Saudi Arabia's Vision 2030 seeks to transition towards a cashless society and increase non-cash transactions to 70% by 2025. The COVID-19 pandemic has further accelerated cashless activities in Saudi Arabia, with e-payments increasing by 75% in the past year. This study explores consumer behavior towards using e-wallets in the post-COVID-19 era by employing the extended Technology Acceptance Model (TAM). The results of an online survey conducted among 303 Saudi citizens were analyzed using SPSS. This study examines the correlation coefficients between the variables and conducts an ANOVA to determine the influence of all variables on consumer behavior towards e-wallets in Saudi Arabia post-COVID-19. The findings indicate that consumer behavior is positively influenced by perceived usefulness, perceived ease of use, lifestyle compatibility, and traceability, whereas cost does not significantly impact the use of e-wallets. Additionally, the results show that approximately 28.1% of the respondents used e-wallet services due to the pandemic. This study adds to the literature by expanding on previous work on the topic and providing detailed insights into the factors of e-wallet acceptability in Saudi Arabia.

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

The healthcare system is a vital element for any community, as it extremely affects the socio-economic development of any country. The current study aims to assess the performance of the healthcare systems of the countries above fifty million citizens in facing the spread of the COVID-19 pandemic since late December 2019. For this purpose, seven scenarios were adopted via the DEA methodology with six variables, which are the number of medical practitioners (doctors and nurses), hospital beds, Conducted Covid-19 tests, affected cases, recovered cases, and death cases. To shed light on the relative efficiency of drivers, the Tobit analysis was used. Besides, the study carried out various statistical tests for the DEA models' findings to validate the choice of the variables and the obtained scores. The DEA results reveal that less than half of the considered countries are relatively efficient. Moreover, the Tobit regression analysis showed that the main impact on the efficiency scores was due to the number of affected and recovered cases. Finally, the results of the tests of Spearman, Mann-Whitney U, and Kruskal-Wallis H indicate the internal validity and robustness of the chosen DEA models. The current study findings raise important implications, which can be helpful for decision makers regarding continuous improvement of performance, in which the findings assert the importance of achieving the best practices regarding relative efficiency through the linkage between the healthcare systems’ resources, and the needed outputs.

This study is aimed to analyze the variables of external environment, organizational resources, organizational capabilities, and business competitiveness. The study priorities strategy and programs as basic for developing the competitiveness of creative industry in Indonesia. The number of respondents who participated in this survey was 200, while the key informants were 10 people. Method of analysis involved descriptive statistics, and analytical hierarchy process (AHP). Then, data were processed by using both IBM SPSS 24, and Expert Choice 11. The results show that creative industry competitiveness has relatively declined during covid-19 pandemic. Although external environment support, organizational resources, and organizational capabilities were at good shape. The priority strategy for competitiveness development should be focused on strengthen the organizational capabilities by considering the dynamics of external environmental factors and internal resource capacity. Then, the priority programs developed sequentially are increasing partnerships with suppliers, distributors and customers, analyzing social and economic aspects, developing human resource capacity, and using information and communication technology in products and services. In addition, another important program is strengthening the supply chain system.

This study analyzed the impact magnitudes and patterns of several intervention events, including eight earthquakes and Covid-19 pandemic, on the number of unloading and loading goods in the three main ports and airports in West Nusa Tenggara Province during 2015-2020. The multi-input intervention models are performed for twelve series data obtained from BPS-Statistics of West Nusa Tenggara Province. The results from the estimated response values show that generally the number of unloading and loading in the three main ports and airports have experienced mixed impact, i.e., negative, and positive impacts. As the main concern in this study, the negative impacts were more experienced by the number of unloading and loading goods in airports than in ports indicating that the supply chain in airports was more vulnerable to intervention. Lombok International Airport and Sultan M Kaharuddin Airport received the most negative impact during the period. Most intervention events have delayed impact patterns that are more experienced by the three airports than the three ports. Started in March 2020, Covid-19 produced the widest and biggest negative impacts. These impacts are even bigger than the impacts produced by the severe earthquakes that occurred in August 2018.

This research aims to show how decision sciences can make a significant contribution on handling the supply chain problem during Covid-19 Pandemic. The paper discusses how robust optimization handles uncertain demand in agricultural processed products supply chain problems within two scenarios during the pandemic situation, i.e., the large-scale social distancing and partial social distancing. The study assumes that demand and production capacity are uncertain during a pandemic situation. Robust counterpart methodology is employed to obtain the robust optimal solution. To this end, the uncertain data is assumed to lie within a polyhedral uncertainty set. The result shows that the robust counterpart model is a computationally tractable through linear programming problem. Numerical experiment is presented for the Bandung area with a case on sugar and cooking oil that is the most influential agricultural processed products besides the main staple food of the Indonesian people, rice.

COVID-19 has caused severe disruptions in global economic activities, and its impacts on stock markets cannot be overemphasized. The study employs market model and event study approach with four events (WHO announcement of COVID-19 as a global health emergency, confirmed infections, confirmed deaths, and vaccination) to examine the reactions of four African regional blocs’ markets to the pandemic from September 1, 2019, to August 31, 2021, to estimate the average abnormal returns of each regional bloc. On the day of the WHO announcement, we document insignificant negative average abnormal returns in the Northern bloc. We also document significant negative average abnormal returns for infections in all but the Northern bloc on the event day. The Western bloc generated the highest significant negative average abnormal return (-43 per cent) on the day COVID-19 death was confirmed on the continent. We finally document insignificant average abnormal returns from weeks 1 to 20 after the first vaccination in the Northern and Eastern blocs. The study recommends that investors, portfolio managers, and speculators not panic during similar pandemics since they can generate significant abnormal returns and diversify their investment holdings across the four regional blocs in Africa, as demonstrated by the COVID-19 pandemic.

The purpose of this study was to examine the effects of key organizational readiness dimensions and extrinsic motivation on the teaching staff’s intention toward the continued voluntary use of e-learning innovations post COVID-19 pandemic. These effects have not received considerable focus in the extant e-learning literature. To mitigate this lack, an integrated model encompassing dimensions from several organizational readiness frameworks and a motivational theory was developed. The model postulated these dimensions as direct determinants of the e-learning innovations continuance intention. A structured questionnaire-based survey was conducted to empirically assess the developed model. The intended population for this survey was composed of teaching staff at a Saudi higher education institution characterized by a wide adoption of e-learning innovations during the pandemic. The 233 valid responses obtained from this population were analyzed using the structural equation modeling method. The results indicated that only two organizational readiness dimensions (i.e., teaching staff readiness and administrative support) and extrinsic motivation were significant positive drivers of the continuance intention to use e-learning innovations. According to these findings, the study emphasizes that the key e-learning stakeholders should develop effective policies and procedures that reinforce the roles of the examined dimensions in promoting such continuance intention, which represents a crucial indicator for the successful implementation of the adopted innovation.

This study examines the effect of the quality of e-services provided by e-commerce on customer satisfaction and loyalty during the COVID-19 pandemic. This study used a quantitative approach to involve 118 respondents who traded in online markets. The sampling method used is purposive sampling. The data were analyzed using a structural equation model using the partial least squares technique. Research results indicate that out of the five hypothetical relationships raised by the researcher, one relationship related to remuneration was found to lack a statistically significant positive effect. Besides that, the remaining four hypothetical relationships, including efficiency, confidentiality, accountability, and customer satisfaction, demonstrated positive and statistically significant. The implication of this study highlights the need to cultivate and improve the quality of e-services in the online market, especially in the context of the COVID-19 pandemic. In this way, businesses can provide consumers with a rich shopping experience, enhancing customer satisfaction and laying the foundation for long-term customer loyalty.