Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/943
Title: Digital Marketing Platforms and Customer Satisfaction: Identifying eWOM Using Big Data and Text Mining
Authors: Kitsios, Fotis
Kamariotou, Maria
Karanikolas, Panagiotis
Grigoroudis, Evangelos
Type: Article
Subjects: FRASCATI::Social sciences::Economics and Business
FRASCATI::Social sciences::Economics and Business::Business and Management
Keywords: customer satisfaction
innovation management
hospitality
big data
text mining
online reviews
Issue Date: 2021
Source: Applied Sciences
Volume: 11
Issue: 17
First Page: 8032
Abstract: 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
Other Identifiers: 10.3390/app11178032
Appears in Collections:Department of Applied Informatics

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