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The purpose of this paper is to present a maturity model developed to assess knowledge sharing (KS) for the Jordanian construction sector.

The research was conducted in three stages. The first stage consisted of the review of literature and documenting variables from the literature that highlight influence on KS in organizations. The second stage was designed for maturity model development by identifying the cultural factors that affect KS in the Jordanian construction sector through questionnaires and interviews. Factor analysis was used to find possible relationships between the cultural variables followed by semi-structured interviews. In the third stage the initial maturity model was refined through another set of semi-structured interviews.

The model presented in the paper includes three levels of maturity. The first level identifies whether the variable barely exists in company’s KS practices. The second level shows the occasional techniques which the company uses to increase KS activities. The final level demonstrates the importance of the variable in affecting KS as being fundamentally ingrained in the company’s vision, mission, strategy and operations.

The research has developed a model that can be used to measure the KS in an organization. Although the model has been applied to the construction industry, it can easily be modified to fit in the other sectors.

Effective cost performance is a crucial criterion measuring successful project management in public-housing projects. This paper aims to analyse the vital underlying factors surrounding the successful cost management process (CMP) outcomes in public housing projects (PHPs).

The research was conducted in three stages. The first stage consisted of a detailed literature review to document success factors affecting cost performances and management. In stage two, brainstorming sessions were undertaken with construction experts knowledgeable in cost management practices and have been involved in PHPs. These sessions were used to refine those success factors for the PHPs settings and define their criticality with respect to the CMP stages using interpretive ranking process. In stage three, focus group sessions were performed to validate the interrelationships of the contextualised critical success factors.

The top three most critical factors for successful implementation and outcomes at all CMP stages in PHPs settings were found to relate to competencies, team qualities and collaborative practices of the project team. Early contractor involvement and effective construction planning and management also emerged relevant to the process.

Government project departments, project managers and construction organisations (consultants and contractors) need to commit and mandate continuous development of cost management competencies for all professionals engaged in PHPs. Channels supporting team integration and collaborative practices between design and construction teams are required to increase the likelihood of successful project cost management practice and outcomes in PHPs.

The research has developed a factor-process relationship model that can be used to improve and evaluate the efficacy of CMP implementation in PHP settings.

The purpose of this paper is to develop a model to understand and facilitate more knowledge sharing (KS) among construction companies in Jordan. Sixteen cultural variables that affect KS were identified through self-administered questionnaires.

Factor analysis was used to find possible relationships between the cultural variables for grouping purposes and to eliminate the cultural variables that do not affect KS. The results of factor analysis were further refined using a brainstorming session and Analytic Hierarchy Process (AHP) was used to prioritise the factors obtained through the factor analysis.

Trust, management and communication were identified as the three most important factors, whilst communication was acknowledged as the least important factor.

This research uses factor analysis and AHP to study the influence of cultural factors on KS. It develops a hierarchy of factors that affect effective KS within the Jordanian context. The paper investigated KS in-depth and highlighted the components that constitute KS in an organisation. Based on extensive literature review, this study found the relative importance of different factors that affect KS. The emphasis on trust was found to be more critical than the presence of a computer-based system. In addition, this is the first paper of this type to look at KS in the context of the Jordanian construction industry.

The purpose of this paper is to present the findings of research carried out as part of an industrial project for selection of an enterprise resource planning (ERP) system in a made‐to‐order (MTO) small‐to‐medium enterprise (SME) scenario. It develops a framework or methodology for selection. It also highlights the areas pertaining to the unique needs of, first, the SME sector and, second, the MTO sector, to be considered while selecting a solution. A work book is developed to provide a structured ERP software selection process for SMEs using a comprehensive literature review plus practical experience. This research is potentially aimed at being useful to other SMEs as a guide for a structured selection process.

A combination of comprehensive literature review and experience of managing the selection process for an ERP system in an SME was employed for the study.

Over the past few years, the number of large companies buying new ERP systems has reached saturation point. This has led to the ERP developers seeking instead other potential markets among SMEs. The MTO scenario within the SME sector is one which is very capricious in terms of demand forecasting, lead times, routings, etc. When selecting a system, an organisation in such a segment needs a tailored methodology and a list of key target areas to consider.

The paper represents a very useful source of practical information for the SME sector to consider when selecting an ERP system.

The paper provides valuable insight into the details of ERP selection, focusing on the peculiarities of the SME MTO sector.

This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.

This paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.

The findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.

The outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.

Specifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.

Local polynomial regression is extremely popular in applied settings. Recent developments in shape-constrained nonparametric regression allow practitioners to impose constraints on local polynomial estimators thereby ensuring that the resulting estimates are consistent with underlying theory. However, it turns out that local polynomial derivative estimates may fail to coincide with the analytic derivative of the local polynomial regression estimate which can be problematic, particularly in the context of shape-constrained estimation. In such cases, practitioners might prefer to instead use analytic derivatives along the lines of those proposed in the local constant setting by Rilstone and Ullah (1989). Demonstrations and applications are considered.

This study aims to focus on model development to analyse key factors affecting trust in virtual project teams (VPTs).

A questionnaire survey was conducted on construction professionals participating in virtual teams. Structural equation modelling technique was performed to establish the effect of relevant factors on trust-building in VPTs.

Team performance is highly affected by the trust among the team members. Trust building can be enhanced by improving the quality of team communication, organisation culture, team bonding and team members’ characteristics.

The model developed in this study would benefit team productivity and team members’ learning in VPTs.

This paper aims to investigate the potential challenges that hinder the effective transfer and sharing of tacit knowledge (knowledge communication [KC]) within a construction supply chain (CSC).

This study identifies six challenges (through literature review) with 15 positive correlations between them. Quantitative methodology is used to validate those challenges and correlations between challenges. First, data are collected through semi-structured e-survey questionnaire. Afterwards, a Frequency and Kruskal–Wallis H test is run for initial validation of identified challenges. A correlation analysis is used to highlight the taxonomic relations between those challenges. Finally, the study establishes the rank order of the first and following challenges.

This study highlights that traditional ways of working with construction organisations are the predominant challenge that hinders effective transferring and sharing of tacit knowledge. The cause of challenges is the fragmented nature of CSC. Also, it brings out the correlation between those challenges. The study draws the conclusion and recommendation to implement KC within a CSC.

The study highlights the challenges that hinder KC in a construction process of a CSC. It establishes that the fragmented nature of the construction sector is not the first challenge that hinders implementation of transferring and sharing of tacit knowledge but somewhat traditional organisation structures and working processes. This is the first paper that investigates and tests the challenges in four dimensions and establishes the rank order of challenges with crucial distinction in a KC approach within a CSC.

Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.

In this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.

The proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.

The model can be used by doctors to identify the exact location of the tumorous region.

The proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.