Airbnb customers’ service quality and satisfaction with big data approach
A desire for sustainability, enjoyment of activities and financial gain, which has sparked a growing interest among researchers and businesses, drives the sharing economy phenomenon. The sharing economy enables people to sell services through reputable online platforms such as Uber or Airbnb. Thi...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/99375/1/DING%20KAI%20-%20IR.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A desire for sustainability, enjoyment of activities and financial gain, which has sparked
a growing interest among researchers and businesses, drives the sharing economy
phenomenon. The sharing economy enables people to sell services through reputable
online platforms such as Uber or Airbnb. This research focuses on Airbnb, a peer-topeer
(P2P) internet platform provider that has become one of the most successful models
in the sharing economy. Because of the unique lodging experience that Airbnb users
pursue, understanding Airbnb users’ perceptions of service quality and satisfaction by
referring to the standards of traditional hotel customers can often be misleading.
Therefore, more research is needed to explore Airbnb customers’ experience. Online
reviews serve as the data source for this research, which provides a representative sample
of individual customers’ personal and unique lodging experiences.
This research is divided into two parts, Study 1 and Study 2. The primary purpose of
Study 1 is to explore the Airbnb service quality attributes that are essential to deliver an
outstanding customer experience. A novel structural topic model (STM) is employed due
to its advantage that enables us to incorporate covariates in the analysis. Study 1
employed STM to extract service quality attributes from 242,020 Airbnb reviews in
Malaysia. This study complements the lack of Airbnb-related research in developing
countries and improves our understanding of the Airbnb service quality attributes in
Malaysia. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated
in this study by mapping identified service quality attributes to five service quality
dimensions of this questionnaire, which contributes to the further modification of this
instrument to suit the Airbnb context. By employing the methodological advantage of
STM, this study extends previous studies by examining the different preferences of
Malaysian and international Airbnb users. The results reconfirm the impact of nationality
on customer preferences. In this study, Airbnb users from Malaysia are found to pay
more attention to the property attributes (e.g., appearance, decoration); international
Airbnb users are found to care more about whether this property is suitable for group
accommodation, which could be associated with Airbnb users’ preferences for group
travel. In addition, this study further examines the changing patterns of identified service
quality attributes during a five-year period. The findings reveal the different changing
patterns of Airbnb users’ perceptions of these attributes, notably, communication with
the host and shopping are found to play an increasingly important role in Airbnb users’
experiences. The extracted service quality attributes perceived by Airbnb users provide
a detailed reference for Airbnb practitioners to develop marketing strategies, property
recommendation systems, and customer service standards.
As for Study 2, it aims to investigate the drivers of satisfaction and dissatisfaction in the
context of Airbnb accommodation, with a focus on Airbnb stay experiences.. The second
study used LDA (Latent Dirichlet Allocation) and supervised LDA (sLDA) to achieve
the study objectives, as these two topic models can effectively assist us in topic
extraction and simultaneous analysis of quantitative data and topic distribution,
respectively. A corpus that comprises 59,766 Airbnb reviews from 27,980 listings in 12
different cities is analyzed by using these two approaches. Unlike previous LDA based
Airbnb studies, this study examines positive and negative Airbnb reviews separately.
The results contribute to Airbnb literatures by revealing the heterogeneity of satisfaction
and dissatisfaction attributes in Airbnb accommodation. In addition, the emergence of
the topic “guest conflicts” in this study leads to a new direction in future sharing
economy accommodation research, which is to study the interactions of different guests
in a highly shared environment. The topic distribution analysis reveals the service
attributes valued by users stay at different types of Airbnb properties, thus providing
hosts operating different types of Airbnb properties with more targeted operational
strategies to increase customer satisfaction. This study determines attributes that have
the strongest predictive power to Airbnb users’ satisfaction and dissatisfaction through
the sLDA analysis, which provides valuable managerial insights into priority setting
when developing strategies to increase Airbnb customer satisfaction.
Last, previous research highlighted the challenges of performing social media analysis,
which is required to process an enormous amount of unstructured big data. Therefore,
two studies in this thesis contribute to the development of the social media literature.
Two user-generated content (UGC) analytical frameworks are developed for companies
to use social media data in their service operation management. The detailed process of
implementing these techniques is provided in this thesis and can serve as a useful
reference for future research. |
|---|