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Exploring barriers to adoption of Virtual Reality through Social Media Analytics and Machine Learning – An assessment of technology, network, price and trialability

Author

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  • Laurell, Christofer
  • Sandström, Christian
  • Berthold, Adam
  • Larsson, Daniel

Abstract

This paper aims to assess how diffusion of Virtual Reality (VR) technology is taking place and identify potential barriers to increased adoption. This is done by utilising Social Media Analytics to collect a data set covering an empirical material of 6044 user-generated content concerning the market‑leading VR headsets Oculus Rift and HTC Vive, and machine learning to identify critical barriers to adoption. Our findings suggest that there is a lack of sufficient technological performance of these headsets and that more applications are required for this technology to take off. We contribute to literature on VR by providing a systematic assessment of current barriers to adoption while also pointing out implications for marketing.

Suggested Citation

  • Laurell, Christofer & Sandström, Christian & Berthold, Adam & Larsson, Daniel, 2019. "Exploring barriers to adoption of Virtual Reality through Social Media Analytics and Machine Learning – An assessment of technology, network, price and trialability," Journal of Business Research, Elsevier, vol. 100(C), pages 469-474.
    • Handle: RePEc:eee:jbrese:v:100:y:2019:i:c:p:469-474
    DOI: 10.1016/j.jbusres.2019.01.017
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    Citations

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    Cited by:

    1. Ali Yuce & Huseyin Arasli & Ali Ozturen & Mustafa Daskin, 2020. "Feeling the Service Product Closer: Triggering Visit Intention via Virtual Reality," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    2. Angélica Pigola & Priscila Rezende da Costa & Luísa Cagica Carvalho & Luciano Ferreira da Silva & Cláudia Terezinha Kniess & Emerson Antonio Maccari, 2021. "Artificial Intelligence-Driven Digital Technologies to the Implementation of the Sustainable Development Goals: A Perspective from Brazil and Portugal," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    3. Wedel, Michel & Bigné, Enrique & Zhang, Jie, 2020. "Virtual and augmented reality: Advancing research in consumer marketing," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 443-465.
    4. Laura Studen & Victor Tiberius, 2020. "Social Media, Quo Vadis? Prospective Development and Implications," Future Internet, MDPI, vol. 12(9), pages 1-22, August.
    5. Zhang, Tao & Li, Gang & Tayi, Giri Kumar, 2023. "A strategic analysis of virtual showrooms deployment in online retail platforms," Omega, Elsevier, vol. 117(C).
    6. Zachlod, Cécile & Samuel, Olga & Ochsner, Andrea & Werthmüller, Sarah, 2022. "Analytics of social media data – State of characteristics and application," Journal of Business Research, Elsevier, vol. 144(C), pages 1064-1076.
    7. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Tahereh Hasani & Norman O’Reilly & Ali Dehghantanha & Davar Rezania & Nadège Levallet, 2023. "Evaluating the adoption of cybersecurity and its influence on organizational performance," SN Business & Economics, Springer, vol. 3(5), pages 1-38, May.

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