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Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Political Corporate Social Responsibility (CSR), based on ideas about deliberative democracy, have been criticised for increasing corporate power and democratic deficits. Yet, deliberative ideals are flourishing in the corporate world in the form of dialogues with a broad set of stakeholders and engagement in wider societal issues. Extractive industry areas, with extensive corporate interventions in weak regulatory environments, are particularly vulnerable to asymmetrical power relations when businesses engage with society. This paper aims to illustrate in what way deliberative CSR practices in such contexts risk enhancing corporate power at the expense of community interests.

This paper is based on a retrospective qualitative study of a Canadian oil company, operating in an Albanian oilfield between 2009 and 2016. Through a study of three different deliberative CSR practices – market-based land acquisition, a grievance redress mechanism and dialogue groups – it highlights how these practices in various ways enforced corporate interests and prevented further community mobilisation.

By applying Laclau and Mouffe’s theory of hegemony, the analysis highlights how deliberative CSR activities isolated and silenced community demands, moved some community members into the corporate alliance and prevented alternative visions of the area to be articulated. In particular, the close connection between deliberative practices and monetary compensation flows is underlined in this dynamic.

The paper contributes to critical scholarship on political CSR by highlighting in what way deliberative practices, linked to monetary compensation schemes, enforce corporate hegemony by moving community members over to the corporate alliance.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This paper aims to explore how open innovation communities contribute to the adoption of building information modelling (BIM) in the construction industry.

The paper presents a cross-case analysis of two construction communities, buildingSMART Norway and the BIM Vietnam Community. Data were collected based on 21 semi-structured interviews conducted with industry experts actively engaged in these two communities. The theoretical basis for the study was open innovation and the institutional intervention model, which delineates institutional actions related to the adoption of new information technology.

The findings show both similarities and differences in the way in which the communities contribute to industrial practice. Both communities use similar knowledge channels and repositories but apply different approaches to innovation creation and diffusion. In addition, trust can support BIM innovation in the community context.

The comparison of buildingSMART Norway and the BIM Vietnam Community in accelerating BIM innovation allows for exploring how open innovation communities support BIM adoption in the construction industry. The findings provide insights for construction communities into creating and diffusing BIM innovation. In addition, the examples of gaining benefits from community innovation activities are useful for construction firms and practitioners.

A lack of suitably qualified Building Information Modelling (BIM) professionals is understood to be a major barrier towards higher uptakes of BIM in the Australian construction industry. In response, Australian universities have tried to integrate the teaching of BIM into construction-related curricula, but with limited success. The acknowledged impediment is the lingering mismatch between what universities offer and what industry actually needs. However, the exact nature of that mismatch has yet to be identified. This study addresses that knowledge gap. It assesses both the current status of BIM competencies among university graduates and explores how BIM education at Australian universities may be improved to deliver BIM work readiness, as required by the industry.

This paper employed a qualitative research approach, utilizing 17 semi-structured interviews with experts in the Australian BIM industry. The Person-Organization (PO) fit theory, which emphasizes the congruence between individual and organizational characteristics, was utilized as a theoretical framework to examine the compatibility between “demand” and “ability” perspectives. The resulting data were analysed using this theoretical framework to gain insights into the PO fit perspectives in relation to BIM industry practices.

Findings reveal that graduates are generally competent regarding the use of BIM software. However, employers require much more than software skills, and expect recruits to have the capability to implement BIM as a process according to information management standards. Specifically, graduates are significantly deficient in matters of BIM protocols, collaboration and coordination, information workflows as well as completion and handover procedures.

This study is the first of its kind that bridges the gap between industry expectations and university education, in the Australian context, moving beyond the common discourse in education literature, which is exclusively focused on assessing students’ perceptions about BIM.

The main purpose of this paper is to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of micro small and medium-sized enterprises (MSMEs) in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.

Structural equation modelling (SEM) through SmartPLS 4.0 was used to generate the standardized parameters to test whether credit counselling mediates the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.

The SEM bootstrap results revealed that credit counselling enhances access to microcredit by 27% to promote survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.

The current study focused only on women MSMEs. Future studies may possibly collect data from all the MSMEs to draw better generalization of the findings within the sector.

The findings can help public finance policy to ensure provision of credit counselling to microentrepreneurs who borrow from different financial institutions to reduce the problem of loan defaults and delinquency rampant in lending. This could be done through conducting routine business education and counselling sessions for microentrepreneurs who often need credit to grow their businesses.

This study is amongst the first few studies to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa in the aftermath of COVID-19 pandemic with data collected from rural Uganda. There is a dearth in literature and theory on the rehabilitative and preventive role of credit counselling in reducing repayment defaults amongst borrowers within the credit market to spur survival of MSMEs seen as the main enabler of economic growth, especially in developing countries. In fact, credit counselling acts as a safety net by substituting financial literacy and education to solve the rampant problem of overindebtedness amongst borrowers who are debt illiterate within the credit market.

Entrepreneurship policy is a vital component of any entrepreneurial ecosystem. However, the specific policy initiatives that have a greater impact on women's entrepreneurship remain unclear in many developing economies. Therefore, this study aims to evaluate the effectiveness of entrepreneurship policies targeted at women’s entrepreneurship in sub-Saharan Africa (SSA).

Employing fuzzy-set qualitative comparative analysis (fsQCA), this paper utilises and analyses secondary data collected by the World Bank's Women, Business, and the Law (WBL) from 1970 to 2020, encompassing 48 countries within SSA.

Through our analysis, we identified two configurations that sufficiently support women's entrepreneurship. First, a combination of gender-based policies focussing on enabling “access to credit” and “signing of contracts”; and second, a blend of policies supporting “signing of contracts”, “business registration”, and “opening a bank account”, represent significant antecedents to supporting women's entrepreneurship. These distinct pathways are crucial to fostering women’s entrepreneurship in the SSA region.

The study's findings indicate that the impact and effectiveness of entrepreneurship policies targeted at women entrepreneurs in developing economies depend on the effectiveness of other policies that are in place.

This study offers new insights into the intricate interrelationship between entrepreneurship policies and women’s entrepreneurship in developing countries by considering the interdependence and combinative value of gender-based policies that effectively support women’s entrepreneurship in sub-Saharan Africa.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

This study examines the impact of chief executive officer (CEO) power, institutional investors and their interaction on green financing provided by Bangladeshi financial institutions and the moderating effect of government policy and CEO political connections on these relations.

We employ ordinary least squares (OLS) regressions and interaction terms among variables of interest for the empirical analysis.

Green financing decreases with CEO power, implying that CEOs of this country’s financial institutions are averse to green loans, whereas institutional investors increase green financing extended by these institutions. The government policy, which includes financial incentives for complying financial institutions, strengthens institutional investors' positive impact on green financing, but it does not change CEOs' aversion to green loans. Institutional investors have a positive moderating effect on the relationship between green finance (GF) and CEO power, but this positive moderating effect is negated in banks where the government owns a stake, possibly because CEOs of state-owned financial institutions are politically connected, which reduces institutional investors’ influence over them.

This study is unique in that it is the first to examine how the interaction among different stakeholders affects green financing in a unique setting. As the literature is almost silent on this topic, the findings of this paper are expected to raise policymakers’ awareness of the obstacles that hamper the efforts of developing countries to go green.