质量(理念)
关系(数据库)
计算机科学
大数据
情绪分析
数据科学
分析
独创性
服务质量
主题模型
用户生成的内容
服务(商务)
数据质量
万维网
知识管理
情报检索
数据挖掘
营销
业务
人工智能
社会化媒体
心理学
哲学
创造力
认识论
社会心理学
作者
Natalia Amat-Lefort,Federico Barravecchia,Luca Mastrogiacomo
出处
期刊:International Journal of Quality & Reliability Management
[Emerald (MCB UP)]
日期:2022-09-09
卷期号:40 (4): 990-1008
被引量:8
标识
DOI:10.1108/ijqrm-01-2022-0024
摘要
Purpose Quality 4.0 is a new paradigm of quality management, which emphasises the need to adapt to recent technological innovations by updating traditional quality approaches. Amongst the most important factors for adopting Quality 4.0 is the leveraging of big data to collect insights and quality perceptions from clients. Therefore, user reviews have emerged as a valuable source of information, which can be analysed through machine learning procedures to uncover latent quality dimensions. Design/methodology/approach This study applies a combination of text mining techniques to analyse Airbnb reviews, identifying service quality attributes and assessing their relation to the users' sentiment. More than two million reviews written by guests in four European cities are analysed. First, topic modelling is applied to find the quality attributes mentioned by reviewers. Then, sentiment analysis is used to assess the positiveness/negativeness of the users' feedback. Findings A total of 37 quality attributes are identified. Four of them show a significant positive relation to the guest's sentiment: apartment views, host tips and advice, location and host friendliness. On the other hand, the following attributes are negatively correlated with user sentiment: sleep disturbance, website responsiveness, thermal management and hygiene issues. Originality/value This paper provides a practical example of how Quality 4.0 can be implemented, proposing a data-driven methodology to extract service quality attributes from user-generated content. Additionally, several attributes that had not appeared in existing Airbnb studies are identified, which can serve as a reference to extend previous quality assessment scales.
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