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<p>The Indian food-tech sector, led by platforms such as Zomato and Swiggy, operates in a volatile environment that shapes consumer perceptions, trust, and decision-making. To quantify platform level decision uncertainty and information ambiguity, this study introduces an entropy-based behavioral framework that uses results of dual fuzzy information theoretic measures. Also, to assess their stability in noisy data, these dual fuzzy measures have been compared with classical entropy measures, namely the Shannon entropy and the Sample entropy. The analysis uses rolling windows of Google Play Store consumer rating data from the year 2018 to 2023 to capture consumer sentiment. To ensure rigor, the Shapiro-Wilk test has been applied to assess normality, complemented by the Welch’s t test and the Mann–Whitney U test. Both tests confirm that Swiggy and Zomato differ significantly across all entropy measures. The convergence of parametric and nonparametric testing, effect size estimation, and non-overlapping confidence intervals indicates that the two platforms operate meaningfully at different levels of consumer-perceived uncertainty. The findings contribute to consumer behaviour and platform competition research by linking information-theoretic measures of rating dynamics to platform-level decision uncertainty and provide practical insights for designing more stable service experiences, pricing strategies, and consumer engagement strategies in digital food delivery services.</p>

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Entropy, uncertainty, and consumer decision dynamics in digital food platforms

  • 期刊论文

  • 发表日期：

    2026年6月19日

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  • Ruchika Lochab,
  • Luckshay Batra

摘要

The Indian food-tech sector, led by platforms such as Zomato and Swiggy, operates in a volatile environment that shapes consumer perceptions, trust, and decision-making. To quantify platform level decision uncertainty and information ambiguity, this study introduces an entropy-based behavioral framework that uses results of dual fuzzy information theoretic measures. Also, to assess their stability in noisy data, these dual fuzzy measures have been compared with classical entropy measures, namely the Shannon entropy and the Sample entropy. The analysis uses rolling windows of Google Play Store consumer rating data from the year 2018 to 2023 to capture consumer sentiment. To ensure rigor, the Shapiro-Wilk test has been applied to assess normality, complemented by the Welch’s t test and the Mann–Whitney U test. Both tests confirm that Swiggy and Zomato differ significantly across all entropy measures. The convergence of parametric and nonparametric testing, effect size estimation, and non-overlapping confidence intervals indicates that the two platforms operate meaningfully at different levels of consumer-perceived uncertainty. The findings contribute to consumer behaviour and platform competition research by linking information-theoretic measures of rating dynamics to platform-level decision uncertainty and provide practical insights for designing more stable service experiences, pricing strategies, and consumer engagement strategies in digital food delivery services.