Search results

This research aims to systematically review studies on significant risks for Critical Infrastructure Projects (CIPs) from selected top-tier academic journals from 2011 to 2023.

In this research, a three-step systematic literature review methodology was employed to analyse 55 selected articles on Critical Infrastructure Risks (CIRs) from well-regarded and relevant academic journals published from 2011 to 2023.

The findings highlight a growing research focus on CIRs from 2011 to 2023. A total of 128 risks were identified and grouped into ten distinct categories: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. In addition, literature reviews combined with questionnaire surveys were more frequently used to identify CIRs than any other method. Moreover, oil and gas projects were the subjects most often explored in the reviewed papers. Furthermore, it was observed that publications from Iran, the USA and China dominated CIRs research, making significant contributions, accounting for 49.65% of the analysed articles.

This research specifically focuses on five types of CIPs (i.e. roadways, bridges, water supply systems, dams and oil and gas projects). Other CIPs like cyber-physical systems or electric power systems, were not considered in this research.

Governments and contracting firms can benefit from the findings of this study by understanding the significant risks associated with the execution of CIPs, irrespective of the nation, industry or type of project. The results of this investigation can offer construction professionals valuable insights to formulate and implement risk response plans in the early stages of a project.

As a novel literature review related to CIRs, it lays the groundwork for future research and deepens the understanding of the multi-faceted effects of these risks, as well as sets practical response strategies.

This paper aims to identify the critical project management success factors and analyze those factors to achieve a sustainable construction industry in Bangladesh.

This study identified 41 major problematic factors from the related literature. In this research, a detailed questionnaire survey was conducted among the experts and stakeholders of the construction industry of Bangladesh. The survey was carried out on a Likert scale and ranked the critical factors using the relative importance index (RII). The 41 problematic factors were divided into five group factors and ranked by the RII index to prioritize the factors. Finally, stakeholders' opinions were analyzed with the critical assessed factors, which was a very effective technique to eliminate the risks and uncertain occurrences in the construction industry of Bangladesh.

The factors analysis revealed that cost overrun, traffic jam, low wedges, slow payment for completed works and financial issues of the owner were leading critical factors in construction projects. Moreover, the critical factors are divided into five-factor groups, namely, financial management, monitoring and feedback, competency management, communication and coordination management, and risk management, which exhibit 0.767, 0.720, 0.711, 0.710 and 0.658 RII values. After all, the stakeholders' opinion suggested that implementing modern tools and techniques can help to avoid the critical situation in the construction industry of Bangladesh.

The construction industry of Bangladesh is moving away from stable construction work day by day. Previously, the potential CSFs were discussed unstructured way. Hence, detecting early warning signals in a structured way has become necessary for the building firm's survival.

Though some scattered critical issues are discussed in different literature, the critical issues of the Bangladeshi construction industry were not investigated extensively. Therefore, this study finds out the potential critical issues of the construction industry of Bangladesh to accumulate such harmful construction issues in a single platform so that the construction industry can have an overview of them with the help of innovative technologies.

The increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.

This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.

Key themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.

Unlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.

The growing demand for housing and infrastructure, as well as the requirement for affordable housing, has been a significant factor, necessitating investigation for sustainable approaches and implementation of alternative construction innovations. Hence, this study aims to identify and assess the drivers for implementing modular construction systems (MCS) in developing countries.

The study adopts a quantitative research approach to seek respondents’ opinions on the factors that can drive the implementation of MCS in developing countries. Accordingly, a structured questionnaire was used as an instrument of data collection based on five Likert scales. The data was analysed using the mean score, one sample t-test, Kruskal–Wallis, factor analysis (FA) and Pearson correlation analysis.

Results show that 15 of the 16 major identified drivers were statistically significant towards implementing MCS, which indicates that the drivers are crucial for implementing MCS in developing countries. However, the Kruskal–Wallis test reveals that the respondents have varying opinions on the identified drivers. FA categorised the drivers into four categories, namely, “management and sustainability”, “key performance”, “know-how and logistics” and “regulations and policies”. A strong relationship among the four categories of drivers was established using Pearson correlation, which indicated that all the drivers’ categories are essential for implementing MCS in developing countries.

This study identified and assessed the drivers towards implementing MCS in developing countries. The study concludes that the identified drivers are essential for implementing MCS in developing countries. Also, the study considers the government the most placed player in driving the implementation of MCS in developing countries.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

In New Zealand, building information modelling (BIM) prevalence is still in its early stages and faces many challenges. This research aims to develop a BIM adoption framework to determine the key factors affecting the success of a BIM project.

Both primary and secondary data were employed in this research, including 21 semi-structured interviews and industry guidelines from the three most well-known global building excellence models (BEMs). The data were analysed through content analysis due to its recognised benefits as a transparent and reliable approach.

Leadership, clients and other stakeholders, strategic planning, people, resources, process and results were identified as seven main categories along with 39 indicators in the BIM adoption framework. Based on the interviewees' perspectives, leadership is considered the most significant category, impacting all of the remaining categories.

Using the developed framework will enhance comprehension of BIM, offering directives for those embracing BIM. This will aid construction stakeholders in being better equipped for BIM projects. Having a skilled BIM manager can lead to the success of construction projects.

This research contributed to the existing body of knowledge by providing the categories with specific factors that assist BIM practitioners in assessing their BIM performance for further BIM practice improvement.

The current gap between the required and available artificial intelligence (AI) professionals poses significant challenges for organisations and academia. Organisations are challenged to identify and secure the appropriate AI competencies. Simultaneously, academia is challenged to design, offer and quickly scale academic programmes in line with industry needs and train new generations of AI professionals. Therefore, identifying and structuring AI competencies is necessary to effectively overcome the AI competence shortage.

A probabilistic topic model was applied to explore the AI competence categories empirically. The authors analysed 1159 AI-related online job ads published on LinkedIn.

The authors identified five predominant competence categories: (1) Data Science, (2) AI Software Development, (3) AI Product Development and Management, (4) AI Client Servicing, and (5) AI Research. These five competence categories were summarised under the developed AI competence framework.

The AI competence framework contributes to clarifying and structuring the diverse AI landscape. These findings have the potential to aid various stakeholders involved in the process of training, recruiting and selecting AI professionals. They may guide organisations in constructing a complementary portfolio of AI competencies by helping users match the right competence requirements with an organisation's needs and business objectives. Similarly, they can support academia in designing academic programmes aligned with industry needs. Furthermore, while focusing on AI, this study contributes to the research stream of information technology (IT) competencies.

This paper aims to investigate the theoretical and practical links between digital twin (DT) application in heritage facilities management (HFM) from a life cycle management perspective and to signpost the future development directions of DT in HFM.

This state-of-the-art review was conducted using a systematic literature review method. Inclusive and exclusive criteria were identified and used to retrieve relevant literature from renowned literature databases. Shortlisted publications were analysed using the VOSviewer software and then critically reviewed to reveal the status quo of research in the subject area.

The review results show that DT has been mainly adopted to support decision-making on conservation approach and method selection, performance monitoring and prediction, maintenance strategies design and development, and energy evaluation and management. Although many researchers attempted to develop DT models for part of a heritage building at component or system level and test the models using real-life cases, their works were constrained by availability of empirical data. Furthermore, data capture approaches, data acquisition methods and modelling with multi-source data are found to be the existing challenges of DT application in HFM.

In a broader sense, this study contributes to the field of engineering, construction and architectural management by providing an overview of how DT has been applied to support management activities throughout the building life cycle. For the HFM practice, a DT-cum-heritage building information modelling (HBIM) framework was developed to illustrate how DT can be integrated with HBIM to facilitate future DT application in HFM. The overall implication of this study is that it reveals the potential of heritage DT in facilitating HFM in the urban development context.

Disasters can affect the important sector of tourism across the world. This study aims to combine qualitative findings from 13 publications to enhance disaster management plans for the security and resilience of the tourist industry.

The authors used Noblit and Hare’s seven-step meta-ethnography method, identifying 13 qualitative studies on tourism disaster management through extensive searches in key databases (WoS, Scopus and Google Scholar), meeting inclusion criteria. NVivo 12 aided in coding, translation and comprehension of related ideas.

Five interrelated and third-ordered conceptual categories were identified: extreme natural and malevolent events, lack of risk management and emerg+ency response, sustainable tourism, tourism resilience factors, disaster awareness and preparedness. Effective strategies demand collaboration, resource allocation and local engagement.

This study offers a novel synthesis of qualitative research in tourism disaster management, enhancing understanding of resilience in the industry. It provides unique insights on cross-cultural dynamics, stakeholder engagement and integrated strategies while showcasing the utility of meta-ethnography in business and management research.

Interdependence on the global economy and rapid technological changes raised the degree of uncertainty and complexity, leading to innovation challenges. Innovation depends on knowledge, and the solution might rest on how sound firms manage it, particularly in emerging markets such as India. The purpose of this paper is to examine how firms implement knowledge management (KM) in highly innovation-oriented firms (biotechnology and pharmaceuticals) and the factors affecting its implementation by examining knowledge interactions between individuals.

This study consists of a systematic literature review, a case study with embedded units and the use of grounded theory to analyse the data. The factors emerging from the results were examined from an individual and organisational lens. Next, complexity theory (CT) was used to understand the impact of these factors in KM by facilitating its incorporation as a system.

The findings of this paper suggest that constant technology adoption increases human-to-technology interaction, higher circulation of existing knowledge and more controlled environments, discouraging individuals from learning or sharing knowledge. From a system perspective, results of this paper suggest that firms self-organise around technology, indicating that innovation decreases as knowledge creation and sharing tend to reduce with lesser social interactions. This study shows the usefulness of using CT in analysing KM for innovation. The performance of the system is analysed based on its constituents and interactions.

This study contributes to advancing CT in KM in the context of innovation in highly knowledge-intensive firms, as few studies were found in the literature.