Modelling Tourists’ Acceptance of Hotel Experience-Enhancement Smart Technologies
Donglin Han,
Huiying (Cynthia) Hou,
Hao Wu and
Joseph H. K. Lai
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Donglin Han: School of International Studies, Renmin University of China, Beijing 100872, China
Huiying (Cynthia) Hou: Department of Management in the Built Environment, Delft University of Technology, 2628 CD Delft, The Netherlands
Hao Wu: Faculty of Architecture Building and Planning, The University of Melbourne, Melbourne, VIC 3010, Australia
Joseph H. K. Lai: Department of Building Services Engineering, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hong Kong
Sustainability, 2021, vol. 13, issue 8, 1-19
Abstract:
Whereas the use of specific smart technologies in various sectors of the tourism industry has been under greater scrutiny in recent years, research that investigates tourists’ acceptance of smart technologies applied as a whole to hotels is largely underexplored. To address this shortfall, a study with a focus on tourists’ acceptance of experience-enhancement smart technologies has been conducted. A technology acceptance model based conceptual framework was developed, followed by a series of interviews with the managerial staff of ten hotels in Hong Kong that adopt experience-enhancement smart technologies. Based on the interview findings, a questionnaire was designed for use in a survey in which data were collected face-to-face from 312 tourists. Structural equation modelling was utilised to reveal the interrelationships between nine technology acceptance parameters. This study contributes knowledge to both real-world practice and research in tourism management.
Keywords: hotel; experience-enhancement smart technology; structural equation modelling; technology acceptance model; tourist (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
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