Εντοπίστηκε ένα σφάλμα στη λειτουργία της ΠΥΞΙΔΑΣ όταν χρησιμοποιείται μέσω του προγράμματος περιήγησης Safari. Μέχρι να αποκατασταθεί το πρόβλημα, προτείνουμε τη χρήση εναλλακτικού browser όπως ο Chrome ή ο Firefox. A bug has been identified in the operation of the PYXIDA platform when accessed via the Safari browser. Until the problem is resolved, we recommend using an alternative browser such as Chrome or Firefox.
Λογότυπο αποθετηρίου

Μεταπτυχιακές Εργασίες

Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/42

Περιήγηση

Πλοήγηση Μεταπτυχιακές Εργασίες ανά Συγγραφέα "Chatzis, Georgios"

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω
Φιλτράρετε τα αποτελέσματα πληκτρολογώντας τα πρώτα γράμματα
Τώρα δείχνει 1 - 1 από 1
  • Μικρογραφία εικόνας
    Τεκμήριο
    Utilization of online customer reviews to identify latent dimensions of customer satisfaction and predict customer recommendation intentions: the case of Greek airline industry
    (2021) Chatzis, Georgios; Athens University of Economics and Business, Department of Marketing and Communication; Karantinou, Kalipso; Painesis, Grigorios; Drossos, Dimitrios
    The market in which airline companies operate, has faced several challenges during the previous years in a global scope. These challenges in combination with the low profit margins of the industry and the appearance of low-cost carriers, have made the market highly competitive. As a result, airline companies try to differentiate themselves from their competitors and achieve competitive advantage, by identifying those dimensions of customer satisfaction which influence passengers loyalty and advocacy.Traditional research methods, employed by airline companies over the previous years, have failed to extract valuable information by analyzing customer feedback and translate it into useful insights. This failure is mainly caused by the rapid growth of web 2.0 applications which changed the way consumers search, produce, consume, and share information. User generated content (UGC) and particularly online customer reviews (OCRs) is considered as a new impulsive, independent and reliable source of information that airline companies should take advantage of.This research utilizes text mining techniques from the field of machine learning (ML) and natural language processing (NLP), such as the Latent Dirichlet Allocation (LDA) model to identify and mine latent dimensions of customer satisfaction as expressed in OCRs. It also uses a lexicon-based sentiment analysis to calculate the sentiment polarity scores of each aforementioned extracted dimension as they are expressed in every OCR. The sentiment polarity scores are then fed in a discriminant function analysis which tests and validates the mined dimensions of customer satisfaction, and ultimately identifies those dimensions which influence customers’ recommendation behavior, the most.The LDA model revealed eight latent dimensions of customer satisfaction, but after the implementation of the discriminant function analysis only six of them namely, Food & Beverages ,Ground Service, Luggage, Cabin crew, Problems, and Customer Service, influence passengers’ recommendation decisions.