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Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

The debate around automation through digital technologies has gathered traction in line with the advancement of Industry 4.0. Blockchain-powered construction progress payment has emerged as an area that can benefit from such automation. However, the challenges inherent in real-time construction payment processes cannot be solely mitigated by blockchain. Including building information modeling (BIM)-based schedule information stored in decentralized storage linked with a smart contract (SC) can allow the efficient administration of payments. Accordingly, this study aims to present an integrated BIM-blockchain system (BBS) to administer decentralized progress payments in construction projects.

A mixed-method approach is adopted, including an extensive literature review, development of the integrated BBS, and a case study with 13 respondents to test and validate the BBS. This study proposes a BBS that extracts the invoices from BIM and pushes them to the decentralized app (dApp) for digital payment to the contractor through the Ethereum blockchain. The Solc npm package was used to compile the backend SC. Next.js was used to create the front end of the dApp. The Web3 npm package is paramount in developing a dApp. A total of 13 construction professionals working on the case study project were engaged through a questionnaire survey to comment on and validate the proposed BBS. A descriptive analysis was conducted on the case study data to apprehend the responses of expert professionals.

The proposed BBS creates an SC, enables sender verification, checks contract complaints, verifies bills, and processes the currency flow based on a coded payment logic. After passing the initial checks, the bill amount is processed and made available for the contractor to claim. Every activity on dApp leaves its trace on the blockchain ledger. A control mechanism for accepting or rejecting the invoice is also incorporated into the system. The case study-based validation confirmed that the proposed BBS could increase payment efficiency (92.3%), tackle financial misconduct (84.6%), ensure transparency and audibility (92.4%), and ensure payment security (61%) in construction projects. A total of 46.2% of respondents were skeptical of the BBS because of its dependency on cryptocurrencies. A further 23.1% of respondents indicated that the price fluctuation of cryptocurrencies is a major barrier to BBS adoption. Others highlighted the absence of legal frameworks for cryptocurrencies’ usage.

This study opens the avenue for the application of dApp for autonomous contract management and progress payments, which is flexible with applications across various construction processes. Overall, it is a potential solution to the endemic problem of cash flow that has devastating consequences for all project stakeholders. This is also aligned with the goals of Industry 4.0, where process automation is a key focus. The study provides a practice application for automated progress payments that can be leveraged in construction projects across the globe.

The purpose of this paper is to analyse whether tourism destination competitiveness is associated with human development using contingency tables. Also, by means of a cluster analysis, it seeks to identify homogeneous groups of tourists.

The analysis relies on the Travel and Tourism Development Index and Human Development Index for 2021 and is applied to 81 countries with a high/very high level of human progress and 36 countries with a medium/low level.

The results show that infrastructure is the tourism dimension that has the greatest influence on human development for all countries, while conditions for doing business and sustainability are relevant to residents’ quality of life only in destinations with a high/very high level of progress. Cluster analysis points to India as a tourist destination that is markedly different from the other countries in its human progress, presenting much higher values for price competitiveness, infrastructure and natural and cultural resources.

It analyses the association between each one of the dimensions of tourism development and the Human Development Index.

This research aims to develop a blockchain smart contract–enabled framework to resolve power imbalance problems in construction payment.

This research adopts a design science research method to develop the blockchain smart contract–enabled framework. The authors then develop a prototype system. Finally, the authors evaluate its performance in solving power imbalance-induced payment problems.

The results show that the prototype system can resolve power imbalance problems in construction payment by allowing project participants to make transparent and decentralized decisions that are self-enforceable by blockchain smart contracts.

This study provides theoretical explanations for how blockchain smart contracts can resolve power imbalances in construction payment; based on that, it proposes a novel blockchain smart contract–enabled method to rebalance the power of stakeholders in construction payment. Thus, it contributes to the body of knowledge on blockchain technology and construction payment.

This study moves beyond a conceptual framework and develops a practical blockchain smart contract system for resolving power imbalances in construction payment, strengthening construction project members' confidence in using blockchain technology.

The proposed blockchain smart contract–enabled solution helps mitigate negative social impacts associated with late payment and non-payment. Furthermore, the research maximizes trust among participants in payment processes to inspire collaborative culture in the construction industry.

This paper introduces a novel blockchain smart contract integrated method, allowing project stakeholders to resolve power imbalance problems in construction payment through decentralized decision-making.

This study examines the differences in the economic stimulus effects, transmission mechanisms, and output multipliers of government consumption, government traditional investment, and government science and technology investment.

This study constructs and estimates a New Keynesian model of endogenous technological progress embedded in the research and development (R&D) and technology transfer sectors. Using Chinese macroeconomic time series data from 1996 to 2019, this study calibrates and estimates the model and analyzes the impulse response function and a counterfactual simulation of expenditure structure adjustment.

The results show that compared with the traditional dynamic stochastic general equilibrium (DSGE) model, the endogenous process of technological progress amplifies the impact of government consumption shock and traditional government investment shock on the macroeconomy, leading to greater economic cycle fluctuations. As government investment in science and technology has positive external spillover effects on firm R&D activities and the application of innovation achievements, it can promote more sustainable economic growth than government consumption and traditional investment in the long run.

This study constructs an extended New Keynesian model with different types of government spending, which includes endogenous technological progress within the R&D and technology transfer sectors, thereby linking fiscal policy, business cycle fluctuations and long-term economic growth. This model can study the macroeconomic impact of fiscal expenditure structure adjustment when fiscal expansion is limited. In the Bayesian estimation of model parameters, this study not only uses macroeconomic variables but also adds a sequence of private R&D investment.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

This study aims to examine the effect of the interplay between national culture and supervisor gender on supervisors’ supportive behaviours towards employees in Europe.

Based on Hofstede’s cultural scores for 34 European countries, two clusters of countries were identified, reflecting two cultural configurations: favouring versus not favouring gender equality (GE). For hypotheses testing, the authors used a sample of 21,335 native employees, obtained from the European Working Conditions Survey.

Women in supervisory positions, compared to men, provided more support to subordinates in terms of respect, recognition, encouraging development and providing feedback. In countries with cultures favouring progress towards GE (small power distance, weak uncertainty avoidance, individualism and indulgence), only respect and recognition were more prevalent. In countries with opposing cultural configurations, other supportive behaviours stood out: coordinating work, providing feedback and helping with work. Furthermore, the impact of supervisor gender on supportive behaviours was influenced by national culture. Gender differences were larger in countries with a culture favouring progress towards GE compared to countries not favouring such progress.

The authors present a pioneering study that delves into national values as they relate to progress towards GE to understand the differences between male and female supervisors in the display of six supportive behaviours towards their subordinates. Their cultural approach nuances some of the predictions of social role theory.

The emergence of the COVID-19 pandemic changed the way in which education was delivered in early 2020, and the impacts of these changes continue to be questionable. The aims of this study were to evaluate: (1) the results obtained by students of the Dentistry course in the progress test carried out both before and after the pandemic, (2) the results obtained by a specific group of students who took the test in 2019–2022, and compare their results and (3) subjects that showed a reduction in the percentage of correct answers when the two tests were compared.

The progress test consisting of 100 multiple choice questions was applied before and after the pandemic to all students in the Dentistry course. The analyses were performed using the IBM SPSS for Statistics v.26 software program. The level of significance of 5% was adopted (<0.05).

The test was applied to 320 students in 2019 and to 272 in 2022, of whom the sample of this study was composed. The mean score values in 2019 (M = 49.10; SD = 12.03) were significantly (p = 0.026) higher than those in the year 2022 (M = 46.97; SD = 12.15), with the disciplines in the area of specific knowledge showing a greater drop in the percentage of correct answers by students. This study showed that the emergency remote education had a negative effect on the academic performance of students, based on the progress testing as an evaluation method.

Many studies that assessed the impacts of the pandemic on teaching were focused on the opinions of students. However, the great advantage of our study was the use of a theoretical assessment tool to verify student performance. The post-pandemic landscape beckons for comprehensive inquiries into these domains. This type of research would be valuable for gathering evidence relative to the performance of students after the emergency remote education.

An S-curve is an essential project-management tool. However, it is difficult to adjust S-curve to deal with a force majeure event. The present study develops four valuable adjustment approaches, designed to achieve a compromise between the views of the client and contractor. These can be used to control projects after a force majeure event.

The present study develops four adjustment approaches, which can be used to achieve a compromise between the views of the client and those of the contractor when controlling projects after a force majeure. To determine the S-curves during a force majeure event, two approaches can be selected: BCWS (budgeted cost of scheduled work)-base approach, or BCWP (budgeted cost of work performed)-base approach. To determine the rest of S-curves after a force majeure event, two approaches can be considered: maintaining the original curve of the remaining BCWS, or allocating the original curve of the remaining BCWS. Based on the validation of three empirical cases, drawn from a professional project-management website, this study confirms the feasibility of four proposed empirical approaches and a selection procedure for S-curve adjustment.

The S-curve-adjustment approaches presented here can be used to deal with cases that are ahead of, on and behind schedule. Using the proposed approaches and selection procedure, contractors can easily revise S-curves and control projects more effectively. To deal with a force majeure event, such as COVID-19, they are strongly advised to adopt the approaches labeled SA-A1 (to adjust the S-curve based on the extension ratio multiplied by the difference in progress during the force majeure) and SA-B1 (to maintain the original curve of the remaining BCWS) for the A/E and E/F curves, respectively.

The proposed approaches can be used in cases of continuous construction during force majeure events. If construction work is totally suspended during such an event, it will be necessary to fine-tune the proposed approaches.

Previous studies have used case-oriented or mathematical-simulation approaches to forecast S-curves. The present study proposes simple approaches that allow the client and contractor to adjust the S-curve easily after a force majeure event. These approaches can be used to adjust work and project-completion targets within an extended duration. Selecting the right S-curve adjustment approach can help to control the remainder of the project, reducing the possibility of delay claims.

This study aims to unpack the progress of board gender diversity among the 3,000 largest US listed firms by market capitalization (i.e. Russell 3000 Index). This study extrapolates four classifications of firms based on the number of women in the boardroom: zero women, one or two women, three plus women and gender balanced. The purpose of this study is to examine where progress has and has not been made, why firms plateau and an agenda for the future.

This study first provides a summative overview of the literature on the benefits of board gender diversity. It then examines progress according to the four classifications, each of which have theoretical underpinnings for whether or not firms can reap the strategic benefits of gender-diverse boardrooms.

Several indices of US publicly traded companies now have women holding between 30% and 33% of the seats in the boardroom. By examining the spread of women on boards according to the four classifications, this study extrapolates three key insights: firms experiencing tokenism (i.e. one or two women in the boardroom) do not have enough women to reap the strategic benefits of diverse boardrooms; firms that have reached a critical mass (three women in the boardroom) are at an impasse and may risk plateauing; and gender-balanced firms are elevated to the status of being role models for other firms. Calls for action and associated action plans accompany these insights.

This study reminds managers and directors of the strategic benefits of gender-diverse boards and offers three critical insights that boards can use to classify what stage they are at on the path toward board gender equality. Based on their classification, calls for action and action plans offer guidance to firms.

This study shifts away from focusing on the average percentage of board seats held by women across all firms and offers new insights on the progress that firms have made according to the number of women in their boardroom.