved in managing PPP risks and can be used to facilitate knowledge reuse and storage beneficial to risk management.

Research limitations/implications

The classes in the risk knowledge ontology model constructed in this research do not yet cover all the information in PPP project risks and need to be further extended. Moreover, only the framework and basic methods needed are developed, while the construction of a working ontology model and the relationship between implicit and explicit knowledge is a complicated process that requires repeated modifications and evaluations before it can be implemented.

Practical implications

The ontology provides a basis for turning PPP risk information into risk knowledge to allow the effective sharing and communication of project risks between different project stakeholders. It can also have the potential to help reduce the dependence on subjectivity by mining, using and storing tacit knowledge in the risk management process.

Originality/value

The apparent suitability of the nine classes of PPP risk knowledge (project model, risk type, risk occurrence stage, risk source, risk consequence, risk likelihood, risk carrier, risk management measures and risk case) is identified, and the proposed construction method and steps for a complete domain ontology for PPP risk management are unique. A combination of criteria- and task-based evaluations is also developed for assessing the PPP risk ontology for the first time.

Details

Construction Innovation , vol. 23 no. 5
Type: Research Article
ISSN: 1471-4175

Keywords

Abstract

Details

Built Environment Project and Asset Management, vol. 13 no. 4
Type: Research Article
ISSN: 2044-124X

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