Higher College of Technology

About: Higher College of Technology is a based out in . It is known for research contribution in the topics: Higher education & Tourism. The organization has 369 authors who have published 379 publications receiving 729 citations.

The impact of gamification adoption intention on brand awareness and loyalty in tourism: The mediating effect of customer engagement

Abstract: Gamification has been used by tourism organisations for marketing purposes to reinforce customer engagement and to achieve brand awareness and loyalty. The current study, using the Unified Theory of Acceptance and Use of Technology, aims to investigate the antecedents and consequences of gamification's adoption intention by tourist organisations, and to examine the mediation effect of customer engagement. A quantitative method was employed using a survey to collect data from a random sample of relevant managers in travel agencies. Using structural equation modelling for data analysis, the findings revealed that tourism organisations have positive intentions to adopt gamification to increase customer engagement and to achieve tourist destinations' brand awareness and loyalty. Implications, limitations, and future research are also addressed.

Sensitivity assessment and optimal economic evaluation of a new COVID-19 compartmental epidemic model with control interventions.

Abstract: Optimal economic evaluation is pivotal in prioritising the implementation of non-pharmaceutical and pharmaceutical interventions in the control of diseases. Governments, decision-makers and policy-makers broadly need information about the effectiveness of a control intervention concerning its cost-benefit to evaluate whether a control intervention offers the best value for money. The outbreak of COVID-19 in December 2019, and the eventual spread to other parts of the world, have pushed governments and health authorities to take drastic socioeconomic, sociocultural and sociopolitical measures to curb the spread of the virus, SARS-CoV-2. To help policy-makers, health authorities and governments, we propose a Susceptible, Exposed, Asymptomatic, Quarantined asymptomatic, Severely infected, Hospitalized, Recovered, Recovered asymptomatic, Deceased, and Protective susceptible (individuals who observe health protocols) compartmental structure to describe the dynamics of COVID-19. We fit the model to real data from Ghana and Egypt to estimate model parameters using standard incidence rate. Projections for disease control and sensitivity analysis are presented using MATLAB. We noticed that multiple peaks (waves) of COVID-19 for Ghana and Egypt can be prevented if stringent health protocols are implemented for a long time and/or the reluctant behaviour on the use of protective equipment by individuals are minimized. The sensitivity analysis suggests that: the rate of diagnoses and testing, the rate of quarantine through doubling enhanced contact tracing, adhering to physical distancing, adhering to wearing of nose masks, sanitizing-washing hands, media education remains the most effective measures in reducing the control reproduction number R c , to less than unity in the absence of vaccines and therapeutic drugs in Ghana and Egypt. Optimal control and cost-effectiveness analysis are rigorously studied. The main finding is that having two controls (transmission reduction and case isolation) is better than having one control, but is economically expensive. In case only one control is affordable, then transmission reduction is better than case isolation. Hopefully, the results of this research should help policy-makers when dealing with multiple waves of COVID-19.

Molecular Docking Reveals Ivermectin and Remdesivir as Potential Repurposed Drugs Against SARS-CoV-2

Abstract: SARS-CoV-2 is a newly emerged coronavirus that causes a respiratory disease with variable severity and fatal consequences. It was first reported in Wuhan and subsequently caused a global pandemic. The viral spike protein binds with the ACE-2 cell surface receptor for entry, while TMPRSS2 triggers its membrane fusion. RNA‐dependent RNA polymerase (RdRp), viral proteases and M proteins are important in different stages of viral replication. Accordingly, they are attractive targets for different antiviral therapeutic agents. Although many antiviral agents have been used in different clinical trials and included in different treatment protocols, the mode of action against SARS-CoV-2 is still not fully understood. Different potential repurposed drugs, including chloroquine phosphate, chloroquine, hydroxychloroquine, ivermectin, remdesivir and favipiravir, were screened in the present study. Molecular docking of these drugs with different SARS-CoV-2 target proteins, including spike and membrane proteins, RdRp, nucleoproteins, viral proteases and nonstructural protein 14 (nsp14), was performed. Moreover, the binding affinities of the human ACE-2 receptor and TMPRSS2 to the different drugs were evaluated. Molecular dynamics simulation and MM-PBSA calculation were also conducted. Ivermectin and remdesivir were found to be the most promising drugs. Our results suggest that both these drugs utilise different mechanisms at the entry and post-entry stages and could be considered potential inhibitors of SARS‐CoV‐2 replication.

Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model

Abstract: The aim of this study was to examine factors that influenced experienced teachers’ intention to use E-learning in their teaching of mathematics. Data were collected using a questionnaire from 161 secondary school mathematics teachers who completed a six-month in-service online training provided by the Indonesian Ministry of Education. The Technology Acceptance Model (TAM) was used as the framework while E-learning experience was included as an additional construct. An extended TAM model was proposed and tested in this study. It consisted of five constructs, namely: intention to use, perceived usefulness, perceived ease of use, attitude toward using, and experience. Data were analyzed using Structural Equation Modelling with SMARTPLS 3.0. The findings showed that attitude toward E-learning use and E-learning experience were the two most significant constructs in predicting E-learning use. Contrary to previous studies, perceived ease of use and perceived usefulness were non-significant factors for the prediction of the behavioral intention. Implications for future research and practices are discussed.