Antecedents of brand advocacy in online food delivery services: An empirical investigation

  • Received March 21, 2022;
    Accepted August 17, 2022;
    Published September 15, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.18(3).2022.12
  • Article Info
    Volume 18 2022, Issue #3, pp. 136-148
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This work is licensed under a Creative Commons Attribution 4.0 International License

Building a solid relationship between a brand and customers has become increasingly prevalent in a firm’s marketing strategy. It has led to a broader and deeper exploration of developing customer relationships by industry practitioners and academic researchers. Drawing on the marketing literature, this empirical study speculates on the possible mechanism underlying the process of building brand advocacy with consumers in online food delivery services. The data were collected from 562 respondents through online questionnaires from consumers of food delivery platforms in Bangkok and Metropolitan areas, Thailand. The survey data were analyzed using Structural Equation Modeling (SEM) to verify the model. The findings indicated the relationship between customer experience quality (brand experience, service experience, and post-purchase experience), brand leadership (perceived quality, perceived innovativeness, perceived value, and perceived popularity), relationship quality (trust, satisfaction, and commitment), and brand advocacy (recommendation, purchase intention, and brand defending). Regarding the investigation, customer experience quality positively affects brand leadership and relationship quality, which, in turn, mediated the pathway from customer experience quality to brand advocacy. The model explained 72% of the variance in brand advocacy. The study recommends that practitioners consider these findings when designing marketing strategies for online platforms.

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    • Figure 1. Conceptual framework
    • Figure 2. SEM result of building brand advocacy in online food delivery
    • Table 1. Discriminant validity assessment
    • Table A1. Mean, SD, Cronbach’s alpha, CR, AVE, and factor loading analysis
    • Conceptualization
      Oranich Kumgliang, Anon Khamwon
    • Data curation
      Oranich Kumgliang, Anon Khamwon
    • Formal Analysis
      Oranich Kumgliang, Anon Khamwon
    • Funding acquisition
      Oranich Kumgliang, Anon Khamwon
    • Investigation
      Oranich Kumgliang, Anon Khamwon
    • Methodology
      Oranich Kumgliang, Anon Khamwon
    • Project administration
      Oranich Kumgliang, Anon Khamwon
    • Resources
      Oranich Kumgliang, Anon Khamwon
    • Validation
      Oranich Kumgliang, Anon Khamwon
    • Visualization
      Oranich Kumgliang, Anon Khamwon
    • Writing – original draft
      Oranich Kumgliang
    • Writing – review & editing
      Oranich Kumgliang, Anon Khamwon
    • Supervision
      Anon Khamwon