Search results

The purpose of this paper is to present a quantified model for influential factors in the collaboration process in Building Information Modelling (BIM). BIM-based Construction Networks (BbCNs), which comprise teams of specialist organisations engaged to execute BIM-related activities, have become the centrepiece of collaboration in construction projects. In BbCNs, however, a lack of effective collaboration among teams remains a major barrier to receiving the full benefits of BIM. Despite this importance, the role of influential factors in collaboration in BbCNs has remained somewhat esoteric in nature in various previous studies, in which the present study attempts to address this gap.

To develop the quantified model for collaboration in BbCNs, primary empirical data was collected from a questionnaire survey of BIM experts in the construction industry. This data was subsequently analysed through the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique using SmartPLS software as a viable and robust package for PLS-SEM analysis.

Quantification of associations to collaboration in BbCNs reveals that unlike the common beliefs espoused in previous studies on collaboration in BbCNs, the lack of BIM-related tools and technologies is no longer the key concern of effective collaboration in BbCNs. Instead, ethical and managerial factors including “ethical approaches”, “liabilities” and “BIM manager role” were found to be in need of more attention for effective collaboration in BbCNs.

Through presenting the first quantified model for collaboration in BbCNs, findings provide a point of reference for practitioners for coaching and managing teams. So too, the findings can be translated into a set of guiding principles for the world of practice for enhancing collaboration in BbCNs.

This paper makes a significant contribution to the field by quantifying the factors that impact collaboration within BbCNs settings. It meticulously assesses the degree of influence wielded by these factors and provides empirical numerical evidence to demonstrate that the lack of BIM tools and software is comparatively less concerning in fostering collaboration within BbCNs when contrasted with human-related factors. This original contribution surpasses prior qualitative evaluations by introducing a systematic framework for ranking and comparing other influential factors, thereby advancing relevant theoretical constructs into the realm of quantitative analysis.

This paper aims to investigate the corrosion resistance of two types of coatings – one is ceria sol coating and the other is ceria sol coating modified by ZnO nanoparticles on 7075 aluminum alloy in 3.5% NaCl solution.

Aluminum alloys were dipped into ceria sol and ceria sol modified by ZnO nanoparticles separately and removed after 10 min from the solutions and dried at 110°C for 30 min and heated at 500 °C for 30 min to form the coatings. The coatings have been characterized by using field emission scanning electron microscopy (FE-SEM), electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS). The EIS tests were performed in a corrosive solution of 3.5% NaCl.

The results showed that the coating of ceria sol modified by ZnO nanoparticles has higher corrosion resistance than the ceria sol coating and the bare sample. Also, the best efficiency is related to aluminum sample immersion after 1 h in NaCl corrosive solution for coating modified by ZnO nanoparticles.

In this research, the modification of ceria sol coating by ZnO nanoparticles had an effect on improving the corrosion behavior of aluminum alloy. It is also understood that modification of coatings is an effective parameter on corrosion resistance.

The ability to use laser powder bed fusion (LPBF) to print parts with tailored surface topography could reduce the need for costly post-processing. However, characterizing the as-built surface topography as a function of process parameters is crucial to establishing linkages between process parameters and surface topography and is currently not well understood. The purpose of this study is to measure the effect of different LPBF process parameters on the as-built surface topography of Inconel 718 parts.

Inconel 718 truncheon specimens with different process parameters, including single- and double contour laser pass, laser power, laser scan speed, build orientation and characterize their as-built surface topography using deterministic and areal surface topography parameters are printed. The effect of both individual process parameters, as well as their interactions, on the as-built surface topography are evaluated and linked to the underlying physics, informed by surface topography data.

Deterministic surface topography parameters are more suitable than areal surface topography parameters to characterize the distinct features of the as-built surfaces that result from LPBF. The as-built surface topography is strongly dependent on the built orientation and is dominated by the staircase effect for shallow orientations and partially fused metal powder particles for steep orientations. Laser power and laser scan speed have a combined effect on the as-built surface topography, even when maintaining constant laser energy density.

This work addresses two knowledge gaps. (i) It introduces deterministic instead of areal surface topography parameters to unambiguously characterize the as-built LPBF surfaces. (ii) It provides a methodical study of the as-built surface topography as a function of individual LPBF process parameters and their interaction effects.

Following the job demands-resources theory, this study investigates the role of female managers in enhancing employee well-being in terms of psychological health via workplace resources.

To accomplish this objective, we conducted a comprehensive literature review to identify key IPS. Subsequently, a fuzzy-based algorithm was employed to prioritize these skills. Following this, we developed an algorithm based on Extreme Gradient Boosting (XGBoost) to predict the quality of workers’ IC. The efficacy of the XGBoost model was assessed by applying it to three real-life construction projects.

Upon application of the model to the case studies, we made the following conclusions: (1) “Leadership Style,” “Listening,” “Team Building” and “Clarifying Expectations” emerged as significant skills and (2) the model accurately predicted workers’ IC quality in over 78% of the cases. This algorithm has the potential to preempt interpersonal conflicts, enhancing job-site productivity, team development and human resources management. Furthermore, it can guide construction managers in designing IPS training programs.

This study contributes to the existing knowledge by addressing the crucial connection between IPS and IC quality in construction projects. Additionally, our novel approach, integrating fuzzy logic and XGBoost, provides a valuable tool for IC prediction. By identifying significant IPS and offering predictive insights, this research facilitates improved communication and collaboration in the construction industry, ultimately enhancing project outcomes.

The chapter explored integrating smart construction techniques in achieving stealth construction objectives, emphasising the development of building cross-sections, visibility management, energy transmission optimisation, and countermeasure implementation. It delved into the multifaceted aspects of smart construction towards achieving stealth construction goals, including environmental protection, enhanced construction safety, accelerated construction duration, cost-effectiveness, and aesthetic considerations. Furthermore, the chapter underscores the importance of leveraging innovative approaches and advanced technologies to meet the evolving demands of stealth construction projects and pave the way for sustainable, safe, and aesthetically pleasing built environments.

Information filtering systems serve as robust tools in the ongoing difficulties associated with overwhelming volumes of data. With constant generation and accumulation of reviews in online communities, the ability to distill and provide valuable insights to assist customers in their search for relevant information is of considerable significance. This study devised an effective review filtering system for a popular online physical experience review site.

This study entailed an investigation of a hybrid approach for a review filtering system augmented with various text mining-based operational variables to extract the linguistic signals of online reviews. Moreover, we devised three ensemble models based on multiple machine learning and deep learning algorithms to build a high-performance review filtering system.

The main findings confirm the effectiveness of using the derived operational variables when reviewing filtering systems. We found that the reviewer’s tendency and history macros, as well as the readability and sentiment of the reviews, contribute significantly to the filtering performance. Furthermore, the proposed three ensemble frameworks demonstrated good efficiency with an average accuracy of 89.39%.

This study provides a methodological blueprint for operationalizing variables in online reviews, covering both structured and unstructured datasets. Incorporating different variables enhances the efficiency of the algorithm and provides a more comprehensive understanding of user-generated content. Furthermore, the study affords a strategic perspective and integrated guidelines for developers seeking to create advanced review filtering systems.

The effects of gendered views on employee voice are of great importance for knowledge sharing within public universities. Yet, they are still neglected by current human resource management and entrepreneurship literature. While strengthening themselves by reinforcing the strengths and opportunities, public universities can generate entrepreneurial opportunities through various knowledge-sharing mechanisms, including social networks and employee voice. This became a crucial lever for public universities to leverage competitive advantages and to support entrepreneurial activities through network knowledge-based sharing. For this purpose, this study aims to examine the various aspects of entrepreneurship via the voice of employees, emphasizing the moderating effect of gender and the mediating role of social networks on the link between employee voice and the entrepreneurial atmosphere of universities.

The authors collected survey data from a cross-sectional sample of 335 employees engaged in entrepreneurship activities within public universities in an emerging economy context and analyzed the data using partial least squares structural equation modeling (PLS-SEM) with the Smart-PLS software.

The PLS-SEM analysis found that different dimensions of the university entrepreneurial climate (communication, knowledge sharing and innovative climate) positively impact members’ voices within public universities. This effect is amplified by social networks, which are crucial for spreading knowledge among faculty, thereby fostering a more open and collaborative academic environment.

When acting, the university top management team should encourage the generation and dissemination of entrepreneurial ideas to nurture a dynamic entrepreneurial atmosphere and social involvement, ultimately supporting sustainable competitive advantages through a culture of strategic knowledge sharing. The results have practical implications for university managers, entrepreneurship education actors, administrators, policymakers and entrepreneurial ecosystem actors, by demonstrating how social networks can amplify the dissemination of ideas and entrepreneurial spirit.

This research explores how entrepreneurship and social networks can help faculty members have a stronger influence in academic settings. It also fills in the gaps in knowledge about how human resource management and entrepreneurship can work together to create a more communicative and innovative academic environment. Additionally, this study brings new ideas to existing literature by looking at how gender differences can affect employee voice, particularly emphasizing the importance of women in leadership roles at universities. This study is also the first to delve into how entrepreneurship and social networks, along with gender perceptions, play a role in shaping the voice of employees in a public university.

Prefabricated construction has been rapidly developing and intensifying the reliance on the supply chain. The pandemic of COVID-19 induced severe disruptions to the supply chain operation and thus attracted the research attention on the supply chain resilience (SCR) under various events. Assessing the resilience of the prefabricated construction supply chain (PCSC) is essential for surviving the shifting disruptive attacks and ensuring consistent, reliable operation. Based on the ripple effect and supply chain performance (SCP), this study aims to develop an assessment model for SCR of PCSC.

Having identified the roles and material flows among stakeholders, a PCSC network is established. Utilizing the ripple effect model, it develops an assessment framework tailored for PCSCs, which then evolves into a comprehensive assessment model for evaluating the SCR by integrating the disruptive influence and the pre-and post-disruption SCP. Case study is then applied to validate the model.

Using SCP metrics and disruptive influence assessment as basic dimensions, the SCR can be assessed and expressed through a vector formula. Operating costs and asset utilization can effectively reflect changes in resilience, paying attention to their real-time changes can provide a better judgment of the current stage of disruptions.

The assessment model of SCR accounts for the ripple effect within prefabricated construction, offering a thorough understanding of how disruptions impact the entire supply chain network. Additionally, this model introduces a novel approach to evaluating SCR in reverse by leveraging SCP metrics instead of direct measurement, thereby minimizing potential biases.

Nowadays, the topic of supply chain network design has gained significant attention as a critical area within the field of supply chain management. Researchers have dedicated their studies to exploring market competitiveness and pursuing greater benefits. This paper presents a multi-objective mathematical model aimed at designing a competitive and sustainable closed-loop supply chain network specifically for perishable products. The proposed model takes into account the competition between two supply chains, considering various factors such as environmental impacts, social performance and resilience.

To address the competitive nature of this model, this study introduces a two-stage approach. In the initial stage, this study uses a game theory approach to determine equilibrium values for competitive decisions. This allows establishing a fair and balanced playing field between the two supply chains. Subsequently, due to the complexity of the proposed model, this study uses a multi-objective metaheuristic algorithm based on the Non-dominated Sorting Genetic Algorithm. This algorithm serves as a robust computational tool to effectively solve the network design problem and optimize the desired objectives.

To evaluate the effectiveness of the proposed model and solution approach, this study provides a numerical example. Through graphical and statistical analysis, this study assesses the performance of the model and its ability to achieve the desired objectives. The results and charts obtained demonstrate the strong performance and efficacy of the proposed model in effectively addressing the challenges associated with designing a competitive and sustainable closed-loop supply chain network for perishable products.

By considering the various factors at play and incorporating them into the model, this study aims to contribute to the existing body of knowledge in supply chain management. The findings provide valuable insights into designing supply chain networks that not only enhance competitiveness but also promote sustainability.

This study represents one of the initial investigations into the evaluation of competitive supply chains, taking into account crucial factors such as environmental, social and resilience. A supply chain that considers these factors can attain greater durability and competitiveness compared to other supply chains.

This paper explores the direct and indirect effects of family emotional, social and organizational support on Innovative Work Behavior (IWB) through psychological capital (Psy.Cap).

Selected by conducting stratified random sampling techniques, 397 employees completed a questionnaire. We used structural equation modeling and multi-group testing by Smart-PLS3 to analyze the data.

Findings reveal that all sources of social-emotional support, including family, supervisor and co-worker support, positively affect Psy.Cap. Moreover, Psy.Cap mediates the effect of family, co-workers and supervisors' emotional support on IWB. The multi-group analysis indicates that all relationships in the model are significant for both groups of males and females; however, there are no significant differences in the link between organizational support and psychological capital, as well as family and co-worker support and innovative work behavior between males and females. The study's results demonstrate the significantly higher impact of family emotional support – Psy.Cap and supervisor support on IWB amongst females compared to their male counterparts.

The implications of this research highlight the importance of considering affective factors on employees’ IWB, as well as the differences between genders in this regard.