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This study aims to develop workplace well-being indexes for construction sites of different project types (infrastructure, high-rise and low-rise). Accordingly, the study objectives are to identify the critical factors that affect workplace well-being at construction sites, compare the critical factors between different project types, categorize the critical factors into subgroups and compute indexes for the critical factors and subgroups.

Data from a systematic literature review and semi-structured interviews with construction industry professionals were used to extract 19 potential factors that affect workplace well-being. Then, a structured questionnaire survey was distributed, and 169 valid responses were collected. Finally, the data were analyzed using normalized mean analysis, agreement analysis, factor analysis and fuzzy synthetic evaluation.

The study findings revealed that there are 11, 11, 8 and 12 critical factors across overall infrastructure, high-rise and low-rise construction projects. Out of those, six critical factors are overlapping across project types, including “general safety and health monitoring,” “salary package,” “timeline of salary payment,” “working hours,” “communication between workers” and “planning of the project.” Accordingly, the critical factors can be categorized into two subgroups within each project type. Finally, the development of indexes shows that infrastructure construction projects have the greatest index compared to other project types.

This study contributes to filling the current knowledge gap by developing workplace well-being indexes at construction sites across different project types. The indexes would assist decision-makers in understanding the current state of workplace well-being. This increases the commitment and recognition of well-being across different construction project types.

Tracking trends in new technology funding patterns is essential for venture scaling. The emerging advanced digital technologies (ADT) such as virtual reality (VR), artificial intelligence (AI), blockchain and Internet-of-things (IoT) promote business innovation adaptations, and in turn, reshape the industrial landscape. To attract nascent funding for such prospective projects among the public, well-articulated project pitches that are equipped with effective marketing communication convey the projects' importance and marketability. Specifically, when the entrepreneurs and the crowdfunding platform users interact via different types of crowdfunding platforms, pitch framing, including the signaling of ADT terms, project location and fundraising goal, becomes imperative to help facilitate crowdfunding success.

Drawing on data collected from six leading US-based equity and reward-based crowdfunding platforms in 2020, an empirical study was performed. Using the text analysis approach, the authors examined the positive effects of incorporating technology orientation on crowdfunding success. While the effect between the project description's signaling of geographic location, fundraising goal and articulation style on fundraising success, while controlling for project and platform characteristics.

The results suggested that the technology-orientated projects are more likely to achieve better fundraising outcomes. Taking crowdfunding platform types, project locations, minimum fundraising goals and articulation with analytical and authentic into consideration, the results still hold.

Building on the theoretical framework of signaling theory, the authors consider the crowdfunding-specific contextual factors to enhance the understanding of the positivity impact of technology orientation. By such addition, it facilitates more effective strategic composition of entrepreneurs' fundraising conversations.

As the foundation of social and economic development, infrastructure development projects are characterized by large initial investment, high technical requirements and thus generally delivered through complex contractor–subcontractor collaboration chains. This study aims to characterize the complexity of collaborative networks between contractors and subcontractors for infrastructure development through comparing the structural characteristics and the formation mechanisms of contractor–subcontractor collaborative networks for the following two different types of infrastructure: public works (PWCN) owned and operated by government agencies, and public utilities (PUCN) owned and operated by nongovernment agencies.

Based on the method of stochastic actor-oriented models and the longitudinal dataset of National Quality Award Projects in China during 2001–2020, this study compares how the structural characteristics of project-based collaborative networks between contractors and subcontractors for the two types of projects are different and how related micro-mechanisms, including both structure-based endogenous network effects and attribute-based exogenous homophily effects (institutional, organizational and geographical homophily), collectively underpin the formation of the networks.

The empirical results provide evidence that while the two networks are both characterized by relatively low levels of network density, PWCN is more globally connected around a minority of superconnected contractors as compared with PUCN. The results further reveal that compared with PUCN, the formation of PWCN is more significantly related to the structure-based anti in-isolates effect, suggesting that PWCN is more open for new entrant subcontractors. With regard to the attribute-based homophily effects, the results provide evidence that while both significantly and positively related to the effects of organizational (same company group) and geographical homophily (same location), the formation of PWCN and PUCN is oppositely driven by the institutional homophily effect (same ownership type).

As an exploratory effort of using network perspective to investigate the formation mechanisms of contractor–subcontractor relationships in the infrastructure development domain, this study contributes to a network and self-organizing system view of how contractors select subcontractors in different types of infrastructure projects. The study also provides insights into how contractor–subcontractor collaborative relationships can be better manipulated to promote the development of complex infrastructure in different contexts.

The exact process of construction projects performance assessment and benchmarking still remains subjective relying on qualitative techniques, which does not allow stakeholders to address the issues and the drawbacks of their respective projects as effectively as possible for performance improvement purposes. Hence, this research aims to establish a unified project performance score (PPS) for assessing and comparing projects performance.

Data were collected from Construction Industry Institute (CII) members and through University of Wisconsin active research projects. Exploratory data analysis was done to investigate the calculated performance metrics and the collected data characteristics. Data were converted into six performance metrics which were used as the independent variables in creating the PPS model. Logistic regression model was developed to generate the unified PPS equation in order to explain the variables that significantly affect construction projects successful post-completion performance. The PPS model was then applied on the collected dataset to benchmark projects in terms of project delivery systems, compensation types and project types in order to showcase the PPS capabilities and possible applications.

The model revealed that construction cost and schedule growth are the most important metrics in assessing projects performance, while RFIs’ processing time and change orders per million dollars were the features with the least effect on the PPS value. The authors found that integrated project delivery (IPD) and target value (TV) projects outperformed all other project delivery and compensation types. While, industrial projects showed the worst performance, as compared to commercial or institutional projects.

The PPS model can be used to assess the performance of any pool of executed projects, and introducing a novel addition to the field of construction business analytics which is a supplementary tool to successful decision making and performance improvement. Additionally, the bidding selection system can be revolutionized from a cost-based to a performance based one using the PPS model to improve the outcomes of the buyout process.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

Master information delivery plan (MIDP) is a key requirement for building information modelling (BIM) execution plan (BEP) that enlists all information deliverables in BIM-based project, containing information about what would be prepared, when, by who, as well as the procedures and protocols to be used. In a well-conceived BEP, the MIDP facilitates collaboration among stakeholders. However, current approaches to generating MIDP are manual, making it tedious, error-prone and inconsistent, thereby limiting some expected benefits of BIM implementation. The purpose of this study is to automate the MIDP and demonstrate a collaborative BIM system that overcomes the problems associated with the traditional approach.

A BIM cloud-based system (named Auto-BIMApp) involving naming that automated MIDP generation is presented. A participatory action research methodology involving academia and industry stakeholders is followed to design and validate the Auto-BIMApp.

A mixed-method experiment is conducted to compare the proposed automated generation of MIDP using Auto-BIMApp with the traditional practice of using spreadsheets. The quantitative results show over 500% increased work efficiency, with improved and error-free collaboration among team members through Auto-BIMApp. Moreover, the responses from the participants using Auto-BIMApp during the experiment shows positive feedback in term of ease of use and automated functionalities of the Auto-BIMApp.

The replacement of traditional practices to a complete automated collaborative system for the generation of MIDP, with substantial productivity improvement, brings novelty to the present research. The Auto-BIMApp involve multidimensional information, multiple platforms, multiple types and levels of users, and generates three different representations of MIDP.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. A framework to measure FEED accuracy does not exist in the literature or in practice, not does systematic data directly linking FEED accuracy to project performance. This paper aims to focus first on gauging and quantifying FEED accuracy, and second on measuring its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction.

A novel measurement scheme was developed for FEED accuracy as a comprehensive assessment of factors related to the project leadership and execution teams, management processes and resources; to assess the environment surrounding FEED. The development of this framework built on a literature review and focus groups, and used the research charrettes methodology, guided by a research team of 20 industry professionals and input from 48 practitioners representing 31 organizations. Data were collected from 33 large industrial projects representing over $8.8 billion of installed cost, allowing for a statistical analysis of the framework's impact on performance.

This paper describes: (1) twenty-seven critical FEED accuracy factors; (2) an objective and scalable method to measure FEED accuracy; and (3) data showing that projects with high FEED accuracy outperformed projects with low FEED accuracy by 20 percent in terms of cost growth in relation to their approved budgets.

FEED accuracy is defined as the degree of confidence in the measured level of maturity of the FEED deliverables to serve as a basis of decision at the end of detailed scope, prior to detailed design. Assessing FEED accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions.

FEED accuracy has not been assessed before, and it turned out to have considerable project performance implications. The new framework presented in this paper is the first of its kind, it has been tested rigorously, and it contributes to both the literature body of knowledge as well as to practice. As one industry leader recently stated, “it not only helped to assess the quality and adequacy of the technical documentation required, but also provided an opportunity to check the organization's readiness before making a capital investment decision.”

Construction delay always causes massive damage to the advancing construction industries, which is no different in the case of Bangladeshi construction industries. This paper aims to investigate the major delay factors causing construction delays in public-funded, mixed and private-funded construction projects of Bangladesh. Also, it offers preventive suggestions from expert stakeholders to reduce the recurrence of delays.

At first, an extensive literature review was conducted to identify the thirty-seven major delay factors categorized under seven groups. A questionnaire was then developed for survey at ongoing construction projects at a different division of Bangladesh. Next, data from 110 respondents were collected, and the delay factors were ranked based on the Relative Importance Index (RII); lastly, probable solutions were suggested for top-ranked delay factors based on opinions from expert stakeholders in the construction sector of Bangladesh.

The overall RII ranking of the 37 delay factors showed “Construction mistakes and defective work,” “Contract modifications by the client” and “Adverse weather condition” as the top three factors causing the delay. For public-funded projects, “Construction mistakes and defective work” and “Slow decision making by a consultant” are the top delay factors. For mixed projects, “Slow decision making of the client” and “Construction mistakes and defective work ranked top, and for private-funded projects, “Financial problems and payment delay of the client” and “Adverse weather condition” ranked top. These nuances of ranking in individual project types ascertain that the causes of delay vary in terms of project features.

The outcome of this project will help identify the significant delay factors based on their severity of effectiveness associated with public-funded, mixed and private-funded projects in Bangladesh. The suggestions regarding preventing these delay factors obtained through the opinions of expert stakeholders can help reduce the effect of these delays in the context of Bangladesh and in countries where the similarity in construction environment prevails.

Previously, studies on construction delays in Bangladesh focused mainly on identifying the delays using qualitative analysis techniques. This study is based on a unique methodology of integrating quantitative research on delay factor identification and qualitative research on preventive measures following the opinions gathered from expert stakeholders in the construction sector.