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Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis

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  • Zhang, Dianfeng
  • Shen, Zifan
  • Li, Yanlai

Abstract

Online fresh retailing enriches people's shopping choices and provides convenience to reduce the risk of infection during the pandemic. Online reviews contain consumers' attention, requirements and sentiments, and in-depth analysis of this information has guiding values for service optimization. To better understand this information, a requirement analysis idea based on the attention and sentiment distribution of online reviews was proposed, namely importance-Kano analysis. Seven different customer requirements were found, including express delivery, cost performance, communication, freshness, flavor, specification and packaging. Flavor and freshness are the most concerned attributes, and they and other attributes all influence satisfaction in their unique ways. Consumers care a lot about the shopping experience and product quality and they have a high degree of product involvement in fresh products. Service improvement should be considered as a systematic project, and the influence of competitive environment, category differences and technological development should not be ignored. A service optimization model was developed based on the concept of total quality management, which was constructed by three layers including supply chain, operation management and consumer experience. The systematic analysis is conducive to in-depth understanding of the complexity, systematical and timeliness nature of fresh product operation management.

Suggested Citation

  • Zhang, Dianfeng & Shen, Zifan & Li, Yanlai, 2023. "Requirement analysis and service optimization of multiple category fresh products in online retailing using importance-Kano analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
  • Handle: RePEc:eee:joreco:v:72:y:2023:i:c:s0969698922003460
    DOI: 10.1016/j.jretconser.2022.103253
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    References listed on IDEAS

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    1. Wu, Linwan, 2019. "Website interactivity may compensate for consumers’ reduced control in E-Commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 253-266.
    2. Ömer Özgür Tort & Özalp Vayvay & Emine Çobanoğlu, 2022. "A Systematic Review of Sustainable Fresh Fruit and Vegetable Supply Chains," Sustainability, MDPI, vol. 14(3), pages 1-38, January.
    3. Wei Hong & Changyuan Zheng & Linhai Wu & Xujin Pu, 2019. "Analyzing the Relationship between Consumer Satisfaction and Fresh E-Commerce Logistics Service Using Text Mining Techniques," Sustainability, MDPI, vol. 11(13), pages 1-16, June.
    4. Guthrie, Cameron & Fosso-Wamba, Samuel & Arnaud, Jean Brice, 2021. "Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    5. Na Hao & H. Holly Wang & Qingjie Zhou, 2020. "The impact of online grocery shopping on stockpile behavior in Covid-19," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 12(3), pages 459-470, August.
    6. Jian-Wu Bi & Yang Liu & Zhi-Ping Fan & Erik Cambria, 2019. "Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7068-7088, November.
    7. Wang, Rebecca Jen-Hui & Malthouse, Edward C. & Krishnamurthi, Lakshman, 2015. "On the Go: How Mobile Shopping Affects Customer Purchase Behavior," Journal of Retailing, Elsevier, vol. 91(2), pages 217-234.
    8. Kaiqi Zhao & Hongxu Shi & Yu Yvette Zhang & Jiping Sheng, 2021. "Fresh Produce E-Commerce and Online Shoppers’ Purchase Intention," Chinese Economy, Taylor & Francis Journals, vol. 54(6), pages 415-429, November.
    9. He, Bo & Gan, Xianghua & Yuan, Kaifu, 2019. "Entry of online presale of fresh produce: A competitive analysis," European Journal of Operational Research, Elsevier, vol. 272(1), pages 339-351.
    10. Haizhou Sun & Jiashi Liu & Zheng Zhang & Tingting Jia & Qi Sun, 2015. "Fresh Food Online Supermarket Format Development Research," Springer Books, in: Runtong Zhang & Zhenji Zhang & Kecheng Liu & Juliang Zhang (ed.), Liss 2013, pages 633-638, Springer.
    11. Dominici, Andrea & Boncinelli, Fabio & Gerini, Francesca & Marone, Enrico, 2021. "Determinants of online food purchasing: The impact of socio-demographic and situational factors," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    12. Lauren Chenarides & Carola Grebitus & Jayson L. Lusk & Iryna Printezis, 2021. "Food consumption behavior during the COVID‐19 pandemic," Agribusiness, John Wiley & Sons, Ltd., vol. 37(1), pages 44-81, January.
    13. Na Hao & H. Holly Wang & Qingjie Zhou, 2020. "The impact of online grocery shopping on stockpile behavior in Covid-19," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 12(3), pages 459-470, August.
    14. Zhao, Meina & Wang, Xuqi, 2021. "Perception value of product-service systems: Neural effects of service experience and customer knowledge," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    15. Kumar, Vikas & Ayodeji, Ogunmola Gabriel, 2021. "E-retail factors for customer activation and retention: An empirical study from Indian e-commerce customers," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    16. Leonardo Salvatore Alaimo & Mariantonietta Fiore & Antonino Galati, 2020. "How the Covid-19 Pandemic Is Changing Online Food Shopping Human Behaviour in Italy," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    17. Beckers, Joris & Weekx, Simon & Beutels, Philippe & Verhetsel, Ann, 2021. "COVID-19 and retail: The catalyst for e-commerce in Belgium?," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    18. Chenyi He & Lijia Shi & Zhifeng Gao & Lisa House, 2020. "The impact of customer ratings on consumer choice of fresh produce: A stated preference experiment approach," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 68(3), pages 359-373, September.
    19. Yunkai Zhai & Xin Song & Yajun Chen & Wei Lu, 2022. "A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies," IJERPH, MDPI, vol. 19(12), pages 1-19, June.
    20. Yang, Luming & Xu, Min & Xing, Lin, 2022. "Exploring the core factors of online purchase decisions by building an E-Commerce network evolution model," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
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