Search results

The present study aims to seek the strategic analysis of the entrepreneurship of agricultural colleges (AC).

In terms of approach, this research was a combination of exploratory and hybrid methods. The present study was conducted in four stages. In the first stage, an open-ended questionnaire was designed to identify the strengths, weaknesses, opportunities and threats of entrepreneurship in AC (qualitative method). In the second stage, the Delphi-Fuzzy questionnaire was designed based on the results obtained from the first stage. In the third stage, the criteria of strengths, weaknesses, opportunities and threats of entrepreneurship of AC were analyzed based on the pairwise comparison (quantitative method) by the sample using a fuzzy hierarchical analysis process (FHAP). In the fourth stage, presented strategies were ranked based on pairwise comparison using FHAP.

From the analysis of weaknesses, strengths, opportunities and threats facing AC for entrepreneurship, 12 strategies were presented in 4 groups of aggressive, conservative, competitive and defensive.

The literature review showed that no research has been done so far to identify strengths, weaknesses, opportunities and threats facing university entrepreneurship, especially AC. So the present study analyzes the weaknesses, strengths, opportunities and threats and proposes practical strategies for moving toward the formation of entrepreneurship AC. According to the gaps in providing SWOT of the AC, the results of this research can pave the way for policy makers and planners in this field.

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.

The conventional project delivery (CPD) approach has been reported in the literature as the most widely used project delivery method in the construction industry globally compared to other delivery methods. However, researchers and practitioners have argued that the approach, specifically during the production of design documentation under the CPD, lacks certain capabilities that ensure quality and enhance project delivery. Therefore, this study aims to use the Ghanaian construction industry to identify the capabilities required of the CPD in practice, particularly during the production of design documentation.

The study design follows a pragmatist philosophy and uses mixed methods based on a deductive approach. Data collection involved a questionnaire survey, followed by semi-structured interviews. Quantitative data analysis used descriptive and inferential statistics, whereas qualitative data analysis used content analysis with the assistance of IBM SPSS and QSR Nvivo 12 Pro.

Findings indicate that there should be incentives for producing good design documentation quality; mandatory coordination of design documentation; improving collaboration among designers; and allowing contractors to make input during the design stage.

The results indicate the need for the identified capabilities to be introduced in the CPD approach to improve design documentation quality.

This study offers a significant insight into the specific capabilities that are required of the CPD approach in practice particularly, in the production of design documentation

Nowadays, designing environmentally compatible buildings with acceptable performance in terms of cost, materials, and energy efficiency is considered crucial for developing sustainable cities. This research aims to identify and rank the most influential factors in the application of Building Information Modeling (BIM) systems in the smartification of green and sustainable buildings.

The present research is applied and descriptive. In this study, we identified the most influential factors in the application of Building Information Modeling (BIM) systems through library studies and expert opinions. Data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy VIKOR method was employed for analysis.

The results show that the most influential factors in the application of Building Information Modeling (BIM) systems in the Smartification of green and sustainable buildings, in order, are: “Energy saving and consumption reduction,” “Increased productivity and efficiency,” “Life-cycle assessment (LCA),” “Eco-friendly design,” “Integration with IoT and other technologies.”

In this study, while addressing the intersection of BIM technology, green building principles, and smart building objectives to optimize the performance of buildings during their life cycle, the most influential factors in the use of this system were ranked based on the criteria of “impact level,” “importance level,” and “availability of necessary tools” for implementation in Kerman. Moreover, solutions for more effectively utilizing this system in the smartification of green and intelligent buildings were proposed.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

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.

Research on green certificate rankings in the MENA region primarily focuses on building scale, relying on the certified project count. This assessment approach overlooks the spatial factor, failing to capture their influence on the urban built environment, thus potentially undermining other efforts not reflected by the project count. This research aims to rank countries in the Middle East and Northern Africa (MENA) region based on their ongoing efforts regarding green neighbourhood certification.

This study employs a three-phase methodology to rank MENA countries' adoption of green neighbourhood certification systems: content analysis, multicriteria analysis (MCA) using the analytical hierarchy process (AHP) and spatial analysis.

Based on the content analysis, four major performance indicators were identified and the conventional ranking using projects count was presented. Using AHP, the MCA could rank the countries in the region according to their unique performance indicators score, clarifying the differences between conventional and AHP-based rankings. Finally, the spatial analysis phase uncovers shortcomings in the traditional ranking method, revealing inaccuracies and misrepresentations for several countries.

The study presents an innovative ranking methodology to monitor the green neighbourhood actions of countries in future development and establish a pioneering framework to evaluate the impact of green certifications within the region.

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.