Search results

The purpose of this paper is to study haemodynamic flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, magnetohydrodynamic (MHD) flow and aneurysm conditions. The findings of this study offer significant insights into the intricate interplay encompassing electro-osmosis, MHD flow, microorganisms, Joule heating and the ternary hybrid nanofluid.

The governing equations are first non-dimensionalised, and subsequently, a coordinate transformation is used to regularise the irregular boundaries. The discretisation of the governing equations is accomplished by using the Crank–Nicolson scheme. Furthermore, the tri-diagonal matrix algorithm is applied to solve the resulting matrix arising from the discretisation.

The investigation reveals that the velocity profile experiences enhancement with an increase in the Debye–Hückel parameter, whereas the magnetic field parameter exhibits the opposite effect, reducing the velocity profile. A comparative study demonstrates the velocity distribution in Au-CuO hybrid nanofluid and Au-CuO-GO ternary hybrid nanofluid. The results indicate a notable enhancement in velocity for the ternary hybrid nanofluid compared to the hybrid nanofluids. Moreover, an increase in the Brinkmann number results in an augmentation in entropy generation.

This study investigates the flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, MHD flow and aneurysm conditions. The governing equations are non-dimensionalised, and a coordinate transformation is applied to regularise the irregular boundaries. The Crank–Nicolson scheme is used to model blood flow in the presence of a ternary hybrid nanofluid (Au-CuO-GO/blood) within the arterial domain. The findings shed light on the complex interactions involving stenosis, MHD flow, aneurysms, Joule heating and the ternary hybrid nanofluid. The results indicate a decrease in the wall shear stress (WSS) profile with increasing stenosis size. The MHD effects are observed to influence the velocity distribution, as the velocity profile exhibits a declining nature with an increase in the Hartmann number. In addition, entropy generation increases with an enhancement in the Brinkmann number. This research contributes to understanding fluid dynamics and heat transfer mechanisms in bifurcated arteries, providing valuable insights for diagnosing and treating cardiovascular diseases.

While the COVID-19 pandemic has impacted the construction industry, it is still unclear from prior studies about adequately positioning the quality assurance (QA) for the post-pandemic era and future pandemics, especially cross-border construction logistics and supply chain (Cb-CLSC). Thus, this study aims to develop a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully.

This is achieved pragmatically through an embedded mixed-method design involving a literature review, survey and interview from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory. The design is further integrated with the partial least squares structural equation modelling (PLS-SEM) approach.

The study revealed 10 critical managerial practices (MPs) to position the QA to be adequate for the post-pandemic and during future pandemics, with the top three including “strict observance of government regulations (MP1)”, “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)” and “modification of contract to cater for uncertainties (MP4)”. This attained a relatively good percentage agreement of 53% between the industry and academia. However, the top four MPs regarded as very effective include “implementing digital collaborative inspections with subcontractors and trades (MP8)”, “implementing a digital centralized document and issue management system (MP7)”, “strict observance to government regulations, including vaccination of workers, social distancing, use of prescribed nose masks, etc. (MP1)” and “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)”. Two underlying components of the MPs were revealed as policy-process (PP)-related practices and people-technology-process (PTP)-related practices, and these can be modelled into a managerial framework capable of effectively positioning the QA to be adequate during pandemics through to the post-pandemic era.

The findings of this study depicted significant theoretical and practical contributions to the proactive management of QA activities during pandemics through to the post-pandemic era. It could empower organisations to pay attention to smartly and innovatively balancing people, processes, pandemic policy and technology to inform decisions to effectively position the QA for the post-pandemic era and survive the risks of future pandemics.

The study contributes to the body of knowledge in that it develops a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully. It is original research with invaluable primary data in the form of surveys and interviews from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory.

This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups.

The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market.

The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives.

To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model.

This paper investigates the joint impact of COVID-19, alliances and digital strategies on bank lending. Additionally, this study examines whether the effect of COVID-19, alliances and digital strategies on bank loans depends on the types of banks.

Using a sample of 92 commercial banks in Indonesia from March 2020 to September 2021, a fixed-effects model (FEM) was used to analyze data.

This study provides robust results regarding the negative impact of the COVID-19 pandemic on bank loans in Indonesian banking. Furthermore, it reveals that collaboration between banks and FinTech does not substantially influence bank lending, despite the rise in proven cases tending to reduce credit expansion. It emphasizes the importance of the development of mobile banking as part of digitalization in boosting loan bank expansion, and this finding is more noticeable in private and small banks.

This study highlights some policy recommendations to improve bank lending during the COVID-19 period, particularly the role of new alliances and digital strategy in involving COVID-19 pandemic mitigation within a novel financial ecosystem.

This study offers a significant contribution to the empirical literature that specifically explores the joint impact of the COVID-19 pandemic, alliances and digital strategies on bank lending in banking.

This paper aims to uncover the key enablers of an agile supply chain in the manufacturing sector amidst disruptions such as pandemics, trade wars and cross-border challenges. The study aims to assess the applicability of existing literature to manufacturing and identify additional industry-specific enablers contributing to the field of supply chain management.

The research methodology is comprehensively described, detailing the utilization of extent literature and semistructured interviews with mid- and top-level executives in a supply chain. The authors ensure the robustness of the data collection process and results interpretation.

The study identifies six essential dimensions of an agile supply chain: information availability, design robustness, external resource planning, quickness and speed, public policy influencing skills and cash flow management. The study provides valuable insights for industry professionals to develop agile supply chains capable of responding to disruptions in a rapidly changing world.

This study is limited by its focus on the manufacturing sector, and future research may explore the applicability of these findings to other industries. By focusing on these essential dimensions identified in the study, managers can develop strategies to improve the agility and responsiveness of their supply chains. In addition, further research may investigate how these enablers may vary in different regions or contexts.

The COVID-19 pandemic has forced executives to reconsider their sourcing strategies and reduce dependence on suppliers from specific geographies. To ensure business continuity, companies should assess the risk associated with their suppliers and develop a business continuity plan that includes multisourcing their strategic materials. Digital transformation will revolutionize the supply chain industry, allowing for end-to-end visibility, real time insights and seamless integration of business and processes. Companies should also focus on creating a collaborative workforce ecosystem that prioritizes worker health and well-being. Maintaining trust with stakeholders is crucial, and firms must revisit their relationship management strategies. Finally, to maintain business leadership and competitiveness during volatile periods, the product portfolio needs to be diversified and marketing and sales teams must work in tandem with product teams to position new products accordingly.

This work contributes substantially to the literature on supply chain agility (SCA) by adding several new factors. The findings result in a more efficient and cost-effective supply chain during a stable situation and high service levels in a volatile situation. A less complex methodology for understanding SCA provides factors with a more straightforward method for identifying well-springs of related drivers. First, the study contributes to reestablish the factors such as quickness, responsiveness, competency, flexibility, proactiveness, collaboration and partnership, customer focus, velocity and speed, visibility, robustness, cost-effectiveness, alertness accessibility to information and decisiveness as applicable factors for SCA. Second, the study suggests a few more factors, such as liquidity management, Vendors’ economic assessment and economic diversity, that are the study’s unique contributions in extending the enablers of SCA. Finally, public policy influencing skills, local administration connects and maintaining capable vendors are the areas that were never considered essential for SCA. These factors have emerged as a vital operational factor during the lockdown, and academicians may consider these factors in the future to assess their applicability.

This study provides new insights for decision-makers looking to enhance the resilience and agility of their supply chains. The identification of unique enablers specific to the manufacturing industry contributes to the existing body of literature on agile supply chains in the face of disruptions.

The purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).

Adopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.

First, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.

By analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

Organizations deal with digital technologies to achieve their strategic goals. The shift toward digitization is a major challenge because it requires companies to create a digital outlook that influences organizational design. As a result, investigation of institutional theory and entrepreneurial orientation theory in the European food industry has become the focus of research in recent times.

To this end, data were collected from 83 companies related to the food industry in the European context. By applying a hybrid phase of the partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods, this study captured the causal–non-linear relationships among the study constructs.

The findings revealed that the variables of institutional theory and entrepreneurial orientation theory affect the adoption of the digital strategy. There is also a dual interaction role for e-business capabilities and digital transformation. The results of non-linear relationships confirmed that digital strategy adoption is highly influenced by digital transformation, followed by risk-taking, digital leadership, e-business capabilities, organizational agility, proactiveness and innovativeness.

The authors provided significant implications for practitioners and academics about the most influential determinants of digital strategy – businesses must move swiftly toward digitization across its various units to achieve their objectives. An organization’s leadership must realize that equipping the employees with necessary skills is the first step toward digitalization.

The current study underscores the digital strategy, which is usually an overlooked area of investigation, in the food industry. The study identifies some important predictors of digital strategy adoption with the interaction’s role of digital transformation and e-business capabilities. Such relationships have been rarely discussed. In addition, the adoption of a hybrid SEM-AAN approach makes the study an original one.

There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers.

Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio.

The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative.

The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study.

Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.