Paper Info
Title
Potentials of changing customer needs in a digital world - a conceptual model and recommendations for action in tourism.
Abstract
While a few studies have already shown the potential benefits of digitization so far, there are no empirically developed approaches demonstrating the potentials of changing customer needs in a digital world in the tourism. This paper develops such a conceptual model, which is able to explore the customer needs in the tourism industry. An empirical, qualitative data collection by expert interviews forms the data basis. Grounded Theory is used to evaluate the transcribed interviews, as this method is particularly suitable for identifying new or unknown relationships. This methodology of qualitative social research serves to derive theories from previously categorized data without using concepts generated from literature. The objective of this study is to provide recommendations for touristic companies that have to face the challenges and the changing market environment through digitization. The main factors influencing the potentials of changing customer needs identified (digital services, digital marketing, data mining and online travel communities) form the conceptual model in order to present recommendations for action. The results of the study show that a personalized approach to the customer on digital communication channels represents an essential requirement in the future provision of services. In order to meet the changing needs, bilateral communication between customers and companies must be guaranteed during the entire customer journey, especially in structurally weak regions. (C) 2018 The Authors. Published by Elsevier Ltd.
Year
DOI
Venue
2018
10.1016/j.procs.2018.08.120
Procedia Computer Science
Keywords
Field
DocType
changing customer need,potentials,digitization,tourism,empirical results,recommendations,experts,qualitative study
Grounded theory,Digitization,Conceptual model,Qualitative property,Market environment,Computer science,Tourism,Knowledge management,Digital marketing,Artificial intelligence,Social research,Machine learning
Conference
Volume
ISSN
Citations 
126
1877-0509
0
PageRank 
References 
Authors
0.34
1
2
Name
Order
Citations
PageRank
Christopher Reichstein151.78
Ralf-Christian Härting25313.55