Search results

The tourism industry's deep interindustry linkages makes it a significant engine of growth for an economy. When aligned with specific sustainability goals, it becomes an effective means of inclusive prosperity and environmental protection. Coming fresh from the lockdown, investors and stakeholders are more conscious of their investing and spending decisions preferring more and more businesses and industries with sustainable business practices (Leal Filho et al., 2022). Specifically, tourism's generation of business and customer values must also extend to generation of value to the environment, society, and even on governance (GIZ, 2020). This chapter, using a system's view approach, demonstrates how activities in the tourism value chain – besides being an engine of growth – can also be the vehicle to attaining environmental, social, and governance (ESG) aspirations.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

This study aimed to identify and assess the barriers to implementing modular construction systems (MCS) in developing country's architecture, engineering and construction (AEC) industry, targeting built environment professionals from Nigeria and South Africa.

The study adopted a quantitative research method, using a structured questionnaire to seek the opinions of the professionals on the identified categories of barriers.

Results indicated that all identified categories of barriers were statistically significant using a one-sample t-test at p = 0.05 significance level which indicates they are critical towards the implementation of MCS in developing countries. Assessment of the opinion of the professionals using the Kruskal–Wallis scale showed that they hold similar views on the barriers to the adoption of MCS. Pearson correlation shows a high correlation coefficient amongst the barrier categories and an acceptable level of significance (p = 0.05).

This study is limited to two significant African countries (Nigeria and South Africa) selected based on the gross domestic product (GDP). Further studies can consider developing countries outside Africa and investigate broader respondents.

The study provides implications on the barriers affecting MCS in developing countries for the academia, industry and government to have an insight into the barriers and make informed decisions and policies.

The research satisfies the need to study the barriers affecting the MCS in developing countries that can mitigate housing deficits. This innovative construction method has been adopted and implemented in developed countries, and the result has been positive.

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.

Sustainable building materials (SBMs) have recently been promoted to foster sustainable construction. While previous studies successfully presented the broad picture of SBM adoption, the question ‘What is the current state of SBM adoption in construction firms?’ was unanswered. Moreover, there is a lack of studies that investigate the impact of firm size on the adoption of SBMs. Therefore, this study aims to examine the level of readiness for SBM adoption in construction firms of different sizes.

Drawing on theoretical lenses and a review of previous studies, the study identified 20 factors affecting the readiness for SBM adoption. These factors were then grouped into market, organization, and employee readiness. Using the questionnaire survey, 229 valid responses were collected. The multiple pairwise comparison test showed significant differences in readiness for SBM adoption in firms of different sizes. The Fuzzy Synthetic Evaluation (FSE) analysis was then used to assess the level of readiness for SBM adoption in construction firms of different sizes.

The analysis of FSE showed that large-sized companies demonstrated the highest level of readiness for SBM adoption, followed by medium-sized and small-sized firms, respectively. These findings imply that larger construction firms may have more resources and organizational capacity to adopt SBMs. Conversely, smaller firms may require additional support or incentives to enhance their readiness for SBM adoption. It also highlights the need for targeted interventions and policies to promote SBM adoption across medium-sized and small-sized firms. Moreover, while market readiness emerged as the least critical category, the findings highlight the pivotal role of employee readiness across all firm sizes.

These findings underscore the importance of prioritizing employee readiness initiatives to facilitate the successful adoption of SBMs. Policymakers and industry stakeholders may need to focus on developing training programs and awareness campaigns tailored to employees, ensuring they are equipped with the necessary knowledge and skills. Moreover, strategies to enhance market readiness should also be explored through regulatory measures and incentives to encourage the adoption of SBMs. Additionally, fostering collaboration and networking among stakeholders can further promote market readiness and accelerate the uptake of SBMs in the construction industry.

Instant crowdsourcing logistics (ICL) platform is an emerging business model in the field of logistics services. Crowdsourcees’ active participation in platform value co-creation is the key to success for this business model. This study aims to explore the influence of platform governance mechanisms on crowdsourcees’ participation in value co-creation on the platform.

Based on governance theory, this study constructed a model of the influence of platform governance mechanisms on crowdsourcees’ value co-creation intentions and discussed the role of community diversity in this model. The survey data of 319 collected from crowdsourcees on China’s well-known ICL platform were analyzed using a partial least squares structural equation model.

The results showed that platform governance mechanisms have a significant influence on crowdsourcees’ value co-creation intentions. Among them, the impact of the welfare resource support mechanism is the largest, followed by the reputation reward and punishment mechanism, while the price coordination mechanism has the least impact. Community diversity has a significant positive moderating effect on the welfare resource support mechanism and crowdsourcees’ value co-creation intentions, but not between other governance mechanisms and crowdsourcees’ value co-creation intentions.

The results provide references for ICL platform to develop a targeted governance mechanism to enhance the positive role of community diversity and reduce the negative impact.

The purpose of this study is to measure the awareness of the global university ranking and its parameters among the faculty members at Dhaka University. This also identifies the challenges linked to university ranking and explores opportunities to overcome these challenges.

The target population of this study is the faculty members of Dhaka University. This study used the online survey questionnaire method, contacting approximately 2,000 faculty members individually through their email addresses. In total, 311 faculty members responded, and the response rate was 15.55%. Data were collected from September to December 2022, and the Statistical Package for the Social Sciences was used to analyze the survey data.

The findings indicate that a majority of the faculty members are acquainted with global university rankings, with Times Higher Education being the most recognized. Faculty members at Dhaka University are confident in their ability to excel in research and citation weighting scores in the ranking parameters. However, challenges such as a lack of incentives, inadequate recruitment policies, limited research funding, minimal collaboration, a scarcity of international students and faculty and limited local journal publications hinder the university’s ranking.

Due to the limited number of responses, the findings may not accurately represent the entire faculty. The findings of this study can guide Dhaka University and other universities to design policies for overall improvement of university ranking.

This is the first time an attempt has been made to measure the awareness of university ranking by collecting primary data in the context of a public university in Bangladesh.

This paper presents a pioneering investigation into the barriers impeding the adoption of reverse logistics (RL) in the Iranian construction industry, addressing a significant gap in the current literature within the country.

Through a meticulous examination of the literature, complemented by 19 in-depth interviews, a battery of barriers to RL implementation was initially identified, distilled into a final set of 30 barriers, which served as the basis for the design of a structured survey questionnaire. Employing Partial Least Squares–Structural Equation Modeling (PLS-SEM), an analysis was conducted on a dataset that comprised of 80 responses. Subsequently, the barriers approved through the PLS- SEM were subjected to prioritization through the application of Fuzzy TOPSIS.

Findings reveal the critical inadequacy of contemporary technology and infrastructure, a foundation essential for the efficacious enactment of RL. This shortfall manifests in procurement and logistics processes plagued by inefficiency and ineffectiveness, as consequences of an immature information and technology ecosystem. Furthermore, the study underscores the dominance of traditional markets where the adoption of secondary materials/products remains sporadic, signaling a crucial area for further research and intervention.

This study offers original insights into the significant hurdles that hinder the adoption and integration of RL practices within the Iranian construction industry, an under-explored area within the RL research domain. For industry practitioners, findings point to recommendations for overcoming these barriers in the specific Iranian context while also offering broader insights and lessons that may be relevant to other countries and similar contexts.

The aim of this study is to utilise the Theoretical Domains Framework (TDF) to identify the main barriers and facilitators of positive attitude towards trying Virtual Active Sports (VAS).

200 individuals took part in an online cross-sectional survey based on 11 domains within the TDF. Linear logistic regression analysis was performed on the participant’s self-reported attitudes and believes. Based on the results from the regression analyses, a list of suggested behaviour change techniques was designed using the Behaviour Change Wheel (BCW) framework and the BCT taxonomy (BCTTv1).

This research suggested that Beliefs about Consequences, Beliefs about Capabilities, Goal Conflict, Coping Planning, and Environmental Context and Resources are the main factors, influencing the positive attitude towards VAS and the self-reported desire to try it in the future. Future interventions were recommended and supported by 22 possible BCTs identified using the BCW approach. The TDF and BCW proved to be useful models for identifying both internal and external factors influencing fitness fans during the adoption of the new sportstech.

The main contribution of the present work was the implementation of a structured and effective approach derived from the healthcare domain to design solutions for behaviour change in the emerging and expanding virtual sports context.

The construction industry is shifting towards becoming more circular by reducing waste, reusing building materials and embracing regenerative solutions for energy generation and biodiversity conservation. Thus, construction professionals must perform diversified services to achieve a circular built environment (CBE). Implementing resource planning and waste management in CBE has already posed challenges to the quantity surveying profession; thus, quantity surveyors (QSs) should be equipped with new roles and competencies to tackle challenges in achieving a CBE. Hence, this study aimed to investigate the functions and competencies of QSs at the design and building material sourcing stages in achieving a CBE according to 3R principles.

The research apprehended a qualitative approach, including two expert interview rounds adhering to the Delphi technique with 14 and 11 experts per each round. Manual content analysis was incorporated to analyse the collected data.

The research findings revealed the important roles and competencies of QSs in achieving a CBE. In addition, essential functions and competencies were categorised according to the 3R principles (reduce, reuse, recycle) to successfully implement CBE during the Design and Building Material Sourcing stages. According to the 3R principles of CBE, cost control, cost planning, feasibility studies, measurement and quantification, risk management, value engineering and innovations and technologies were well-received by most interviewees as essential roles of QSs during the Design and Building Material Sourcing stages. Further, basic/mandatory competencies (personal, interpersonal, professional practice and business skills), economic analysis, BIM management, cost management and risk management are highly regarded by experts for QS as important competencies to achieve CBE.

With new construction trends, QSs must enhance their conventional roles and competencies and search for new skills and competencies. Those skills and competencies could be linked to the CE concept, either directly or indirectly. Since QS holds a prominent place in sustainable construction in CBE, adapting to changes in the construction industry such as CBE is timely for QSs. Further, there is a shortage of literature regarding QSs' roles and competencies in achieving a CBE; thus, this study will contribute by identifying new avenues for QSs in achieving a CBE in the practice and the research.