Scholarlink

<p>Customer satisfaction (CS) in accommodation-sharing platforms such as Airbnb is a critical factor influencing repeat usage and platform reputation. However, previous research has yielded limited insight on the predictors of customer satisfaction in this context. This study employs machine learning (ML) algorithms like ensemble models, to analyse 51,707 Airbnb listings across ten European cities, distinguishing between core and peripheral service factors. The findings extend Expectation–Confirmation Theory (ECT) by demonstrating how core and peripheral attributes contribute differently to post-consumption evaluations. Results reveal that the service factors with the greatest values for estimating the overall customer satisfaction scores are cleanliness (core), distance from city center and metro station (core), and the price of the listing (core). Practical implications are discussed are discussed for hosts, while specific recommendations are provided for platform managers and policymakers in relation to service quality and customer retention.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

What factors predict the satisfaction ratings of Airbnb users in ten European cities: a machine learning approach

  • 期刊论文

  • 发表日期：

    2026年2月2日

查看全文
  • Nagihan Cakmakoglu Arici,
  • Mehmet Ali Koseoglu

摘要

Customer satisfaction (CS) in accommodation-sharing platforms such as Airbnb is a critical factor influencing repeat usage and platform reputation. However, previous research has yielded limited insight on the predictors of customer satisfaction in this context. This study employs machine learning (ML) algorithms like ensemble models, to analyse 51,707 Airbnb listings across ten European cities, distinguishing between core and peripheral service factors. The findings extend Expectation–Confirmation Theory (ECT) by demonstrating how core and peripheral attributes contribute differently to post-consumption evaluations. Results reveal that the service factors with the greatest values for estimating the overall customer satisfaction scores are cleanliness (core), distance from city center and metro station (core), and the price of the listing (core). Practical implications are discussed are discussed for hosts, while specific recommendations are provided for platform managers and policymakers in relation to service quality and customer retention.