Motivations and barriers to embracing augmented reality: An exploratory study with Vietnamese retailers

  • Received May 5, 2022;
    Accepted June 27, 2022;
    Published July 15, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.18(3).2022.03
  • Article Info
    Volume 18 2022, Issue #3, pp. 28-37
  • TO CITE АНОТАЦІЯ
  • Cited by
    1 articles
  • 696 Views
  • 264 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The most crucial key to successfully approaching customers is enhancing the interaction experience between customers and retailers. This study explores the motivations for adopting augmented reality (AR) in retailing small and medium-sized retailers in Vietnam. A structured questionnaire was delivered to a total sample of 302 Vietnamese retailers and got 215 clean and valid responses. The survey was conducted both online and offline for ten months, from February 2021 to December 2021. The chosen surveyors are retailing managers and owners of retailing firms. These firms sell fashion products, technology gadgets, and household products. The data were statistically analyzed using Smart PLS software and the partial least equation structural model. The findings indicate three direct, positive, and significant factors that influence the retailer’s AR adoption, including (1) organizational attitude toward AR, (2) organizational innovativeness, and (3) competition pressure in which organizational attitude toward AR and organization innovativeness are two critical motivational drivers. The competition pressure has been identified as the challenge barrier. The cost barriers affect organizational attitude toward AR but do not significantly influence AR adoption. Along with theoretical contributions, this paper also gave some theoretical and practical implications for retailers who have the intention to adopt AR and integrate AR into their current retailing system.

view full abstract hide full abstract
    • Figure 1. Proposed research model
    • Figure 2. Structural model
    • Table 1. Outer loadings, Cronbach’s alpha, composite reliability, and AVE
    • Table 2. HTMT
    • Table 3. R2, Q2, and SMRM
    • Table 4. Hypotheses testing result
    • Conceptualization
      Hai Ninh Nguyen
    • Data curation
      Hai Ninh Nguyen
    • Formal Analysis
      Hai Ninh Nguyen
    • Funding acquisition
      Hai Ninh Nguyen
    • Investigation
      Hai Ninh Nguyen
    • Methodology
      Hai Ninh Nguyen
    • Project administration
      Hai Ninh Nguyen
    • Resources
      Hai Ninh Nguyen
    • Software
      Hai Ninh Nguyen
    • Supervision
      Hai Ninh Nguyen
    • Validation
      Hai Ninh Nguyen
    • Visualization
      Hai Ninh Nguyen
    • Writing – original draft
      Hai Ninh Nguyen
    • Writing – review & editing
      Hai Ninh Nguyen