ABSTRACT
User-Generated-Content (UGC) has gained increasing attention as an important indicator of business success in the tourism and hospitality sectors. Previous literature has analyzed travelers' satisfaction through quantitative approaches using questionnaire surveys. Another direction of research has explored the dimensions of satisfaction based on online customers' reviews using the machine learning approach. This study aims to present a new method that combines machine learning and survey-based approaches for customers' satisfaction analysis during the COVID-19 outbreak. In addition, we investigate the moderating role of service quality on the relationship between hotels' performance criteria and customers' satisfaction. To achieve this, the Latent Dirichlet Allocation (LDA) was used for textual data analysis, k-means was used for data segmentation, dimensionality reduction approach was used for the imputation of the missing values, and fuzzy rule-based was used for the prediction of satisfaction level. Following that, a survey-based approach was used to validate the research model by distributing the questionnaire and analyzing the collected data using the Structural Equation Modeling technique. The result of this research presents important contributions from the methodological and practical perspectives in the context of customers' satisfaction in tourism and hospitality during the COVID-19 outbreak. The outcomes of this research confirm the significant influence of the quality of services during the COVID-19 crisis on the relationship between hotel services and travellers' satisfaction.
ABSTRACT
The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs);leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.
ABSTRACT
The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.
ABSTRACT
The global business scenario seems to be gloomy due to the economic uncertainty caused by the COVID-19 pandemic. The COVID-19 pandemic has impacted many economic sectors and a country's national GNP, including the tourism industry. The question is whether the influencing factors for firms involved in the tourism industry, especially in developing countries, ensure their future survival. The main aim of this paper is to examine the role of internal resources and external environmental factors on the firm performance of small–medium enterprises (SMEs) in the tourism industry, with a specific focus on SME hotels. Based on a survey carried out among hotel owners or key managerial staff in Saudi Arabia and using partial least squares (PLS), the study aimed to attain the objective of this study. Results from the statistical analysis indicate that both internal and external environmental factors have a positive impact on the performance of SME hotels. The results also revealed a more significant impact from the external environmental factors in influencing firm performance than internal resources. Implications, limitations, and recommendations for future scientific investigation are put forward.
ABSTRACT
Purpose>This study explored the relationship between local food consumption value and satisfaction with local food, leading to behavioral intention. Moreover, tourist's involvement is used as a mediator, and COVID-19 fear moderates between satisfaction with local food and behavioral intention.Design/methodology/approach>Structural equation modeling (SEM) technique presents researchers with extra flexibility and better research conclusions. This study used Partial Equation Modeling SEM to test the proposed hypotheses. The convenience sampling technique was used to collect data, and 339 questionnaires were part of the final analysis.Findings>The results reveal that local food consumption value is positively associated with local food satisfaction except for emotional value. Satisfaction on local food significantly determined tourist's involvement and behavioral intention. Tourist's involvement is positively related to behavioral intention. Despite this, COVID-19 fear significantly decreases behavioral intention. Tourist's involvement significantly mediates, and COVID-19 fear moderates between satisfaction with local food and behavioral intention significantly.Practical implications>The results of our research will support scholars and practitioners to recognize the importance of factors that influence people's intention to eat local food. Besides, our research offers a significant policy to get maximum benefits for the tourism industry in Pakistan.Originality/value>To the author's knowledge, our study initially incorporates a research model in the COVID-19 pandemic and covers local food consumption value, satisfaction on local food, tourist's involvement and COVID-19 fear to determine the behavioral intention of people to eat local food. Besides, consumption value theory was used to build a research framework.
ABSTRACT
Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers' online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. This research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. The results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more efficient in addressing the sparsity problem and presenting more accurate results. The results of the method evaluation are presented and discussed, and several suggestions are provided for future studies. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Tourism and hospitality have been recognized as leading economic sectors globally. Before the outbreak of COVID-19, it was estimated that the tourism and hospitality sector was growing by around 4% each year. Although the economic-efficiency-led hypothesis of the tourism and hospitality sector is strong, there is another perspective related to tourism and hospitality. That is, tourism and hospitality are not as 'green';as they were supposed to be. Indeed, this sector is known for its outsized carbon footprint. It is estimated that, if not managed efficiently, the GHG contribution of the tourism sector will grow in the future. Specifically, the hotel business accounts for 1% of total global greenhouse gas emissions (GHG), which is huge. Responding to these significant issues, this study investigates the relationship between the corporate social responsibility (CSR) activities of a hotel enterprise and employees' pro-environmental behavior (PEB). The mediating role of environmental-specific transformational leadership (ESTFL) and the moderating role of green perceived organizational support (GPOS) were also tested in the above relationship. The data were collected by the employees through a self-administered questionnaire. The hypothesized relations were statistically investigated by using structural equation modeling (SEM). The findings revealed that CSR activities of a hotel not only influence employees' PEB directly, but the mediating role of ESTFL was also significant. At the same time, the conditional indirect role of GPOS was also confirmed. This study offers different theoretical and practical insights, which have been discussed in detail.
ABSTRACT
Online reviews have been used effectively to understand customers' satisfaction and preferences. COVID-19 crisis has significantly impacted customers' satisfaction in several sectors such as tourism and hospitality. Although several research studies have been carried out to analyze consumers' satisfaction using survey-based methodologies, consumers' satisfaction has not been well explored in the event of the COVID-19 crisis, especially using available data in social network sites. In this research, we aim to explore consumers' satisfaction and preferences of restaurants' services during the COVID-19 crisis. Furthermore, we investigate the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. We applied a new approach to achieve the objectives of this research. We first developed a hybrid approach using clustering, supervised learning, and text mining techniques. Learning Vector Quantization (LVQ) was used to cluster customers' preferences. To predict travelers' preferences, decision trees were applied to each segment of LVQ. We used a text mining technique; Latent Dirichlet Allocation (LDA), for textual data analysis to discover the satisfaction criteria from online customers' reviews. After analyzing the data using machine learning techniques, a theoretical model was developed to inspect the relationships between the restaurants' quality factors and customers' satisfaction. In this stage, Partial Least Squares (PLS) technique was employed. We evaluated the proposed approach using a dataset collected from the TripAdvisor platform. The outcomes of the two-stage methodology were discussed and future research directions were suggested according to the limitations of this study.
ABSTRACT
Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers' trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers' reviews in TripAdvisor's COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k-means to segment the users' data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample.
ABSTRACT
To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits.
ABSTRACT
The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers' satisfaction through survey-based and online customers' reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers' reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers' satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers' reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers' concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers' reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.
ABSTRACT
COVID-19 is a kind of SARS-CoV-2 viral infectious pneumonia. This research aims to perform a bibliometric analysis of the published studies of vitamins and trace elements in the Scopus database with a special focus on COVID-19 disease. To achieve the goal of the study, network and density visualizations were used to introduce an overall picture of the published literature. Following the bibliometric analysis, we discuss the potential benefits of vitamins and trace elements on immune system function and COVID-19, supporting the discussion with evidence from published clinical studies. The previous studies show that D and A vitamins demonstrated a higher potential benefit, while Selenium, Copper, and Zinc were found to have favorable effects on immune modulation in viral respiratory infections among trace elements. The principles of nutrition from the findings of this research could be useful in preventing and treating COVID-19.
Subject(s)
Clinical Trials as Topic , Trace Elements/pharmacology , Vitamins/pharmacology , Bibliometrics , COVID-19/epidemiology , Humans , Immune System/drug effectsABSTRACT
The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers' decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers' decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers' decision making. We conclude with the implications of our study for future research and practice.
ABSTRACT
Emerging in China in late 2019, the new COVID-19 virus infection epidemic is growing rapidly and new cases are reported around the world. The first cases were linked to a wet market, and subsequently, the virus has spread rapidly in China through human-to-human transmission, and the universal impact of the COVID-19 virus is now spreading worldwide. The disease originated from COVID19 is a type of viral pneumonia that is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Currently, no clinically approved antiviral drugs have been introduced for SARS-CoV-2 infection. Identifying the mechanism of action of the virus and its interaction with the immune system will help prevent and treat the disease. In other words, understanding the disease and its effect on the immune system will improve disease management. The immune system has a fundamental protective function against most infectious diseases such as SARS-CoV-2. This study investigates the effectiveness of Complementary and Alternative Medicines (CAMs) in boosting immune response against infection diseases. The role of vitamins in COVID-19 in its early stages is also investigated and the previous research findings are reported. The result of this study is important especially for the patients with COVID-19 who may found CAMs as effective way in boosting immune response against this virus and useful option for management and treatment of COVID-19 in its early stages. We suggest that further studies through consumers' experience analysis on CAMs are required to come to robust conclusions in the effectiveness of CAMs for management and treatment of COVID-19. [ABSTRACT FROM AUTHOR] Copyright of Biomedical Research (0970-938X) is the property of Scientific Publishers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)