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This study was designed to provide insights into how barriers to small‐ to medium‐sized enterprises’ (SMEs’) participation can be addressed by training providers by examining evidence from a training programme offered over a period of 21 months. Previous research findings identifying influences on SME participation in training have been used as an analytical framework to examine an experience of training design and delivery in practice. Discussion and analysis of project data within the framework above identifies support for some of the relationships between SME participation and these influences. SMEs have taken up the training, participation grew over time, and positive feedback was received. More respondents reported an intention to participate in future training than had taken part in prior training. It therefore seems that the approach taken has been successful in overcoming barriers to SME participation. The paper concludes by discussing the implications of the findings for learning approaches that promote SME take up.

The study examines the management of competitive funding by UK local authorities. This funding, which is additional to core funding, is seen by authorities to be worth securing because it gives them the ability to provide services over and above those that must be provided to meet legislative requirements. However the evidence shows that considerable pressures are being placed upon local authorities as a result. These can be distinguished according to whether they are outside a local authority’s control, such as lateness of approval notification, or within their control, such as the provision of bidding activity cost information. Whilst competition remains as a feature of resource allocation, these pressures must be addressed by external agencies and local authorities as appropriate. The article concludes with a series of proposals for improved practice.

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

This study examines the direct and indirect effect mechanisms of how using smartphones for social media is associated with college students' civic engagement via levels of communication network heterogeneity and social capital. In addition, this study tests whether such indirect effects mechanisms are moderated by the need to belong.

The study analyzes data from an online survey (N = 580) conducted at a public university. The PROCESS macro is used to examine the mediation association between mobile social media use, communication network heterogeneity, social capital, and civic engagement and the moderated mediation conditional upon need to belong.

College students who often use smartphones for social media were more likely to communicate with people who have different socio-demographic characteristics and different opinions. There was also a positive mediation mechanism between smartphone use for social media, network heterogeneity, social capital and civic engagement, which means that college students who often use mobile social media are more likely to communicate with heterogeneous others and develop a sense of social capital, which in turn led to greater levels of civic engagement. Importantly, these indirect effects of smartphone use for social media on civic engagement were stronger for those with greater levels of need to belong.

The findings of the current study are significant given that little is known about how young adults' mobile social media use is associated with communication network heterogeneity and civic engagement in their everyday life. The research expands the research agenda by investigating the most popular interactive communication media platforms.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

Building upon social capital theoretical foundation, the principal purpose of present research is to construct a conceptual research model and uncover the intricate relationships between WeChat usage, social capital and civic participation behaviors among young people.

A large cross-sectional valid data from 1,238 young people in mainland China were gathered to examine the hypothesized research model. Descriptive analyses, correlation analysis and structural equation modeling (SEM) were sequentially undertaken to determine dynamic interrelationships among main constructs.

The empirical findings demonstrate that WeChat usage positively impacts young people's social interaction ties, the guanxi network and shared goals. Moreover, social interaction ties and shared goals are positively associated with their online civic engagement. Furthermore, the guanxi network is positively associated with both online and offline civic engagement.

Theoretically, this research is the first to explicitly explore the influence mechanism of WeChat use intensity on different dimensions of individual-level social capital and civic engagement. Practically, these obtained results can be beneficial to the comprehending of whether and how WeChat is a resource for social capital and civic involvement.

Despite mobile social media, especially WeChat, has achieved immense popularity in contemporary mainland China, the potential influence of this emerging technology use on individuals' differential dimensions of social capital and civic engagement has not been systematically investigated. The study may not only offer fresh insight into innovative features of WeChat but also contribute to broader controversies regarding the impact of mobile-mediated communication on individuals' daily civic lives.

Food and beverage (F&B) small and medium-sized enterprises (SMEs) must diversify their markets and obtain predictable sources of revenues to withstand difficult and volatile periods such as the post-pandemic geopolitical scenario, recently burdened by the Russian-Ukrainian conflict. On the other hand, another strand of the literature suggests that public procurement could be considered a great source of income, enabling solid contracts, revenues and cash-flow stability. Therefore, this paper aims to explore the role of public procurement, the adoption of e-commerce platforms and their interactions in affecting the exporting performances of SMEs operating in the F&B sector.

The study retrieves data from 2,186 Italian F&B manufacturing SMEs relying on Margò by Cribis database. Therefore, it conducts a structured equational model (SEM) to test the developed hypotheses empirically.

The findings reveal that digital selling platforms positively affect exports, whereas public procurement negatively affects F&B SMEs exports. Nonetheless, findings underline that the interaction between public procurement and the adoption of digital selling platforms dampens public procurement's negative effects on exports.

This study brings an original contribution to the F&B literature by conducting empirical research on an extensive sample of firms from one of the most influential countries in the F&B vertical, Italy, with officially registered data. More importantly, to the best of the authors' knowledge, this study pioneers the investigation of the relationship between public procurement and e-commerce platforms in affecting F&B SMEs' export performances.