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(wow) Words Of Wonders Level 2071 Answers
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Date Received: 20 October 2021 / Date Revision: 24 November 2021 / Date Accepted: 27 November 2021 / Date Published: 2 December 2021
In the field of cultural tourism, there is growing interest in applying data-driven approaches to measure service quality dimensions through online reviews. To date, research using content analysis of user-generated online reviews to assess the environmental quality aspects of cultural tourism has been largely manual. These studies did not compare different analysis methods when content analysis was automated. Our article enters this field by comparing two different automated content analysis methods to assess which is more suitable for assessing quality dimensions using user-generated ratings in an empirical setting of 100 Italian museums. Specifically, we compare a top-down approach to content analysis based on supervised taxonomy based on policymaker guidelines and a bottom-up approach based on unsupervised topic models created by online reviewers. A comparison of the resulting dimensions of museum quality shows that the bottom-up approach reveals additional dimensions of quality compared to the top-down approach. The difference between top-down and bottom-up approaches to museum quality assessment has sparked a critical debate about the contribution of data analysis to cultural tourism decision-making.
Online user reviews; visitor perception; museum quality dimensions; user choice quality dimensions; text modeling; online text analysis; user-generated content; data science; text mining; cultural tourism
In the field of cultural tourism, there is increasing interest in applying data-driven approaches to understand tourist perceptions (e.g. [1, 2, 3, 4, 5, 6, 7]). Research in this area provides different insights into tourist expectations [1], tourist opinions [8] or service quality dimensions [9]. Although these analyzes are common in tourist facilities such as hotels (e.g. [10]), there is much less evidence for evaluating quality dimensions in museum consumer perceptions. This is mainly due to the lack of a clear definition of quality dimensions for museums [11], as opposed to hotels, which are based on pre-defined dimensions such as cleanliness, location, room, value and service online reviews, platforms (eg [10]).
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Although still understudied, museums are an important area of research in the field of tourism, as these facilities increase the attractiveness of destinations [12] and contribute to the economic development of tourist areas [13]. The literature on identifying dimensions of museum quality from museum visitors’ online perceptions via online reviews is limited, as most available contributions focus on customer satisfaction analyzes (e.g. [14, 15]) and surveys (e.g. [16, 17, 18]) on. ]).
Online ratings have long been an important source of data analysis in the field of tourism (e.g. [19, 20]), but research on these data sources is mainly based on numerical ratings provided by rating platforms in museum settings (e.g. 21]. However, online Reviews are primarily characterized by textual data, i.e. reviews written by tourists on travel experiences. Although text data are valuable data sources for measuring visitor experience (e.g. [20]), automated analysis of these data sources is rarely performed in museum settings. In fact, artificial online feedback analysis methods have recently been used to study visitor perception, not just customer satisfaction surveys (eg [9, 22, 23, 24]). For example, a study in [9] used online reviews to examine service quality dimensions of museums, but content analysis was done manually. Although automated text analysis tools have been shown to be useful for examining quality dimensions in various application settings (e.g. [19, 20]), to the best of our knowledge there are still few studies analyzing online reviews to automatically Dimensions identifying museum quality as expressed by visitors through online polls.
To fill these gaps, this study compares two different approaches to automated text analysis of TripAdvisor data, referred to here as top-down and bottom-up, to assess which approach is more suitable for assessing quality dimensions. Through user-generated content in an empirical context of 100 Italian museums. Top-down approaches are based on a predefined set of expected service quality dimensions, while bottom-up approaches aim to identify hidden quality aspects.
The first research question (RQ1) assesses quality dimensions of museums using a top-down approach; that is, a set of predefined dimensions is defined by decision makers (i.e., policy makers), and we use keyword-based classifier to analyze expected dimensions in online review text. The second research question (RQ2) uses a bottom-up approach to assess quality dimensions of museums; that is, latent quality dimensions are derived directly from textual descriptions of visitor experiences based on Latent Dirichlet Allocation (LDA) [25], while There is no set of predefined quality dimensions identified. The third research question (RQ3) compares the results of the two approaches, showing when the bottom-up approach outperforms the top-down approach, thereby critically discussing the implications of different automation approaches. Data analysis can aid in decision making.
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Our study contributes to the discussion of the impact of different data analysis methods on organizational decision-making. The paper is structured as follows: Chapter 2 presents the literature on the role of online user-generated data in tourism quality assessment, with a particular focus on museums. Section 3 presents the methodology, detailing the available datasets and the two analytical methods used to analyze the online data. Section 4 presents the results and Section 5 provides a critical review of them.
In the cultural tourism literature, online reviews have become an invaluable source of data for studying the quality of experience dimension; this data allows for the collection of large amounts of user data without asking visitors for this information, since visitors voluntarily share this information in a highly personalized manner. Content [26]. This is in contrast to customer satisfaction surveys, which require constructing questions and scales to measure experience dimensions with numerical ratings (eg, [16, 17, 18, 27, 28, 29, 30]).
In addition to the recognition that online reviews are useful for understanding consumers and their perceptions of cultural experiences, research on cultural tourism has increased significantly in recent years, and the literature on the topic can be divided into two main directions. In the first stream, online reviews are mostly consumed manually by encoding content and manually categorizing online reviews (e.g. [9, 23]). The second stream automatically uses online reviews, but the method varies by study. Several studies have taken a top-down approach to online review content by automatically searching for predefined quality aspects in datasets (e.g. [31, 32, 33]). Other studies have taken a bottom-up approach to the content of online reviews, looking for dimensions of experience without defining a priori a set of quality dimensions (e.g. [7, 24, 26, 34, 35]).
The existence of different automated data analysis methods raises questions about how they differ and whether one of the two methods is more suitable than the other [36]. Our study compares top-down
