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An integrated TOPSIS-MOORA-based performance evaluation methodology for the key service providers in sharing economy:case of Airbnb superhosts

机译:基于集成的Topsis-Moora绩效评估方法,适用于分享经济的主要服务提供商:Airbnb超强案例

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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.
机译:目的 - 在分享经济的背景下,Airbnb的超级阶段作为一种现象成功的故事,这已经改变了旅游业,并获得了顽固的人气。适当的性能评估和超强稳定性的分类对于激励超强峰来保持更高的服务质量至关重要。本文的主要目的是设计一个基于集成的多轨道决策(MCDM)方法的绩效评估和分类框架,用于Airbnb的超级卓越,并研究各种上下文因素的变化,如价格,列表和取消政策穿过超强骨头。设计/方法/方法 - 这项工作考虑了三种加权技术,平均值,熵和基于评论的方法,以确定因素的重量。对于每个加权技术,已经开发了基于集成的Topsis-Moora的性能评估方法和分类框架。拟议的方法已经应用于纽约市超级数据的绩效评估,使用来自Airbnb的真实数据。结果 - 从绩效评估的角度来看,使用非参数Wilcoxon签名 - 等级测试,已经突出了设计综合方法而不是采用单一方法的重要性。根据上下文专用的调查结果,已经观察到最高类别中的价格和上市人数最高。实际意义 - 建议的方法促进了排行榜的设计来激励服务提供商更好地执行。此外,它可以适用于其他住宿共享经济平台和乘车共享平台。原创性/价值 - 这是第一个为在旅游业背景下为分享经济的服务提供商提供绩效评估和分类框架的第一项工作。

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