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      Exploring Consumer Behavior in Virtual Reality Tourism Using an Extended Stimulus-Organism-Response Model

      1 , 1 , 2
      Journal of Travel Research
      SAGE Publications

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          Abstract

          Although virtual reality (VR) is an emerging technology in tourism, little research has been conducted on what factors make consumers visit destinations presented by VR. To address this gap in the literature, this study developed a theoretical framework including authentic experience, cognitive and affective responses, attachment, and visit intention with VR tourism using a stimulus-organism-response (SOR) theory. The results revealed significant impacts of authentic experience on cognitive and affective responses, indicating that authentic experience is an important factor in VR tourism. The study identified cognitive and affective responses as significant mediators in predicting attachment and visit intention. The results demonstrated that the intention to visit places shown in VR tourism was influenced by attachment to VR. Cognitive response had a stronger influence than affective response on the intention to visit a destination in VR. This study sheds light on why potential tourists visit destinations shown in VR.

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          Most cited references106

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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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              A power primer.

              One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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                Author and article information

                Contributors
                Journal
                Journal of Travel Research
                Journal of Travel Research
                SAGE Publications
                0047-2875
                1552-6763
                February 2020
                December 26 2018
                February 2020
                : 59
                : 1
                : 69-89
                Affiliations
                [1 ]College of Hotel & Tourism Management, Kyung Hee University, Seoul, Republic of Korea
                [2 ]Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester, United Kingdom
                Article
                10.1177/0047287518818915
                74b6651b-349f-46ef-8e32-8586bf5879a9
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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