Exploring the Service Quality of Airbnb

Sun, Sunny; Zheng, Chen; Schuckert, Markus; Law, Rob

doi:10.3727/108354219x15652651367424

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…Their findings indicated that Airbnb has multiple SQ attributes associated with website, host and facility that produce distinctive effects on customer satisfaction. Sun et al (2019) based on a qualitative design identified accuracy, cleanliness, rooms and facilities, location, personalized service and value as the six dimensions of measuring Airbnb SQ.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although a limited number of research papers have explored the whole SQ concept of Airbnb, there are some studies that considered just some of service-related factors separately, including peer-to-peer interactions (Moon et al , 2019), quality and quantity attributes of hosts (Sun et al , 2019), transaction experience (Liang et al , 2018), trust (Ert and Fleischer, 2019), value co-creation (Jeannette and Barbara, 2017), hospitality experience (Sthapit and Jiménez-Barreto, 2018) and customer experience and review system (Cheng and Jin, 2019). Table 1 summarizes the service-related attributes of Airbnb which have been reviewed through analysis of the available literature to date.…”

Section: Literature Reviewmentioning

confidence: 99%

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A big data approach to map the service quality of short-stay accommodation sharing

Ranjbari

Esfandabadi

Scagnelli

2020

IJCHM

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Purpose The purpose of this paper is to map the service quality (SQ) of Airbnb, to provide additional insight for such top player of short-stay accommodation in the sharing economy context. Design/methodology/approach A mixed-method approach is used in two phases. In the qualitative phase, 112,138 online review comments of Airbnb guests were analyzed to generate the service attributes. In the quantitative phase, an online survey (n = 814) was conducted to calculate the performance and importance values of extracted attributes to plot them in an Importance-Performance Analysis (IPA) matrix. Findings A holistic image of the Airbnb extracted service attributes was presented through the IPA plot. Four types of SQ strategies were proposed, considering the actions priority. “Price reasonability” was the most important service attribute of Airbnb for guests, whereas “Check-in flexibility” was the best performed one. Practical implications The results shed light on the most relevant SQ attributes of Airbnb and proposed suitable strategies that can prioritize relevant stakeholders’ actions and decisions. The study significantly contributes to all decision makers involved in the short-stay accommodation sharing industry to further understand and develop SQ. Originality/value This research, using a comprehensive hybrid method, opens a lens to see more clearly the positioning of different attributes of Airbnb service from importance and performance viewpoints. As a contribution, the SQ of Airbnb was mapped by conducting an IPA for the first time in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A big data approach to map the service quality of short-stay accommodation sharing

Ranjbari

Esfandabadi

Scagnelli

2020

IJCHM

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“…In a complementary line of research, Barber and Scarcelli (2010) identified cleanliness as an influencing factor of the perceived service quality. The more recent studies by Sun et al (2019) and Lari et al (2020) corroborate such finding by exploring two major online hospitality platforms, Airbnb and TripAdvisor, respectively. Additionally, since airport hotels are intrinsically connected to airports, issues related to transportation to the city center as well as flight related issues are also highly relevant from a service quality perspective (Tam et al, 2008).…”

Section: 2perceived Hotel Service Qualitymentioning

confidence: 62%

Service quality in airport hotel chains through the lens of online reviewers

Moro

Lopes

Esmerado

et al. 2020

Journal of Retailing and Consumer Services

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Airport hotel chains target the specific and important segment of accommodation near airports, thus benefiting from travelers seeking to stay near an airport. This study addresses service quality by analyzing TripAdvisor online reviews over units from both a high-end and a low-end chain in five European cities (Amsterdam, Brussels, Frankfurt, London, Paris). Using text mining and topic modeling, ten heat matrices were drawn (one per unit) to summarize the main services characterizing the computed topics. Seven hypotheses grounded on existing literature were tested, from which some interesting findings emerged (e.g., related to transfer services, staff, food and beverage, cleanliness, and punctuality). This study contributes to the standardization versus adaptation debate by unveiling a globalized strategy in staff management and breakfast services, while bar services adopt a localized strategy. Transportation services, while not offered by the hotels, are frequently mentioned, which signals hoteliers to interact with local authorities to improve accessibilities.

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“…e check-in score indicates whether the user is satisfied with the check-in process upon arrival. According to Ranjbari et al and Sun et al [46,61], this indicator is considered among the key points of customer satisfaction with the quality of service offered by Airbnb.…”

Section: Check-inmentioning

confidence: 99%

A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP

Chiny

Bencharef

Hadi

et al. 2021

Applied Computational Intelligence and Soft Computing

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Understanding the determinants of satisfaction in P2P hosting is crucial, especially with the emergence of platforms such as Airbnb, which has become the largest platform for short-term rental accommodation. Although many studies have been carried out in this direction, there are still gaps to be filled, particularly with regard to the apprehension of customers taking into account their category. In this study, we took a machine learning-based approach to examine 100,000 customer reviews left on the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). However, the data collected do not give any information on the category to which the customer belongs to. So, we applied natural language processing (NLP) algorithms to the reviews in order to find clues that could help us segment them, and then we trained two regression models, multiple linear regression and support vector regression, in order to calculate the coefficients acting on each of the 6 elementary scores (precision, cleanliness, check-in, communication, location, and value) noted on Airbnb, taking into account the category of customers who evaluated the performance of their accommodation. The results suggest that customers are not equally interested in satisfaction metrics. In addition, disparities were noted for the same indicator depending on the category to which the client belongs to. In light of these results, we suggest that improvements be made to the rating system adopted by Airbnb to make it suitable for each category to which the client belongs to.

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Exploring the Service Quality of Airbnb

Cited by 9 publications

References 12 publications

A big data approach to map the service quality of short-stay accommodation sharing

A big data approach to map the service quality of short-stay accommodation sharing

Service quality in airport hotel chains through the lens of online reviewers

A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP

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