Search results

The purpose of this research is to determine the competitive advantages of higher education institutions (HEIs) and create a new methodology to rank universities according to the competitive advantages.

The research determines the competitive advantages of HEIs by analysing expert opinions through a semi-structured interview form, matches codes and themes to performance indicators using Saldana's two-cycle coding methods, evaluates content validity through Lawshe and reveals the item weights of the ranking with analytical hierarchy process (AHP). Simple additive weighting (SAW) and Technique for Order of Preference by Similarity (TOPSIS) methods were used for ranking universities.

Seven dimensions stand out in regard to what should be considered while ranking HEIs: research and publication, education, management, infrastructure, financial resources, human resources and social and economic contribution. Under the 7 dimensions, 69 indicators were determined.

The research provides a scientific reference point where HEIs can compare themselves with other HEIs regarding where they are in the sector, especially in terms of competitive advantages.

Although there are many different ranking methods that rank universities in the national and international literature, almost all these methods are largely based on the outputs of the university such as the number of publications, the number of patents, the number of projects, etc. A framework which ranks universities by considering different aspects of the institution, such as management, human resources and financial resources, has not been developed yet. In this respect, this research aims to fill this gap in the literature.

This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.

Global urbanization has accelerated due to the persistent trend of rural-to-urban migration in search of better prospects and livelihoods, which has had serious negative effects on the environment, especially in rapidly developing economies. Hence, the purpose of the study is to analyse the relationship between urbanization, economic growth, consumption of renewable energy and carbon emissions with careful examination, particularly in the context of India, where urban population growth has skyrocketed.

This study uses econometric methods like Granger causality analysis and the ARDL bound tests, to analyse the intricate relationships between the selected time series variables for India from 1970 to 2022.

This research highlights the difficult task of striking a balance between economic development and environmental preservation by emphasizing the crucial role that urbanization and economic expansion play in causing carbon emissions. India’s urbanization trajectory presents a significant policy problem that calls for a move towards renewable energy sources to successfully decrease carbon emissions. Moreover, this research indicates a two-way causal relationship between economic growth, urbanization and carbon emissions, pointing to the intricate interactions between these variables during the developmental stage.

Despite India’s per capita emissions remaining below the global average, this study highlights the mounting policy challenge of balancing economic development with environmental sustainability as urbanization persists. The paper emphasizes the need for India to invest in renewable energy capacity to replace non-renewable sources and mitigate the carbon footprint of its growing energy demands. Collaborative efforts between India and the developed world to facilitate access to clean energy technologies are crucial for India to achieve sustainable growth in the long run.

To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on sustainable development to attain carbon neutrality. Furthermore, these studies have not considered the role of urbanization and non-renewable energy in addressing the challenge of sustainability issues in an emerging country like India. Hence, this study is a comprehensive study that addresses the research gap in these directions.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health.

A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses.

The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships.

This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.

The purpose of current analytical work is to identify the critical barriers in social isolation in India amid Coronavirus infection disease (COVID) outbreak using the fuzzy-analytical hierarchical process (AHP) method.

The conventional AHP is insufficient for tackling the vague nature of linguistic assessment. Fuzzy AHP had been developed to resolve the hierarchical fuzzy problems, avoiding its risks on performance. In AHP, all comparisons are not included; thus, to find the priority of one decision variable over other, triangular fuzzy numbers are used.

A total of eight critical barriers in social distancing in India during COVID-19 have been compared and ranked. Dense population has emerged as the most culpable barrier in social isolation in India amid COVID outbreak followed by compulsion for pecuniary earning and general incautiousness. A total of eight critical barriers in social distancing in India during COVID-19 in four categories (societal barriers, insufficient facilitation barriers, growth-related barriers and population related barriers) have been compared and ranked.

On the basis of the numeral values, “growth-related barriers” attained top position followed by “population-related barriers” and “insufficient facilitation barriers.” The current work has explored the possible factors which can become key game changers to control the pace of spread of the pandemic.

The purpose of this paper is to examine the quantum of research papers and the citations these papers received for the plant Jatropha curcas Linn.

Articles published on Jatropha curcas Linn during 1987–2016 were downloaded from Science Citation Index-Expanded (SCIE) by using the keyword Jatropha* on October 18, 2017. The search resulted in 4,276 records in all. The authors analyzed only 4,111 documents which were published as review articles, research articles and proceeding papers using the complete count methodology. The data were analyzed to examine the pattern of growth of output, most prolific countries, institutions and authors. It also identified highly cited authors and journals used for communicating research results.

The study indicates that India, China and Brazil are the main contributors to the field and the pattern of growth indicates a steep rise in publication output especially in the last block of 2015–2016. Most of the prolific institutions and authors were also located in these countries. However, the impact of output was different from the pattern of output. The publication output is scattered in more than 1,000 journals published from different parts of the globe.

The plant of Jatropha curcas Linn is a highly useful plant as a source of biofuel energy. This is the second study in English language on this plant and has used a large set of publication data as compared to the first. The findings of the study may be useful for policy makers as well as for researchers working in the field of biofuel energy.

The purpose of this paper is to assess the relative effectiveness of bilateral and multilateral concessional debts on economic growth in 32 sub-Saharan African (SSA) countries over the period 1985–2016.

The recently developed dynamic panel autoregressive distributed lag models which comprise three different estimators, the mean group, pooled mean group (PMG) estimator and dynamic fixed effect, were applied to estimate the model. Following these estimators, the Hausman test was employed to determine the efficient and consistent estimator.

The results showed that bilateral concessional debts had a negative impact on growth. From the findings, a 1 percent increase in bilateral concessional debts induced economic growth to decline by 38.1 percent points in the short run, and by 7.1 percent points in the long run; convergence to long-run equilibrium adjusted at the speed of 90 percent on an annual basis. Multilateral concessional debts were found to have a positive impact on growth both in the short and long run. The coefficient of the error term was negatively signed and indicates that deviations from the long-run equilibrium path were being corrected at the speed of 89.4 percent annually.

To the authors’ best knowledge, empirical studies that specifically seek to examine how bilateral and multilateral concessional debts impacted on growth are yet to attract the attention of researchers. As a result, this study will complement related extant growth studies, especially in the case of SSA.