A multi-method analytical approach to predicting young adults' intention to invest in mHealth during the COVID-19 pandemic.

Mobile-based health (mHealth) systems are proving to be a popular alternative to the traditional visits to healthcare providers. They can also be useful and effective in fighting the spread of infectious diseases, such as the COVID-19 pandemic. Even though young adults are the most prevalent mHealth user group, the relevant literature has overlooked their intention to invest in and use mHealth services. This study aims to investigate the predictors that influence young adults' intention to invest in mHealth (IINmH), particularly during the COVID-19 crisis, by designing a research methodology that incorporates both the health belief model (HBM) and the expectation-confirmation model (ECM). As an expansion of the integrated HBM-ECM model, this study proposes two additional predictors: mobile Internet speed and mobile Internet cost. A multi-method analytical approach, including partial least squares structural equation modelling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA), and machine learning (ML), was utilised together with a sample dataset of 558 respondents. The dataset-about young adults in Bangladesh with an experience of using mHealth-was obtained through a structured questionnaire to examine the complex causal relationships of the integrated model. The findings from PLS-SEM indicate that value-for-money, mobile Internet cost, health motivation, and confirmation of services all have a substantial impact on young adults' IINmH during the COVID-19 pandemic. At the same time, the fsQCA results indicate that a combination of predictors, instead of any individual predictor, had a significant impact on predicting IINmH. Among ML methods, the XGBoost classifier outperformed other classifiers in predicting the IINmH, which was then used to perform sensitivity analysis to determine the relevance of features. We expect this multi-method analytical approach to make a significant contribution to the mHealth domain as well as the broad information systems literature.

The Russia-Saudi Arabia oil price war during the COVID-19 pandemic.

The COVID-19 pandemic damaged crude oil markets and amplified the consequences of uncertainty stemming from the Russia-Saudi Arabia oil price war in March-April of 2020. We investigate the impacts of the oil price war on global crude oil markets. By doing so, we use the daily futures and spot prices in three major crude oil markets - West Texas Intermediate, European Brent, and Oman - to perform a systematic analysis of the impacts of the oil price war on them. The event study method, a well-established analytical tool to measure the impacts of a given event on markets, is used in this study. The results indicate that information leakage plays an important role in the impacts of the price war. The outbreak of and truce following the price war have asymmetrical impacts on the markets; negative impacts generated by information leakage during the outbreak are generally more durable than the positive ones it generated during the truce. Furthermore, the magnitude of the impacts on futures markets is negatively correlated with the time-to-maturity of futures. Finally, negative crude oil prices affect West Texas Intermediate crude oil markets the most. Our findings generally show that market participants could perceive and assimilate market changes and adjust their expectations, which restrained the impacts that should have occurred within the oil price war.

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.

Recently, the destructive impact of Coronavirus 2019, commonly known as COVID-19, has affected public health and human lives. This catastrophic effect disrupted human experience by introducing an exponentially more damaging unpredictable health crisis since the Second World War (Kursumovic et al. in Anaesthesia 75: 989-992, 2020). Strong communicable characteristics of COVID-19 within human communities make the world's crisis a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection from spreading (e.g., by isolating the patients). This situation indicates improving the auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a widely used technique for pneumonia because of its expected availability. The artificial intelligence-aided images analysis might be a promising alternative for identifying COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on the most recent modified CNN architecture (DenseNet-121) to predict COVID-19. The results outperformed 92% accuracy, with a 95% recall showing acceptable performance for the prediction of COVID-19.

Impact of "e-Learning crack-up" perception on psychological distress among college students during COVID-19 pandemic: A mediating role of "fear of academic year loss".

While literature reveals the positive perception of e-Learning, this study examined and assessed the impact of e-Learning crack-up perceptions on psychological distress among college students during COVID-19 pandemic. Kessler psychological distress scale (K10) was used to evaluate stress symptoms. This study first conducted an online focus group discussion (OFGD) with the target population to develop the scale of "e-Learning crack-up" and "fear of academic year loss". Afterward, a questionnaire was developed based on OFGD findings. An online survey was conducted amongst college students in Bangladesh using a purposive sampling technique. Results show that "e-Learning crack-up" perception has a significant positive impact on student's psychological distress, and fear of academic year loss is the crucial factor that is responsible for psychological distress during COVID-19 lockdown. This study can provide an understanding of how "e-Learning crack-up" and "Fear of academic year loss" influence college students' mental health. Theoretically, this study extends and validated the scope of Kessler's psychological distress scale with two new contexts. Practically, this study will help the government and policymakers identify the student's mental well-being and take more appropriate action to address these issues.

Investigating Factors Affecting Elderly's Intention to Use m-Health Services: An Empirical Study.

BACKGROUND: m-Health as an important part of e-health has recently become one of the most influential initiative in healthcare sector all over the world. In developing countries healthcare service providers started to provide m-health services from the last few years. Despite the widespread acceptance of mobile phones, the adoption of m-health among elderly is significantly low in developing countries. However, little research has been conducted to explore factors influencing elderly's intention to use m-health services particularly in developing countries. The objective of this study is to identify the factors that influence the elderly's intention to use m-health services. MATERIALS AND METHODS: To assess elderly's intention to use m-health services, this study applied the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from participants of age 60 years and above. The partial least square method based on structural equation modeling was used to analyze data. RESULTS: The study found that performance expectancy, effort expectancy, social influence, and perceived credibility (p < 0.05) had significant influence on elderly's intention to use m-health services. However, facilitating condition (p > 0.05) had no significant influence on elderly's intention to use m-health services. CONCLUSIONS: The findings of this study may become beneficial for the governments, policy makers, and healthcare service providers in developing countries.

Investigating the determinants of Chinese adult children's intention to use online health information for their aged parents.

BACKGROUND: The increasing number of older people and the dissemination of health information via the Internet have emerged and both are challenging to Chinese society. Available online health information highlights the importance of decision making processes, specially in relation to the elderly who almost have no online presence and depend on their adult children's help. The researchers mostly focus on parents' health information search for their children, however, they overlook the adult children's intention to use online health information for their aged parents. OBJECTIVE: This study fills this gap by extending the Theory of Planned Behavior (TPB) to identify the determinants of adult children's intention to use online health information for their aged parents. METHOD: Relying on survey method, the data were collected from teachers and students at different participating Universities in Wuhan, China. The Partial Least Squares (PLS), a structural equation modeling technique, was employed to test the research model. RESULTS: This study found that attitude, subjective norm, perceived behavioral control and risk (p<0.05) were the predictors of intention to use online health information, whereas, trust (p>0.05) was not listed among the predictors. CONCLUSIONS: This study is a significant addition to the literature, in that it confirms the utility of the TPB with additional variables in predicting adults' children intention to use online health information for their aged parents.

Investigating factors influencing the adoption of e-Health in developing countries: A patient's perspective.

PURPOSE: The aim of the study was to investigate factors that influence the adoption and use of e-Health applications in Bangladesh from citizens' (patients') perspectives by extending the technology acceptance model (TAM) to include privacy and trust. METHODS: A structured questionnaire survey was used to collect data from more than 350 participants in various private and public hospitals in Dhaka, the capital city of Bangladesh. The data were analyzed using the partial least-squares (PLS) method, a statistical analysis technique based on structural equation modeling (SEM). RESULTS: The study determined that perceived ease of use and perceived usefulness and trust (p < 0.05) were significant factors influencing the intention to adopt e-Health. Privacy (p > 0.05) was identified as a less significant factor in the context of e-Health in Bangladesh. The findings also revealed that gender was strongly associated with the adoption and use of e-Health services. CONCLUSIONS: The findings of the present study contribute to the development of strategies and policies to enhance e-Health services in Bangladesh. Furthermore, as a result of the generic approach used in this study, the acceptance model developed can be easily modified to investigate the adoption of e-Health in other developing countries.

Cultural Influence on Adoption and Use of e-Health: Evidence in Bangladesh.

BACKGROUND: E-health is an important initiative among the public and private hospitals in Bangladesh in the last few years. The factors influencing e-health adoption have been a well-investigated research area in both developed and developing countries. However, there have been only a few studies exploring the role of cultural factors in the adoption and use of e-health, particularly in developing countries. In this study, we investigated the influence of culture on the adoption of e-health in Bangladesh. MATERIALS AND METHODS: This study developed a more adequate research framework by integrating Hofstede's cultural dimension model and the Technology Acceptance Model (TAM). A structured questionnaire was used to collect data from respondents in different private and public hospitals in Bangladesh. The partial least squares method, a statistical analysis technique based on the Structural Equation Model, was used to analyze the collected data. RESULTS: The study found that cultural dimensions such as Power Distance, Masculinity, and Restraint had significant impacts on Intention to Use e-Health, whereas Uncertainty Avoidance, Collectivism, and Pragmatism had no significant impact on Intention to Use e-Health in Bangladesh. The results also revealed that Perceived Usefulness was a significant indicator of e-health adoption decisions, whereas Perceived Ease of Use was an insignificant predictor of e-health adoption. CONCLUSIONS: The findings of this study may assist governments, organizations, and policy makers to understand the key factors affecting e-health adoption and to develop strategies and policies to enhance e-health services in Bangladesh.

Multistep-ahead air passengers traffic prediction with hybrid ARIMA-SVMs models.

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy.

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Electricity load forecasting using support vector regression with memetic algorithms.

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.