In the cultural tourism field, there has been an increasing interest in exploiting data from online reviews adopting data-driven approaches finalized at understanding visitors’ perceptions. To date, the sparse studies on the measurement of online perception are mainly based on manual approaches to content analysis and do not compare the visitor and policy maker perspectives. This study addresses this gap, evaluating museum quality dimensions from online reviews of 100 Italian museums over a time-period of one year. Exploiting both a “top-down” approach to the analysis – supervised classification based on policy makers’ guidelines – and “bottom-up” approach – unsupervised topic model of online words of reviewers – the resulting quality dimensions are compared, allowing authors to discuss the potential to inform policy making through a user-generated data. Our research contributes to the discussions on the impact that different data analytics approaches have in supporting organizations’ decision making processes, by: (1) demonstrating that online reviews can actually provide valuable insights for the evaluation of service quality dimensions defined by decision makers; and (2) showing that a bottom-up approach starting directly from textual expressions in reviews is able to identify further dimensions and quality aspects, that go beyond the typical service-centred analysis performed by businesses and institutions.

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

Deborah Agostino;Marco Brambilla;Silvio Pavanetto;Paola Riva
2021

Abstract

In the cultural tourism field, there has been an increasing interest in exploiting data from online reviews adopting data-driven approaches finalized at understanding visitors’ perceptions. To date, the sparse studies on the measurement of online perception are mainly based on manual approaches to content analysis and do not compare the visitor and policy maker perspectives. This study addresses this gap, evaluating museum quality dimensions from online reviews of 100 Italian museums over a time-period of one year. Exploiting both a “top-down” approach to the analysis – supervised classification based on policy makers’ guidelines – and “bottom-up” approach – unsupervised topic model of online words of reviewers – the resulting quality dimensions are compared, allowing authors to discuss the potential to inform policy making through a user-generated data. Our research contributes to the discussions on the impact that different data analytics approaches have in supporting organizations’ decision making processes, by: (1) demonstrating that online reviews can actually provide valuable insights for the evaluation of service quality dimensions defined by decision makers; and (2) showing that a bottom-up approach starting directly from textual expressions in reviews is able to identify further dimensions and quality aspects, that go beyond the typical service-centred analysis performed by businesses and institutions.
Online user reviews; Visitor perception; Museum quality dimensions; User-driven quality dimensions; Text modelling; Online text analytics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1210783
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