Kemajuan ketara dalam inovasi teknologi telah mentransformasikan pelbagai tugasan yang dahulunya dilakukan secara manual dan konvensional. Perubahan ini telah menjadikan teknologi kecerdasan buatan (AI) digunakan secara meluas dalam periklanan untuk meningkatkan kecekapan dan memenuhi permintaan pasaran. Berbanding dengan penghasilan iklan konvensional, AI dilihat sebagai teknologi yang banyak memudahkan proses secara optimum, penyelenggaraan yang minimum, pembantu maya, mengesan penipuan serta turut berfungsi sebagai pengesan anomali. Bagaimanapun, kebimbangan terhadap kesan AI terhadap proses pengiklanan semakin meningkat kerana berlakunya perubahan fungsinya yang dianggap lebih sistematik dan pantas. Terdapat risiko dan kesan buruk penggunaan AI dalam periklanan yang menyebabkan keengganan pemain industri untuk menerima pakai teknologi ini. Justeru, kajian ini dijalankan untuk melihat peranan dan pengaruh AI serta cabarannya terhadap industri periklanan dengan mengambil kira beberapa risiko moral dan kebimbangan privasi melalui kaedah temubual mendalam dengan 11 pengamal periklanan di Malaysia. Pengamal periklanan yang dipilih adalah berdasarkan pengalaman mereka berdepan dengan teknologi ini, yang akhirnya memberikan dapatan yang holistik terhadap kajian. Terdapat empat tema yang dicerap berdasarkan sesi temubual dan tema-tema tersebut mencakupi implikasi aplikasi AI dalam industri periklanan serta isu-isu yang melibatkan etika. Kajian ini turut mencadangkan beberapa ruang baharu yang boleh diterokai pada masa hadapan seperti langkah untuk mengelakkan risiko moral dan cara terbaik untuk mengkaji semula isu berkenaan hak cipta.

Kata kunci: Kecerdasan buatan (AI), Periklanan, Kecerdasan buatan periklanan, Privasi, Keselamatan data pengguna

Abstract: The significant advancements in technology innovation have led to the transformation of various manual tasks and processes that have existed for decades, to the extent that artificial intelligence (AI) technology is now widely used in advertising to enhance efficiency and meet market demands. Compared to traditional advertising creation, AI offers several significant benefits, especially in Malaysia, where AI has brought transformative changes to various industries, including advertising. AI has facilitated optimization processes, maintenance prevention, virtual assistants, fraud detection, and anomaly detection. However, concerns about the impact of artificial intelligence on advertising processes are increasing, manifesting as systematic restructuring. There are risks and negative effects of using AI in advertising that lead to industry players' reluctance to adopt this technology. Therefore, this study was conducted to examine the role and influence of artificial intelligence on the advertising industry and to explore the downsides of AI, considering several moral risks and privacy concerns through in-depth interviews with 11 advertising practitioners in Malaysia. Advertising practitioners were selected for this study based on their experience with this technology, ultimately providing rich findings for the research. The total of four themes emerged based on the interview sessions, with these themes addressing the application of AI in the advertising industry and ethical issues. This study also suggests several new areas that can be explored in the future, such as steps to mitigate moral risks and best practices for addressing copyright issues.

Keywords: Artificial intelligence (AI), Advertising, Artificial intelligence advertising, Privacy, User data security

References

Audry, S., & Ippolito, J. (2019). Can artificial intelligence make art without artists? Ask the viewer. Arts, 8(1), https://doi.org/10.3390/arts8010035

Bansal, S., Gupta, M. (2023). Towards using artificial intelligence in neuromarketing. In M.Gupta, P. Jindal, & S. Bansal (Ed.), Promoting consumer engagement through emotional branding and sensory marketing. IGI Global.

Başev, E.S. (2024). The role of artificial intelligence (AI) in the future of the advertising industry: Applications and examples of AI in advertising. International Journal of Education Technology and Scientific Researches, 9(26), 167-183. http://dx.doi.org/10.35826/ijetsar.729

Braun, V., & Clarke, V. (2013). Successful qualitative research: An introductory practical guide. London: Sage

Campbell, C., Plangger, K., Sands, S., Kietzmann, J., & Bates, K. (2022). How deepfakes and artificial intelligence could reshape the advertising industry: The coming reality of AI fakes and their potential impact on consumer behavior. Journal of Advertising Research, 62(3), 241–251. https://doi.org/10.2501/JAR-2022-017

Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing, 39(8), 1529–1562. hppts://doi.org/10.1002/mar.21670

Choi, J. A., & Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3), 175–180. https://doi.org/10.1016/j.icte.2020.04.012

Creswell, J. W. (2014). Research design : Qualitative, quantitative, and mixed methods approaches. Sage Publication, Inc.

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Sage.

Creswell, J. W., Hanson, W. E., Plano, V. L. C., & Morales, A. (2007). Qualitative research designs selection and implementation. The Counseling Psychologist, 35(2), 236-264. https://doi.org/10.1177/0011000006287390

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs ,C., Crick T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Vigneswara Ilavarasan, P., Janssen, M., Jones, P., Kumar Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 1–47. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Enberg, J. (2019, March 28). Digital ad spending. eMarketer. https://bit.ly/37HW9R5.

Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial intelligence in advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. Sage Open, 13(4). https://doi.org/10.1177/21582440231210759

Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005.

Huh, J., & Malthouse, E. C. (2020). Advancing computational advertising: Conceptualization of the field and future directions. Journal of Advertising, 49(4), 67–76. https://doi.org/10.1080/00913367.2020.1795759

Jaiwant, S. V. (2023). The changing role of marketing: Industry 5.0-the game changer. Transformation for sustainable business and management practices: Exploring the spectrum of industry 5.0. Emerald Publishing Limited.

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155. https://doi.org/10.1177/0008125619859317

Lai, Z. (2021). Research on advertising core business reformation driven by artificial intelligence. Journal of Physics, 1757(1), 012018. https://doi.org/10.1088/1742-6596/1757/1/012018

Lau, M. (2024, February 28). How is AI in Malaysia (2024)? LEAD. https://thelead.io/artificial-intelligence/how-is-ai-in-malaysia-2023/

Laux, J., Stephany, F., Russell, C., Wachter, S., & Mittelstadt, B. (2022). The concentration-after-personalisation index (CAPI): Governing effects of personalisation using the example of targeted online advertising. Big Data & Society, 9(2), 1–15. https://doi.org/10.1177/20539517221132535

Li, H. (2019). Special section introduction: Artificial intelligence and advertising. Journal of Advertising, 48(4), 333-337. https://doi.org/10.1080/00913367.2019.1654947

Malthouse, E., & Copulsky, J. (2023). Artificial intelligence ecosystems for marketing communications. International Journal of Advertising, 42(1), 128–140. https://doi.org/10.1080/02650487.2022.2122249

Meron, Y. (2022). Graphic design and artificial intelligence: Interdisciplinary challenges for designers in the search for research collaboration. In D.

Lockton, S. Lenzi, P. Hekkert, A. Oak, J. Sádaba, & P. Lloyd (Eds.), Design Research Society International Conference 2022 (pp. 1-16). DRS2020 Bilbao. https://doi.org/10.21606/drs.2022.157

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. Jossey-Bass.

Merriam, S. B., & Tisdell, E. J. (2015). Qualitative Research: A Guide to Design and Implementation. Wiley.

Merriam, S. B., & Tisdell, E. J. (2016). Qualitative Research: A Guide to Design and Implementation (4th ed.). Jossey Bass.

Mühlhoff, R., & Willem, T. (2023). Social media advertising for clinical studies: Ethical and data protection implications of online targeting. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231156127

Neuman, W. L. (2011). Social research methods: Qualitative and quantitative approaches. Allyn and Bacon.

Nie, S. (2017). Artificial intelligence ushers in a new era for marketing. China’s Foreign Trade, 6, 20-23.

Nikolajeva, A., & Teilans, A. (2021). Machine learning technology overview in terms of digital marketing and personalization. ECMS 2021 Proceedings European Council for Modeling and Simulation. http://doi.org/10.7148/2021

Nwachukwu, D. (2023). Evaluating the influence of artificial intelligence marketing on customer satisfaction with products and services of telecommunication companies in Port Harcourt, Rivers State, Nigeria. International Journal of Management and Marketing Systems, 3(9). https://doi.org/272614566711391

Nwachukwu, D. & Affen, M. P. (2023). Artificial intelligence marketing practices: The way forward to better customer experience management in Africa. International Academy Journal of Management, Marketing and Entrepreneurial Studies, 9(2). https://doi.org/ 27214256637924

Paley, J. (2017). Phenomenology as qualitative research - A critical analysis of meaning attribution. Routledge.

Peter, M. K., & Dalla Vecchia, M. (2021). The digital marketing toolkit: A literature review for the identification of digital marketing channels and platforms. Springer International Publishing. https://doi.org/10.1007/978-3-030-48332-6_17

Qin, X., & Jiang, Z. (2019). The impact of AI on the advertising process: The Chinese experience. Journal of Advertising, 48(4), 38–46. https://doi.org/10.1080/00913367.2019.1652122

Rodgers, S. (2021). Themed issue introduction: Promises and perils of artificial intelligence and advertising. Journal of Advertising, 50(1), 1-10. https://doi.org/10.1080/00913367.2020.1868233

Samuel, A., White, G.T.R., Thomas, R., & Jones, P. (2021). Programmatic advertising: An exegesis of consumer concerns. Computers in Human Behavior, 116. https://doi.org/10.1016/j.chb.2020.106657.

Tahoun, N., & Taher, A. (2021). Artificial intelligence as the new realm for online advertising. Advance. https://doi.org/ 10.4018/978-1-6684-5844-0.ch004

Thorson, E., & Rodgers, S. (2019). Advertising theory in the digital age. Routledge, Taylor and Francis Group.

Wimmer, R.D., & Dominick, J.R. (2013). Mass media research: An introduction. Cengage Learning.

Wiredu, J. (2023). An investigation on the characteristics, abilities, constraints, and functions of artificial intelligence (ai): The age of chatgpt as an essential ultramodern support tool. Information and Management, 108(3), 62614–62620. https://doi.org/10.37118/ijdr.22689.05.2023

Yu, Y. (2021). Advances in Social Science, Education and Humanities Research. Atlantis Press. 10.2991/assehr.k.220105.037

Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li C., Zhang, M., Dam, S. K., Thwal, C. M., Tun ,Y. L., Huy, L. L., Kim D., Bae, S.-H., Lee, L-H., Yang, Y.,

Shen, H. T., Kweon I. S., & Hong, C. S. (2023). A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need? arXiv preprint arXiv:2303.11717

Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7, 439–457. https://doi.org/10.1007/s40747-020-00212-w