SavedAdd your journal

Journals / Advances in Hospitality and Tourism Research (AHTR) / 2021 / Cilt: 9 - Sayı: 1

A Big-Data Analysis of Public Perceptions of Service Robots Amid Covid-19

Yaozhi ZHANG
Pages
234–242
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results reveal that while there are supporters and opponents toward robotic services during the pandemic, the overall public sentiment is neutral, and confirm that the health factor and a series of social-cultural factors encompassing the employment concern, political influence, and cultural norm should be involved as more significant variables for COVID-Tourism research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This research note investigates public perceptions of robotic services in the hospitality and tourism industry in the context of COVID-19.
📊
Key Findings & Data:The results reveal that while there are supporters and opponents toward robotic services during the pandemic, the overall public sentiment is neutral, and confirm that the health factor and a series of social-cultural factors encompassing the employment concern, political influence, and cultural norm should be involved as more significant variables for COVID-Tourism research.
💡
Conclusion & Impact:Some practical suggestions for robotic services amidst COVID-19 are accordingly put forward.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This research note investigates public perceptions of robotic services in the hospitality and tourism industry in the context of COVID-19. Relevant comments from YouTube videos were crawled and analysed by Natural Language Processing (NLP) techniques including explorative analysis, sentiment analysis, and topic modelling. The results reveal that while there are supporters and opponents toward robotic services during the pandemic, the overall public sentiment is neutral, and confirm that the health factor and a series of social-cultural factors encompassing the employment concern, political influence, and cultural norm should be involved as more significant variables for COVID-Tourism research. Some practical suggestions for robotic services amidst COVID-19 are accordingly put forward.

🔖

Cite This Article

ZHANG, Y. (2022). A Big-Data Analysis of Public Perceptions of Service Robots Amid Covid-19. Advances in Hospitality and Tourism Research (AHTR), Cilt: 9 - Sayı: 1, 234–242. https://ajindex.com/articles/429420

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Abdelhamied, H. H. S. (2011). Customers' perceptions of floating restaurants in Egypt. Anatolia, 22(1), 1-15. https://doi.org/10.1080/13032917.2011.556212
  2. Amatulli, C., De Angelis, M., & Stoppani, A. (2019). Analyzing online reviews in hospitality: data-driven opportunities for predicting the sharing of negative emotional content. Current issues in tourism, 22(15), 1904-1917. https://doi.org/10.1080/13683500.2019.1594723
  3. Gössling, S., Scott, D., & Hall, C. M. (2021). Pandemics, tourism and global change: a rapid assessment of COVID-19. Journal of sustainable tourism, 29(1), 1-20. https://doi.org/10.1080/09669582.2020.1758708
  4. Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., Pesonen, J., Zanker, M., & Xiang, Z. (2020). e-Tourism beyond COVID-19: a call for transformative research. Information technology & tourism, 22(2), 187-203. https://doi.org/10.1007/s40558-020-00181-3
  5. Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism management 59, 467-483. https://doi.org/10.1016/j.tourman.2016.09.009
  6. Ivanov, S., Gretzel, U., Berezina, K., Sigala, M., & Webster, C. (2019). Progress on robotics in hospitality and tourism: a review of the literature. Journal of Hospitality and Tourism Technology, 10(4), 489-521. https://doi.org/10.1108/JHTT-08-2018-0087
  7. Ivanov, S., Webster, C., & Garenko, A. (2018). Young Russian adults' attitudes towards the potential use of robots in hotels. Technology in Society, 55, 24-32. https://doi.org/10.1016/j.techsoc.2018.06.004
  8. Jiang, Y., & Wen, J. (2020). Effects of COVID-19 on hotel marketing and management: a perspective article. International journal of contemporary hospitality management, 32(8), 2563-2573. https://doi.org/10.1108/IJCHM-03-2020-0237
  9. Kuo, C.-M., Chen, L.-C., & Tseng, C.-Y. (2017). Investigating an innovative service with hospitality robots. International Journal of Contemporary Hospitality Management, 29(5), 1305-1321. https://doi.org/10.1108/IJCHM-08-2015-0414
  10. Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51. https://doi.org/10.1016/j.ijhm.2019.01.005
  11. Seyitoğlu, F., & Ivanov, S. (2020). Service robots as a tool for physical distancing in tourism. Current issues in tourism, 1-4. https://doi.org/10.1080/13683500.2020.1774518
  12. Shin, H., & Kang, J. (2020). Reducing perceived