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Influence of Panoramic Vr Software on Tourists' on-Site Travel Intention: an Integrated Model Based on Tam and Idt

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DOI: 10.23977/csoc.2022.020108 | Downloads: 30 | Views: 1184

Author(s)

Zhao Xinyu 1

Affiliation(s)

1 Shenzhen Campus, Jinan University, Shenzhen, China

Corresponding Author

Zhao Xinyu

ABSTRACT

Using the panoramic VR Palace Museum section of the digital Palace Museum mini program of the Palace Museum, this paper explored the influence of the use of panoramic VR scenic tour software on tourists' on-site travel intention by integrating technology acceptance models and innovation diffusion theory, and introducing variables such as perceived playfulness and subjective norms. The findings of this study were as follows. Perceived usefulness and perceived playfulness are key factors influencing tourists' behavioral intention for on-site travel. Perceived ease of use and consumer innovativeness had a significant positive influence on the perceived usefulness. Consumer innovativeness and subjective norms had a significant positive influence on the perceived ease of use and perceived playfulness. Finally, the paper concluded with recommendations based on the conclusions, with a view to informing tourism enterprises in their digital transformation.

KEYWORDS

Panoramic vr, On-site travel intention, Consumer innovativeness, Technology acceptance models, Innovation diffusion theory

CITE THIS PAPER

Zhao Xinyu, Influence of Panoramic Vr Software on Tourists' on-Site Travel Intention: an Integrated Model Based on Tam and Idt. Cloud and Service-Oriented Computing (2022) Vol. 2: 55-61. DOI: http://dx.doi.org/10.23977/csoc.2022.020108.

REFERENCES

[1] Hjalager Anne Mette. A review of innovation research in tourism[J]. Tourism Management, 2009, 31(1): 1-12.
[2] ZHOU B, ZHOU L Q, WU M Y. Influence of augmented reality on tourists' tourism intention in the context of smart tourism: a revised model based on technology acceptance model[J]. Journal of business economics, 2017, (02):71-79.
[3] XU F F, HUANG L. Tourists' willingness to use smart tourist attractions system: an integrated model based on TAM and TTF[J]. Tourism Tribune, 2018, 33(08):108-117.
[4] LIU H Y, YAN M J. Influence of short mobile videos on tourist behavioral intentions [J]. Tourism Tribune, 2021, 36(10): 62-73.
[5] DONG X W, YE Z J, XU N N, WANG Y L, GUAN J J, CHEN J. Tourists' intention to book freelance tour guide online based on technology acceptance model and technology readiness index[J]. Tourism Tribune, 2020, 35(07):24-35.
[6] YAO Y H, LUAN W X. Factors influencing yacht tourism consumption behavior based on the TAM-IDT model[J]. Tourism Tribune, 2019, 34(02):60-71.
[7] Fred D. Davis and Richard P. Bagozzi and Paul R. Warshaw. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models [J]. Management Science, 1989, 35(8): 982-1003.
[8] HE W Y, HE R. Empirical study of influence factors of public market diffusion on new energy vehicles: based on TAM and IDT theory [J]. Journal of Dalian University of Technology (Social Sciences), 2015, 36(03):28-33.
[9] Yuanquan Li and Jiayin Qi and Huaying Shu. Review of Relationships Among Variables in TAM[J]. Tsinghua Science & Technology, 2008, 13(3): 273-278.
[10] Yu J , Ha I , Choi M , et al.. Extending the TAM for a t-commerce[J]. Information & Management, 2004, 42(7): 965-976.
[11] JiWon Moon and YoungGul Kim. Extending the TAM for a World-Wide-Web context[J]. Information & Management, 2001, 38(4): 217-230.
[12] Chia-Lin, Hsu, Kuo-Chien, et al. The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators[J]. Information Systems and e-Business Management, 2012, 10(4): 549-570.
[13] Kenneth C.C. Yang. Exploring factors affecting the adoption of mobile commerce in Singapore[J]. Telematics and Informatics, 2004, 22(3): 257-277.
[14] Xiang Y, Wu X, Chen Q. Personal innovativeness and initial adoption of M-Commerce: Toward an integrated model. IEEE, 2008.
[15] Subin Im and Charlotte H. Mason and Mark B. Houston. Does innate consumer innovativeness relate to new product/service adoption behavior? The intervening role of social learning via vicarious innovativeness[J]. Journal of the Academy of Marketing Science, 2007, 35(1): 63-75.
[16] Ajzen I. The theory of planned behavior[J]. Organizational Behavior & Human Decision Processes, 1991, 50(2):179–211.
[17] Hauptmann S, Gerlach L. Microblogging as a Tool for Networked Learning in Production Networks, 2010.
[18] Timothy Teo. The Impact of Subjective Norm and Facilitating Conditions on Pre-Service Teachers' Attitude toward Computer Use: A Structural Equation Modeling of an Extended Technology Acceptance Model[J]. Journal of Educational Computing Research, 2009, 40(1): 89-109.

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