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Actualizing big data analytics for smart cities: A cascading affordance study
Institution:1. School of Economics and Management, Beijing Jiaotong University, China;2. School of Information Systems and Technology Management, UNSW Business School, UNSW Sydney, Australia;3. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China;1. Department of Computer and Information Science, Higher Colleges of Technology, Dubai, United Arab Emirates;2. Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Ontario, Canada;3. Department of International Studies, Glendon College, York University, Toronto, ON, M4N 3M6, Canada;4. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, B3H4R2, Canada;5. Faculty of Management, Dalhousie University, Halifax, Nova Scotia, B3H4R2, Canada;1. University of Nevada, Las Vegas, Las Vegas, NV, 89154, United States;2. Towson University, Towson, MD, 21252, United States;3. Western Carolina University, Cullowhee, NC, 28723, United States;4. University of Virginia, Charlottesville, VA, 22903, United States;1. School of Economics and Management, Beihang University, China;2. UNSW Business School, University of New South Wales, Australia;3. Baylor University, USA;4. Lund University, Sweden;1. Business and Law, Manchester Metropolitan University, Manchester, M15 6BH, UK;2. Informatics Research Centre, University of Reading, Reading, RG6 6UD, UK;3. King Abdulaziz University, Jeddah, Saudi Arabia;4. Information Technology University, Jeddah, Lahore, Pakistan;1. Data Science, 84.51°, United States;2. Logistikum, University of Applied Sciences Upper Austria, Austria;3. Mississippi State University, United States;4. California State University Long Beach, United States
Abstract:This study investigates how Big Data Analytics (BDA) can be leveraged to support a city’s transformation into a smart destination. We conduct an in-depth case study of a city-in-transformation and adopt the perspective of technology affordances to uncover the varying opportunities enabled by BDA to facilitate the attainment of smart tourism goals. Our findings unveil three types of BDA affordances and demonstrate how these affordances are actualized in a cascading manner to enable informed decisions and a sustainable development of smart tourism. Implications are presented for future investigation of the affordances of BDA in smart tourism, as well as for policy makers and practitioners who engage in the development of innovative tourism services for the smart citizens.
Keywords:Big data analytics  Cascading affordances  Smart cities  Smart tourism
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