Search results

The purpose of this paper was to study laminar fluid flow and convective heat transfer in a conical gap at small conicity angles up to 4° for the case of disk rotation with a fixed cone.

In this paper, the improved asymptotic expansion method developed by the author was applied to the self-similar Navier–Stokes equations. The characteristic Reynolds number ranged from 0.001 to 2.0, and the Prandtl numbers ranged from 0.71 to 10.

Compared to previous approaches, the improved asymptotic expansion method has an accuracy like the self-similar solution in a significantly wider range of Reynolds and Prandtl numbers. Including radial thermal conductivity in the energy equation at small conicity angle leads to insignificant deviations of the Nusselt number (maximum 1.23%).

This problem has applications in rheometry to experimentally determine viscosity of liquids, as well as in bioengineering and medicine, where cone-and-disk devices serve as an incubator for nurturing endothelial cells.

The study can help design more effective devices to nurture endothelial cells, which regulate exchanges between the bloodstream and the surrounding tissues.

To the best of the authors’ knowledge, for the first time, novel approximate analytical solutions were obtained for the radial, tangential and axial velocity components, flow swirl angle on the disk, tangential stresses on both surfaces, as well as static pressure, which varies not only with the Reynolds number but also across the gap. These solutions are in excellent agreement with the self-similar solution.

Present study aims to extend the lattice Boltzmann method (LBM) to simulate radiation in geometries with curved boundaries, as the first step to simulate radiation in complex porous media. In recent years, researchers have increasingly explored the use of porous media to improve the heat transfer processes. The lattice Boltzmann method (LBM) is one of the most effective techniques for simulating heat transfer in such media. However, the application of the LBM to study radiation in complex geometries that contain curved boundaries, as found in many porous media, has been limited.

The numerical evaluation of the effect of the radiation-conduction parameter and extinction coefficient on temperature and incident radiation distributions demonstrates that the proposed LBM algorithm provides highly accurate results across all cases, compared to those found in the literature or those obtained using the finite volume method (FVM) with the discrete ordinates method (DOM) for radiative information.

For the case with a conduction-radiation parameter equal to 0.01, the maximum relative error is 1.9% in predicting temperature along vertical central line. The accuracy improves with an increase in the conduction-radiation parameter. Furthermore, the comparison between computational performances of two approaches reveals that the LBM-LBM approach performs significantly faster than the FVM-DOM solver.

The difficulty of radiative modeling in combined problems involving irregular boundaries has led to alternative approaches that generally increase the computational expense to obtain necessary radiative details. To address the limitations of existing methods, this study presents a new approach involving a coupled lattice Boltzmann and first-order blocked-off technique to efficiently model conductive-radiative heat transfer in complex geometries with participating media. This algorithm has been developed using the parallel lattice Boltzmann solver.

This study aims to investigate to role of empowering leadership and psychological empowerment on nurses' work engagement and affective commitment.

Self-administered questionnaire data from 231 nurses working in a university hospital in Saudi Arabia were analysed using a cross-sectional research design using structural equation modelling (SEM) to assess the relationship between empowering leadership (EL), affective commitment (AC) and work engagement (WE) while testing for the mediating role of psychological empowerment (PE).

SEM analysis demonstrated that EL significantly relates to AC. AC similarly significantly relates to WE. Further, the results showed that PE substantially mediates the relationship between EL and WE. There is no significant direct relationship found between EL and WE.

The study findings are essential for nursing managers. They illustrate that nurses become more committed to their organisation and, in return, more engaged with their work when they receive EL. Therefore, nursing managers could train their leaders to practice EL as increased WE has been found to result in other positive work attitudes such as reduced turnover intention.

This study corroborates the relationships between EL, AC and WE, as well as the mediating role of PE. However, this research is unique as the long-established relationship between EL and WE was not supported. It shows that the propositions of leader-member exchange theory may not hold for unique non-Western contexts, in this case, Saudi Arabia.

This paper aims to detect the most profitable, i.e. optimal turning points (TPs), from the history of time series using a binary integer programming (BIP) model. TPs prediction problem is one of the most popular yet challenging topics in financial planning. Predicting profitable TPs results in earning profit by offering the opportunity to buy at low and selling at high. TPs detected from the history of time series will be used as the prediction model’s input. According to the literature, the predicted TPs’ profitability depends on the detected TPs’ profitability. Therefore, research for improving the profitability of detection methods has been never given up. Nevertheless, to the best of our knowledge, none of the existing methods can detect the optimal TPs.

The objective function of our model maximizes the profit of adopting all the trading strategies. The decision variables represent whether or not to detect the breakpoints as TPs. The assumptions of the model are as follows. Short-selling is possible. The time value for the money is not considered. Detection of consecutive buying (selling) TPs is not possible.

Empirical results with 20 data sets from Shanghai Stock Exchange indicate that the model detects the optimal TPs.

The proposed model, in contrast to the other methods, can detect the optimal TPs. Additionally, the proposed model, in contrast to the other methods, requires transaction cost as its only input parameter. This advantage reduces the process’ calculations.

Organizations experience various issues with the optimum use of data. This study is qualitative research to identify and provide a helpful pattern for increasing the performance of sustainable supply chain management (SSCM).

The statistical population in the qualitative section includes managers and experts in the supply chain (SC) and food production. The data were collected via semi-structured interviews, and data saturation happens after the tenth interview. Then, the data were coded using grounded theory and qualitative research analysis. 384 questionnaires were distributed among employees via random sampling. SmartPLS software is used to investigate and analyze the relationships in the mentioned model through 13 core categories.

The findings indicate that organizational productivity and SC deficiencies are among the effective factors in the SSCM primarily identified by this study. Moreover, the findings propose that industry SC, macro policies, organizational performance, social factors, economic factors, organizational factors, political factors, technological factors, production and customer are likely to positively impact the SSCM, which have previously been documented by studies.

The model and concepts extracted from the responses of research participants show well that there are reasons and motivations for increasing the performance of SSCM. Also, the designed model shows well that the motives and reasons for turning to this system are satisfied due to its implementation.

This study explores the connection between intellectual capital (IC) components and the competitive advantage (CA) of the hospitality sector in Jordan through the mediating role of service innovation as well as the moderating role of big data analytics capabilities.

Data were collected through a self-administered questionnaire from the hospitality sector with a sample of 402 respondents. Data were analysed using SmartPLS, a bootstrapping technique was used to analyse the data. The mediating effect for service innovation and the moderating effect for big data analytics capabilities were performed.

The results showed that the proposed moderated-mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between human capital, structural capital and relational capital and the CA as well as a mediating effect of service innovation. The findings confirmed that there is a moderating relationship for big data analytics capabilities between service innovation and CA. The results illustrate the importance of IC and service innovation in enhancing CA in the Jordanian hospitality sector in light of the big data analytics capabilities.

This cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalisation of the limitation's results, and the results are limited to one sector.

This research developed a theoretical model to incorporate IC components, service innovation, big data analytics capabilities and CA. This paper offers new theoretical and practical contributions that add value to the innovation and CA literature by testing the moderated-mediation model of these constructs in the hospitality sector which has been greatly affected by the coronavirus disease 2019 (COVID-19) pandemic. This study is distinguished from other studies by highlighting the role of IC and service innovation in enhancing CA as service innovation contributes to the formation of many organisational advantages in the Jordanian hospitality sector.

There are a variety of benefits associated with the use of YouTube for learning purposes, such as YouTube is a free open-access tool students can use to facilitate their learning. This study investigates whether an attitudinal factor (i.e. perceived usefulness) and the factor's antecedents, resource quality factors (i.e. credibility, currency, coverage and relevance), normative factor (i.e. subjective norm) and control factor (i.e. perceived ease of use) all affect college freshmen's behavioral intentions to use YouTube for academic learning purposes.

This research employs the theory of planned behavior (TPB) to explore the attitudinal, normative and control factors associated with college freshmen's behavioral intentions to use YouTube for academic learning. After developing a quantitative survey given to 182 college freshmen in a Southeastern institution in the United States of America, structural equation modeling (SEM) was used to examine the seven hypotheses and the research constructs.

The results indicate that attitudinal factor (i.e. perceived usefulness) and its antecedents, resource quality factors (i.e. currency, coverage and relevance) and normative factor (i.e. subjective norm) have a statistically significant effect on college freshmen's intentions to use YouTube for academic learning purposes.

This study suggests that individual motivations (i.e. perceived usefulness and subjective norm) and resource quality factors (i.e. currency, coverage and relevance) play into college freshmen's decisions to use YouTube for learning purposes, while other research indicates that the system or application itself factors into students' decisions to use technology for learning.

This study suggests that college freshmen are more likely to use YouTube for academic learning purposes when the freshmen hold favorable attitudes about the platform and when the freshmen believe the freshmen's peers are also using YouTube to supplement in-class learning.

This is an initial study that focuses on college freshmen's behavioral intentions to use YouTube for academic learning purposes. This research demonstrates the roles that peers as well as resource quality factors play in students' decisions to use specific technology to enhance the students' learning.

Extant literature on knowledge sharing in higher education institutions (HEIs) concentrates on non-behavioral perspectives and indicates that academics continue to hoard knowledge despite being given incentives to bolster knowledge sharing behavior (KSB). This study aims to examine KSB among academics from a behavioral perspective through the lenses of the theory of planned behavior, perceived trust and organizational climate.

Self-administered questionnaires were distributed to 12 private universities using the drop-off/pick-up approach, resulting in 405 usable responses, which were analyzed using covariance-based structural equation modeling.

Academics’ salient beliefs – that is, behavioral beliefs, normative beliefs and control beliefs – significantly influence their attitude, subjective norms and perceived behavioral control (PBC). Attitude, subjective norms, PBC, perceived trust and organizational climate directly influence knowledge sharing intention (KSI), whereas attitude, KSI, subjective norms and PBC directly influence KSB. Noteworthily, KSI is a mediator in the relationships between attitude, subjective norms and PBC with KSB.

This study makes a seminal contribution through the novel conceptualization and theoretical generalizability of the theory of planned behavior by which HEIs can reinforce their competitiveness and global position by enhancing KSB among academics using a profound behavioral strategy.

This paper aims to analyze relative contribution of intensive margin (IM) and extensive margin (EM) to growth in India's agricultural exports for the period 2001 to 2020. It also analyses the determinants of IM and EMs through a standard gravity model.

The study uses export data from United Nations Comtrade, which is accessed through World Integrated Trade Solution (WITS) software. Data for the period 2001 to 2020 were compiled for analysis using the Harmonized System (HS) of commodity classification system at the six-digit level. This study decomposed the contribution of IM and EM in the growth of Indian agricultural trade by using Hummels and Klenow's approach. After performing the export decomposition analysis, the authors analyze the factors influencing IM and EM by using the Tobit regression model and Poisson pseudo-maximum-likelihood (PPML) method of estimation.

The EM grew at 1.24% per annum, while the intensive margin (IM) increased by 0.23%. The contribution of growth at the EM increased from 58.8% in 2001 to 70.2% in 2020. Export growth along the IM was relatively high for animal products and agricultural raw materials, while growth at the EM was an important contributor to the export growth of horticultural and processed agricultural products. There was a positive and significant effect of the free trade agreement (FTA) on export margins.

More disaggregated commodity-specific studies on value chain analysis would provide valuable insights into the issues hindering exports and realizing the untapped export potential.

There is a scarcity of holistic and recent studies illustrating the role of IM and EMs in agricultural trade growth, covering a large number of commodities and geographies associated with Indian agricultural trade. The study would be helpful to the stakeholders in facilitating informed policy decisions.