Determinants of consumer motivation to use online food delivery apps: An empirical investigation of Bangladesh

  • Received February 4, 2023;
    Accepted March 22, 2023;
    Published April 20, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.19(2).2023.06
  • Article Info
    Volume 19 2023, Issue #2, pp. 63-72
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This work is licensed under a Creative Commons Attribution 4.0 International License

This study aims to investigate the influencing elements of consumers’ behavioral intention to use online food delivery apps in Bangladesh. MS Excel and SPSS were used to calculate the relevant information. The targeted population of this study is the current users of online food delivery apps in Bangladesh. The final sample size is 368, with a response rate of 92%. The information was gathered from the respondents through a web-based survey in Google Forms. Due to the nature of the study object, the purposeful sampling method has been used and is quantitative and exploratory. The results show that five predictors affect consumers’ intention to use food delivery apps. The findings demonstrate that social influence, perceived trust, perceived safety, performance expectancy, and effort expectancy significantly affect the consumers’ usage intention of food delivery apps. The study also found that perceived trust is the strongest predictor of usage intention among five intention predictors. However, following an extensive literature review, only a few studies have been conducted in this context, so there is a deficiency in investigating key influencing factors of users’ motivation to adopt online food delivery apps in Bangladesh. Therefore, this study could be indispensable for app delivery operators, governmental and non-governmental organizations, businesses, and researchers to make policies and strategies to create intention among consumers to use online food delivery apps.

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    • Figure 1. Theoretical framework
    • Figure 2. Regression results
    • Table 1. Reliability and validity analysis
    • Table 2. Demographic analysis
    • Table 3. Regression coefficients
    • Table 4. Summary of hypotheses testing
    • Conceptualization
      Mohammed Julfikar Ali, Md. Mobarak Karim
    • Data curation
      Mohammed Julfikar Ali, Md. Mobarak Karim
    • Investigation
      Mohammed Julfikar Ali, Md. Mobarak Karim
    • Methodology
      Mohammed Julfikar Ali, Md. Mobarak Karim
    • Software
      Mohammed Julfikar Ali, Md. Mobarak Karim
    • Supervision
      Mohammed Julfikar Ali, Md. Atikur Rahaman
    • Writing – original draft
      Mohammed Julfikar Ali, Md. Atikur Rahaman, Wasib Bin Latif, Issa Ahammad, Md. Mobarak Karim
    • Writing – review & editing
      Mohammed Julfikar Ali, Md. Mobarak Karim
    • Formal Analysis
      Md. Atikur Rahaman, Wasib Bin Latif, Issa Ahammad
    • Funding acquisition
      Md. Atikur Rahaman, Wasib Bin Latif, Issa Ahammad
    • Project administration
      Md. Atikur Rahaman, Wasib Bin Latif, Issa Ahammad
    • Resources
      Md. Atikur Rahaman, Wasib Bin Latif
    • Validation
      Md. Atikur Rahaman, Wasib Bin Latif, Issa Ahammad
    • Visualization
      Md. Atikur Rahaman, Wasib Bin Latif, Issa Ahammad