Your browser does not support JavaScript!

Doctoral theses

Current Record: 7 of 31

Back to Results Previous page
Next page
Add to Basket
[Add to Basket]
Identifier 000430223
Title Data-driven innovation : location and big data analytics for knowledge extraction in tourism destinations
Alternative Title Καινοτομία δεδομένων : ανάλυση τοποθεσίας και μεγάλων δεδομένων για την εξόρυξη γνώσης σε τουριστικούς προορισμούς
Author Βασσάκης, Κωνσταντίνος
Thesis advisor Πετράκης Εμμανουήλ
Reviewer Τσιώτας Γεώργιος
Κοπανάκης Ιωάννης
Abstract This Ph.D. thesis is written and submitted to the Department of Economics of the University of Crete, Greece, as a partial fulfillment of my obligations as a PhD Candidate. It consists of three separate chapters that study the applications of location and big data analytics for knowledge extraction in tourism destinations.The first chapter of the thesis entitled, "Big data Analytics: Applications, prospects and challenges", studies the role and impact of big data analytics in innovation and value creation. The tremendous increase of data through the Internet of Things (connected devices) has led to a “big-data era”, where big data analytics are applicable in every sector and economy globally. The growing expansion of available data is commonly accepted, while valuable knowledge arising from the information comes from big data analysis processes. The prospects of big data analytics are important and the benefits for data-driven organizations are significant determinants for competitiveness and innovation performance. However, there are considerable obstacles to adopting a data-driven approach and get valuable knowledge through big data. The second chapter of the thesis entitled, "Knowledge Extraction through Location & Big Data Analytics: the case of Crete", explores the knowledge extraction using location and big data analytics techniques. Nowadays, tourists generate massive volumes of data (big data) during their visit to an urban destination. However, there is little knowledge of their spatial activity and perceptions. Enterprises and organizations in hospitality and tourism are able to exploit actual behavioural data - perceptions derived from big data generated in real-time from online data sources in contrast to traditional customer surveys based on questionnaires. An innovative approach is demonstrated using the case study of Crete by integrating big data techniques, location intelligence and social media transforming tourist experiences into valuable assets (new knowledge extraction) for quicker and more efficient decision making. More specifically, the approach introduces the combination of textual and photo analytics with data derived from media sharing and textual social networks, introduces social big data analytics such as social engagement, sentiment analysis, topic/label detection combined with spatio-temporal features to provide more insights about tourist destinations. Research findings demonstrate how this novel approach of location and big data analytics, in contrast to traditional tourist surveys and conventional spatio-temporal data, can provide new and valuable knowledge. Implications arising from the study are significant assets for tourism SMEs, DMOs and other tourism stakeholders in the search of innovative marketing strategies for demonstrating the added value of destination, strengthening destination branding and gaining a competitive advantage against other rival tourist destinations. The third chapter of the thesis entitled, "Big Data Analytics for Tourism Destinations: A comparative analysis through Location-Based Social Networks", investigates the user-generated data in Location-based Social Networks (LBSNs) that can be a great resource of knowledge for understanding people’s behaviour details and movement flows in tourism destinations. Nowadays, local authorities and tourism enterprises are using conventional methods like surveys and opinion polls for collecting data and strategic decision making. Despite the benefits of these approaches, they present significant disadvantages such as the sample size is small and they are time - consuming. Focusing on tourism and location-based social media networks, this chapter reveals a novel approach to leverage massive unstructured data for knowledge extraction. In contrast to the conventional spatio-temporal data, big social media data offer dynamically to innovation and value creation through improving the strategic decision-making process of tourism destination stakeholders. The approach integrates location and big data analytics techniques and it is implemented based upon geotagged user-generated data shared on the two largest islands in the Mediterranean Sea, the island of Crete (Greece), and the island of Cyprus that are popular summertime tourist destinations. The comparison between two tourist destinations with common characteristics provides additional insights into the potential of each destination and areas of improvement. Practical implications are arising through the efficient spatio-temporal and demographic analysis of tourist movement in both tourism destinations for improving the strategic decision making of stakeholders like local authorities and tourism SMEs leading to innovation and value creation. In addition, DMOs can leverage the new knowledge for developing innovative marketing strategies, strengthening destination branding and gaining a competitive advantage against rival tourism destinations.
Language English
Subject Big data
Big data analytics
Location-based social networks
Social media
Tourism destinations
Ανάλυση μεγάλων δεδομένων
Κοινωνικά δίκτυα
Μεγάλα δεδομένα
Μικρομεσαίες επιχειρήσεις (ΜΜΕ)
Τουριστικοί προορισμοί
Issue date 2019-12-11
Collection   School/Department--School of Social Sciences--Department of Economics--Doctoral theses
  Type of Work--Doctoral theses
Permanent Link Bookmark and Share
Views 103

Digital Documents
No preview available

Download document
View document
Views : 1