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Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application

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  • Chatterjee, Swagato
  • Goyal, Divesh
  • Prakash, Atul
  • Sharma, Jiwan

Abstract

In the digital era, online channels have become an inevitable part of healthcare services making healthcare/health-product e-commerce an important area of study. However, the reflections of customer-satisfaction and their difference in various subgroups of this industry is still unexplored. Additionally, extant literature has majorly focused on consumer surveys for customer-satisfaction research ignoring the huge data available online. The current study fills these gaps. With 186,057 reviews on 619 e-commerce firms from 29 subcategories of healthcare/health-product industry posted in a review-website between 2008 and 2018, we used text-mining, machine-learning and econometric techniques to find which core and augmented service aspects and which emotions are more important in which service contexts in terms of reflecting and predicting customer satisfaction. Our study contributes towards the healthcare/health-product marketing and services literature in suggesting an automated and machine-learning-based methodology for insight generation. It also helps healthcare/health-product e-commerce managers in better e-commerce service design and delivery.

Suggested Citation

  • Chatterjee, Swagato & Goyal, Divesh & Prakash, Atul & Sharma, Jiwan, 2021. "Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application," Journal of Business Research, Elsevier, vol. 131(C), pages 815-825.
    • Handle: RePEc:eee:jbrese:v:131:y:2021:i:c:p:815-825
    DOI: 10.1016/j.jbusres.2020.10.043
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    as
    1. John G. Lynch Jr., 2006. "Accessibility-Diagnosticity and the Multiple Pathway Anchoring and Adjustment Model," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 33(1), pages 25-27, June.
    2. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    3. Joel B. Cohen & Americus Reed II, 2006. "A Multiple Pathway Anchoring and Adjustment (MPAA) Model of Attitude Generation and Recruitment," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 33(1), pages 1-15, June.
    4. Hasford, Jonathan & Farmer, Adam, 2016. "Responsible you, despicable me: Contrasting competitor inferences from socially responsible behavior," Journal of Business Research, Elsevier, vol. 69(3), pages 1234-1241.
    5. Mouwen, Arnoud, 2015. "Drivers of customer satisfaction with public transport services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 1-20.
    6. Westbrook, Robert A & Oliver, Richard L, 1991. "The Dimensionality of Consumption Emotion Patterns and Consumer Satisfaction," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(1), pages 84-91, June.
    7. Laros, Fleur J.M. & Steenkamp, Jan-Benedict E.M., 2005. "Emotions in consumer behavior: a hierarchical approach," Journal of Business Research, Elsevier, vol. 58(10), pages 1437-1445, October.
    8. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    9. Xu, Xun, 2020. "Examining an asymmetric effect between online customer reviews emphasis and overall satisfaction determinants," Journal of Business Research, Elsevier, vol. 106(C), pages 196-210.
    10. Byrd, Erick T. & Canziani, Bonnie & (Jerrie) Hsieh, Yu-Chin & Debbage, Keith & Sonmez, Sevil, 2016. "Wine tourism: Motivating visitors through core and supplementary services," Tourism Management, Elsevier, vol. 52(C), pages 19-29.
    11. Filieri, Raffaele, 2016. "What makes an online consumer review trustworthy?," Annals of Tourism Research, Elsevier, vol. 58(C), pages 46-64.
    12. Mowen, John C. & Park, Sojin & Zablah, Alex, 2007. "Toward a theory of motivation and personality with application to word-of-mouth communications," Journal of Business Research, Elsevier, vol. 60(6), pages 590-596, June.
    13. Ahani, Ali & Nilashi, Mehrbakhsh & Yadegaridehkordi, Elaheh & Sanzogni, Louis & Tarik, A. Rashid & Knox, Kathy & Samad, Sarminah & Ibrahim, Othman, 2019. "Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 331-343.
    Full references (including those not matched with items on IDEAS)

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    6. Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    7. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
    8. Petrescu, Maria & Ajjan, Haya & Harrison, Dana L., 2023. "Man vs machine – Detecting deception in online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    9. Çiğdem Sıcakyüz, 2023. "Analyzing Healthcare and Wellness Products’ Quality Embedded in Online Customer Reviews: Assessment with a Hybrid Fuzzy LMAW and Fermatean Fuzzy WASPAS Method," Sustainability, MDPI, vol. 15(4), pages 1-41, February.
    10. Hu, Hui & Xu, Jiajun & Liu, Mengqi & Lim, Ming K., 2023. "Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning," Journal of Business Research, Elsevier, vol. 156(C).
    11. Markus Binder & Bernd Heinrich & Marcus Hopf & Alexander Schiller, 2022. "Global reconstruction of language models with linguistic rules – Explainable AI for online consumer reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2123-2138, December.
    12. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    13. Minnu F. Pynadath & T. M. Rofin & Sam Thomas, 2023. "Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3241-3272, August.
    14. Nan Yang & Nikolaos Korfiatis & Dimitris Zissis & Konstantina Spanaki, 2024. "Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach," Annals of Operations Research, Springer, vol. 339(1), pages 631-662, August.
    15. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Samad, Sarminah & Alrizq, Mesfer & Alyami, Sultan & Abosaq, Hamad & Alghamdi, Abdullah & Akib, Noor Adelyna Mohammed, 2022. "Factors impacting customer purchase intention of smart home security systems: Social data analysis using machine learning techniques," Technology in Society, Elsevier, vol. 71(C).
    16. Zaman, Mustafeed & Vo-Thanh, Tan & Nguyen, Chi T.K. & Hasan, Rajibul & Akter, Shahriar & Mariani, Marcello & Hikkerova, Lubica, 2023. "Motives for posting fake reviews: Evidence from a cross-cultural comparison," Journal of Business Research, Elsevier, vol. 154(C).

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