Travel bubbles to maintain safe space for international travel during crisis – emotions reflected in Twitter posts

Chen, Jinyan; Becken, Susanne; Stantić, Bela

doi:10.1080/13683500.2022.2089546

Cited by 9 publications

(5 citation statements)

References 36 publications

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“…R. Li et al, in their recent research, outline that sentiments accentuate only the broad category of emotions as positive, negative and neutral; therefore, future studies should pursue creating a more granular form of emotion representation [28]. The identification of the emotions of customers at a granular level from their online reviews is crucial to properly exploit the power of social media [29]. These models rely on previously annotated data to train the machine learning model and lack deeper exploration of the derived emotions which could be used for decision-making.…”

Section: Sentiment Analysis Vs Deep Emotion Modellingmentioning

confidence: 99%

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

Adikari,

Nguyen,

Nawaratne

et al. 2024

Applied Sciences

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The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making.

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Section: Sentiment Analysis Vs Deep Emotion Modellingmentioning

confidence: 99%

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

Adikari,

Nguyen,

Nawaratne

et al. 2024

Applied Sciences

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“…However, only a few studies have analysed the usefulness of big data from Twitter to Twitter and tourism demand analyse tourism demand (e.g. Bign e et al, 2019;Sulong et al, 2022;Yang et al, 2022) and define management approaches and business responses to the COVID-19 pandemic in real-time (Chen et al, 2023;Yang and Han, 2021). Previous literature has recognised Twitter's representativeness as a concern (Beninger and Lepps, 2014), but some authors recognise its interest if a contextual interpretation is made (Tromble, 2019).…”

Section: Social Media As a Source Of Prediction Datamentioning

confidence: 99%

Twitter's capacity to forecast tourism demand: the case of way of Saint James

Mendieta-Aragón,

Navío-Marco,

Garín-Muñoz

2024

EJMBE

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PurposeRadical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.Design/methodology/approachThis study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.FindingsThe results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.Originality/valueThis study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

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“…Research on travel bubbles during the COVID-19 pandemic has garnered limited attention from tourism researchers, particularly regarding destination image (Rasoolimanesh et al, 2021) and behavioural intentions (Girish et al, 2022). These researchers emphasised the need for proper guidelines related to cleanliness and services to ensure that guests feel safe and comfortable during their visits (Chen, 2022). In this study, the researchers focused on Langkawi Island in Malaysia as a successful travel bubble program destination (Tourism Malaysia, 2022).…”

Section: Travel Bubblementioning

confidence: 99%

Travelling During Travel Bubble: Assessing the Interrelationship between Cognitive, Affective, Unique Image, and Future Revisit Intention

Asyraff,

Hanafiah,

Md Zain

2024

JoTS

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This research study utilises Mehrabian and Russell's Stimulus-Organism-Response (SOR) model to explore the connections between various components of a destination image (cognitive, affective, and unique) and the intention of travellers to revisit Malaysia. A total of 402 responses were collected using purposive sampling. The Partial-Least Square-Structural Equation Modelling (PLS-SEM) method was employed to examine the research model and its proposed hypotheses. The findings reveal that the cognitive and unique aspects of the destination image significantly influence the affective image. This quantitative analysis confirms that the destination image's unique and affective components significantly impact the intention to revisit. Interestingly, the cognitive image does not significantly impact the intention to revisit. The bootstrapping analysis demonstrates that the affective image mediates the relationship between the cognitive image and the intention to revisit. However, the unique image, indirectly through affective image, does not influence the intention to revisit. This study contributes to our understanding of the application of the extended SOR model by Mehrabian and Russell in the context of tourism, particularly regarding destination image dimensions in different settings, such as islands and travel bubble destinations. From a practical standpoint, the travel bubble is seen as an effective crisis management strategy employed by certain countries to stimulate their tourism industry in the midst of the COVID-19 pandemic. This study provides valuable insights for policymakers and stakeholders regarding creating a positive experience while maintaining appropriate preventive measures within the travel bubble program.

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Travel bubbles to maintain safe space for international travel during crisis – emotions reflected in Twitter posts

Cited by 9 publications

References 36 publications

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

Twitter's capacity to forecast tourism demand: the case of way of Saint James

Travelling During Travel Bubble: Assessing the Interrelationship between Cognitive, Affective, Unique Image, and Future Revisit Intention

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