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Quality 4.0: big data analytics to explore service quality attributes and their relation to user sentiment in Airbnb reviews

Natalia Amat-Lefort (Universitat Internacional de Catalunya, Barcelona, Spain) (Science Based Business (SBB), Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands)
Federico Barravecchia (Department of Management and Production Engineering (DIGEP), Politecnico di Torino, Turin, Italy)
Luca Mastrogiacomo (Department of Management and Production Engineering (DIGEP), Politecnico di Torino, Turin, Italy)

International Journal of Quality & Reliability Management

ISSN: 0265-671X

Article publication date: 9 September 2022

Issue publication date: 23 March 2023

962

Abstract

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.

Keywords

Citation

Amat-Lefort, N., Barravecchia, F. and Mastrogiacomo, L. (2023), "Quality 4.0: big data analytics to explore service quality attributes and their relation to user sentiment in Airbnb reviews", International Journal of Quality & Reliability Management, Vol. 40 No. 4, pp. 990-1008. https://doi.org/10.1108/IJQRM-01-2022-0024

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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