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Building information modelling (BIM) has been established in the literature as a successful platform that creates an intelligent virtual model for processing data from conceptual design through construction to operational stage of a facility. However, its adoption for facilities management (FM) provision in Nigeria has been slow due to inherent barriers. The aim of this paper is to (1) assess and categorise using factor analysis BIM for FM barriers and (2) model the barriers using stakeholders' personal/professional attributes.

Anchored on quantitative research design, 205 copies of structured questionnaire were distributed to key stakeholders and facilities managers in Nigeria's three strategic cities while 135 valid responses were received giving a response rate of 65.8%. Data collected were analysed using descriptive statistics while multiple regression analysis was used to model the barriers. Kruskal Wallis test was used to test the only hypothesis postulated for the study.

The study established lack of awareness of BIM for FM, poor supporting infrastructure for Internet services, and lack of education and training as the top three rated barriers militating against adoption of BIM for FM in Nigeria while corruption, widespread mistakes and errors and cultural issues were established as the three least rated barriers. Besides, findings also established eight underlying factors that explained 23 barrier factors used for the study which were subsequently used to develop eight regression models. In effect, gender, professional affiliation, organisation, experience, education, expertise, BIM for FM project type, and location were found to statistically predict the 8 extracted factors driving perceived barriers of BIM for FM adoption in Nigeria.

The study has provided a framework of barrier factors to help stakeholders identify specific barriers for which appropriate measures can be taken to ameliorate consequences of the perceived barriers. Meanwhile, an improved and rejuvenated advocacy on inherent benefits of BIM for facilities management by frontline stakeholders could potentially steer up interests and increased participation of stakeholders on BIM for FM.

The unique study developed the first ever regression model that links BIM for FM barriers to professional attributes of facilities management stakeholders in Nigeria.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

Although many health and safety (H&S) studies have widely examined safety risk perception in the construction industry, few studies have explored how this perception influences site workers' risk-taking behaviours during construction. This study aims to examine how construction site workers perceive and judge safety risks in risk-taking behaviours of site workers for intervention safety policy framework that may encourage safe work.

The study employed Pictorial-based Q-Methodology, which documented 63 picture scenarios of risk-taking behaviours from building sites and submitted them for validation from H&S inspectors. In total, 33 pictures emerged as having great potential to cause harm. After using these 33 pictures to elicit data from randomised site workers, the study used Frequency Tabulation, Relative Importance Index (RII) and Kruskal–Wallis Test to analyse the collected data. To fully explain the analysed data for deeper understanding, the study conducted Focus Group Discussions (FGDs) with these site workers to share the thoughts of site workers on these pictures.

Two distinctive pictures emerged from these analyses: one showing risk-taking behaviour likely to contract internal and skin disease and the other likely to fall from height. One of the implications is that construction site workers are unfamiliar with the dangerous contaminants in the materials the site workers use to work, which can potentially harm the site workers' skin and internal organs. Hence, site workers continue engaging in risk-taking behaviours. The other is that site workers are aware of and can mention catastrophic physical injuries attached to site workers' jobs. However, site workers continue engaging in risk-taking behaviours because of site workers' safety plights and rely on the favour and mercies of a supreme being as coping strategies to escape from these physical injuries.

This study is original in that the study uses picture scenarios of risk-taking behaviours to amass an empirical-based understanding of how site workers perceive and respond to H&S risks during construction. This piece of evidence is missing in the numerous research studies in this area. Again, the findings contribute to the state-of-the-art literature regarding risk-taking behaviours on construction sites.

This study develops a robust model to measure intellectual capital efficiency (ICE). It also analyzes ICE across Gulf companies, sectors and countries.

This study uses data envelopment analysis (DEA), the Malmquist productivity index (MPI), difference tests and additional analyses on a dataset consisting of 276 firm-year observations.

The findings indicate that the study model is robust to additional analysis. The results show significant differences in ICE between firms during the study period and noteworthy differences between countries, where the Qatari and Bahraini firms achieved the best ICE compared to other countries.

The results of this study have significant ramifications for increasing knowledge of ICE analysis models among relevant parties. In addition, the findings may affect trading strategies because investors and financiers are motivated by the potential for lucrative financial returns on their investments in companies that prioritize ICE strategies.

This research contributes to the literature by proposing a robust model for estimating the ICE. It also compares ICE across Gulf companies, industries and countries to shed light on their ICE challenges.

The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.

The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.

Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.

The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.

Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

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.

Micro, small and medium-sized enterprises are the live wire of construction industry in developing countries. These classes of establishments are most affected by economic contraction and turmoil, thus affecting their performance and survivability. Hence, the purpose of this study is to investigate and prioritize the survival determinants of construction consulting organization during economic contraction in Nigeria using quantity surveying firms as a focal point.

The study adopted the descriptive-survey design and quantitative data were collected through questionnaire purposely administered to quantity surveying firms in the Southwestern part of Nigeria. The data obtained from 99 quantity surveying firms on survival determinants were analysed using various statistical analysis such as mean score, standard deviation, Mann–Whitney U test, Kruskal–Wallis H test, and so on. Principal component analysis was used to identify the principal components of survival determinants, while the factors were prioritized using fuzzy synthetic evaluation (FSE).

The result of the analysis reveals eight factors that significantly determines the survival of firms during the period of economic contraction. Furthermore, the eight grouped factors were prioritized accordingly namely firm's innovation and diversification, ownership structure and networking, education level and management skills, and so on.

This study investigated the survival determinants of quantity surveying firms and prioritized it with the opinions of principal partners in quantity surveying establishments. As against obtaining large survey responses from all quantity surveyors in the study area that may not have practical experience of managing firms, the limited responses received provide valid basis to broaden the horizon of professionals and other stakeholders on the key determinants for firms to survive economic turmoil.

This study contributes to the body of knowledge by providing information on prioritized factors that must be considered in an appropriate order by quantity surveying firms to survive economic contraction.

The insidious proliferation of online misinformation represents a significant societal problem. With a wealth of research dedicated to the topic, it is still unclear what determines fake news sharing. This paper comparatively examines fake and accurate news sharing in a novel experimental setting that manipulates news about terrorism.

The authors follow an extended version of the uses-and-gratification framework for news sharing, complemented by variables commonly employed in fake news rebuttal studies.

Logistic regression and classification trees revealed worry about the topic, media literacy, information-seeking and conservatism as significant predictors of willingness to share news online. No significant association was found for general analytical thinking, journalism skepticism, conspiracy ideation, uses-and-gratification motives or pass-time coping strategies.

The current results broaden and expand the literature examining beliefs in and sharing of misinformation, highlighting the role of media literacy in protecting the public against the spread of fake news.

This is, to the authors’ knowledge, the first study to integrate a breadth of theoretically and empirically driven predictors of fake news sharing within a single experimental framework.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2022-0693

Lean construction (LC) is an innovative approach in the construction industry that has shown significant success in developed countries. Although LC has potential in the construction sector of Pakistan, it has not been extensively explored. This study aims to address this knowledge gap by identifying and predicting current lean practices and assessing the strengths and weaknesses of LC implementation in Pakistan.

Using robust statistical methods to analyze 92 valid responses, the study reveals that approximately 54% of lean practices are currently in use in the construction industry of Pakistan, with a population mean ranging from 52.7% to 55.6%.

Surprisingly, the research identifies instances where some construction firms in Pakistan are implementing LC practices, even though they have only a limited understanding of its underlying principles. Notably, certain subprinciples, such as visual management, top management commitment to change, employee training, process cycle time reduction and production optimization, are less integrated within the construction industry. Exploring the possibility of implementing LC, recommendations for strategies to implement LC in Pakistan are suggested, aligning with the conceptual model proposed by the researchers.

The novelty of this work offers insights that can serve as a comprehensive guide for developing nations. It provides a structured approach to assess and benchmark LC practices, which, in turn, can contribute to a more efficient and effective construction industry. Moreover, the strategies proposed in this research can aid developing countries in the efficient implementation of LC. This will have a positive implication for both economic and developmental outcomes.