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https://ruomo.lib.uom.gr/handle/7000/943
Τίτλος: | Digital Marketing Platforms and Customer Satisfaction: Identifying eWOM Using Big Data and Text Mining |
Συγγραφείς: | Kitsios, Fotis Kamariotou, Maria Karanikolas, Panagiotis Grigoroudis, Evangelos |
Τύπος: | Article |
Θέματα: | FRASCATI::Social sciences::Economics and Business FRASCATI::Social sciences::Economics and Business::Business and Management |
Λέξεις-Κλειδιά: | customer satisfaction innovation management hospitality big data text mining online reviews |
Ημερομηνία Έκδοσης: | 2021 |
Πηγή: | Applied Sciences |
Τόμος: | 11 |
Τεύχος: | 17 |
Πρώτη Σελίδα: | 8032 |
Επιτομή: | Big data analytics provides many opportunities to develop new avenues for understanding hospitality management and to support decision making in this field. User-generated content (UGC) provides benefits for hotel managers to gain feedback from customers and enhance specific product attributes or service characteristics in order to increase business value and support marketing activities. Many scholars have provided significant findings about the determinants of customers’ satisfaction in hospitality. However, most researchers primarily used research methodologies such as customer surveys, interviews, or focus groups to examine the determinants of customers’ satisfaction. Thus, more studies must explore how to use UGC to bridge the gap between guest satisfaction and online reviews. This paper examines and compares the aspects of satisfaction and dissatisfaction of Greek hotels’ guests. Text analytics was implemented to deconstruct hotel guest reviews and then examine their relationship with hotel satisfaction. This paper helps hotel managers determine specific product attributes or service characteristics that impact guest satisfaction and dissatisfaction and how hotel guests’ attitudes to those characteristics are affected by hotels’ market positioning and strategies. |
URI: | https://doi.org/10.3390/app11178032 https://ruomo.lib.uom.gr/handle/7000/943 |
ISSN: | 2076-3417 |
Αλλοι Προσδιοριστές: | 10.3390/app11178032 |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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applsci-11-08032-v2.pdf | 260,49 kB | Adobe PDF | Προβολή/Ανοιγμα |
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