IDEAS home Printed from https://ideas.repec.org/a/aes/amfeco/v27y2025i69p675.html
   My bibliography  Save this article

Good Practice for Product Management Decision-Making: Using Amazon’s Dataset and ChatGPT

Author

Listed:
  • Mitja Bervar

    (University of Primorska, Koper, Slovenia)

  • Tine Bertoncel

    (University of Primorska, Koper, Slovenia)

Abstract

This study examines how Chat Generative Pre-trained Transformer (ChatGPT) can be effectively utilised as a good practice to analyse Amazon reviews within the creative and cultural industry category of Design, particularly Arts, Crafts & Sewing. By focusing on specific products and reviews extracted from a dataset containing over 800,000 products and nearly nine million reviews, ChatGPT identified common themes and customer issues such as software problems and product reliability. When we compared ChatGPT’s results with those from manual data reviews, we found that ChatGPT was adept at identifying main topics but sometimes missed detailed insights or altered the original reviews when providing examples. This indicates that while ChatGPT can quickly highlight important areas for product managers, it should be used in conjunction with human analysis to achieve a comprehensive understanding. Nonetheless, the results demonstrate that when considering group creativity, ChatGPT can be a valuable member.

Suggested Citation

  • Mitja Bervar & Tine Bertoncel, 2025. "Good Practice for Product Management Decision-Making: Using Amazon’s Dataset and ChatGPT," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 27(69), pages 675-675, April.
    • Handle: RePEc:aes:amfeco:v:27:y:2025:i:69:p:675
    as

    Download full text from publisher

    File URL: http://www.amfiteatrueconomic.ro/temp/Article_3429.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Karkkainen, H. & Elfvengren, K., 2002. "Role of careful customer need assessment in product innovation management--empirical analysis," International Journal of Production Economics, Elsevier, vol. 80(1), pages 85-103, November.
    2. Shushu Xie & Yingxue Zhao & Lin Zhao & Xingyuan He, 2024. "Do Online Reviews Always Incentivise Remanufacturers to Improve Quality in a Competitive Environment?," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 903-903, August.
    3. Rajaram, Kumaran & Tinguely, Patrick Nicolas, 2024. "Generative artificial intelligence in small and medium enterprises: Navigating its promises and challenges," Business Horizons, Elsevier, vol. 67(5), pages 629-648.
    4. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    5. Vasile Dinu, 2024. "Innovative Application of Artificial Intelligence in Business Impacting Socio-Economic Progress," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(66), pages 398-398, Aprilie.
    Full references (including those not matched with items on IDEAS)

    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. Onofrei, George & Filieri, Raffaele & Kennedy, Lorraine, 2022. "Social media interactions, purchase intention, and behavioural engagement: The mediating role of source and content factors," Journal of Business Research, Elsevier, vol. 142(C), pages 100-112.
    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. Shobhit Kakaria & Aline Simonetti & Enrique Bigne, 2024. "Interaction between extrinsic and intrinsic online review cues: perspectives from cue utilization theory," Electronic Commerce Research, Springer, vol. 24(4), pages 2469-2497, December.
    4. Rosillo-Díaz, Elena & Muñoz-Rosas, Juan Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of heuristic–systematic cues on the purchase intention of the electronic commerce consumer through the perception of product quality," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    5. Muhammad Rifki Shihab & Audry Pragita Putri, 2019. "Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy," Electronic Commerce Research, Springer, vol. 19(1), pages 159-187, March.
    6. Gaurav Khatwani & Gopal Das, 2016. "Evaluating combination of individual pre-purchase internet information channels using hybrid fuzzy MCDM technique: demographics as moderators," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 12(1), pages 28-49.
    7. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    8. Book, Laura A. & Tanford, Sarah & Chang, Wen, 2018. "Customer reviews are not always informative: The impact of effortful versus heuristic processing," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 272-280.
    9. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    10. Pooja Katyal & Reetika Sehgal, 2025. "Unraveling the impact of online consumer reviews on consumer buying behavior," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(1), pages 330-345, January.
    11. Azer, Jaylan & Anker, Thomas & Taheri, Babak & Tinsley, Ross, 2023. "Consumer-Driven racial stigmatization: The moderating role of race in online consumer-to-consumer reviews," Journal of Business Research, Elsevier, vol. 157(C).
    12. Filieri, Raffaele & Lin, Zhibin & Pino, Giovanni & Alguezaui, Salma & Inversini, Alessandro, 2021. "The role of visual cues in eWOM on consumers’ behavioral intention and decisions," Journal of Business Research, Elsevier, vol. 135(C), pages 663-675.
    13. Bhukya, Ramulu & Paul, Justin, 2023. "Social influence research in consumer behavior: What we learned and what we need to learn? – A hybrid systematic literature review," Journal of Business Research, Elsevier, vol. 162(C).
    14. Ray, Arghya & Bala, Pradip Kumar & Chakraborty, Shibashish & Dasgupta, Shilpee A., 2021. "Exploring the impact of different factors on brand equity and intention to take up online courses from e-Learning platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    15. Song, Yongming & Li, Guangxu & Li, Tie & Li, Yanhong, 2021. "A purchase decision support model considering consumer personalization about aspirations and risk attitudes," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    16. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    17. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    18. Wong, Christina W.Y. & Wong, Chee Yew & Boon-itt, Sakun, 2013. "The combined effects of internal and external supply chain integration on product innovation," International Journal of Production Economics, Elsevier, vol. 146(2), pages 566-574.
    19. Yang Liu & Xingchen Ding & Maomao Chi & Jiang Wu & Lili Ma, 2024. "Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach," Information Technology & Tourism, Springer, vol. 26(1), pages 59-87, March.
    20. Viglia, Giampaolo & Abrate, Graziano, 2017. "When distinction does not pay off - Investigating the determinants of European agritourism prices," Journal of Business Research, Elsevier, vol. 80(C), pages 45-52.

    More about this item

    Keywords

    artificial intelligence; managerial decision-making; product management; creative industries; ChatGPT; good practice;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D79 - Microeconomics - - Analysis of Collective Decision-Making - - - Other
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

    Statistics

    Access and download statistics

    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:aes:amfeco:v:27:y:2025:i:69:p:675. 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: Valentin Dumitru (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    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.