IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v131y2021icp815-825.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296320307049
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2020.10.043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marina Covalenco (Dobrovolschi), 2024. "Health Tourism Industry: Segmentation, Differentiation and Communication," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 418-425, August.
    2. Ruan, Yanya & Mezei, József, 2022. "When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    3. Xin Ye & Xiaoyan Su & Zhijun Yao & Lu-an Dong & Qiang Lin & Shuo Yu, 2023. "How Do Citizens View Digital Government Services? Study on Digital Government Service Quality Based on Citizen Feedback," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
    4. T. M. Rofin & Sreejith Alathur, 2024. "Optimal online channel configuration for a Manufacturer under price and lead time-sensitive demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(2), pages 121-139, April.
    5. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ray, Arghya & Bala, Pradip Kumar & Rana, Nripendra P., 2021. "Exploring the drivers of customers’ brand attitudes of online travel agency services: A text-mining based approach," Journal of Business Research, Elsevier, vol. 128(C), pages 391-404.
    2. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    3. Brady, Michael K. & Robertson, Christopher J. & Cronin, J. Joseph, 2001. "Managing behavioral intentions in diverse cultural environments: an investigation of service quality, service value, and satisfaction for American and Ecuadorian fast-food customers," Journal of International Management, Elsevier, vol. 7(2), pages 129-149.
    4. Heyes, Anthony & Kapur, Sandeep, 2012. "Angry customers, e-word-of-mouth and incentives for quality provision," Journal of Economic Behavior & Organization, Elsevier, vol. 84(3), pages 813-828.
    5. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    6. Minseok Choi & Kyeongseok Han & Jeongil Choi, 2015. "The effects of product attributes and service quality of transportation card solutions on service user’s continuance and word-of-mouth intention," Service Business, Springer;Pan-Pacific Business Association, vol. 9(3), pages 463-490, September.
    7. Klaus Schoefer & Adamantios Diamantopoulos, 2008. "Measuring experienced emotions during service recovery encounters: construction and assessment of the ESRE scale," Service Business, Springer;Pan-Pacific Business Association, vol. 2(1), pages 65-81, March.
    8. Daekil Kim & Byoungsoo Kim, 2018. "An Integrative View of Emotion and the Dedication-Constraint Model in the Case of Coffee Chain Retailers," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    9. Gelbrich, Katja, 2011. "I Have Paid Less Than You! The Emotional and Behavioral Consequences of Advantaged Price Inequality," Journal of Retailing, Elsevier, vol. 87(2), pages 207-224.
    10. Prayag, Girish & Mills, Hamish & Lee, Craig & Soscia, Isabella, 2020. "Team identification, discrete emotions, satisfaction, and event attachment: A social identity perspective," Journal of Business Research, Elsevier, vol. 112(C), pages 373-384.
    11. Lova Rajaobelina & Isabelle Brun & Nour Kilani & Line Ricard, 2022. "Examining emotions linked to live chat services: The role of e-service quality and impact on word of mouth," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(3), pages 232-249, September.
    12. Arnold, Mark J. & Reynolds, Kristy E. & Ponder, Nicole & Lueg, Jason E., 2005. "Customer delight in a retail context: investigating delightful and terrible shopping experiences," Journal of Business Research, Elsevier, vol. 58(8), pages 1132-1145, August.
    13. Sweeney, Jill & Swait, Joffre, 2008. "The effects of brand credibility on customer loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 15(3), pages 179-193.
    14. Organ, Kate & Koenig-Lewis, Nicole & Palmer, Adrian & Probert, Jane, 2015. "Festivals as agents for behaviour change: A study of food festival engagement and subsequent food choices," Tourism Management, Elsevier, vol. 48(C), pages 84-99.
    15. María Rojas & María Camarero, 2006. "Experience and satisfaction of visitors to museums and cultural exhibitions," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 3(1), pages 49-65, June.
    16. J. Andrew Petersen & V. Kumar & Yolanda Polo & F. Javier Sese, 2018. "Unlocking the power of marketing: understanding the links between customer mindset metrics, behavior, and profitability," Journal of the Academy of Marketing Science, Springer, vol. 46(5), pages 813-836, September.
    17. Greenwell, T. Christopher & Fink, Janet S. & Pastore, Donna L., 2002. "Assessing the Influence of the Physical Sports Facility on Customer Satisfaction within the Context of the Service Experience," Sport Management Review, Elsevier, vol. 5(2), pages 129-148, November.
    18. Frank, Björn & Herbas Torrico, Boris & Enkawa, Takao & Schvaneveldt, Shane J., 2014. "Affect versus Cognition in the Chain from Perceived Quality to Customer Loyalty: The Roles of Product Beliefs and Experience," Journal of Retailing, Elsevier, vol. 90(4), pages 567-586.
    19. Waites, Stacie F. & Farmer, Adam & Collier, Joel, 2024. "Good people good planet: Investigating the interconnection between social and environmental sustainability," Journal of Business Research, Elsevier, vol. 184(C).
    20. Kuo-Yan Wang & Mu-Lan Ma & Jing Yu, 2021. "Understanding the perceived satisfaction and revisiting intentions of lodgers in a restricted service scenario: evidence from the hotel industry in quarantine," Service Business, Springer;Pan-Pacific Business Association, vol. 15(2), pages 335-368, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:131:y:2021:i:c:p:815-825. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.