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Research Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

Assessing consumer preferences for postharvest quality of leafy greens and climacteric fruits in rapid delivery systems: A conjoint analysis

DOI
https://doi.org/10.14719/pst.9944
Submitted
9 June 2025
Published
27-08-2025 — Updated on 29-09-2025
Versions

Abstract

The rapid growth of quick commerce (q-commerce) is changing urban grocery retailing by guaranteeing delivery of essentials, including fresh fruits and vegetables in 10-30 min. However, speed of delivery has implications for postharvest management of highly perishable food items such as leafy greens and climacteric fruits (i.e., bananas, mangoes, tomatoes and peaches). There is a significant lack of research examining how physiological quality preservation methods influence consumer preferences for highly perishable items such as leafy greens and climacteric fruits. This research fills this gap by investigating urban consumers' preferences regarding the trade-off between delivery speed, price and quality preservation on q-commerce platforms. The study was conducted in the Coimbatore district in Tamil Nadu. A structured survey of 110 urban consumers in Coimbatore, Tamil Nadu, was conducted using convenience sampling. Choice-Based Conjoint (CBC) analysis under the Random Utility Theory framework was applied using an orthogonal design with three attributes: delivery speed, quality preservation methods and pricing. Consumer utility values were estimated using the Multinomial logit model (MNL) with significance testing at p<0. 05.  The results indicated that delivery speed held the highest relative importance (38 %, p<0.001) in consumer preferences, followed by quality preservation methods (32 %, p<0.001) and price sensitivity (30 %, p<0.001). Exploratory Factor Analysis (EFA) revealed that physiological quality indicators such as freshness, ripeness and shelf life were the most influential latent factor (explaining 37.5 % of variance). Garrett Ranking identified inconsistent ripening (mean = 69.68) and high delivery charges (mean = 56.60) as the top barriers to purchasing fresh produce via quick commerce platforms.

References

  1. 1. Ganapathy V, Gupta C. Critical success factors for quick commerce grocery delivery in India: an exploratory study. Sustainability, Agri, Food and Environmental Research. 2024;12(X):Article 691. https://doi.org/10.7770/safer-V12N1-art691
  2. 2. Harris F, Singh J, Rettie R. Online grocery shopping: the influence of situational factors. Eur J Mark. 2009;43(9/10):1205–19. https://doi.org/10.1108/03090560910976447
  3. 3. Porat R, Lichter A, Terry LA, Harker R, Buzby J. Postharvest losses of fruit and vegetables during retail and in consumers' homes: quantifications, causes and means of prevention. Postharvest Biol Technol. 2018;139:135–49. https://doi.org/10.1016/j.postharvbio.2017.11.019
  4. 4. Malhotra V. Impact of the 10-minute grocery delivery incentive on consumer habits, demand and road accidents. SSRN Electron J. 2022. https://doi.org/10.2139/ssrn.4161893
  5. 5. Lee SK, Kader AA. Preharvest and postharvest factors influencing vitamin C content of horticultural crops. Postharvest Biol Technol. 2000;20(3):207–20. https://doi.org/10.1016/S0925-5214(00)00133-2
  6. 6. Grashuis J, Skevas T, Segovia MS. Grocery shopping preferences during the COVID-19 pandemic. Sustainability. 2020;12(13):5369. https://doi.org/10.3390/su12135369
  7. 7. Harter A, Stich L, Spann M. The effect of delivery time on repurchase behavior in quick commerce. Journal of Service Research. 2025;28(2):211–27. https://doi.org/10.1177/10946705241236961
  8. 8. Tyrväinen O, Karjaluoto H. Online grocery shopping before and during the COVID-19 pandemic: a meta-analytical review. Telemat Inform. 2022;101839. https://doi.org/10.1016/j.tele.2022.101839
  9. 9. Watada AE, Ko NP, Minott DA. Factors affecting the quality of fresh-cut horticultural products. Postharvest Biol Technol. 1996;9(2):115–25. https://doi.org/10.1016/S0925-5214(96)00041-5
  10. 10. Mahajan PV, Caleb OJ, Singh Z, Watkins CB, Geyer M. Postharvest handling systems and technologies to maintain quality and safety of fresh fruits and vegetables. Annu Rev Food Sci Technol. 2022;13:337–63.
  11. 11. Ramesh T, Sethuraman G. Ethylene management in quick commerce supply chains: challenges and innovations. Postharvest Biol Technol. 2023;196:112162.
  12. 12. Chandrasekar V, Kumar S. Impact of handling practices on physiological deterioration of leafy vegetables in quick commerce supply chains. Plant Sci Today. 2024;11(2):142–56.
  13. 13. McFadden D. The measurement of urban travel demand. J Public Econ. 1974;3:303–28. https://doi.org/10.1016/0047-2727(74)90003-6
  14. 14. Oyatoye E, Otike-Obaro AE, Nkeiruka GE. Using conjoint analysis to study the factors important to university students in Nigeria when they select a laptop computer. Peer Rev Univ Lagos Niger. 2016.
  15. 15. Vag A. Simulating changing consumer preferences: a dynamic conjoint model. J Bus Res. 2007;60(8):904–11. https://doi.org/10.1016/j.jbusres.2007.02.012
  16. 16. De Pelsmaeker S, Schouteten JJ, Lagast S, Dewettinck K, Gellynck X. Is taste the key driver for consumer preference? A conjoint analysis study. Food Qual Prefer. 2017;62:323–31. https://doi.org/10.1016/j.foodqual.2017.02.018
  17. 17. Shuwetha R, Muralidharan C, Selvanayaki S, Kavitha PS, Giridhari VVA. Assessing consumer preferences for plant-based ice cream: a conjoint analysis of soy and almond milk varieties. Plant Sci Today. 2024;12(sp1):1–7. https://doi.org/10.14719/pst.6029
  18. 18. Ong AKS, Prasetyo YT, Libiran MADC, Lontoc YMA, Lunaria JAV, Manalo AM, et al. Consumer preference analysis on attributes of milk tea: a conjoint analysis approach. Foods. 2021;10:1382. https://doi.org/10.3390/foods10061382
  19. 19. Johnson LW, Ringham L, Jurd K. Behavioural segmentation in the Australian wine market using conjoint choice analysis. Int Mark Rev. 1991;8(4). https://doi.org/10.1108/EUM0000000001541
  20. 20. Sethuraman R, Kerin RA, Cron WL. A field study comparing online and offline data collection methods for identifying product attribute preferences using conjoint analysis. J Bus Res. 2005;58:602–10. https://doi.org/10.1016/j.jbusres.2003.09.009
  21. 21. Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods. 1999;4(3):272–93. https://doi.org/10.1037/1082-989X.4.3.272
  22. 22. Ao W, Jamir BK. Application of the Garrett ranking technique in studying the problems of bamboo cultivation: a case study of Mokokchung district, Nagaland. Indian J Hill Farming. 2020;33(2):311–15.

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