The Contribution of Online Reviews for Quality Evaluation of Cultural Tourism Offers: The Experience of Italian Museums

Deborah Agostino, Marco Brambilla, Silvio Pavanetto and Paola Riva
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Deborah Agostino: Economics and Industrial Engineering, Department of Management, Politecnico di Milano, Via Lambruschini 4/b, 20156 Milano, Italy
Marco Brambilla: Data Science Lab, Informazione e Bioingegneria, Dipartimento di Elettronica, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
Silvio Pavanetto: Informazione e Bioingegneria, Dipartimento di Elettronica, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
Paola Riva: Economics and Industrial Engineering, Department of Management, Politecnico di Milano, Via Lambruschini 4/b, 20156 Milano, Italy

Sustainability, 2021, vol. 13, issue 23, 1-20

Abstract: In the cultural tourism field, there has been an increasing interest in adopting data-driven approaches that are aimed at measuring the service quality dimensions through online reviews. To date, studies measuring quality dimensions in cultural tourism settings through content analysis of online user-generated reviews are mainly based on manual approaches. When the content analysis is automated, these studies do not compare different analytical approaches. Our paper enters this field by comparing two different automated content analysis approaches to evaluate which of the two is more adequate for assessing the quality dimensions through user-generated reviews in an empirical setting of 100 Italian museums. Specifically, we compare a ‘top-down’ content analysis approach that is based on a supervised classification built on policy makers’ guidelines and a ‘bottom-up’ approach that is based on an unsupervised topic model of the online words of reviewers. The resulting museum quality dimensions are compared, showing that the ‘bottom-up’ approach reveals additional quality dimensions compared with those obtained through the ‘top-down’ approach. The misalignment of the results of the ‘top-down’ and ‘bottom-up’ approaches to quality evaluation for museums enhances the critical discussion on the contribution that data analytics can offer to support decision making in cultural tourism.

Keywords: online user reviews; visitor perception; museum quality dimensions; user-driven quality dimensions; text modelling; online text analytics; user-generated content; data science; text mining; cultural tourism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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