Article: Determining the factors affecting customer satisfaction using an extraction-based feature selection approach

The coronavirus disease 2019 (COVID-19) causes tremendous damages to the world, including threats to human's health and daily activities. Most industries have been affected by this pandemic, particularly the tourism industry. The online travel agencies (OTAs) have suffered from the global tourism market crisis by air travel lockdown in many countries. How online travel agencies can survive at stake and prepare for the post-COVID-19 future has emerged as an urgent issue. This study aims to examine the critical factors of customers' satisfaction to OTAs during the COVID-19 pandemic. A text mining method for feature selection, namely LASSO, was used to deal with online customer reviews and to extract factors that shape customers' satisfaction to OTAs. Results showed that refunds, promptness, easiness and assurance were ranked as the most competitive factors of customers' satisfaction, followed by bad reviews & cheap and excellent service & comparison. New factors to customers' satisfaction were revealed during the global tourism recession. Findings provide OTAs guidelines to reset services priorities during the pandemic crisis.

Citing Articles

Effect of COVID-19 Pandemic on Patients Who Have Undergone Liver Transplantation: Retrospective Cohort Study.

Akbulut S, Yagin F, Sahin T, Garzali I, Tuncer A, Akyuz M J Clin Med. 2023; 12(13).

PMID: 37445501 PMC: 10342746. DOI: 10.3390/jcm12134466.

References

1.

Sigala M . Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. J Bus Res. 2020; 117:312-321. PMC: 7290228. DOI: 10.1016/j.jbusres.2020.06.015. View

2.

Hao F, Xiao Q, Chon K . COVID-19 and China's Hotel Industry: Impacts, a Disaster Management Framework, and Post-Pandemic Agenda. Int J Hosp Manag. 2020; 90:102636. PMC: 7405826. DOI: 10.1016/j.ijhm.2020.102636. View

3.

Piccinelli S, Moro S, Rita P . Air-travelers' concerns emerging from online comments during the COVID-19 outbreak. Tour Manag. 2021; 85:104313. PMC: 8647352. DOI: 10.1016/j.tourman.2021.104313. View

4.

Wang T . A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso. PeerJ Comput Sci. 2021; 7:e732. PMC: 8507474. DOI: 10.7717/peerj-cs.732. View

5.

Makarov I, Gerasimova O, Sulimov P, Zhukov L . Dual network embedding for representing research interests in the link prediction problem on co-authorship networks. PeerJ Comput Sci. 2021; 5:e172. PMC: 7924522. DOI: 10.7717/peerj-cs.172. View

6.

Chen M, Chang J, Chen L, Shen E . The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach. J Ambient Intell Humaniz Comput. 2021; 13(6):3083-3101. PMC: 7986645. DOI: 10.1007/s12652-021-03146-4. View

7.

Foroudi P, H Tabaghdehi S, Marvi R . The gloom of the COVID-19 shock in the hospitality industry: A study of consumer risk perception and adaptive belief in the dark cloud of a pandemic. Int J Hosp Manag. 2023; 92:102717. PMC: 9998105. DOI: 10.1016/j.ijhm.2020.102717. View

8.

Zhang K, Hou Y, Li G . Threat of infectious disease during an outbreak: Influence on tourists' emotional responses to disadvantaged price inequality. Ann Tour Res. 2020; 84:102993. PMC: 7365089. DOI: 10.1016/j.annals.2020.102993. View

9.

Luo Y, Xu X . Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. Int J Hosp Manag. 2021; 94:102849. PMC: 8586815. DOI: 10.1016/j.ijhm.2020.102849. View

10.

Ugur N, Akbiyik A . Impacts of COVID-19 on global tourism industry: A cross-regional comparison. Tour Manag Perspect. 2020; 36:100744. PMC: 7474895. DOI: 10.1016/j.tmp.2020.100744. View

Determining the Factors Affecting Customer Satisfaction Using an Extraction-based Feature Selection Approach