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Public-private partnership (PPP) power projects are associated with varying risk factors. This paper aims to develop a fuzzy quantitative risk allocation model (QRAM) to guide decision-making on risk allocation in PPP power projects in Ghana.

A total of 67 risk factors and 9 risk allocation criteria were established from literature and ranked in a two-round Delphi survey using questionnaires. The fuzzy synthetic evaluation method was used in developing the risk allocation model.

The model’s output variable is the risk allocation proportions between the public body and private body based on their capability to manage the risk factors. Out of the 37 critical risk factors, the public sector was allocated 12 risk factors with proportions = 50%, while the private sector was allocated 25 risk factors with proportions = 50%.

To the best of the authors’ knowledge, this research presents the first attempt in Ghana at endeavouring to develop a QRAM for PPP power projects. There is confidence in the model to efficiently allocate risks emanating from PPP power projects.

This study provides a comprehensive analysis of the transition from Building Information Modelling (BIM) to digital twins (DT) in the construction industry. Specifically, the research explores the current state (themes and trends) and future directions of this emerging research domain.

A multi-stage approach was employed that combines scientometric and systematic review approaches. The scientometric analysis involves quantitative assessment of scientific publications retrieved from the Web of Science database – using software tools like VOSviewer and HistCite. The systematic review involved a rigorous synthesis and evaluation of the existing literature to identify research gaps, themes, clusters and future directions. Clusters obtained from the scientometric analysis of the co-occurrence network were then used as a subject base for a systematic study.

Emergent findings reveal a rapidly growing interest in BIM-DT integration, with over 90% of publications since 2020. The United Kingdom, China and Italy are the leading contributing countries. Five prominent research clusters identified are: (1) Construction 4.0 technologies; (2) smart cities and urban environments; (3) heritage BIM and laser scanning; (4) asset and facility management; and (5) energy and sustainability. The study highlights the potential of BIM-DT integration for enhancing project delivery, asset management and sustainability practices in the built environment. Moreover, the project’s life cycle operation phase has garnered the most attention from researchers in this field compared to other phases.

This unique study is comprehensive in its approach by combining scientometric and systematic methods to provide a quantitative and qualitative evaluation of the BIM-DT research landscape. Unlike previous reviews that focused solely on facility management, this study’s scope covers the entire construction sector. By identifying research gaps, challenges and future directions, this study establishes a solid foundation for researchers exploring this emerging field and envisions the future landscape of BIM-DT integration in the built environment.

This study examines green absorptive capacity as an important intervening variable that elucidates the relationship between green supply chain management (GSCM) practices (specifically, green purchasing, customer cooperation and investment recovery) and firm performance.

Drawing from the theoretical underpinnings of the natural-resource-based view theory and information processing theory, a research model is developed and tested using data obtained from 368 manufacturing firms in Ghana. Data analysis was conducted using structural equation modeling.

The results indicate that green purchasing, customer cooperation and investment recovery have a direct positive and significant effect on firm performance. Additionally, green purchasing and customer cooperation have a positive and significant effect on green absorptive capacity but investment recovery does not. Further, the results show that the paths from green purchasing and customer cooperation to firm performance are positively mediated by green absorptive capacity.

The study reveals to supply chain managers that green absorptive capacity is an important conduit through which firms can achieve enhanced firm performance from GSCM initiatives.

This study makes a contribution by integrating the absorptive capacity literature and green management literature and establishes green absorptive capacity as a mechanism through which GSCM practices enhance firm performance.

The slow adoption of unmanned aerial vehicles (UAVs) in the construction industry, particularly in developing countries like Nigeria, underscores the need for a deeper understanding of the critical factors influencing their adoption. This study aims to identify these factors using the Technology-Organization-Environment (TOE) framework and address uncertainties in their prioritization through Fuzzy Synthetic Evaluation (FSE). The utility of this approach lies in its ability to provide construction organizations with actionable insights to enhance operational efficiency and competitiveness through effective UAV adoption.

A post-positivist philosophical stance was adopted, wherein quantitative data were gathered from construction professionals in Nigeria via a questionnaire survey. The collected data were analyzed using the Cronbach alpha test as a measure of internal consistency and the FSE test to synthesize critical drivers for the adoption of UAVs.

The study found that drivers related to technology and organization are the most critical drivers. This implies that variables related to technology and organization warrant a higher level of focus if UAVs are to continue gaining popularity within the construction industry. Additionally, this study identified that logistic management, construction monitoring and site surveying represent the most critical areas of UAV application within the construction industry.

The emphasis on technology and organizational drivers as critical factors suggests that construction companies should prioritize investments in technology infrastructure and cultivate an organizational culture that embraces innovation. This may involve providing training to construction professionals to enhance their technological skills and fostering a leadership culture that champions technology adoption.

This study introduces novelty by applying the TOE framework, which has received limited attention in UAV adoption studies within construction. Additionally, the use of FSE addresses uncertainties in prioritizing critical drivers, particularly relevant in developing countries facing unique technological challenges. By assigning priority to these factors, this research lays the groundwork for a more informed and strategic approach to UAV adoption.

Addressing Environmental, Social and Governance (ESG) issues has become a critical aspect of business strategy. Since ESG has primarily focused on ratings and measures for reporting, there is a scarcity of methods to assist stakeholders in better comprehending corporate risk and addressing ESG-related issues and problems. The purpose of this paper is to propose a new model to narrow the critical gap.

This study is based on several well-known structural frameworks for managing risks and projects in various industries. Two case studies on topics related to environment (E) and social (S) responsibility are used to demonstrate the practical implementation of the CEPAR® model.

The CEPAR® model, a trademarked five-step methodology (the Challenge-Evaluation-Planning-Action-Review model) was developed by the International Chamber of Sustainable Development (ICSD). The method and guidelines are outlined for easier appreciation by stakeholders of corporations to analyze ESG-related challenges and dilemmas, then able to make principled decisions, take actions, and review the outcomes. Each phase of the new model adheres to the theoretical and practical frameworks for problem-solving and decision-making, emphasizing the iterate process of addressing challenges, evaluating materiality, planning actions, taking actions, and reviewing the outcomes.

The new model is applicable for business corporations and organizations seeking to gain insight and tackle crucial ESG issues, ultimately improving their short- and long-term decision-making and business opportunities.

We explore the design risk factors and associated managerial practices driving collaborative risk management for design efficacy in green building projects. By illuminating project design risk as an important project risk category in its own right, the study contributes to our understanding of optimising design efficacies for collaborative project risk management.

The study comprises exploratory interviews conducted with 27 industry project practitioners involved in the design and delivery/implementation of Green Star-certified building projects in South Africa.

The findings discursively highlight seven sources of design risk. We also identify seven specific collaborative risk management practices for design efficacy emerging from a consideration of how risk environments vary in the Green Star-certified projects, each with its own project design risk implications.

The study advances our understanding of how collaborations emerging from particular relational yet context-specific practices can be optimised to strengthen project risk management.

The study examines the project success factors for leadership behavioural practices and communication impacting project success, providing empirical evidence to address the challenges in the digitalized environment in the Australian construction sector.

A quantitative approach was employed to collect survey data from 109 project managers and followers (project engineers, supervisors, team members, and senior managers) with diverse project management experience in the construction sector. An exploratory factors analysis/multivariate regression/relative importance index/t-test was used to identify the key project success factors and validate the study's results.

Data analysis identified four key project success factors: (1) Relationship Management, (2) Leading by Example, (3) Self-Management, and (4) Effective Communication, along with seventeen “behavioural practices attributes” impacting project success positively and significantly and emphasizing inclusiveness, relationship building, self-feedback objectivity, sharing information, collaboratively resolving disputes, and controlling emotions that significantly impact project success.

The study's results will address the industry's challenges in the complex digitalized environment and specific issues experienced in the construction industry: delays and inefficiencies, supply chain management, communication barriers with multicultural workforce and safety protocols implementation, regulatory and safety compliance, infrastructure demands, skills shortages, sustainability, and new technology adoption to achieve project success.

The quantification of research findings, employing an innovative approach, underscores the distinctive nature of this study. The key success factors will help formulate innovative practices using stakeholder analysis, communication plans, conflict resolution strategies, promoting collaboration, safety leadership, providing cultural awareness, and enhancing the decision-making process to face challenges in the construction sector.

In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance.

SL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments.

We verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively.

In this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.

This study developed a new analytical model to quantify the influence of business intelligence (BI) adoption on bank performance. An in-depth review of academic literature revealed a significant research gap exists in investigating BI's performance impacts, especially in the under-studied Indian banking context. Additionally, customer relationship management (CRM) was incorporated as a moderating variable given banks' large customer databases.

A survey was administered to 413 employees across leading Indian banks to collect empirical data for evaluating the conceptual model. Relationships between variables were analysed using partial least squares structural equation modelling (PLS-SEM). This technique is well-suited for theory building with smaller sample sizes and non-normal data.

Statistical analysis supported the hypothesised positive effect of BI adoption on bank performance dimensions including growth, internal processes, customer satisfaction, and finances. Furthermore, while CRM did not significantly moderate this relationship, its inclusion represents an incremental contribution to the limited academic literature on BI in Indian banking.

The model provides a quantitative basis for strategies leveraging BI's performance benefits across the variables studied. Moreover, the literature review revealed an important knowledge gap and established a testable framework advancing BI theory in the Indian banking context. Significant future research potential exists through model replication, expansion, and empirical verification.

This research thoroughly reviewed existing academic literature to develop a novel testable model absent in prior studies. It provides a robust conceptual foundation and rationale for ongoing scholarly investigation of BI's deployment and organisational impacts.

The purpose of our study is to investigate the effects of politically-connected boards (PCBs) on over-(under-)investment in labor. We also examine the impacts of the supervisory board (SB)’s optimal tenure on the association between PCBs and over-investment in labor.

We constructed the proxy for PCBs using a dummy variable set to 1 (one) if a firm has politically-connected boards and zero (0) otherwise. For the robustness check, we used the number of politically-connected members on the boards as the proxy for PCBs.

We find that the presence of PCBs reduces over-investment in labor. Consistent with our prediction, we found no significant association between PCBs and under-investment in labor. We also find that the SB with optimal tenure strengthens the negative association between PCBs and over-investment in labor. In our channel analysis, we find that the presence of PCB mitigates over-investment in labor through a higher dividend payout ratio.

Due to the unavailability of data in firms’ annual reports regarding the number of poorly-skilled and highly skilled employees, we were not able to examine the effect of low-skilled and high-skilled employees on over-investment in labor. Also, we were not able to examine over-(under-)investment in labor by drawing a distinction between general (generalist) and firm-specific human capital (specialist) as suggested by Sevcenko, Wu, and Kacperczyk (2022). Generally, it is more difficult for managers to hire highly-skilled employees, specialists in particular, thereby driving the choice of either over- or under-investing in the labor forces. In addition, in the firms’ annual reports, there is no information regarding temporary employees. Therefore, if and when such data become available, this would provide another avenue for future research.

Our study offers several practical implications and insights to stakeholders (e.g. insiders or management, shareholders, investors, analysts and creditors) in the following ways. First, our study highlights significant differences between capital investment and labor investment. For instance, labor investment is considered an expense rather than an asset (Wyatt, 2008) because, although such investment is human capital and is not recognized on the firm’s balance sheet (Boon et al., 2017). In addition, labor investment is characterized by: its flexibility which enables firms to make frequent adjustments (Hamermesh, 1995; Dixit & Pindyck, 2012; Aksin et al., 2015), its non-homogeneity since every employee is unique (Luo et al., 2020), its direct impact on morale and productivity of a firm (Azadegan et al., 2013; Mishina et al., 2004; Tatikonda et al., 2013), and its financial outlay which affects the ongoing cash flows of a firm (Sualihu et al., 2021; Khedmati et al., 2020; Merz & Yashiv, 2007). Second, our findings reveal that the presence of PCBs could help to reduce over-investment in labor. However, if managers of a firm choose to under-invest in labor in order to obtain better profit in the short-term through cost saving, they should be aware of the potential consequences of facing a financial loss when a new business opportunity suddenly arises which requires a larger labor force. Third, our findings help stakeholders to re-focus on the labor investment. This is crucial due to the fact that labor investment is often neglected by those stakeholders because the expenditure of labor investment is not recognized on the firm’s balance sheet as an asset. Instead, it is written off as an expense in the firm’s income statement. Fourth, our findings also provide insightful information to stakeholders, suggesting that an SB with optimal tenure is more committed to a firm, and this factor plays an important role in strengthening the negative association between PCBs and over-investment in labor.

First, our findings provide a valuable understanding of the effects of PCBs on over-(under-)investment in labor. Stakeholders could use information disclosed in the financial statements of a publicly-listed firm to determine the extent of the firm’s investment in labor and PCBs, and compare this information with similar firms in the same industry sector. Second, our findings give a better understanding of the association between investment in labor and political connections , which are human and social capital that could determine the long-term survival and success of a firm. Third, for shareholders, the appointment of board members with political connections is an important strategic decision to build political capital, which is likely to have a long-term impact on the financial performance of a firm; therefore, it requires thoughtful consultation with firm insiders.

Our findings highlight the role of PCBs in reducing over-investment in labor. These findings are significant because both investment in labor and political connections as human and social capital can play an important role in determining the long-term survival and success of a firm.