Search results

This study aims to report the quality of Bangladesh's science and technology universities (STUs) in ensuring sustainable employment of graduates during the Fourth Industrial Revolution (4IR) by assessing their vigilance in skill development activities and exploring students’ perspectives on the university.

This research used mixed methods: a close-ended five-point Likert scale questionnaire to understand students’ perspectives and a thematic analysis of the interviews of students, faculties, policymakers and relevant stakeholders. The data was collected through a random sampling method where 1,000 university students took part in the quantitative analysis along with other respondents for the qualitative portion. The analysis was done with a 99% confidence level and a 4.5% margin of error.

Bangladesh's STUs still have a long way to go to ensure quality education and generate sustainable employment for their graduates. The universities' preparation to comprehend the 4IR is not at the expected level. In addition, despite students' favourable perception of universities as providers of qualified labour, there is a significant mismatch between supply and demand.

This research has some limitations regarding time and resources. Due to the limited number of responses from a few universities, this study's findings might only apply to some of the STUs of other countries. This study provided several recommendations for providing quality education to the STUs of Bangladesh.

The findings of this study indicate that there is still a crucial gap between the initiatives of STUs and the employment market, which prevents graduates from offering the necessary skills to achieve sustainable employment. The findings also support the idea of significant changes in the approach of these universities to address the mentioned issues.

This study suggests collaboration among social actors, relevant stakeholders, STU authorities, education experts and government officials to ensure a demand-based curriculum for the students. The relevant stakeholders should come forward to ensure advanced technologies and internet connectivity in the STUs.

The data set used in this study is significantly large and varies in the number of institutions, departments and socioeconomic backgrounds of the students, faculties, policymakers and various stakeholders. Furthermore, the capacity of scientific and technology institutions to guarantee sustainable employment through quality assurance in education has also not been recently evaluated in Bangladesh.

Due to the lack of consensus on influential variables for real estate appraisal, which varies from one country to another based on the national preferences and customs of each country, this study aims to identify the most influential variables affecting condominium apartment real estate appraisal within the context of Al Bireh city, Palestine.

The methodology adopts a cross-sectional quantitative approach, entailing the administration of an online questionnaire survey to 103 buyers and appraisers. The questionnaire aims to evaluate 32 variables concerning their impact on condominium real estate appraisal. Out of these, 25 are derived from three specific previous studies, and the remaining 7 are identified through various studies or by the authors, taking into account the local context and the geopolitical situation in Palestine. Respondents assign scores to these variables on a five-point Likert scale, ranging from 1 to 5, where 1 indicates less influence and 5 signifies the most influence. Variables with an arithmetic mean score exceeding 4 are deemed the most influential.

The findings underscore 16 and 17 influential variables as perceived by buyers and appraisers, respectively. Notably, 13 variables are common, including aspects such as parking, elevator, neighborhood, floor apartments, finishing quality, construction material, condition, building apartments, area, open sides, building floors, colonies and age.

The primary constraint of this study is its dependence on insights solely from buyers and appraisers, disregarding input from other stakeholders like investors or developers. The questionnaire lacks vital respondent characteristics, such as gender and occupation, impeding the analysis of variable dependence on participant attributes. Although some additional influential variables are suggested through the responses of an open-ended question, the questionnaire is not repeated, leaving their influence unassessed. This study's focus on Al Bireh city limits the opportunity for result comparison with other cities, diminishing its generalizability.

The implications of this research are twofold: to provide stakeholders with a checklist for variables influencing apartment price value and to guide data collection related to the real estate appraisal sector, facilitating its use as input in advanced appraisal methods such as artificial intelligence with a view to improving overall performance. Obtaining an informed, mature and accurate appraisal has direct economic, business and financial impacts at the level of policymakers and individuals.

To the best of the authors' knowledge, this study is the inaugural endeavor in Palestine focusing on identifying pivotal factors influencing condominium apartment appraisal values. This study concludes by presenting a checklist comprising the most influential variables, offering utility to various stakeholders, including buyers, appraisers and developers. In addition, the questionnaire incorporates an open-ended question, soliciting respondents' input on additional variables they believe impact the appraisal process.

This study investigates the evolution of skilled personnel in airline operations driven by technological advancements. It aims to elucidate the changing personnel demands necessitated by technological innovations in the ground and flight services.

The impact of technological advancements on aviation services has been broadly outlined. Secondary sources were used to identify the relationship between technology and human resources in aviation and categorize the current situation. However, the main narrative was based on the author’s observations.

The progression of technology in air transportation has led to a reduction in the number of personnel involved and the time spent on human interactions. Technological advancements in aviation have predominantly affected three crucial domains: back offices, ground services, and flight services. A future trend foresees a substantial shift toward self-service in ground services, contributing to streamlined processes with minimal errors.

Airlines must consider candidates' ability to adapt to technological changes during the hiring process to enhance operational efficiency and customer satisfaction. The current staff should be supported by training programs to facilitate their adaptation to technology.

This study provides a theoretical framework regarding changes in personnel requirements due to technological applications in aviation, the integration of technology into the sector, and the adaptation of current personnel to these technologies.

This perspective resonates with scholars engaged in the realms of aviation and tourism. This study assesses technological progress from both managerial and customer perspectives.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.

A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.

Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.

The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.

This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

The aim of this study is to understand health-care facilities’ practice of deferring maintenance and to identify the relationship between deferred maintenance and hospital profitability.

Financial statements from hospitals in Washington State were analyzed. Differences in building and equipment values were compared to the capital renewal investment benchmark. Then, linear regression analysis was conducted to identify correlations between deferring or sustaining maintenance and a hospital’s profitability.

The majority of hospitals in Washington State practice deferred maintenance, investing less in annual facility capital renewal than benchmark amounts. Hospitals in deferred and sustained maintenance states do not significantly differ in terms of facility size and plant operating expenses but there is a statistically significant difference in profit margins. Furthermore, a linear relationship exists between the level of investment in facility renewal and overall hospital expenses, revenue and profit.

The findings of this research can be used to support fiscal policies related to maintaining aging health-care facility infrastructure. The findings can also be used to overcome barriers to securing capital budgets that are sufficient to optimize the safety and performance of the built environment.

Hospitals in a state of deferred maintenance return higher profits than do hospitals in a state of sustained maintenance. Nevertheless, hospital spending on deferred maintenance backlog reduction generates a positive return on investment. While hospitals may achieve higher returns, in the short term, by spending less on facility capital renewal and focusing instead on higher-revenue-generating opportunities, additional research in necessary to understand the long-term costs of deferring facility infrastructure repairs and replacement.

Considering the construction industry’s vital role in economic development and social consequences, this study seeks to pinpoint critical barriers hindering Turkey’s sustainable construction (SC). Although several studies highlighted the barriers to SC worldwide, none identified the critical factors. By identifying and understanding these barriers, the research aims to comprehensively understand practices and formulate strategic recommendations to promote sustainable construction.

A systematic approach is adopted to achieve the research objectives. The study involves identifying potential barriers to SC with a systematic literature review. A questionnaire was organized and distributed via e-mail to architects, civil engineers, and contractors. The criticality of identified barriers was determined with normalized mean value analysis, and critical barrier factors (CBFs) to SC were isolated with exploratory factor analysis. Finally, the effect size of these factors was quantified through structural equation modeling.

This study identified 32 critical barriers hindering the SC in the Turkish construction industry among 49 barriers. Furthermore, this study revealed six CBFs to SC that are “inadequate supervision and control of SC,” “fear of transition to sustainable construction and disruptions in adoption,” “lack of educational opportunities,” “return on investment and financial bias,” “awareness and knowledge gap about SC,” and “lack of demand from stakeholders.” Among them, “awareness and knowledge gap about SC,” “fear of transition to SC,” “lack of educational opportunities,” “lack of demand from stakeholders,” and “inadequate supervision and control of SC” were determined as the very highly crucial CBFs hindering SC.

Although some studies have identified the barriers to SC qualitatively and quantitatively, studies have yet to provide insights into the critical barrier factors hindering SC. Therefore, this study comprehensively and quantitatively determines the relevant CBFs to SC using exploratory factor analysis and utilizes confirmatory factor analysis and structural equation modeling to present a structural model of how critical factors affect the SC.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

The aim of this study is to investigate technological and innovation orientation contribution to the development and sustainability of the industrial sector.

The authors gathered the perspectives of many experts who were aware enough of their company’s technical and innovation orientations to participate in this study to understand how technology and innovation orientations may affect sustainability and development. These people included the company managers, accounting department heads, IT department workers and employees in the innovation department. This was accomplished by distributing a thorough questionnaire intended to gather their perspectives.

The study’s results highlight the significant positive relationship between technological and innovation orientation. Moreover, the study demonstrates that both technological and innovation orientation were found to positively impact the sustainability and development of the industrial sector.

This study provides practical insights for policymakers, industrial managers and innovation supporters in Jordan. Managers can use these insights to reassess technology adoption and innovation strategies. Additionally, investing in staff skills and technology readiness can boost efficiency, competitiveness and long-term growth.

To the best of the authors’ knowledge, this study is pioneering research to shed light on the connection between technological orientation, innovation orientation and sustainability and development in the industrial sector, providing valuable insights for policymakers and practitioners alike.

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.

The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.