Antecedents of behavioral intention to use online food delivery services: an empirical investigation


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The online food delivery market in India perseveres to grow at a sustained pace. The business has unique dynamics and challenges with the spike in orders during weekends, meeting delivery schedules during peak demand, offering deep discounts to address wavering customer loyalty, reducing cash burns, and managing food quality inconsistency. In contrast, the fast-paced life and the rise of millennials in the workforce is likely to assure a promising future for the food aggregators. The above backdrop has led the researchers to pursue this study. An empirical study was carried out to explore the consumption occasion and the antecedents of online food ordering in the select cities in Karnataka, India. The data was collected from 385 respondents through telephonic and mail survey using a structured questionnaire. The responses were analyzed using exploratory factor analysis and multiple regression. The result of the study indicated a positive association between the constructs ‘buying motives’, ‘aggregator attractiveness’, and customer satisfaction. The variation in customers` satisfaction is largely attributable to the convenience of order placing, food quality, availability of food and restaurant reviews, offers and discounts, faster home delivery, and the wide choice of restaurants listed on the aggregator’s website. Additionally, the aggregator attractiveness showed a higher impact on customer satisfaction as compared to buying motives.

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    • Table 1. Correlation of variables under aggregator attractiveness
    • Table 2. Discriminant analysis of aggregator attractiveness
    • Table 3. Correlation of variables under buying motives
    • Table 4. Discriminant analysis of variables under buying motives
    • Table 5. Demographic profile
    • Table 6. Reliability indicators
    • Table 7. KMO and Bartlett’s test
    • Table 8. Summary of total variance
    • Table 9. Rotated component matrix
    • Table 10. Construct wise factor loadings
    • Table 11. Regression analysis of hypothesis H1
    • Table 12. Model summary
    • Table 13. Regression analysis of hypothesis H2
    • Table 14. Summary of the regression model
    • Table 15. Cross-tabulation among age and online food ordering occasion
    • Table 16. Chi-squared tests
    • Data curation
      Vinish P, Slima Pinto
    • Formal Analysis
      Vinish P, Iqbal Thonse Hawaldar , Slima Pinto
    • Methodology
      Vinish P, Iqbal Thonse Hawaldar
    • Software
      Vinish P, Slima Pinto
    • Writing – original draft
      Vinish P, Prakash Pinto, Slima Pinto
    • Conceptualization
      Prakash Pinto, Iqbal Thonse Hawaldar
    • Investigation
      Prakash Pinto, Slima Pinto
    • Project administration
      Prakash Pinto
    • Validation
      Iqbal Thonse Hawaldar
    • Writing – review & editing
      Iqbal Thonse Hawaldar