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In the last decade, medical tourism has increased tremendously. After assessing the alarming levels of spread and severity, the World Health Organization declared the new coronavirus (COVID-19) outbreak as a global pandemic. The COVID-19 outbreak has affected many of the major destinations and sources of medical tourism. Patients who have planned to travel abroad for affordable medical care are canceling or postponing trips due to this pandemic. During this pandemic, it is important to obtain new medical tourists through effective destination branding. Neuromarketing techniques and tools have been effective in studying the consumers’ behaviors, particularly in the decision-making process. It is found that neuromarketing tools can assist the decision-makers to accurately measure the consumers’ behaviors in relation to the conventional techniques. This paper investigates the role of neuromarketing techniques in the event of the COVID-19 outbreak for destination marketing. Accordingly, a decision-making model is developed for destination marketing in the event of the COVID-19 outbreak. The data is analyzed by a fuzzy multi-criteria decision-making model, Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The results showed that accuracy and quality of information are the most important factors in implementing neuromarketing techniques for medical tourism destination marketing in the event of the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Wearable devices have attracted a great deal of attention and popularity among academics and decision-makers in the last decade. The potential of wearable technology to improve health efficiency and cut healthcare costs has been demonstrated in several studies. Wearable devices have a great value for detecting, tracking, and controlling the spread of infectious diseases such as COVID-19. Based on the confirmation of expectations and technology acceptance theories, this study has developed a theoretical model to study user perceptions of wearable healthcare devices. The data collected from 163 study samples were examined using the Classification and Regression Tree (CART) technique. The study results showed that the security and privacy factor is important for the adoption of wearable healthcare devices in the event of COVID-19. [ABSTRACT FROM AUTHOR]

In this study, we present a multi-criteria decision-making approach for the evaluation of the performance of airline service quality. Our method is developed using Analytic Hierarchy Process (AHP) technique. We systematically reviewed the previous studies which have been conducted for airline service quality performance. Accordingly, a set of quality criteria under ten dimensions were identified to be evaluated by the proposed approach. By the use of AHP, we select the most important quality criteria for assessing the service quality performance of the airport. The evaluation results of the main dimensions showed that safety and reliability, passenger service, and airline website are the most important quality criteria for improving airline efficiency. In addition, the results by AHP analysis showed that baggage delivery time, up-to-date aircraft and in-flight facility, courtesy of employees, on-time performance, online purchase process, value for money, handling of delayed flight, promptness of booking, the convenience of buying ticket and loyalty program are the most important indicators respectively in passenger service, in-flight service, employees safety and reliability, airline website, pricing policy, responsiveness, booking service, ticketing service, and promotions. The finding of this study and the proposed multi-criteria decision-making approach make important contributions to the literature by helping to identify quality criteria for improving the service quality in airports. [ABSTRACT FROM AUTHOR]

As a novel technology, Augmented Reality (AR) has gained a lot of attention in the education sector. Augmented reality in the education field offers new ways of learning and teaching. Augmented reality eliminates the gap between the real world and the virtual world. Regardless of the increasing number of studies in the augmented reality area, there is still a lack of studies that integrate and synthesize this body of knowledge. Therefore, this study aimed to conduct a systematic literature review to gain insights into existing empirical studies of AR and understanding the current state of research regarding AR in education. From the review of the studies, we recognized 40 primary studies focusing on AR in the education field. The results of this study disclose a growth in the number of augmented reality studies during the last three years. Moreover, several challenges and numerous advantages of using augmented reality technology in the educational setting were extracted and discussed. The results of this study can help scholars by providing a roadmap of augmented reality technology and highlighting guidelines for the successful implementation of the augmented reality systems in educational settings. [ABSTRACT FROM AUTHOR]

This research investigates the effect of environmental sustainability on students' wellbeing and learning performance. A comp rehensive literature is conducted to identify those environmental sustainability factors for the development of a new model for learning performance. We measure the effect of environmental sustainability on the students wellbeing. We aim to reveal the interrelationships among the environmental sustainability factors to better measure the effect of wellbeing. In addition, we find the relationship between wellbeing and learning performance. To do so, we adopt Decision Making Trial and Evaluation Laboratory system to show whether an improvement on the students wellbeing will effect on the students learning effectiveness. The data is collected from the female students at a university in Malaysia. The results showed that students' learning performance can be significantly influenced by environmental sustainabil ity. The proposed model will help the universities managers and practitioners to consider the best solutions to improve the environmental sustainability of universities for students' wellbeing which is the key target of managing the socio-economic systems. [ABSTRACT FROM AUTHOR]

Since social and environmental conditions have been changed dramatically in recent years, the spectrum of diseases caused by infections is also changing at a rapid pace. The Internet of Things (IoT) is a new concept which enables users with wearable devices to monitor healthcare. Wearable devices have attracted a great deal of attention and popularity among academics and industry in the last decade. The potential of wearable technology has previously been proven in improving health efficiency and reducing healthcare costs. Wearable devices are believed to be of a very strong value, both for detection and for the tracking and control of the spread of infectious diseases such as COVID-19. Regardless of the importance of wearable devices, only a few number of studies have revealed the usefulness of wearable devices in COVID-19 outbreak. As many of people are not aware of wearable health devices advantages as a mean of tracking their health, as well as using online health communities in critical conditions with limited access to the doctors in hospitals, these types of healthcare technology should be widely introduced and advertised through online retailing shops to improve the individuals’ awareness and knowledge of these devices. This can be effectively done by knowledge sharing through social media and intelligent agents in online retailing websites. One of the intelligent systems in online retailing websites is recommendation agents which would be helpful in this situation. In case of wearable health devices to be recommended to the users the in the event of outbreaks, the recommendation systems in online retailing websites must be adaptable and aware to the event of outbreaks and consider the users’ demands in this situations. This study aims to investigate the advantages of wearable devices in the event of outbreaks or disasters for healthcare. In addition, the role of recommendation agents in introducing and recommending these devices is explored. Finally, this study reveals some shortcomings of current recommendation agents and provides appropriate solutions for effectiveness of these systems in the event of COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

To tackle the current environmental problems, the adoption of Green IT is considered as a reasonable effort for organizations which can improve their economic performance. To investigate the previous studies that have been done on Green IT, a Systematic Literature Review (SLR) was undertaken to identify, evaluate and interpret all the existing research studies related to the specific research topic, phenomenon of interest, or study area. The data was collected from the primary studies published during the year 2010 to 2019 in the form of the conference, journals, and other online sources. A total of 135 primary studies were included based on the defined inclusion, exclusion, and quality criteria. This research summarises and organizes the existing literature published related to Green IT based on the defined keywords and research question. This will provide a roadmap to guide future studies on Green IT and highlight directions for the successful implementation of Green IT in organizations. [ABSTRACT FROM AUTHOR]

Advances in technology have affected all sectors of the economy and environments of countries around the globe. Research studies of the relationship between environmental sustainability, information technology (IT), and information systems (IS) under the terms of Green IT and Green IS have grown exponentially in the IS research community in the past seven years. This paper provides a systematic academic literature review of the organization-level adoption of Green IT/IS to understand and summarize the existing studies related to the field with the aim of understating better the research field, categorizing the studies and identifying some research opportunities and gaps for future research. To provide a robust study, we have applied the upper echelon theory (UET) to formulate potential research questions for future researchers. [ABSTRACT FROM AUTHOR]

Hospital Information System (HIS) is an integrated Information System (IS) designed to enhance clinical, financial and administrative functions of a hospital. HIS is vital to the healthcare sector especially in public hospitals as they need to serve the public with high-quality healthcare services. Therefore, it is important to investigate the adoption of HIS in public hospitals. The aim of this study is to provide a literature review of adoption theories (Technology Organization Environment Framework, Institutional Theory and Human Organization Technology Fit Model) in IS domains and potential factors that influencing adoption of HIS framework in public hospitals with emphasis on organizational context perspective. [ABSTRACT FROM AUTHOR]

Global research is being directed towards addressing sustainable development topics. Organizations face an increasing pressure for practicing environment-friendly behaviour. Green Information Technology (IT) is an example of such practices that aims to enhance the organizational performance and sustainability. Regardless of the significance of this technology, inadequate number of studies have focused on its adoption in Malaysian organizations. Therefore, the aim of this study is to investigate the factors that influence the Green IT adoption practices and its impact on organizational performance through the lens of Resource-Based View (RBV) theory. Based on previous literature and the Resource-Based View (RBV) theory, six factors were identified for adopting Green IT in Malaysian organization context. The research model hypothesizes that institutional pressure, organizational strategy, openness, environmental performance, economic performance, and competitive advantage can affect the individual perception of Green IT adoption. The proposed hypotheses in this study will be tested in the upcoming future. [ABSTRACT FROM AUTHOR]

Diabetes has been one of the leading health problems in all over the world. Diabetic retinopathy is the most common retinal vascular disease. Supervised data mining techniques have been successful in detecting diabetic retinopathy through a set of datasets. However, the most methods developed by supervised methods do not support the ensemble learning of data. The aim of this paper is to take the advantages of ensemble learning and develop a new method for diabetic retinopathy using data mining techniques. We use Non-linear Iterative Partial Least Squares for data dimensionality reduction, Self-Organizing Map for clustering task and ANFIS ensemble to classify unlabeled retinal images with high accuracy. We evaluate our method on a publicly available Messidor dataset and present our results in comparison with the latest results of previous studies. For classification task, features of retinal images used for experimental analysis have been extracted by two algorithms, anatomical part recognition and lesion detection. The experimental analysis showed that the proposed method is robust in classifying the retinal images with Accuracy= 0.915, Sensitivity=0.946 and Specificity=0.917. The results of experimental analysis also demonstrated that our method performance is superior to Neural Network (NN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Trees (DT), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The hybrid intelligent system has potential to assist medical practitioners in the healthcare practice for early detection of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

This study aims to provide a multi-criteria decision making approach to evaluate the hotel website through a set of important quality factors. We use Loiacono's WebQual™ scale to examine the quality of hotel websites. The data is collected from the users of the world's largest travel site, TripAdvisor. We adopt a multi-criteria decision making approach, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to perform data analysis on the collected data. Results revealed that trust, response time, transaction capability and informational fit-to-task are the most important factors from the customers' perspectives. These findings offer useful implications for hotel website managers to consider the identified factors in the design of websites to attract and retain the consumers for long term. [ABSTRACT FROM AUTHOR]

In the current business practice, recommender agents are widely used in e-commerce domain to actively recommend the right items to online users. Traditional Collaborative Filtering (CF) recommender systems are developed based on single ratings which are used to match similar users based on their past ratings. Although these types of recommender systems have been successfully implemented in healthcare context, however the use of multi-criteria CF for health product recommendation has been rarely explored. The aim of this paper is to propose a new recommendation method based on multi-criteria CF to enhance the predictive accuracy of recommender systems in healthcare domain using clustering, dimensionality reduction and prediction machine learning methods. To do so, we develop a knowledgebased system to predict the users' overall assessment value of health products using Mamdani's fuzzy inference technique. Accordingly, we used Classification and Regression Trees (CART) to discover the fuzzy rules to be used in the fuzzy rule-based technique. To improve the recommendation accuracy of proposed multi-criteria CF, we apply a clustering technique and ensembles of fuzzy rule-based prediction models. We also use a robust dimensionality reduction technique, Higher Order Singular Value Decomposition (HOSVD), to find the similar users and products in each cluster to solve the sparsity issue. We test the accuracy of recommendation method on two health products datasets with three criteria, Product Brand, Product Price and Product Quality, crawled from the online health products stores. Our experiments confirm that the proposed method can be a promising and effective intelligent system for healthcare products recommendation. [ABSTRACT FROM AUTHOR]