Success model of online food delivery system: The role of brand image in customer responses

  • Received March 2, 2022;
    Accepted May 16, 2022;
    Published June 1, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.18(2).2022.13
  • Article Info
    Volume 18 2022, Issue #2, pp. 148-160
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This work is licensed under a Creative Commons Attribution 4.0 International License

There is a growing interest in understanding the factors affecting the success of online food delivery (OFD) systems because online food ordering has increased considerably in recent years. Hence, the purpose of this study is to investigate the effect of brand image on customer satisfaction and purchase intention based on the stimulus-organism-response (S-O-R) framework by adopting DeLone and McLean IS success model. A convenience sample of 251 respondents, who use the most popular OFD applications in 3 largest cities of Turkey, was surveyed by an online self-administered structured questionnaire. The results were first organized as descriptive statistics for observed variables and frequencies of demographic variables. In the second phase, confirmatory factor analysis (CFA) followed by structural equation modeling (SEM) was used to test the measurement and structural model. The results reveal that among OFD system success factors, only the system and service quality positively influence brand image, accounting for 46% of the variance. On the other hand, this study could not validate the proposed positive effect of information quality on brand image. For the role of brand image in customer responses, the findings evidence a significant positive effect of brand image on both customer satisfaction and intention to use. The variable explains 34% and 22% of the variance in satisfaction and purchase intention, respectively. In line with these results, this paper concludes that brand image can be introduced into the e-commerce success model as a new variable due to its partial mediating role and significant effects on customer responses.

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    • Figure 1. Proposed research model
    • Table 1. Demographic profile of respondents
    • Table 2. Composite variable – construct correlations and average variance extracted (AVE)
    • Table 3. Measurement model results
    • Table 4. Results of path analysis
    • Conceptualization
      Ezgi Erkmen, Nida Turegun
    • Data curation
      Ezgi Erkmen
    • Formal Analysis
      Ezgi Erkmen
    • Investigation
      Ezgi Erkmen, Nida Turegun
    • Methodology
      Ezgi Erkmen
    • Project administration
      Ezgi Erkmen
    • Resources
      Ezgi Erkmen, Nida Turegun
    • Software
      Ezgi Erkmen
    • Supervision
      Ezgi Erkmen
    • Validation
      Ezgi Erkmen
    • Writing – original draft
      Ezgi Erkmen, Nida Turegun
    • Writing – review & editing
      Ezgi Erkmen, Nida Turegun
    • Visualization
      Nida Turegun