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Pazarlamada Üretken Yapay Zekâ: Yapay Zekâ Halüsinasyonlarının Gizli Tehlikesi

Year 2024, Volume: 8 Issue: 2, 133 - 158, 31.12.2024
https://doi.org/10.7596/jebm.1588897

Abstract

Bu çalışma, pazarlama alanında Üretken Yapay Zekânın (ÜYZ) dönüştürücü etkisini hem önemli fırsatlarını hem de barındırdığı zorlukları vurgulayarak incelemektedir. ÜYZ, otomatik içerik üretimi, kişiselleştirilmiş müşteri deneyimleri ve ileri düzey veri analitiği yoluyla pazarlama stratejilerini geliştirerek verimliliği ve etkileşimi artırmaktadır. Ancak, Yapay Zekâ modellerinin gerçekçi ancak yanlış veya yanıltıcı bilgi üretmesi olarak tanımlanan Yapay Zekâ halüsinasyonları, marka itibarına zarar verme, tüketici güveninin sarsılması ve olası yasal sonuçlar gibi önemli riskler taşımaktadır. Çalışma, literatür incelemesi ve vaka analizleri kullanarak Üretken Yapay Zekânın pazarlamadaki çift yönlü etkilerini, yani potansiyel avantajları ve ilişkili riskleri ele almaktadır. Çalışma, Yapay Zekâ halüsinasyonları ile ilişkili riskleri azaltmak için, yanlış çıktıları tespit etmek ve düzeltmek için teknik çözümler, doğruluğu sağlamak için insan denetimi ve etik ile yasal düzenlemelere uyum gibi kapsamlı risk yönetimi stratejileri önermektedir. Bu sayede, ÜYZ'nin avantajlarını dengeli bir şekilde kullanarak marka bütünlüğünü korumak ve müşteri güvenini sürdürmek hedeflenmektedir.

References

  • Aporia. (2023). AI Chatbot Hallucinations: Understanding and Mitigating Risks. Retrieved from https://www.aporia.com/learn/chatbot-hallucinations/
  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
  • Bernard, A. (2023). AI in ecommerce: True one-on-one personalization is coming. CMSWire. Retrieved April 21, 2023.
  • Bhattacharya, R. (2024). Strategies to mitigate hallucinations in large language models. Applied Marketing Analytics, 10(1), 62–67.
  • Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2021). The ML Test Score: A rubric for ML production readiness and technical debt reduction. Proceedings of the IEEE International Conference on Big Data.
  • Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... & Amodei, D. (2020). Language models are few-shot learners.
  • Butler, R. (2023). Where are marketers on the GAI adoption curve? CMSWire. Retrieved May 4, 2023.
  • Chaffey, D. (2020). AI in digital marketing: How businesses are using AI to create personalized experiences. Smart Insights.
  • Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing: Strategy, implementation, and practice (7th ed.). Pearson Education.
  • Chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2024). Toward trustworthy artificial intelligence (TAI) in the context of explainability and robustness. ACM Computing Surveys.
  • Christensen, J., Hansen, J. M., & Wilson, P. (2024). Understanding the role and impact of generative artificial intelligence (AI) hallucination within consumers’ tourism decision-making processes. Current Issues in Tourism, 1–16.
  • Cichonski, P., Millar, T., Grance, T., & Scarfone, K. (2012). Computer security incident handling guide. National Institute of Standards and Technology.
  • Coca-Cola Company. (2023, March 16). Coca-Cola invites digital artists to create real magic using new AI platform. Retrieved from https://www.coca-colacompany.com/media-center/coca-cola-invites-digital-artists-to-create-real-magic-using-new-ai-platform
  • DataDance. (2024). Turning data into gold: 10 exceptional AI marketing campaign examples. Retrieved from https://datadance.ai/applications/turning-data-into-gold-10-exceptional-ai-marketing-campaign-examples
  • De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91–105.
  • Deloitte. (2024a). Training your chatbot correctly: What are the legal implications of a chatbot that provides wrong information? Retrieved from https://blogs.deloitte.ch/tax/2024/05/training-your-chatbot-correctly-what-are-the-legal-implications-of-a-chatbot-that-provides-wrong-inf.html
  • Deloitte. (2024b). AMD CMO on opportunities, challenges of using AI in marketing. The Wall Street Journal. Retrieved from https://deloitte.wsj.com/cmo/amd-cmo-on-opportunities-challenges-of-using-ai-in-marketing-8845225f
  • Digital Agency Network. (2024). Top AI-generated advertising campaigns from famous brands. Retrieved from https://digitalagencynetwork.com/top-ai-generated-advertising-campaigns-from-famous-brands
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71, Article 102642.
  • Elgammal, A. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.
  • Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In Machine learning and the city: Applications in architecture and urban design (pp. 535–545).
  • Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., & Greenspan, H. (2018). GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 321, 321–331.
  • Fujitsu. (2023, September 26). Fujitsu launches new technologies to protect conversational AI from hallucinations and misuse. Retrieved from https://www.fujitsu.com/global/about/resources/news/press-releases/2023/0926-02.html
  • Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2018). Datasheets for datasets.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
  • Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 1323512.
  • Hall, A. (2024, April 12). False advertising: 7 legal risks your marketing team must know. Retrieved from https://aaronhall.com/false-advertising-7-legal-risks-your-marketing-team-must-know/
  • Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., ... & Vosoughi, S. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38.
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
  • Kaur, J., & Nagina, R. (2024). Harnessing AI: Ethically transforming service marketing with responsible practices. In Integrating AI-Driven Technologies Into Service Marketing (pp. 239–264). IGI Global.
  • Kietzmann, J., Paschen, J., & Treen, E. R. (2018). Artificial intelligence in advertising: How marketers can leverage AI to deliver personalized content. Journal of Advertising Research, 58(3), 263–267.
  • Lambrecht, A., & Tucker, C. E. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2964–2981.
  • Lomas, N. (2023, May 10). Clearview fined again in France for failing to comply with privacy orders. TechCrunch. Retrieved from https://techcrunch.com/2023/05/10/clearview-ai-another-cnil-gspr-fine/
  • Luo, J., Hong, T., & Yue, M. (2018). Real-time anomaly detection for very short-term load forecasting. Journal of Modern Power Systems and Clean Energy, 6(2), 235–243.
  • Malhan, S., & Agnihotri, S. (2023). Consumer acceptance of artificial intelligence constructs on brand loyalty in online shopping: Evidence from India. In Hybrid Intelligent Systems (pp. 814–823). Springer.
  • Mantelero, A. (2018). AI and Big Data: A blueprint for a human rights, social and ethical impact assessment. Computer Law & Security Review, 34(4), 754–772.
  • Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon Books.
  • Mills, K., & Robson, K. (2020). Brand management in the era of fake news: Narrative response as a strategy to mitigate reputational damage. Journal of Product & Brand Management, 29(3), 345–358.
  • Müller, V. C. (2020). Ethics of artificial intelligence and robotics. In The Stanford Encyclopedia of Philosophy (E. N. Zalta, Ed.). Retrieved from https://plato.stanford.edu/entries/ethics-ai/
  • Neff, G. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 10, 4915–4931.
  • Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of GPT-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
  • NYP. (2024, November 15). Coca-Cola ripped for ugly AI-generated Christmas commercial: "Dystopian nightmare." Retrieved from https://nypost.com/2024/11/15/lifestyle/coca-cola-ripped-for-ugly-ai-generated-christmas-commercial-dystopian-nightmare/
  • Orange. (n.d.). Data and AI ethics council: Representing responsible AI. Retrieved from https://www.orange.com/en/data-and-ai-ethics-council
  • Prompt.security. (2023, May). 8 real world incidents related to AI. Retrieved from https://www.prompt.security/blog/8-real-world-incidents-related-to-ai
  • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog.
  • Sherly Steffi, L., Subha, B., Kuriakose, A., Singh, J., Arunkumar, B., & Rajalakshmi, V. (2024). The impact of AI-driven personalization on consumer behavior and brand engagement in online marketing. In Harnessing AI, Machine Learning, and IoT for Intelligent Business: Volume 1 (pp. 485–492). Springer Nature Switzerland.
  • Speedy Brand. (2024). AI in advertising: Examples of exceptional AI-powered marketing campaigns. Retrieved from https://speedybrand.io/blogs/aI-in-advertising-examples
  • Sullivan, B. (2023). Salesforce’s Einstein GPT: A new era of AI-driven insights in CRM. TechTarget.
  • Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024). AI hallucination: Towards a comprehensive classification of distorted information in artificial intelligence-generated content. Humanities and Social Sciences Communications, 11(1), 1–14.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  • Wen, Y., & Laporte, S. (2024). Experiential narratives in marketing: A comparison of generative AI and human content. Journal of Public Policy and Marketing.
  • West, D. M. (2019). The future of work: Robots, AI, and automation. Brookings Institution Press. Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023, April). Synthetic lies: Understanding AI-generated misinformation and evaluating algorithmic and human solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–20).

Generative Artificial Intelligence in Marketing: The Invisible Danger of AI Hallucinations

Year 2024, Volume: 8 Issue: 2, 133 - 158, 31.12.2024
https://doi.org/10.7596/jebm.1588897

Abstract

This study explores the transformative impact of Generative Artificial Intelligence (GAI) on the marketing highlighting both its significant opportunities and inherent challenges. GAI enhances marketing strategies through automated content creation, personalized customer experiences, and advanced data analytics, thereby increasing efficiency and engagement. However, the phenomenon of AI hallucinations—where AI models produce realistic yet incorrect or misleading information—poses substantial risks, including damage to brand reputation, erosion of consumer trust, and potential legal ramifications. To mitigate the risks associated with AI hallucinations, the study proposes comprehensive risk management strategies that include technical solutions to detect and correct erroneous outputs, human oversight to ensure accuracy, and adherence to ethical and regulatory frameworks. By balancing the advantages of GAI with robust measures to address AI-generated inaccuracies, organizations can harness its full potential while safeguarding their brand integrity and maintaining trust with customers.

References

  • Aporia. (2023). AI Chatbot Hallucinations: Understanding and Mitigating Risks. Retrieved from https://www.aporia.com/learn/chatbot-hallucinations/
  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
  • Bernard, A. (2023). AI in ecommerce: True one-on-one personalization is coming. CMSWire. Retrieved April 21, 2023.
  • Bhattacharya, R. (2024). Strategies to mitigate hallucinations in large language models. Applied Marketing Analytics, 10(1), 62–67.
  • Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2021). The ML Test Score: A rubric for ML production readiness and technical debt reduction. Proceedings of the IEEE International Conference on Big Data.
  • Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... & Amodei, D. (2020). Language models are few-shot learners.
  • Butler, R. (2023). Where are marketers on the GAI adoption curve? CMSWire. Retrieved May 4, 2023.
  • Chaffey, D. (2020). AI in digital marketing: How businesses are using AI to create personalized experiences. Smart Insights.
  • Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing: Strategy, implementation, and practice (7th ed.). Pearson Education.
  • Chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2024). Toward trustworthy artificial intelligence (TAI) in the context of explainability and robustness. ACM Computing Surveys.
  • Christensen, J., Hansen, J. M., & Wilson, P. (2024). Understanding the role and impact of generative artificial intelligence (AI) hallucination within consumers’ tourism decision-making processes. Current Issues in Tourism, 1–16.
  • Cichonski, P., Millar, T., Grance, T., & Scarfone, K. (2012). Computer security incident handling guide. National Institute of Standards and Technology.
  • Coca-Cola Company. (2023, March 16). Coca-Cola invites digital artists to create real magic using new AI platform. Retrieved from https://www.coca-colacompany.com/media-center/coca-cola-invites-digital-artists-to-create-real-magic-using-new-ai-platform
  • DataDance. (2024). Turning data into gold: 10 exceptional AI marketing campaign examples. Retrieved from https://datadance.ai/applications/turning-data-into-gold-10-exceptional-ai-marketing-campaign-examples
  • De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91–105.
  • Deloitte. (2024a). Training your chatbot correctly: What are the legal implications of a chatbot that provides wrong information? Retrieved from https://blogs.deloitte.ch/tax/2024/05/training-your-chatbot-correctly-what-are-the-legal-implications-of-a-chatbot-that-provides-wrong-inf.html
  • Deloitte. (2024b). AMD CMO on opportunities, challenges of using AI in marketing. The Wall Street Journal. Retrieved from https://deloitte.wsj.com/cmo/amd-cmo-on-opportunities-challenges-of-using-ai-in-marketing-8845225f
  • Digital Agency Network. (2024). Top AI-generated advertising campaigns from famous brands. Retrieved from https://digitalagencynetwork.com/top-ai-generated-advertising-campaigns-from-famous-brands
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71, Article 102642.
  • Elgammal, A. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.
  • Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In Machine learning and the city: Applications in architecture and urban design (pp. 535–545).
  • Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., & Greenspan, H. (2018). GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 321, 321–331.
  • Fujitsu. (2023, September 26). Fujitsu launches new technologies to protect conversational AI from hallucinations and misuse. Retrieved from https://www.fujitsu.com/global/about/resources/news/press-releases/2023/0926-02.html
  • Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2018). Datasheets for datasets.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
  • Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 1323512.
  • Hall, A. (2024, April 12). False advertising: 7 legal risks your marketing team must know. Retrieved from https://aaronhall.com/false-advertising-7-legal-risks-your-marketing-team-must-know/
  • Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., ... & Vosoughi, S. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38.
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
  • Kaur, J., & Nagina, R. (2024). Harnessing AI: Ethically transforming service marketing with responsible practices. In Integrating AI-Driven Technologies Into Service Marketing (pp. 239–264). IGI Global.
  • Kietzmann, J., Paschen, J., & Treen, E. R. (2018). Artificial intelligence in advertising: How marketers can leverage AI to deliver personalized content. Journal of Advertising Research, 58(3), 263–267.
  • Lambrecht, A., & Tucker, C. E. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2964–2981.
  • Lomas, N. (2023, May 10). Clearview fined again in France for failing to comply with privacy orders. TechCrunch. Retrieved from https://techcrunch.com/2023/05/10/clearview-ai-another-cnil-gspr-fine/
  • Luo, J., Hong, T., & Yue, M. (2018). Real-time anomaly detection for very short-term load forecasting. Journal of Modern Power Systems and Clean Energy, 6(2), 235–243.
  • Malhan, S., & Agnihotri, S. (2023). Consumer acceptance of artificial intelligence constructs on brand loyalty in online shopping: Evidence from India. In Hybrid Intelligent Systems (pp. 814–823). Springer.
  • Mantelero, A. (2018). AI and Big Data: A blueprint for a human rights, social and ethical impact assessment. Computer Law & Security Review, 34(4), 754–772.
  • Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon Books.
  • Mills, K., & Robson, K. (2020). Brand management in the era of fake news: Narrative response as a strategy to mitigate reputational damage. Journal of Product & Brand Management, 29(3), 345–358.
  • Müller, V. C. (2020). Ethics of artificial intelligence and robotics. In The Stanford Encyclopedia of Philosophy (E. N. Zalta, Ed.). Retrieved from https://plato.stanford.edu/entries/ethics-ai/
  • Neff, G. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 10, 4915–4931.
  • Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of GPT-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
  • NYP. (2024, November 15). Coca-Cola ripped for ugly AI-generated Christmas commercial: "Dystopian nightmare." Retrieved from https://nypost.com/2024/11/15/lifestyle/coca-cola-ripped-for-ugly-ai-generated-christmas-commercial-dystopian-nightmare/
  • Orange. (n.d.). Data and AI ethics council: Representing responsible AI. Retrieved from https://www.orange.com/en/data-and-ai-ethics-council
  • Prompt.security. (2023, May). 8 real world incidents related to AI. Retrieved from https://www.prompt.security/blog/8-real-world-incidents-related-to-ai
  • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog.
  • Sherly Steffi, L., Subha, B., Kuriakose, A., Singh, J., Arunkumar, B., & Rajalakshmi, V. (2024). The impact of AI-driven personalization on consumer behavior and brand engagement in online marketing. In Harnessing AI, Machine Learning, and IoT for Intelligent Business: Volume 1 (pp. 485–492). Springer Nature Switzerland.
  • Speedy Brand. (2024). AI in advertising: Examples of exceptional AI-powered marketing campaigns. Retrieved from https://speedybrand.io/blogs/aI-in-advertising-examples
  • Sullivan, B. (2023). Salesforce’s Einstein GPT: A new era of AI-driven insights in CRM. TechTarget.
  • Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024). AI hallucination: Towards a comprehensive classification of distorted information in artificial intelligence-generated content. Humanities and Social Sciences Communications, 11(1), 1–14.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
  • Wen, Y., & Laporte, S. (2024). Experiential narratives in marketing: A comparison of generative AI and human content. Journal of Public Policy and Marketing.
  • West, D. M. (2019). The future of work: Robots, AI, and automation. Brookings Institution Press. Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023, April). Synthetic lies: Understanding AI-generated misinformation and evaluating algorithmic and human solutions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–20).
There are 52 citations in total.

Details

Primary Language English
Subjects Digital Marketing, Marketing Technology
Journal Section Articles
Authors

Burak Yaprak 0000-0001-9831-0813

Early Pub Date December 31, 2024
Publication Date December 31, 2024
Submission Date November 22, 2024
Acceptance Date December 27, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Yaprak, B. (2024). Generative Artificial Intelligence in Marketing: The Invisible Danger of AI Hallucinations. Ekonomi İşletme Ve Yönetim Dergisi, 8(2), 133-158. https://doi.org/10.7596/jebm.1588897