Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation

• Published in: International journal of hospitality management Vol. 91; p. 102676
• Main Authors: Ding, Kai, Choo, Wei Chong, Ng, Keng Yap, Ng, Siew Imm
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2020
• Subjects: Airbnb; Online reviews; Service quality; Structural topic model; Text mining; Text mining; Structural topic model; Airbnb; Service quality; Online reviews
• ISSN: 0278-4319; 1873-4693
• DOI: 10.1016/j.ijhm.2020.102676

•Customer reviews can serve an valuable data source to explore Airbnb service quality.•A novel technique used to extracts service quality attributes from online customer reviews.•Four service attributes that are not reported in the previous Airbnb studies are identified in the present study.•Malaysian and international Airbnb users are found to have different emphasis on service quality attributes.•A modified SERVQUAL questionnaire developed by Akbaba (2006) is cross-validated by using a different source of data.
This study employs the structural topic model to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. 22 service related topics were extracted from the corpus and four topics have not appeared in previous Airbnb studies. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by comparing its service quality attributes with the results of the topic modelling, which indicates that this MSQ can cover general Airbnb service quality attributes. This study also examines the different preferences of Malaysian and international Airbnb users and the changing patterns of the top six service quality attributes during a five-year period. The findings reveal that Malaysian Airbnb users care more about the appearance and location of the property, and international Airbnb users pay more attention to whether the property can accommodate a group of people. In addition, communication with the host is found to play an increasingly important role in Airbnb users’ lodging experiences.