ChatGPT và marketing: Phân tích phản ứng của công chúng thông qua mạng xã hội Twitter
Nội dung chính của bài viết
Tóm tắt
Nghiên cứu này phân tích tác động của trí tuệ nhân tạo (AI) đến lĩnh vực Marketing, đặc biệt sau sự ra đời của ChatGPT vào năm 2022. Bằng cách khai thác dữ liệu từ hơn 450.000 tweet về AI và Marketing, nghiên cứu sử dụng phương pháp mô hình hóa chủ đề (CTM) để đánh giá phản ứng của công chúng trước và sau khi triển khai ChatGPT, từ năm 2021 đến đầu tháng 4 năm 2023. Kết quả cho thấy, ChatGPT đã kích thích sự quan tâm đáng kể từ cộng đồng, với lượng tweet tăng vọt. Tuy nhiên, sự hào hứng ban đầu dần giảm do lo ngại về giới hạn và rủi ro của công nghệ AI. Trước khi ChatGPT xuất hiện, ứng dụng AI trong Marketing chưa có xu hướng rõ ràng; sau đó, các cuộc thảo luận tập trung vào các chủ đề như ảnh hưởng lâu dài của AI, hiệu quả của các công cụ AI trong sản xuất nội dung và vai trò của AI trong tối ưu hóa Marketing qua SEO. Nghiên cứu cũng nhấn mạnh các mối lo ngại về mất việc làm và các vấn đề xã hội do AI gây ra.
Abstract
This study analyzes the impact of Artificial Intelligence (AI) on the field of Marketing, particularly following the emergence of ChatGPT in 2022. By leveraging data from over 450,000 tweets about AI and Marketing, the study utilizes Correlated Topic Modeling (CTM) to assess public reactions before and after the deployment of ChatGPT, from 2021 to early April 2023. The findings show that ChatGPT sparked significant interest from the public, with a sharp increase in related tweets. However, the initial excitement gradually waned due to concerns over the limitations and risks of AI technology. Before ChatGPT’s appearance, AI applications in Marketing lacked clear trends; subsequently, discussions became centered on topics such as the long-term impact of AI, the effectiveness of AI tools in content production, and the role of AI in optimizing Marketing through SEO. The study also highlights concerns over job losses and social issues arising from AI.
Từ khóa
ChatGPT; Marketing; Phản ứng của công chúng; Phân tích chủ đề; Trí tuệ nhân tạo (AI)
Chi tiết bài viết
Lĩnh vực kinh tế (JEL Codes)
M15 - IT Management - Business Administration, M30 - General - Marketing and Advertising, O33 - Technological Change: Choices and Consequences • Diffusion Processes - Innovation • Research and Development • Technological Change • Intellectual Property Rights
Tài liệu tham khảo
Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90-108. https://doi.org/10.1016/j.aci.2014.10.001
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Blei, D., & Lafferty, J. (2006). Correlated topic models. Advances in neural information processing systems, 18, 147. https://www.cs.cmu.edu/afs/cs/usr/lafferty/www/pub/ctm.pdf
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Esch, P. and Black, J. (2021). Artificial intelligence (AI): revolutionizing digital marketing. Australasian Marketing Journal, 29(3), 199-203. https://doi.org/10.1177/18393349211037684
Financial Times (2023). Elon Musk and other tech experts call for ‘pause’ on advanced AI systems. Accesed at 8th April 2023. https://www.ft.com/content/3f584019-7c51-4c9c-b18f-0e0ac0821bf7
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., Singh, M., Mehta, H., Ghosh, S. K., Baker, T., Parlikad, A. K., Lutfiyya, H., Kanhere, S. S., Sakellariou, R., Dustdar, S., Ran, O., & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19. https://doi.org/10.1016/j.iot.2022.100514
Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292-1307. https://doi.org/10.1016/j.ipm.2018.05.006
Hashimoto, K., Kontonatsios, G., Miwa, M., & Ananiadou, S. (2016). Topic detection using paragraph vectors to support active learning in systematic reviews. Journal of biomedical informatics, 62, 59-65. https://doi.org/10.1016/j.jbi.2016.06.001
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49, 30-50. https://doi.org/10.1007/s11747-020-00749-9
Hoffman, D. L., Moreau, C. P., Stremersch, S., & Wedel, M. (2022). The rise of new technologies in marketing: A framework and outlook. Journal of Marketing, 86(1), 1-6. https://doi.org/10.1177/00222429211061636
Hossain, A., Akter, S., Yanamandram, V., & Gunasekaran, A. (2022). Operationalizing artificial intelligence-enabled customer analytics capability in retailing. Journal of Global Information Management, 30(8), 1-23. https://doi.org/10.4018/jgim.298990032
John, G., & Scheer, L. K. (2021). Commentary: Governing technology-enabled omnichannel transactions. Journal of Marketing, 85(1), 126–130. https://doi.org/10.1177/0022242920972071
Karami, A., & Pendergraft, N. M. (2018). Computational analysis of insurance complaints: Geico case study. arXiv. https://doi.org/10.48550/arXiv.1806.09736
Karami, A., Gangopadhyay, A., Zhou, B., & Kharrazi, H. (2018). Fuzzy approach topic discovery in health and medical corpora. International Journal of Fuzzy Systems, 20, 1334-1345. https://doi.org/10.1007/s40815-017-0327-9
Karami, A., Lundy, M., Webb, F., & Dwivedi, Y. K. (2020). Twitter and research: A systematic literature review through text mining. IEEE Access, 8, 67698-67717. https://doi.org/10.1109/ACCESS.2020.2983656
Kim, J. H., Kim, J., Baek, T. H., & Kim, C. (2025). ChatGPT personalized and humorous recommendations. Annals of Tourism Research, 110. https://doi.org/10.1016/j.annals.2024.103857
Krystal, H. (2023). ChatGPT sets record for fastest-growing user base - analyst note. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2). https://doi.org/10.1016/j.jjimei.2021.100017
McLean, G., Osei-Frimpong, K., & Barhorst, J. (2021). Alexa, do voice assistants influence consumer brand engagement? Examining the role of AI powered voice assistants in influencing consumer brand engagement. Journal of Business Research, 124, 312-328. https://doi.org/10.1016/j.jbusres.2020.11.045
Miklosik, A., Kuchta, M., Evans, N., & Zak, S. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. Ieee Access, 7, 85705-85718. https://doi.org/10.1109/ACCESS.2019.2924425
Mogaji, E. and Nguyen, N. (2021). Managers' understanding of artificial intelligence in relation to marketing financial services: insights from a cross-country study. The International Journal of Bank Marketing, 40(6), 1272-1298. https://doi.org/10.1108/ijbm-09-2021-0440
Morgan, N. A. (2019). Researching marketing capabilities: Reflections from academia. AMS Review, 9, 381–385. https://doi.org/10.1007/s13162-019-00158-4
Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). User interactions with chatbot interfaces vs. Menu-based interfaces: An empirical study. Computers in Human Behavior, 128. https://doi.org/10.1016/j.chb.2021.107093
Paul, S. P., Aggarwal, S., & Jha, S. (2022). A survey on the use of AI-enabled techniques in digital marketing. In AIP Conference Proceedings (Vol. 2555, No. 1). AIP Publishing. https://doi.org/10.1063/5.0108607
Pereira, V., & Bamel, U. (2021). Extending the resource and knowledge-based view: A critical analysis into its theoretical evolution and future research directions. Journal of Business Research, 132, 557–570. https://doi.org/10.1016/j.jbusres.2021.04.021
Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15–26. https://doi.org/10.1016/j.ijresmar.2019.08.002
Schuetzler, R. M., Grimes, G. M., Giboney, J. S., & Rosser, H. K. (2021). Deciding whether and how to deploy chatbots. Mis Quarterly Executive, 20(1). https://doi.org/10.17705/2msqe.00039
Valdez, D., Pickett, A. C., & Goodson, P. (2018). Topic modeling: latent semantic analysis for the social sciences. Social Science Quarterly, 99(5), 1665-1679. https://doi.org/10.1111/ssqu.12528
Webb, F., Karami, A., & Kitzie, V. (2018). Characterizing diseases and disorders in Gay Users' tweets. arXiv. https://doi.org/10.48550/arXiv.1803.09134
Wernerfelt, B. (1984). A resource-based view of the frm. Strategic Management Journal, 5(2), 171-180. https://doi.org/10.1002/smj.4250050207
Wilden, R., & Gudergan, S. P. (2015). The impact of dynamic capabilities on operational marketing and technological capabilities: Investigating the role of environmental turbulence. Journal of the Academy of Marketing Science, 43(2), 181–199. https://doi.org/10.1007/s11747-014-0380-y
Zarifhonarvar, A. (2024). Economics of chatgpt: A labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics, 3(2), 100-116. https://doi.org/10.1108/JEBDE-10-2023-0021