Abstract:
Purpose – In the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal
success story that has transformed the tourism industry and garnered humongous popularity. Proper
performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain
higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making
(MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and
to study the variation in various contextual factors such as price, number of listings and cancelation policy
across the superhosts.
Design/methodology/approach – This work considers three weighting techniques, mean, entropy and
CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated
TOPSIS-MOORA-based performance evaluation method and classification framework have been developed.
The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New
York City using real data from Airbnb.
Findings – From the perspective of performance evaluation, the importance of devising an integrated
methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon
signed-rank test. As per the context-specific findings, it has been observed that the price and the number of
listings are the highest for the superhosts in the topmost category.
Practical implications – The proposed methodology facilitates the design of a leaderboard to motivate
service providers to perform better. Also, it can be applicable in other accommodation-sharing economy
platforms and ride-sharing platforms.
Originality/value – This is the first work that proposes a performance evaluation and classification
framework for the service providers of the sharing economy in the context of tourism industry.