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Article
Publication date: 21 September 2015

Abdullah Albogamy and Nashwan Dawood

The risk factors associated with clients have a major impact on the successful delivery of a project from early design to the construction and operation stages. Risk management…

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Abstract

Purpose

The risk factors associated with clients have a major impact on the successful delivery of a project from early design to the construction and operation stages. Risk management studies conducted so far have not succeeded in providing an effective risk assessment methodology for clients in analysing and managing the risk factors that cause both project delays and cost overruns. So, the purpose of this paper is to provide a methodology for a client-based risk management model.

Design/methodology/approach

A conceptual framework is designed by integrating the findings from a literature review and a construction industry survey in the Kingdom of Saudi Arabia. The framework includes the risk identification, risk analysis and mitigation strategy, which are the key components of the model. The model of the framework is developed by integrating the analytical hierarchy process (AHP) and Monte Carlo simulation (MCS) underpinned within an @Risk program.

Findings

A case study is used to demonstrate the proposed methodology; the results found that the model helps to analyse and quantify the impact of risk factors, and also to assist in taking a suitable risk mitigation strategy, particularly at the early design stage in the construction process.

Practical implications

The model is applicable to both public and private clients when they need to know the possible project duration in a new construction project, and to take some proactive actions to avoid the adverse effect of client risk factors at the early stage of the project.

Originality/value

The model is expected to help in understanding the nature, and analysing the influence, of client risk factors that cause project delays and cost overruns. The development of the methodology for managing the client-based risk in construction processes at the early design stage is the key value of the study.

Details

Engineering, Construction and Architectural Management, vol. 22 no. 5
Type: Research Article
ISSN: 0969-9988

Keywords

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Article
Publication date: 6 February 2025

Mohammad Hossein Ronaghi and Marzieh Ronaghi

Artificial Intelligence (AI) technology, having powerful capabilities and rapid development, has been able to move the structures of businesses and organizational processes…

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Abstract

Purpose

Artificial Intelligence (AI) technology, having powerful capabilities and rapid development, has been able to move the structures of businesses and organizational processes towards intelligent automation. The role of digital transformation in universities and educational institutions has an increasing trend. New business structures and the digitization of processes, other than the advantages they bring about, might have different effects on the environment and sustainability. This study aims to identify the effective factors on AI adoption and the effect of using this technology in educational institutions and universities on their sustainable performance.

Design/methodology/approach

This research is applied using a quantitative approach. Universities selected for the study were ranked by Quacquarelli Symonds (QS). Of the 111 QS listed universities in the Middle East in 2023, 30 universities were randomly selected, and the research questionnaire was emailed to 50 people (administrative, educational and research staff) from each university. Information related to the level of AI technology acceptance and use was collected using a questionnaire among the university staff and faculty members; moreover, their relationship with universities’ sustainable performance scores was assessed. Path analysis and Smart PLS software have been used for data analysis.

Findings

The research findings showed that factors of technology performance, enjoyment, trust, social influence and organizational capabilities all have positive effect on AI adoption at universities. Also, the adoption of AI is considered as an effective factor in improving university sustainable performance. Therefore, based on exact data analysis using AI, universities can manage their activities and better control their environmental performance. Also, the use of AI can be effective in the availability to sustainable education in universities and the establishment of social justice in society. Accordingly, to facilitate executive processes and decision-making, policymakers in the field of science and university principals can improve administrative, educational and research processes via investing on AI, in addition to improving environmental activities and sustainable development.

Originality/value

The theoretical contribution of this research, other than designing an AI acceptance model for universities includes evaluating the relationship between using AI and university sustainable performance.

Details

Asian Education and Development Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-3162

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