IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v60y2021ics026840122100075x.html
   My bibliography  Save this article

Consumer engagement via interactive artificial intelligence and mixed reality

Author

Listed:
  • Sung, Eunyoung (Christine)
  • Bae, Sujin
  • Han, Dai-In Danny
  • Kwon, Ohbyung

Abstract

The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.

Suggested Citation

  • Sung, Eunyoung (Christine) & Bae, Sujin & Han, Dai-In Danny & Kwon, Ohbyung, 2021. "Consumer engagement via interactive artificial intelligence and mixed reality," International Journal of Information Management, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:ininma:v:60:y:2021:i:c:s026840122100075x
    DOI: 10.1016/j.ijinfomgt.2021.102382
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S026840122100075X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2021.102382?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    2. Rahman, Muhammad Sabbir & Bag, Surajit & Hossain, Md Afnan & Abdel Fattah, Fadi Abdel Muniem & Gani, Mohammad Osman & Rana, Nripendra P., 2023. "The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    3. Chunlin Yuan & Shuman Wang & Yue Liu, 2023. "AI service impacts on brand image and customer equity: empirical evidence from China," Journal of Brand Management, Palgrave Macmillan, vol. 30(1), pages 61-76, January.
    4. Baabdullah, Abdullah M. & Alalwan, Ali Abdallah & Algharabat, Raed S. & Metri, Bhimaraya & Rana, Nripendra P., 2022. "Virtual agents and flow experience: An empirical examination of AI-powered chatbots," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Liu, Hua & Ma, Ruili & He, Guangyao & Lamrabet, Abdesslam & Fu, Shaoling, 2023. "The impact of blockchain technology on the online purchase behavior of green agricultural products," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    6. Yuan, Chunlin & Zhang, Chenlei & Wang, Shuman, 2022. "Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    7. Eleonora Pantano & Jamie Carlson & Konstantina Spanaki & George Christodoulides, 2024. "Guest editorial: More supportive or more distractive? Investigating the negative effects of technology at the customer interface," Post-Print hal-04478502, HAL.
    8. Liu, Xiaohui & He, Xiaoyu & Wang, Mengmeng & Shen, Huizhang, 2022. "What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics," Technology in Society, Elsevier, vol. 70(C).
    9. Balakrishnan, Janarthanan & Abed, Salma S. & Jones, Paul, 2022. "The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ininma:v:60:y:2021:i:c:s026840122100075x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.