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PurposeIn 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/approachThis 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.FindingsFrom 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 implicationsThe 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/valueThis 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.

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PurposeThis study aims to analyze the socio-ecological policy trade-off caused by technological innovations in the post-COVID-19 era. The study outcomes are utilized to design a comprehensive policy framework for attaining sustainable development goals (SDGs).Design/methodology/approachStudy is done for 100 countries over 1991–2019. Second-generation estimation method is used. Innovation is measured by total factor productivity, environmental quality is measured by carbon dioxide (CO2) emissions and social dimension is captured by unemployment.FindingsInnovation–CO2 emissions association is found to be inverted U-shaped and innovation–unemployment association is found to be U-shaped.Research limitations/implicationsThe study outcomes show the conflicting impact of technological innovation leading to policy trade-off. This dual impact of innovation is considered during policy recommendation.Practical implicationsThe policy framework recommended in the study shows a way to address the objectives of SDG 8, 9 and 13 during post-COVID-19 period.Social implicationsPolicy recommendations in the study show a way to internalize the negative social externality exerted by innovation.Originality/valueThis study contributes to the literature by considering the policy trade-off caused by innovation and recommending an SDG-oriented policy framework for the post-COVID-19 era.