Exploring Airbnb service quality attributes and their asymmetric effects on customer satisfaction

Ju, Yongwook; Back, Ki‐Joon; Choi, Youngjoon; Lee, Jin Soo

doi:10.1016/j.ijhm.2018.07.014

Cited by 200 publications

(168 citation statements)

References 44 publications

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“…This finding contributed to the understanding of finer-grained value dimensions contributing to IVF outcomes in the context of Airbnb. Overall, this finding supported recent studies which indicated that the host's friendly behaviour (Ju et al, 2019) and host-guest interaction was of utmost importance in Airbnb guest experience (Lalicic & Weismayer, 2018;Sthapit & Jiménez Barreto, 2018a).…”

Section: Driver 2: Hosts' Actionssupporting

confidence: 90%

“…Studies have suggested that the Airbnb platform provides answers to frequently asked questions and offers 24/7 customer service to help guests and hosts resolve problems (Ju, Back, Choi, & Lee, 2019). However, several review posts showed that guests felt that Airbnb's customer support failed to properly or promptly resolve their problems, for example, offering compensation during the service failure, which resulted in a second service failure.…”

Section: Driversmentioning

confidence: 99%

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Interactive value formation: drivers and outcomes from Airbnb guests’ perspectives

Sthapit

Björk

2020

Scandinavian Journal of Hospitality and Tourism

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This study explores interactive value formation, particularly the underlying drivers of three value outcomes in the Airbnb context: co-creation, co-destruction and co-recovery. The study focuses on reviews posted online by Airbnb guests in English. These posts contained customers' positive and negative experiences with Airbnb on Trustpilot. The data analysis uncovered two main themes that reflected the drivers of value co-creation, codestruction and co-recovery (company's customer service and hosts' actions). First, after a service failure, many guests experienced value co-destruction because they felt that Airbnb's customer service agents could not solve their problems in a timely and proper manner, while the use of successful recovery efforts by the service agents served as an antidote to value codestruction, thereby contributing to value co-recovery. Second, host's friendly behaviour, including prompt communication between the host and the guest, led to value co-creation. On the contrary, inadequate communication and unethical actions by the host generated value co-destruction among the guests and resulted in a decline in their well-being. The findings suggest that particular value dimensions can individually act as a source of either value co-creation or co-recovery, while their inadequate integration in the interactive value formation processes leads to value co-destruction.

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Section: Driver 2: Hosts' Actionssupporting

confidence: 90%

Section: Driversmentioning

confidence: 99%

Interactive value formation: drivers and outcomes from Airbnb guests’ perspectives

Sthapit

Björk

2020

Scandinavian Journal of Hospitality and Tourism

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“…Using questionnaires on 202 international tourists to Phuket, one study identified the importance of several factors, including tangibles, convenience, assurance, understanding and caring, the supply of adequate service, general satisfaction, and loyalty [35]. A mixed method analysis of 16,430 Airbnb reviews followed by 322 online surveys found that service quality attributes could be broken down into those regarding the website, host, and facility [36]. An analysis of customer experience factors across several countries found wide agreement rather than divergence regarding the factors, including the stay, host, place, location, apartment, room, and city [37].…”

Section: Customer Experience In Peer-to-peer Accommodationmentioning

confidence: 99%

Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA

Sutherland

Kiatkawsin

2020

Sustainability

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This study inductively analyzes the topics of interest that drive customer experience and satisfaction within the sharing economy of the accommodation sector. Using a dataset of 1,086,800 Airbnb reviews across New York City, the text is preprocessed and latent Dirichlet allocation is utilized in order to extract 43 topics of interest from the user-generated content. The topics fall into one of several categories, including the general evaluation of guests, centralized or decentralized location attributes of the accommodation, tangible and intangible characteristics of the listed units, management of the listing or unit, and service quality of the host. The deeper complex relationships between topics are explored in detail using hierarchical Ward Clustering.

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“…The study also grouped these factors into three types (satisfiers, dissatisfiers if absent, and hybrids) and compared differences among three market segments (Turkish, German and Russian). Ju et al (2019) revealed that Airbnb guests' satisfaction was influenced by quality factors including facility service, host service, web efficiency, and web responsiveness. Attributes of each factor have asymmetric links to guests' satisfaction.…”

Section: Satisfaction Studies In Hospitalitymentioning

confidence: 99%

Why are Chinese and North American guests satisfied or dissatisfied with hotels? An application of big data analysis

Ying

Chan

2020

IJCHM

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Purpose The paper aims to identify the emergent themes of hotel guests’ satisfaction, to compare the distribution of the attributes of the themes between Chinese and North American guests and to compare the importance of the themes for different satisfaction levels between Chinese and North American guests from a cross-cultural perspective. Design/methodology/approach By adopting Python (a computer language), the word-frequency method was used to identify emergent themes of hotel guests’ satisfaction. Topic modeling was adopted to compare the attributes distribution of each theme and the features of satisfaction between Chinese and North American guests. Findings First, three themes were identified including functionality, staff and price. Functionality can be further categorized into five subthemes, namely, room, travel, food, environment and hotel facility. Second, the distribution of the attributes of the themes between Chinese and North American guests was compared from a cross-cultural perspective. Chinese guests tend to mention both lifestyles- and social norms–related attributes and expect personalized service, while North American guests mainly prefer to describe lifestyle-related attributes and prefer standardized service. Third, the study compared the changing importance of the themes (functionality, staff and price) for different satisfaction levels between Chinese and North American guests. As the satisfaction level decreases, the importance of functionality decreases, that of staff increases and that of price remain stable for Chinese guests. In contrast, the importance of each theme has fluctuated mildly from the high to the low satisfaction level for North American guests. Practical implications Proposed managerial implications are to highlight lifestyle- and social norms-related attributes, as well as personalized service for Chinese guests. However, lifestyle-related attributes and standardized service should be facilitated for North American guests. Specific suggestions were made to help improve hotel performance such as the good performance of functional-related attributes, which could enhance satisfaction and better staff performance, which would reduce dissatisfaction. Originality/value By mining big data, this study investigated hotel guests’ satisfaction from a dynamic instead of a static perspective. This study provides some rare insights into differences in key attributes influencing satisfaction levels of Chinese versus North American guests staying in luxury hotels in China. This study also takes a novel approach to examine the dynamics of the importance of the various themes at different satisfaction levels, and contrast these dynamics between Chinese and North American guests. The findings offer valuable insight into market segmentation and management in the hospitality industry.

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Exploring Airbnb service quality attributes and their asymmetric effects on customer satisfaction

Cited by 200 publications

References 44 publications

Interactive value formation: drivers and outcomes from Airbnb guests’ perspectives

Interactive value formation: drivers and outcomes from Airbnb guests’ perspectives

Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA

Why are Chinese and North American guests satisfied or dissatisfied with hotels? An application of big data analysis

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