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.
Highlights
In the cultural tourism field, there has been an increasing interest in adopting datadriven approaches to understand visitors’ perceptions (e.g., [1,2,3,4,5,6,7])
Scanning the content of these online reviews classified as Other Aspects, we found that these reviews were addressing many other aspects rather the one identified by the policy maker: the five quality dimensions defined by the policy maker are related to the services offered by the museum, such as ticketing, communication and activities, while the museum public does not necessarily underline only these service-related aspects but rather refers to additional aspects
The ‘top-down’ approach is based on a predefined set of expected service quality dimensions that are defined by the decision maker; once defined, these dimensions are automatically searched for within the dataset of online reviews, here by implementing an automated supervised keyword-based non-overlapping multiclass classifier for the Italian text of the reviews
Summary
In the cultural tourism field, there has been an increasing interest in adopting datadriven approaches to understand visitors’ perceptions (e.g., [1,2,3,4,5,6,7]) The research in this area offers several insights into the expectations of visitors [1], the opinions of travellers [8] or dimensions of service quality [9]. These analyses are quite diffused for touristic attractions such as hotels (e.g., [10]), there is much less evidence on the evaluation of quality dimensions as seen through users’ perceptions within museums. The literature on the identification of museum quality dimensions from online perceptions of museum visitors through online reviews is limited because most available contributions mainly focus on customer satisfaction analyses (e.g., [14,15]) and surveys (e.g., [16,17,18])
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