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      Customer Acceptance of Autonomous Vehicles in Travel and Tourism

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          Abstract

          Since the COVID-19 pandemic has significantly increased the use of personal vehicles in travel, adoption of self-driving autonomous vehicles can radically transform the travel industry. Thus, this study develops and tests a conceptual autonomous vehicle acceptance model that identifies hedonic motivation, trust in autonomous vehicles and social influence as critical determinants of performance expectancy, perceived risk and emotions, which determine travelers’ intentions to use autonomous vehicles (AVs) utilizing the Cognitive Appraisal Theory and the Artificially Intelligent Device Use Acceptance model as conceptual frameworks. Findings indicate that trust is the most powerful determinant of performance expectancy and essential to decrease risk perceptions. Furthermore, performance expectancy and hedonic motivation are critical determinants of travelers’ positive emotions, which in turn determines the acceptance of AVs. Contribution to theoretical knowledge and implications for practice are provided, and limitations and recommendations for future studies are discussed.

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          Most cited references83

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            Common method biases in behavioral research: A critical review of the literature and recommended remedies.

            Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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              Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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                Author and article information

                Contributors
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                Journal
                Journal of Travel Research
                Journal of Travel Research
                SAGE Publications
                0047-2875
                1552-6763
                February 28 2021
                : 004728752199357
                Affiliations
                [1 ]School of Hospitality & Tourism Management, Faculty of Arts and Social Science, University of Surrey, Guildford, United Kingdom
                [2 ]School of Tourism and Hospitality, University of Johannesburg, Johannesburg, South Africa
                [3 ]School of Hospitality Business Management, Carson College of Business, Washington State University, Pullman, WA, USA
                Article
                10.1177/0047287521993578
                dba60d92-274e-4b39-9d79-58bf6c67d7ca
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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