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Consumer's adoption of virtual reality technologies for marine conservation: Motivational and technology acceptance perspectives

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  • Yuen, Kum Fai
  • Chua, Jessana
  • Li, Kevin X.
  • Wang, Xueqin

Abstract

As the marine ecosystem deteriorates over time, more efforts are made to raise public awareness. Virtual reality technology in marine ecotourism (VRTME) enhances the travel experiences of those affected by the COVID-19 pandemic. This study adopts Maslow's hierarchy of motivational needs and the technology acceptance model to investigate the factors influencing consumers' behavioural intention (BI) towards VRTME and examines their interrelationships. An online survey was conducted with 451 people, and the results were analysed using structural equation modelling. The findings show how motivational constructs, namely, environmental knowledge, biospheric values, risk perception of marine pollution, social susceptibility, and green self-image, as mediated by perceived usefulness (PU), affect the adoption of VRTME. Perceived ease of use (PEOU) further impacted PU. The total effect analysis indicated that PU had the strongest influence, followed by PEOU, social susceptibility, biospheric values, environmental knowledge, green self-image, and risk perception of marine pollution. To substantiate the constructs motivating BI, this study combined two disparate research paradigms centred on the environmental needs and technological characteristics of VRTME. Overall, this study contributes to the existing literature on consumers' adoption of VRTME by introducing new perspectives and recommending policies to raise public awareness and support ongoing conservation efforts.

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  • Yuen, Kum Fai & Chua, Jessana & Li, Kevin X. & Wang, Xueqin, 2022. "Consumer's adoption of virtual reality technologies for marine conservation: Motivational and technology acceptance perspectives," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:tefoso:v:182:y:2022:i:c:s0040162522004140
    DOI: 10.1016/j.techfore.2022.121891
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    1. Huang, Dan & Jin, Xin & Coghlan, Alexandra, 2021. "Advances in consumer innovation resistance research: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Sabrina Stöckli & Doris Hofer, 2020. "Susceptibility to social influence predicts behavior on Facebook," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    3. Nikhil Menon & Yu Zhang & Abdul Rawoof Pinjari & Fred Mannering, 2020. "A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(3), pages 253-278, April.
    4. Yuen, Kum Fai & Bin Saidi, Muhammad Syukri & Bai, Xiwen & Wang, Xueqin, 2021. "Cruise transport service usage post COVID-19: The health belief model application," Transport Policy, Elsevier, vol. 111(C), pages 185-196.
    5. FIORILLO, Damiano & SENATORE, Luigi, 2016. "Self Image and Environmental Attitude and Behavior," CELPE Discussion Papers 140, CELPE - CEnter for Labor and Political Economics, University of Salerno, Italy.
    6. Alan T. Sorensen, 2006. "Social learning and health plan choice," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 929-945, December.
    7. Cui, Lixin & Wang, Yonggui & Chen, Weiming & Wen, Wen & Han, Myat Su, 2021. "Predicting determinants of consumers' purchase motivation for electric vehicles: An application of Maslow's hierarchy of needs model," Energy Policy, Elsevier, vol. 151(C).
    8. Alan T. Sorensen, 2006. "Social learning and health plan choice," RAND Journal of Economics, The RAND Corporation, vol. 37(4), pages 929-945, December.
    9. Yuen, Kum Fai & Wang, Xueqin & Ma, Fei & Wong, Yiik Diew, 2019. "The determinants of customers’ intention to use smart lockers for last-mile deliveries," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 316-326.
    10. Ponzoa, José M. & Gómez, Andrés & Villaverde, Silvia & Díaz, Vicente, 2021. "Technologically empowered? perception and acceptance of AR glasses and 3D printers in new generations of consumers," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Wang, Xueqin & Wong, Yiik Diew & Liu, Feng & Yuen, Kum Fai, 2021. "A push–pull–mooring view on technology-dependent shopping under social distancing: When technology needs meet health concerns," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    13. Xianwei Liu & Yang Zou & Jianping Wu, 2018. "Factors Influencing Public-Sphere Pro-Environmental Behavior among Mongolian College Students: A Test of Value–Belief–Norm Theory," Sustainability, MDPI, vol. 10(5), pages 1-19, May.
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    3. Hoang, Sinh Duc & Dey, Sandeep Kumar & Tučková, Zuzana & Pham, Tien Phat, 2023. "Harnessing the power of virtual reality: Enhancing telepresence and inspiring sustainable travel intentions in the tourism industry," Technology in Society, Elsevier, vol. 75(C).
    4. Saif, Mashaal A.M. & Hussin, Nazimah & Husin, Maizaitulaidawati Md & Muneer, Amgad & Alwadain, Ayed, 2024. "Beyond conventions: Unravelling perceived value's role in shaping digital-only banks' adoption," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    5. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    6. Wang, Xueqin & Wong, Yiik Diew & Liu, Feng & Yuen, Kum Fai, 2023. "Consumers' paradoxical motives of co-creation: From self-service technology to crowd-sourcing platform," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    7. Allal-Chérif, Oihab & Puertas, Rosa & Carracedo, Patricia, 2024. "Intelligent influencer marketing: how AI-powered virtual influencers outperform human influencers," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    8. Yohan Wismantoro & Vincent Didiek Wiet Aryanto & Imang Dapit Pamungkas & Nanda Adhi Purusa & Amron & Amalia Nur Chasanah & Usman, 2022. "Virtual Reality Destination Experiences Model: A Moderating Variable between Wisesa Sustainable Tourism Behavior and Tourists’ Intention to Visit," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    9. Fang, Xiaohan & Zhang, Ying & Yang, Jiajia & Zhan, Guohua, 2024. "An evaluation of marine economy sustainable development and the ramifications of digital technologies in China coastal regions," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 554-570.

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