TY  - BOOK
AU  - Thelwall, Mike
AU  - Sigala, Marianna
AU  - Rahimi, Roya
TI  - Big Data and Innovation in Tourism, Travel, and Hospitality
SN  - 9789811363412
PY  - 2019///
CY  - 
PB  - Springer
KW  - CLASS G - GEOGRAPHY, ANTHROPOLOGY, RECREATION
KW  - Big data
KW  - Management
KW  - Tourism
KW  -  Hospitality
N1  - Managerial Approaches, Techniques, and Applications; Includes bibliographical references; TOC:--; Composite Indicators for Measuring the Online Search Interest by a Tourist Destination--; Developing Smart Tourism Destinations with the Internet of Things--; Big Data in Online Travel Agencies and Its Application Through Electronic Devices--; Big Data for Measuring the Impact of Tourism Economic Development Programmes: A Process and Quality Criteria Framework for Using Big Data--; Research on Big Data, VGI, and the Tourism and Hospitality Sector: Concepts, Methods, and Geographies--; Sentiment Analysis for Tourism--; Location-Based Social Network Data for Tourism Destinations--; Identifying Innovative Idea Proposals with Topic Models-A Case Study from SPA Tourism--; Customer Data and Crisis Monitoring in Flanders and Brussels--; Analyzing Airbnb Customer Experience Feedback Using Text Mining--; Big Data as a Game Changer: How Does It Shape Business Intelligence Within a Tourism and Hospitality Industry Context?--; Strengthening Relational Ties and Building Loyalty Through Relational Innovation and Technology: Evidence from Spanish Hotel Guests--; Big Data and Its Supporting Elements: Implications for Tourism and Hospitality Marketing--
N2  - This book brings together multi-disciplinary research and practical evidence about the role and exploitation of big data in driving and supporting innovation in tourism. It also provides a consolidated framework and roadmap summarising the major issues that both researchers and practitioners have to address for effective big data innovation. ; The book proposes a process-based model to identify and implement big data innovation strategies in tourism. This process framework consists of four major parts: 1) inputs required for big data innovation; 2) processes required to implement big data innovation; 3) outcomes of big data innovation; and 4) contextual factors influencing big data exploitation and advances in big data exploitation for business innovation
UR  - https://link.springer.com/book/10.1007/978-981-13-6339-9
ER  -