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Many studies have analyzed the asymmetric relationship between hotel service quality attributes and satisfaction, and tried to find consumers' satisfiers and dissatisfiers, aiming at enhancing satisfaction more efficiently. However, the impact of COVID-19 has led to changes in consumers' expectations for hotel services, and the above findings might face invalidation. To date, we are still unknown whether consumers' satisfiers/dissatisfiers changed. This study fills this research gap by mining online reviews of five-star hotels from Shanghai and Beijing before and during the pandemic. The results found that the asymmetric categories of Sleep quality and Internet/Wi-Fi attributes have changed. Specifically, Sleep quality and Internet/Wi-Fi became dissatisfiers from hybrids, indicating that hoteliers only need to maintain their normal performance, and do not invest too many resources because they bring no satisfaction;attributes like Room, decoration, etc. remain hybrids. Additionally, we performed opinion extraction for the dissatisfier attributes to further understand what causes consumer dissatisfaction. The results contribute to the literature by shedding light on the pandemic's impacts on the asymmetric effects of service quality attributes. The cause analysis can help hoteliers develop specific measures to mitigate the adverse effects of COVID-19.
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Our prior research showed that patient experience-as reported by Google, Yelp, and the Hospital Consumer Assessment of Healthcare Providers and Systems survey-is associated with health outcomes. Upon learning that COVID-19 mortality rates differed among U.S. geographic areas, we sought to determine if COVID-19 outcomes were associated with patient experience. We reviewed daily, U.S.-county-level-accrued COVID-19 infections and deaths during the first year of the pandemic using each locality's mean online patient review rating, correcting for county-level demographic factors. We found doctor star ratings were significantly associated with COVID-19 outcomes. We estimated the absolute risk reduction (ARR) and relative risk reduction (RRR) for each outcome by comparing the real-world-observed outcomes, observed with the mean star rating, to the outcomes predicted by our model with a 0.3 unit higher average star rating. Geographic areas with higher patient satisfaction online review ratings in our models had substantially better COVID-19 outcomes. Our models predict that, had medical practices nationwide maintained a 4-star average online review rating-a 0.3-star increase above the current national average-the U.S may have experienced a nearly 11% lower COVID-19 infection rate and a nearly 17% lower death rate among those infected.
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Lately, online users have become avid content creators and consumers of brands and firms through reviews. These serve as sources to peers and reflect reviewers' satisfaction levels with different elements of the experiences. Thus, online reviews are considered reliable sources of information to understand customers' perceptions. Using hybrid methodologies and text-mining techniques is recommended to obtain useful knowledge based on user-generated data. This study intended to understand customers' perceptions of restaurant service and unveil any change that might occur driven by the COVID-19 pandemic. Therefore data was collected on TripAdvisor for two different periods - before and after the pandemic, using the top restaurants of two island tourism destinations. An upturn in customer satisfaction during Covid-19 and changes in attribute weight in the overall evaluation was observed. Therefore, the attribute "service" acquires even more special relevance. This methodology is considered adequate to meet the objectives defined, allowing to obtain relevant conclusions;it might also be applied to other phenomena and sectors.
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Introduction - In today's intense competitive environment, businesses that want to have a sustainable competitive advantage must put the customer at the centre of all their activities and create customer loyalty by offering products and services that will provide customer satisfaction. One of the key elements of ensuring customer satisfaction is the effective handling of customer complaints, which is defined as the customers expressing their dissatisfaction with unmet expectations and unsatisfied needs verbally or in writing. The concept of a complaint as a response of customers' dissatisfaction with the products and services they experience is an invaluable feedback mechanism for businesses to resolve issues relating to their products and services. Aim - The aim of this chapter is to emphasise the importance of the concept of complaint as an important part of customer relations management and an effective marketing tool for the tourism sector. As a service sub-sector, the simultaneous production and consumption of services in the tourism sector ensures customer satisfaction more than concrete products, due to their inseparable nature. For this reason, handling, evaluating and finalising customer complaints has an important function and value in providing the necessary information for tourism enterprises to become aware of their deficiencies and mistakes. Complaint management has started to play an even more critical role for the tourism industry in preventing customer losses due to dissatisfaction, especially considering the shrinkage in demand in the sector due to the COVID-19 pandemic. Method - Firstly the concept of complaint and the importance of complaints for businesses were explained, customer complaint behaviour and the factors affecting this behaviour were examined in detail, the concept of online complaint was mentioned and the subject was evaluated in terms of tourism businesses. Results - While the effective management and resolution of complaints should be seen as a goal by every tourism business, it is vital that they understand customer complaint behaviours, the factors affecting this behaviour and how complaints should be managed in a way that will result in favour of the business. Conclusion - A complaint management process that enables customers to easily report their complaints to businesses and produces solutions as soon as possible will positively affect customer satisfaction. In this context, in order to reduce the negative effects on tourism enterprises, especially through the pandemic, business need to have clear and easy-to-access procedures, provide a quick response, show reliability and consistency in providing a solution, keeping the complainant informed of progress, have employees who can communicate with empathy and courtesy, have enough employees to deal with the situation, and adopt proactive approaches to prevent complaints rather than reducing the volume of complaints. Originality/Value - This research contributes to the literature in terms of complaining behaviour, examining the factors affecting this behaviour and emphasising the importance of the concept of complaints in the tourism sector. In addition, the research is important in terms of examining the contributions of an effective complaint management system in reducing the negative effects of the COVID-19 pandemic on the tourism sector, which is one of the sectors mostly affected on a global scale.
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This research examined the relationship of dark personality traits of narcissism, Machiavellianism, and psychopathy with online review generation, consumption, and distribution. Using TripAdvisor.com as a primary platform of data collection, we collected online reviews of 670 hotels located in New York City posted between 2003 and 2020. Reviews posted after March 2020 were excluded from consideration owing to the circumstantial effects of COVID-19 impacting data differently. Using Pennebaker, Francis & Booth's (2001) closed vocabulary lexical definitions of Linguistic Inquiry and Word Count, we studied linguistic styles of review posters as a correlate of DT traits. Our results evidenced that individuals with dark personality traits are naturally more predisposed towards being dissatisfied and prone to negative online reviews but differences in the three dark personality traits were determined in terms of review consumption and distribution. This study opens avenues for scholarly debate on how personality theories can inform tourist behavior and furnish important insights for practitioners in the tourism and hospitality industry. © 2023 Elsevier Ltd
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Purpose: The COVID-19 pandemic has disrupted numerous businesses and upended the lives and shopping habits of most consumers. This study aims to examine the price sensitivity and the efficacy of online reviews during a pandemic crisis. Design/methodology/approach: This study borrowed from the regulatory focus theory and heuristic-systematic model and used a unique longitudinal sample of 320,000 product/day observations from the jeans category, collected before and during the pandemic, to investigate how consumers' online shopping behavior changed during the pandemic. Findings: The results of several hierarchical linear modeling analyses revealed that during the pandemic consumers were less price-sensitive and more willing to pay price premiums for jeans. Furthermore, consumers were more (less) likely to be influenced by online review volume than valence. Finally, the results of a post-hoc study highlighted the potential role of regulatory focus as the underlying psychological mechanism explaining the effect of the pandemic. Originality/value: This research contributes to the digital marketing and regulatory-focus literatures by showing that the COVID-19 pandemic may have triggered a prevention-focus state of mind and prompted consumers to place a greater value on online review volume than valence when shopping online (for jeans). Furthermore, this paper contributes to the pricing literature by offering further evidence that the pandemic may have inclined consumers to be less price-sensitive. © 2023, Emerald Publishing Limited.
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PurposeGiven the disruption of the COIVD-19 pandemic in higher education, this study seeks to understand possible changes in students' ratings and textual reviews of higher education institutions posted on Niche College Rankings (niche.com) prior to and after the COVID-19 pandemic.Design/methodology/approachThis study utilized a text analytics technique to identify the positive and negative keywords of students' sentiments expressed in their textual reviews provided on niche.com. After identifying the positive and negative sentimental keywords, this study performed ordinal logistic regressions and analyzed the statistical effects of these positive and negative sentimental keywords on the types of student ratings of a higher education institution.FindingsResults from 15,666 online reviews provided by students on niche.com indicate the following. First, eight positive sentimental keywords such as "outstanding” and "love” have a significant impact on students' positive ratings of a higher education institution prior to COVID-19, whereas eight positive sentimental keywords such as "amazing” and helpful” have a significant impact on students' positive ratings of a higher education institution after COVID-19. Second, twenty-eight negative sentimental keywords such as "difficult” and "frustrating” have a significant impact on students' negative ratings of a higher education institution prior to COVID-19, whereas thirty negative sentimental keywords such as "complex” and "hate” have a significant impact on student negative ratings of a higher education institution after COVID-19.Originality/valueThis study is one of the first few studies investigating higher education institution ratings and reviews provided by students. Additionally, this study provides an understanding of student positive and negative sentiments expressed in textual reviews posted prior to and after the COVID-19 pandemic. By doing so, this study provides a basis for future research seeking to understand student textual reviews of higher education institutions. Additionally, this study offers higher education administrators some recommendations that may foster student positive campus experience while minimizing negative sentiments.
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This study extracted airline data from several online source to examine operational and service strategy of the airline industry during the COVID-19 pandemic. The results have suggested that airlines were losing market shares in this pandemic situation except for those with high assets. In addition, this study utilizes text analytics techniques to provide insight into service characteristics that distinguish positive from negative reviews. The results suggest that satisfied travelers are demanding services with high empathy and responsiveness, while negative reviewers frequently complain about insufficient operational aspects such as ground operations, mishandled baggage, system glitches, and staff management on handling cancellation. Copyright © 2022, IGI Global.
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The Covid-19 pandemic has impacted many people's lives. Many researches have studied the impact of the pandemic on customer opinion change regarding services, yet there are still few researches regarding the change towards products. As a product category that experienced a significant increase in sales since the pandemic began, headphones have become a suitable product category to analyze the change. To analyze the change, this paper aims to discover the topics that customers discuss in their reviews. Latent Dirichlet Allocation (LDA) is selected as the topic modeling method to obtain the topics (i.e., aspects of a product) that are discussed in the customer reviews. In the case study, six topics that are discussed by customers are discovered, i.e., Durability Issues, Usage Contexts, Noise Cancellation, Features, Quality, and Customer Service. The monthly proportion of sentences that discuss a topic provides the topic trend. Among those six topics, the discussion about the Usage Contexts topic has increased since the beginning of the pandemic, while the other topics do not show a clear trend related to the pandemic. SentiWordNet is selected as the sentiment analysis method to capture the positive and negative sentiment towards the topics. Among the six topics, the Durability Issues and Noise Cancellation topics showed an improved sentiment after the pandemic began, while the sentiment for Usage Contexts, Features, and Quality topics worsened. Future research may be suggested to explain the worsening trend for those topics, especially the Usage Contexts topic that gained significant negativity after the pandemic began. © 2022 IEEE.
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The coronavirus disease (COVID-19) has impacted the hotel industry in all aspects. However, the changes in hotel customer satisfaction deserve additional attention. Using online hotel reviews, this study explores the difference between the influencing factors of customer satisfaction before and after the COVID-19 outbreak. By use of Latent Dirichlet Allocation and sentiment analysis, factors that affect customer satisfaction and their emotional strength before and after the COVID-19 outbreak are extracted. Then, multiple regression models are established to compare the differences of the impact of each factor on hotel customer satisfaction in different periods. According to the aforesaid study, hotel customer satisfaction and its influencing factors have changed significantly during the pandemic;hotel customer satisfaction during the pandemic is mainly influenced by service quality. Accordingly, strategies are proposed for hotel managers to improve their customer satisfaction during the COVID-19. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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The impact of the second Covid-19 wave on guests' evaluations of hotel services is scarce and mainly focused on Asia. We compared positive and negative comments of 1196 pre-pandemic reviews with 1065 reviews written during the pandemic of hotels in the heart of Europe. We found a significant drop in overall guest scores;however, guests commenting positively on the hotel's handling of the pandemic, rated their stay significantly higher. Furthermore, we observed substantial shifts in guests' comments on certain hotel attributes. Our findings provide, therefore, useful insights for hotel managers into guests' expectations and hotel experiences under strict safety conditions. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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Purpose: The purpose of this paper is exploring the effects of segment dynamic and temporal dynamic triggered by the COVID-19 pandemic on classifying service quality attributes, thereby formulating improvement strategies to satisfy customers and respond to threats. Design/methodology/approach: Given the dynamics of the attractive quality theory, this paper designs a framework with four phases by embedding techniques of text mining and deep learning based on evidence from online reviews. Findings: This paper figures out dynamics of service quality attributes for distinct segments and their dynamic proportion along with different stages of the pandemic. Another finding demonstrates segment dynamic and temporal dynamic effects of sentiments toward service quality attributes on customer satisfaction under the impacts of pandemic. Classification results and improvement strategies are derived for varying segments at different pandemic situations. Practical implications: This paper reveals dynamic effects on classifying service quality attributes, which contributes to assisting hospitality practitioners from different segments in improving service quality when facing with the challenges of crisis and potential risks. Originality/value: Given hospitality industry is time- and segment-sensitive, the authors achieve the quantification of dynamics of attractive quality theory and extend it into hospitality marketing and crisis management from the perspective of dynamics with evidence from online reviews. © 2022, Emerald Publishing Limited.
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Purpose: The ongoing COVID-19 pandemic has led to drastic changes in the lives of customers. Social isolation, financial difficulties, fear of being infected and many other factors have caused the psychological well-being of customers to deteriorate. By taking up the role of online reviews in the regulation of consumers' moods, this study aims to examine the changes that have occurred in online product ratings, as well as the negative tone and word counts of product reviews during the COVID-19 pandemic. Design/methodology/approach: This study examines the online reviews of 321 products in the pre-COVID, immediate COVID and extended COVID periods. This paper compares the changes that have taken place in product evaluations via various analysis of variance analyses. The authors also test the effect of COVID-related deaths on product evaluations via regression analyses. Findings: The results indicate that online product ratings decreased sharply just after the outbreak of COVID-19. The study also found that the tone of reviews was found to be more negative and the length of reviews appeared to be longer in comparison to the pre-COVID-19 period. The results also revealed that the product type (experience vs search) moderated the effect of the pandemic in online reviews and the impact of COVID-19 on online product reviews diminished in the later stages of the ongoing pandemic. Practical implications: Managers should be aware of the detrimental impact of pandemics on online product reviews and be more responsive to customer problems during the early stages of pandemics. Originality/value: To the best of the authors' knowledge, this is the first study that analyzes the effects of a pandemic on online product ratings and review content. As such, this study offers a timely contribution to the marketing literature. © 2021, Emerald Publishing Limited.
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Purpose: This paper aims to examine the impact of online reviews on behavioral intentions via perceived risk. Perceived risk is both analytical and emotional. Stimulus–organism–response (S–O–R) framework guided this study to explore the interaction between online reviews, perceived risk and behavioral intentions. Design/methodology/approach: The conceptual model proposed in this research has been validated using confirmatory factor analysis (CFA) and structural equation modeling to assess the measurement model and the validity of the scale, based on primary responses collected from 473 travelers. Findings: Findings of this study suggest the role of online consumer reviews in reducing the perceived risk associated with experience dominant services like tourism. Process model test proves the mediating role of perceived risk between online reviews and behavioral intentions. Results indicate the significance of online review in lowering the perceived risk leading to positive behavioral intentions. Practical implications: Destination marketing organizations (DMOs) should understand the role of online reviews in effectively reducing risk and uncertainty, thereby influencing behavioral intentions. Originality/value: This paper is unique in attempting to empirically examine the mediating role of perceived risk between online reviews and behavioral intentions. The study is a forerunner in using S–O–R framework to test the interaction between online review, perceived risk and behavioral intention. © 2023, Neha Yadav, Sanjeev Verma and Rekha Chikhalkar.
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Online consumer reviews play a pivotal role in boosting online shopping. After Covid-19, the e-commerce industry has been grown exponentially. The e-commerce industry is greatly impacted by the online customer reviews, and a lot of work has been done in this regard to identify the usefulness of reviews for purchasing online products. In this proposed work, predicting helpfulness is taken as binary classification problem to identify the helpfulness of a review in context to structural, sentimental, and voting feature sets. In this study, the authors implemented various leading ML algorithms such as KNN, LR, GNB, LDA and CNN. In comparison to these algorithms and other existing state of art methods, CNN yielded better classification results, achieving highest accuracy of 95.27%. Besides, the performance of these models was also assessed in terms of precision, recall, F1 score, etc. The results shown in this paper demonstrate that proposed model will help the producers or service providers to improve and grow their business.
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With the popularity of the Internet and the growing complexity of COVID-19, more and more patients tend to consult doctors online. With the difficulty of doctor selection caused by a massive amount of information, this study proposes a hybrid multi-criteria decision-making framework, which can model patients' emotional intensity through heterogeneous information and rank doctors. Firstly, online reviews (ORs) are transformed into probabilistic linguistic term sets through sentiment analysis. Then, new score functions are proposed considering the nonlinear influence of doctors' information and the patients' negative bias toward ORs. Next, a method of weight determination combining the Term Frequency Inverse Document Frequency and the Decision-making Trial and Evaluation Laboratory method is proposed. Finally, the proposed score functions are applied to the Combined Compromise Solution (CoCoSo) method to aggregate information and rank doctors. The proposed method is verified in a case study on haodf.com. The results show that considering the emotional intensity of heterogeneous information will make the recommendations more realistic. Comparative analysis and sensitivity analysis are further performed to illustrate the availability and effectiveness of the proposed method.
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The COVID-19 pandemic caused a massive fall in demand and revenues and changed consumers' perceptions and expectations. Recovery from such a scenario demands attention from researchers, industry practitioners, and hotel management. This paper explored how online reviews, hotel trust, brand image, and perceived health risks affected consumers' hotel booking intentions during COVID-19. The cross-sectional study included 319 participants. Using PLS-SEM, the results showed that all factors affected hotel booking intention, except perceived health risk. The results can be helpful for managerial implications as they identified the critical factors for enhancing the hotel booking intention among prospective customers during the COVID-19 pandemic. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.
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Online consumer reviews play a pivotal role in boosting online shopping. After Covid-19, the e-commerce industry has been grown exponentially. The e-commerce industry is greatly impacted by the online customer reviews, and a lot of work has been done in this regard to identify the usefulness of reviews for purchasing online products. In this proposed work, predicting helpfulness is taken as binary classification problem to identify the helpfulness of a review in context to structural, sentimental, and voting feature sets. In this study, the authors implemented various leading ML algorithms such as KNN, LR, GNB, LDA and CNN. In comparison to these algorithms and other existing state of art methods, CNN yielded better classification results, achieving highest accuracy of 95.27%. Besides, the performance of these models was also assessed in terms of precision, recall, F1 score, etc. The results shown in this paper demonstrate that proposed model will help the producers or service providers to improve and grow their business. © 2022 Taru Publications. All rights reserved.
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Hotel guests' experience and satisfaction are important aspects of the hospitality industry. It is influenced by several hotel attributes. With the emergence of Covid-19 pandemic, changes in important attributes for customers need to be studied. This study utilized textual reviews and ratings from Tripadvisor for hotels in Indonesia in 2019 and 2021. Topic modeling and opporunity algorithm were applied to identify important attributes, calculate each attribute's level of importance and satisfaction, and conduct opportunity. This study found there are changes in the important attributes before and during the pandemic. Finally, the opportunity algorithm was computed to find attributes that need to be improved. This study found that the latest service opportunities in Indonesia arising from the Covid-19 pandemic are 'health protocol' and hotel facilities such as fitness/gym centers and internet connections to improve room comfort. © 2022 IEEE.
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A positive customer journey experience is necessary to maintain customer loyalty in online retailing. After the outbreak of Covid-19, there has been a significant increase in the number of customers who buy online groceries. Due to the anonymity and convenience throughout the customer journey, E-grocery shopping platforms have become a reliable source for gathering online customer reviews. In the study, we used text mining and machine learning (ML) models to an e-grocery customer review database from the Amazon Fresh website to forecast customer feelings in the data set. To be more specific, this study aimed to determine whether the customers are satisfied with the online purchase of products or not. Further, the study aims to analyze whether the customers would recommend the purchased products or not. For sentiment analysis a sample of 78,619 reviews was used. We used a linguistic approach consisting of ML and dictionary scoring algorithms to forecast customers' sentiment based on their reviews. Topic modeling (TM) on 3,26,120 customer reviews was used to reveal 'themes' from customer reviews to grasp a better knowledge of customers experiences. © 2022 IEEE.