Predicting Student's Satisfaction towards Hybrid Learning in Informatics

This paper used regression and moderation approaches to evaluate the student's satisfaction with informatics towards the hybrid learning in their study. Multiple Linear Regression (MLR) identified student satisfaction based on hybrid learning difficulty and benefit ($p < 0.001$). Linear Regression (LR) found hybrid learning benefits impacted the student's satis-faction significantly $(p < 0.001$). Student's $t$-test also revealed that Overall Satisfaction (OS) significantly affected hybrid learning's satisfaction ($p < 0.001$). Analysis of Co-variants (ANCOVA) also proved that hybrid learning's benefit ($p < 0.001$) and OS ($p < 0.05$) significantly influenced student satisfaction. The paper also proved that hybrid learning's benefits positively correlate with student satisfaction (0.596). The slopes of 'Yes' and 'No' are substantially different from one another when the probability value of 0.22 $(p > 0.05$). Hence, no moderator (OS) affects the relationship's strength between the benefit and satisfaction of hybrid learning. The paper also revealed that hybrid learning's difficulty has a negative correlation (-.18), and the benefit of hybrid learning is positively associated with student satisfaction (.66). Implementing a hybrid learning mode during Covid-19 periods significantly impacted student satisfaction and the decision taken by the administration was also meaningful. © 2023 IEEE.

Smart Tourism using Attractive and Safe Travel Recommendation Technology

The tourism sector was among the most affected sector during the COVID-19 pandemic and has lost up to USD 5.87 billion potential revenue. Since many countries closed the borders, including Indonesia, by applying travel restrictions and thus tourists postponed their visits. Whereas vaccine distribution has shown good progress as the vaccination percentage in Jakarta and Bali has shown promising results since the majority of its population has been vaccinated, and it helps many industries, including tourism, recover. However, the pandemic might change tourist behavior. In addition, information about tourist destinations is spread poorly in various sources, and it psychologically affects tourists' decision to visit. Many works have been published to address this issue with the recommendation system. However, it does not provide geopolitical variables such as PPKM in Indonesia to ensure safeness for the tourist. Therefore, this research aims to enhance innovations in the tourism industry by considering the geopolitics factor into the system using Multiple Linear Regression. The result of this research demonstrates the effectiveness of geopolitics added variable on three different cities Jakarta, Java, and Bali. It can be implemented in a wide area in Indonesia. For further research, the proposed model can be used in a wide area in Indonesia and developed for a more comprehensive recommendation system. © 2022 IEEE.

Assessment of changes in air quality in Bangalore, India, during the Covid-19 pandemic lockdown: Statistical modelling

COVID-19 pandemic has resulted in a halt to the daily lifestyle of people around the world and bound them to abide by the lockdown measures enforced to prevent the disease from further spreading. In India also, lockdown has been enforced from March 2020. As a result, the level of air pollutants in the atmosphere goes on decreasing. To know the air quality pattern of Bangalore city, ten stations around the city were selected. Air quality data of these stations has been availed from the Central Pollution Control Board (CPCB) of India website. Box chart concept of graphical representation has been applied to show the range of temporal variation of the air pollutants selected (CO, NO2, Ozone, PM2.5, PM10 and SO2) for the study area over two distinct periods (pre-lockdown and post-lockdown). It has been observed that all the pollutants level were drastically or significantly reduced except for SO2 which showed mixed behavior during the entire study period probably due to no restriction on the operation of power plants. GIS based contour mapping is done for each pollutant over the entire study area and separately for two distinct periods (pre-lockdown and post-lockdown). It was found that, change in CO level over the entire study area was significant and the reason behind it was complete restriction on vehicular movement which is the primary reason for CO emission in atmosphere. Reduction in PMs and ozone was also noticeable, but change in SO2 over the entire study area was almost insignificant. To find out the probable sources of pollution during the lockdown and before the lockdown period and the most significant parameters statistical approach has been adopted. The whole data set has been grouped based on similarity and divided into three distinct clusters for both pre-lockdown and post-lockdown period separately using Hierarchical Agglomerative Cluster Analysis (HACA) concept. Principal Component Analysis (PCA) was done for each of the clusters and each time period considered. From the results of PCA it can be confirmed that the most significant parameters were PM10, PM2.5, ozone and SO2. Results suggest that the probable sources of pollution during pre-lockdown period were vehicular emissions, power plants, industrial activities etc. In contrast, during post-lockdown period the sources of pollution were power plants, construction sites and household pollution only. MLR (Multiple Linear Regression) models were developed to predict Air Quality Index (AQI). Most of the models showed good fit with adjusted R2 value more than 0.9. Regression coefficient (R2) values for PM10 followed PM2.5 were highest in each cluster. © 2023 Author(s).

The technology acceptance model and beyond: factors influencing physicians' intention to use teleconsultations

Background/Aims: The COVID-19 pandemic accelerated the implementation of telehealth and virtual care services. Clinicians must be comfortable using this technology in order for it to be developed effectively and implemented consistently. This study evaluated the influence of various factors, including those theorised in the technology acceptance model, on physicians' intention to use teleconsultations in their clinical practice in Chennai, India. Methods: A snowball sampling method was used to distribute an online survey to physicians in Chennai, India. The survey measured respondents' intention to use teleconsultations (dependent variable), along seven independent variables relating to this technology (perceived usefulness, perceived ease of use, physicians' attitudes, social influences, facilitating conditions, perceived compatibility with the clinical area and trust). A total of 165 responses were collected. Results were analysed using descriptive and correlational statistics, along with multiple linear regression. Results: All seven independent variables were found to be significantly associated with the dependent variable (P<0.01). Multiple linear regression analysis indicated that the independent variables accounted for 67.8% of the variance in respondents' intention to use teleconsultations. Conclusions: Physicians' intention to use teleconsultations is complex and multi-faceted. Although the factors theorised by the technology acceptance model were significantly associated with intention to use telemedicine, other factors were also found to be important, including social influences, external facilitating factors, perceived compatibility with the clinical area and personal trust in technology.

Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, "machine speed” and "fabric width” are determined as the most important variables by using these tools. Then, optimum values for "machine speed” and "fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

IT reliability as a source of sustainability for organisations operating during the COVID-19 pandemic

The article aims to show that reliable IT support was crucial for the survival and sustainability of organisations during the COVID-19 pandemic. The article considers the negative effect of the crisis caused by the COVID-19 pandemic on the organisational sustainability of an organisation (i.e., organisational performance through employee job performance). It explores the role of IT reliability in mitigating such a negative effect. To verify the hypotheses, the empirical studies were performed during the COVID-19 crisis with 1160 organisations operating in Poland, Italy and the USA. The data were analysed using multiple linear regression models with mediators and moderators. The results confirmed that due to the ability to limit the severity of a crisis-induced negative effect on employee job performance (influencing organisational performance), IT reliability could be considered a mitigator for the negative effect of the COVID-19 crisis on the sustainability of organisations. The results indicate that IT reliability should be fostered among organisations operating during the COVID-19 pandemic to maintain sustainability. © 2023 Katarzyna Tworek, published by Sciendo.

2d Qsar Approach to Develop Newer Generation Small Molecules Active against Sars-Covid

We are in the half past of 2022, but still, we are facing the coronavirus pandemic situation. When a patient is hospitalized, only some FDA-approved drugs were administered to cure the patient. In treating coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors, and various monoclonal antibodies are present. But all the molecules used in the treatment were not so effective in fully curing the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches played an essential role. 2D QSAR studies related to anti-SARS-CoV-2 molecule development, some QSAR models observed with good statistical parameters such as R2: 0.748, cross-validated Q2 (LOO): 0.628, external predicted R2: 0.723 and another model suggested with R2: 0.764, Q2: 0.627 and Rm2: 0.610, Q2 (F1): 0.727, Q2 (F1): 0.652, MAE score: 0.127. We developed a new 2D QSAR model with a higher number of molecules and greater statistical parameters. A dataset of 84 anti-SARS-CoV2 molecules was obtained from literature followed by descriptor calculation PADEL software;the QSAR model was generated using the Modelability index, dataset pretreatment, division, MLR equation, validation, and Y randomization test. The model was pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP with R2 and Q2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298. Atoms E-state and electronic features of the molecules directly related to anti-SARS-CoV-2 drug activity. It can be easily concluded that we want to develop a small molecule effective against SARS-CoV-2 disease in the near future.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Continuous effort is dedicated to clinically and computationally discovering potential drugs for the novel coronavirus-2. Computer-Aided Drug Design CADD is the backbone of drug discovery, and shifting to computational approaches has become necessary. Quantitative Structure–Activity Relationship QSAR is a widely used approach in predicting the activity of potential molecules and is an early step in drug discovery. 3-chymotrypsin-like-proteinase 3CLpro is a highly conserved enzyme in the coronaviruses characterized by its role in the viral replication cycle. Despite the existence of various vaccines, the development of a new drug for SARS-CoV-2 is a necessity to provide cures to patients. In the pursuit of exploring new potential 3CLpro SARS-CoV-2 inhibitors and contributing to the existing literature, this work opted to build and compare three models of QSAR to correlate between the molecules' structure and their activity: IC50 through the application of Multiple Linear Regression(MLR), Support Vector Regression(SVR), and Particle Swarm Optimization-SVR algorithms (PSO-SVR). The database contains 71 novel derivatives of ML300which have proven nanomolar activity against the 3CLpro enzyme, and the GA algorithm obtained the representative descriptors. The built models were plotted and compared following various internal and external validation criteria, and applicability domains for each model were determined. The results demonstrated that the PSO-SVR model performed best in predictive ability and robustness, followed by SVR and MLR. These results also suggest that the branching degree 6 had a strong negative impact, while the moment of inertia X/Z ratio, the fraction of rotatable bonds, autocorrelation ATSm2, Keirshape2, and weighted path of length 2 positively impacted the activity. These outcomes prove that the PSO-SVR model is robust and concrete and paves the way for its prediction abilities for future screening of more significant inhibitors' datasets. © 2023, The Author(s) under exclusive licence to Associação Brasileira de Engenharia Química.

Measuring Evidence Affecting the Financial Stability of Airport Operations in Malaysia

The indicator of bankruptcy exposure for airport operations in Malaysia is calculated by using Altman's Z'-score. Financial and non-financial attributes related to the bankruptcy exposure show multicollinearity, and the redundant information was identified and removed. The common period for the variables is from 1999-2021, which includes the period of COVID-19 pandemic. Models with a combination of financial and non-financial attributes further reduce the deviation between the estimated standard deviation of the residuals and the marginal standard deviation of the bankruptcy risk in comparison to models without the combination. The best model provides improvements in terms of the mean of the absolute errors (MAE), mean of absolute percentage errors (MAPE), and mean absolute scaled errors (MASE). Furthermore, all determinants in the best model are statistically significant. We suggest that the opportunity for optimisation, including total movements of passenger, cargo and mail, could reduce the company's bankruptcy exposure. Findings indicate that reducing the financial leverage could improve the financial distress risk while liquidity, net operating margin, and asset turnover are positively contributed to the financial stability of the largest airport operator in Malaysia. If the marginal average of annual exposures to bankruptcy of 4.04% continues linearly into the future, the company is expected to transition from being financially stable to experiencing financial distress in 2030. © 2022 IEEE.

Impact of Remote Audit, Computer Literacy, and Audit Software Skill to Audit Quality during Pandemic of Covid-19

In order to face industrial revolution 4.0 auditors must start developing their skills. In the past, auditors have used remote audit only to reach remote places. However, currently Covid-19 comes and encourages even more the use of technology and provides an opportunity to rethink the way audits are conducted. In this study, researchers wanted to know how remote audit, computer literacy and audit software skill has affected audit quality. This research is quantitative in nature, by processing data using primary data obtained from distributing questionnaires to auditors who work at public accounting firms in Jabodetabek. Statistical analysis used multiple linear regression, previously carried out a feasibility test through validity, reliability and classical assumption tests. The results showed that the variables of remote audit, computer literacy and audit software skill had a significant effect on the audit quality. © 2022 ACM.

Analysis of Impact of Competency, Professionalism, and Audit Deadline on Remote Audit Effectiveness

Auditors must begin to develop their skills to face industry 4.0. The spread of Covid-19 has further encouraged auditors to conduct remote audits and provides an opportunity to rethink the way audits are conducted. In this study, the researcher wanted to find out how the influence of competence, professionalism, and audit deadlines on the effectiveness of remote audits. This research is quantitative, with data processing using primary data obtained from distributing questionnaires to auditors who work at Public Accounting Firms in Jakarta. Statistical analysis using multiple linear regression, before conducting a feasibility test through validity, reliability and classical assumption tests. The results showed that the variables of competence, professionalism, and audit time limit had a significant effect on the effectiveness of remote auditing. © 2022 ACM.

Technological Innovation and Performance of MSMEs in Panama: Study in Times of Covid-19

This paper presents the results of an empirical study, conducted during the months of February to March 2021, with the objective of analyzing innovative activity in product, process and organizational innovation and its impact on the performance of MSMEs. An online survey was conducted with the participation of 430 companies stratified according to sector (Industry, Construction, Commerce and Services) and size (micro, small and medium enterprises). The model used was multiple linear regression, whose response variable was company performance and the explanatory variables were product, process and organizational innovations. The results showed that process and organizational innovations were statistically significant, while product innovations were not significant. The coefficients were positive, implying that the higher the degree of process and organizational innovation, the higher the company's performance is expected to be. These innovations included changes in production processes, acquisition of new equipment, improvements in management, new procurement and purchasing processes, and changes in sales and commercial management. All these innovations were made during the pandemic period. © 2022 IEEE.

Modeling of COVID-19 death rate using various air pollutants: A multiple linear regression approach

Air pollution is a significant health risk, especially for vulnerable populations such as children, people with chronic illnesses, the elderly, and the economically and socially disadvantaged. Furthermore, air pollution has enormous social costs that we all bear in the form of premature deaths, low productivity, sick leave, and other strains on the healthcare system. The primary sources of air pollution are traffic, home fires, and industry. Measuring NO2 levels in air pollution reveals the extent of pollution caused by traffic, particularly diesel vehicles, which are the primary source of NO2. COVID-19 rates are rising in areas with high levels of air pollution, according to mounting evidence. Toxic contaminants can make people more susceptible to COVID-19. The causal relationship between air pollution and COVID-19 cases has yet to be established, but experts warn that long-term exposure will undoubtedly make people more susceptible to lung infections. Air pollution has been linked to an increase in cancer, heart disease, stroke, diabetes, asthma, and other comorbidities by inducing cellular damage and inflammation throughout the body. All of these factors increase the risk of death in COVID-19 patients. As a result, air quality parameters must be predicted and monitored. To predict results, this study proposes a statistical-based machine learning approach. Using multiple linear regression (MLR), Decision Tree (D.T.), and Random Forest (R.F.), the experimental results achieved 80%, 73%, and 65% accuracy on the dataset, respectively. © 2023 Wiley Periodicals LLC.

Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Indonesia declares COVID-19 Pandemic by the World Health Organization (WHO) from March 2020. This has a very impact, one of which is on the continuity of the world of education. This research aims to foretell the impact of hybrid teaching methods used at SMK Cendikia Cirebon City during the COVID-19 period on student achievement. Teachers' materials, honesty, enthusiasm, and IT backing are all factors in determining the quality of education. Multiple linear regression with root-mean-squared error as the dependent variable is used in this study (RMSE). Experiments conducted on 122 participants yielded an RMSE of 0.375 and a correlation level of 0.440 for each attribute, with test samples comprising 10% and training samples comprising 90%. As a result, the use of this multiple linear regression model can be suggested for foreseeing the introduction of a hybrid learning model to enhance educational quality. © 2023 International Information and Engineering Technology Association. All rights reserved.

A Study to Analyse Covid-19 Outbreak Using Multiple Linear Regression: A Supervised Machine Learning Approach

Introduction: Globally, COVID-19 have impacted people's quality of life Machine learning have recently be-come popular for making predictions because of their precision and adaptability in identifying diseases. This study aims to identify significant predictors for daily active cases and to visualise trends in daily active, positive cases, and immunisations. Material and methods: This paper utilized secondary data from Covid-19 health bulletin of Uttarakhand and multiple linear regression as a part of supervised machine learning is performed to analyse dataset. Results: Multiple Linear Regression model is more accurate in terms of greater score of R2 (=0.90) as com-pared to Linear Regression model with R2 =0.88. The daily number of positive, cured, deceased cases are significant predictors for daily active cases (p <0.001). Using time series linear regression approach, cumulative number of active cases is forecasted to be 6695 (95% CI: 6259-7131) on 93rd day since 18 Sep 2022, if similar trend continues in upcoming 3 weeks in Uttarakhand. Conclusion: Regression models are useful for forecasting COVID-19 instances, which will help governments and health organisations address this pandemic in future and establish appropriate policies and recommen-dations for regular prevention. © 2023 National Journal of Community Medicine.

The Use of Interpreters in Health Centers: A Mixed-Methods Analysis

Health centers serve millions of patients with limited English proficiency (LEP) through highly variable language services programs that reflect patient language preferences, the availability of bilingual staff, and very limited sources of third-party funding for interpreters. We conducted a mixed-methods study to understand interpreter services delivery in federally qualified health centers during 2009–2019. Using the Uniform Data System database, we conducted a quantitative analysis to determine characteristics of centers with and without interpreters, defined as staff whose time is devoted to translation and/or interpreter services. We also analyzed Medicaid-relevant policies' association with health centers' interpreter use. The qualitative component used a sample of 28 health centers to identify interpreter services models. We found that the use of interpreters, as measured by the ratio of interpreter full-time equivalents per patients with LEP, decreased between 2009 and 2019. We did not find statistically significant relationships between interpreter staffing and number of patients with LEP served, or in our examination of Medicaid-relevant policies. Our qualitative analysis uncovered homegrown models with varying program characteristics. Key themes included the critical role of bilingual staff, inconsistent interpreter training, and the reasonably smooth transition to virtual interpretation during COVID-19.

The effect of diversification on company value during a global health crisis: evidence from the COVID-19 pandemic

PurposeThe authors study the valuation effect of corporate diversification in the initial phase of the COVID-19 pandemic in 2020 in Europe.Design/methodology/approachApplying a cross-sectional regression model to a sample of public companies headquartered in the European Union, the authors investigate the existence of and the change in a diversification discount between 2018 and 2020. By applying the Excess Q methodology, the authors make an industry adjustment of diversified companies to measure the value effect of corporate diversification.FindingsThe authors find an economically and statistically significant diversification discount that increases from an average Excess Q of -0.05 in 2019 to -0.10 in 2020. The diversified companies' inferior fundamental financial performance in 2020 accompanies the discount. The results deviate from those of previous research, which mostly show a decrease in the diversification discount in economic crises, and thereby, shed doubt on whether diversification provides insurance against pandemic-induced adverse value effects.Originality/valueThe study distinguishes the role of corporate diversification during recessionary periods by establishing that the valuation effect of diversification depends on the nature of the crisis. The analysis incorporates criticism of previous studies concerning a biased methodology and uniform data source by applying the Excess Q methodology and using FactSet industry segment data.

Academic Procrastination, Depressive Symptoms and Suicidal Ideation in University Students: A look during the Pandemic

Objective: The aim is to determine the relationship between academic procrastination, depressive symptoms and suicidal ideation in students of the Faculty of Health Sciences. Method: It was a non-experimental and cross-sectional study of correlational scope. The non-probabilistic convenience sample, made up of 578 participants between 16 and 30 years old (69% female), completed the Academic Procrastination Scale, the Positive and Negative Suicidal Ideation Inventory (PANSI) and the Beck Depression Inventory (BDI-II). Frequencies and percentages were estimated at a descriptive level, the partial correlation coefficient and multiple linear regression were utilized to examine the associations between academic procrastination and suicidal ideation. Results: Subjects with a higher score of academic procrastination and BDI-II reported higher scores for suicidal ideation than those with a lower score (P < 0.01). A positive significant relationship was found between total academic procrastination and its subscales and suicidal ideation (P < 0.01). This correlation remained significant after controlling for depression (P < 0.05). Moreover, multiple linear regression revealed that academic procrastination, its subscales and depressive symptoms could explain about 20% of the total suicidal ideation in university students (R2 = 0.198). Conclusion: Increased levels of academic procrastination increase suicidal ideation in college students during the pandemic. These results suggest the need to create interventions for the prevention of this problem in the fields of educational and public health.

هم هگیری کووید- 19 در شهر رشت در سال 14اختلال استرس پس از آسیب حساسیت اضطرابی و تا بآوری در کارکنان خدمات درمانی طی

Background The coronavirus disease 2019 (COVID-19) pandemic has extensively affected the public physical and mental health, especially the medical staff working in the COVID-19 wards of hospitals. Objective This study aims to evaluate the relationship between post-traumatic stress disorder (PTSD), anxiety sensitivity (AS), and resilience among hospital medical staff in Rasht, during the COVID-19 pandemic. Methods This is an analytical cross-sectional study. The study population consists of all medical staff working in the COVID-19 wards of Poursina and Razi hospitals in Rasht, Iran. Of these, 94 participated who were selected using a convenience sampling method. Weathers et al.'s post-traumatic stress disorder checklist, Reiss et al.'s AS index, and Connor-Davidson resilience scale were used for data collection. The data were analyzed using Pearson correlation test and multiple linear regression analysis. Results There was a significant negative correlation between PTSD and resilience (r = -0.405, P = 0.001). and a significant positive correlation between PTSD and AS (r=0.633, P=0.001). The results of multiple analyses showed that resilience (B=-0.208, P=0.004) and AS (B=0.574, P=0.001) could significantly explain the PTSD in medical staff. Conclusion Resilience can be an important protective factor against PTSD in hospital medical staff during the COVID-19 pandemic. The medial staff with AS may experience the symptoms of PTSD more.

Factors Affecting E-Wallet Usage in Sarawak

E-wallet is a part of the fintech solution that provides customer convenience through fast speed of transaction using RFID technology, QR code and stable internet connection. The usage of e-wallet in Malaysia and Sarawak has risen in the past few years and accelerated during the Covid-19 pandemic crisis due to the needs of contactless payment to reduce the spread of the virus. However, the usage of e-wallet and factors that affect the usage of e-wallet in Sarawak has not been identified through research. Therefore, this research aims to identify the factors influencing e-wallet usage and evaluate the factors using Technology Acceptance Model (TAM) affecting Sarawakians perception towards e-wallet in general and towards Sarawak's own e-wallet app, SarawakPay. Data collection of online questionnaires have been distributed and 111 responses was analyzed using descriptive analysis and correlation analysis as well as multiple linear regression to obtain mean and standard deviation values. Five formulated hypotheses were tested using multiple linear regression showing a significant and positive relationship among the TAM variables. Descriptive analysis results have also shown that top factors affecting usage of SarawakPay is performance expectancy and perceived value. The result from this research indicates that the usage of e-wallet in Sarawak is still at medium level. The evaluation using TAM shows that there is significant relationship between the Technological and Social factors to the usage of e-wallet in Sarawak. © 2022 IEEE.