Reklam Metni Yazımında Yapay Zekâ İnsana Karşı

Year 2023, Volume: 8 Issue: 22, 850 - 862, 28.10.2023

Aykut Yılmaz

https://doi.org/10.25204/iktisad.1345154

Abstract

Bu araştırma insan üretimi olan reklam metniyle yapay zekalara (YZ) yazdırılan reklam metinlerinin etkinliğinin karşılaştırılması amacıyla yapılmıştır. Bu kapsamda, kolayda örnekleme yöntemiyle 208 üniversite öğrencisine anket uygulanarak veriler toplanmıştır. YZlar tarafından üretilmiş iki reklam metni ile firmanın orijinal reklamında kullanılan metin katılımcılara sunulmuştur. Her bir reklam metni için ölçek ifadelerini cevaplamaları istenmiştir. Toplanan veriler üzerinde regresyon analizi uygulanmıştır. Analiz sonuçlarına göre, katılımcılar üzerinde en etkili olan reklam metni küçük farkla da olsa ChatGPT'ye yazdırılan reklam metnidir. Sonuç olarak, YZlar reklam metni yazımı konusunda kısıtlı veri kullanılmasına rağmen insana yakın başarı göstermiştir. Reklam metni yazımında YZ kullanımının sektöre etkinlik ve verimlilik kazandıracağı düşünülmektedir.

Keywords

Reklam, yapay zeka, tüketici ilgilenimi, satın alma niyeti

Artificial Intelligence vs Human in Advertisement Text Writing

Abstract

This research was conducted to compare the effectiveness of human-generated advertisement text and artificial intelligence (AI)-generated advertisement texts. In this context, data were collected by applying a questionnaire to 208 university students using convenience sampling method. In the questionnaire form, consumer involvement and purchase intention scales were included. Two advertising texts produced by AI and the text used in the original advertisement of the company were presented to the participants. They were asked to answer the scale statements for each ad text. Regression analysis was applied on the collected data. According to the results of the analysis, the most effective ad text on the participants is the ad text printed on ChatGPT, even if with a small difference. As a result, AIs have shown close to human-like success in ad text writing despite using limited data. It is thought that the use of AI in ad text writing will bring efficiency and productivity to the sector.

Keywords

Advertisement, artificial intelligence, consumer involvement, purchase intention

References

• Adam, M., Wessel, M. and Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31, 427-445. https://doi.org/10.1007/s12525-020-00414-7
• Albus, J. S. (1991). Outline for a theory of intelligence. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 473-509. https://doi.org/10.1109/21.97471
• Altunışık, R., Coşkun, R., Bayraktaroğlu, S. and Yıldırım, E. (2012). Sosyal bilimlerde araştırma yöntemleri SPSS uygulamalı. Sakarya Yayıncılık.
• American Marketing Association. (2011). Marketing dictionary. August 15, 2023, https://marketing-dictionary.org/a/advertising/
• American Marketing Association. (2017). Definition of marketing. August 14, 2023, https://www.ama.org/the-definition-of-marketing-what-is-marketing/
• Armstrong, G., Kotler, P. and Opresnik, M. O. (2023). Marketing: An introduction (Fifteenth Edition ed.). Pearson Education Limited.
• Bentler, P. M. and Chou, C.-P. (1987). Practical issues in structural modeling. Sociological methods & research, 78-117. https://doi.org/10.1177/0049124187016001004
• Bhosale, S. S., Salunkhe, A. G. and Sutar, S. S. (2020). Artificial intelligence and its application in different areas. International Journal of Advance and Innovative Research, 35-39. https://doi.org/10.1002/9781119761655.ch9
• Borsci, S., Malizia, A., Schmettow, M., Van Der Velde, F., Tariverdiyeva, G., Balaji, D. and Chamberlain, A. (2022). The chatbot usability scale: The design and pilot of a usability scale for interaction with AI-Based conversational agents. Personal and Ubiquitous Computing, 26, 95-119. https://doi.org/10.1007/s00779-021-01582-9
• Bradley, F. (1995). Marketing management: Providing, communicating and delivering value. Prentice Hall Europe.
• Büyüköztürk, Ş. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve uygulamada eğitim yönetimi, 470-483.
• Çakır, V. (2007). Tüketici ilgilenimini ölçmek. Selçuk İletişim, 163-180.
• DeVellis, R. F. (2003). Scale development: Theory and applications (Second Edition b.). SAGE Publications, Inc.
• Dodds, W. B., Monroe, K. B. and Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of marketing research, 307-319. https://doi.org/10.1177/002224379102800305
• Flathery, K. E., Lassk, F., Lee, N., Marshall, G. W., Moncrief, W. C., Mulki, J. P. and Pullins, E. B. (2018). Sales scholarship: Honoring the past and defining the future (Key takeaways from the 2018 American Marketing Association Faculty Consortium: New Horizons in Selling and Sales Management). Journal of Personal Selling & Sales Management, 38(4), 413-421. https://doi.org/10.1080/08853134.2018.1537796
• Fogel, D. B. (1995). Review of computational intelligence: Imitating life [book reviews]. Proceedings of the IEEE, 83(11), 1588-1592. https://doi.org/10.1109/JPROC.1995.481636
• Gerwal, D. S. (2014). A critical conceptual analysis of definitions of artificial intelligence as applicable to computer engineering. IOSR Journal of Computer Engineering, 16(2), 9-13.
• Gudwin, R. R. (2000). Evaluating intelligence: A computational semiotics perspective. IEEE International conference on systems, man and cybernetics, (pp. 2080-2085). Nashville, Tenessee. https://doi.org/10.1109/ICSMC.2000.886422
• Haenlein, M. and Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
• Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2014). Multivariate data analysis. Pearson Education Limited.
• Hsu, P.-F., Nguyen, T. K., Wang, C.-Y. and Huang, P.-J. (2023). Chatbot commerce-How contextual factors affect Chatbot effectiveness. Electronic Markets, 33(1), 1-22. https://doi.org/10.1007/s12525-023-00629-4
• Kalaycı, Ş. (2014). SPSS uygulamalı çok değişkenli istatistik teknikleri. Asil Yayın Dağıtım.
• Kalicanin, K., Colovic, M., Njegus, A. and Mitic, V. (2019). Benefits of artificial intelligence and machine learning in marketing. International scientific conference on information technology and data related research (pp. 472-477). Singidunum University.
• Kaplan, A. and Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
• Kerin, R. A. and Hartley, S. W. (2016). Marketing: The core. McGraw-Hill Education.
• Kerin, R. A. and Hartley, S. W. (2023). Marketing (Sixteenth Edition ed.). McGraw Hill LLC.
• Kietzmann, J., Paschen, J. and Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263–267. https://doi.org/10.2501/JAR-2018-035
• Klein, K. and Martinez, L. F. (2022). The impact of anthropomorphism on customer satisfaction in chatbot commerce: An experimental study in the food sector. Electronic Commerce Research, 1-37. https://doi.org/10.1007/s10660-022-09562-8
• Kline, P. (1994). An easy guide to factor analysis. Routledge.
• Kotler, P., Armstrong, G. and Balasubramanian, S. (2023). Principles of marketing (Nineteenth Edition ed.). Pearson Education Limited.
• Kumar, V., Rajan, B., Venkatesan, R. and 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
• Lemon, K. N. and Verhoef, P. J. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
• Lindstorm, M. (2008). Buyology. (Ü. Şensoy, Çev.) Optimist Yayınları.
• Martinez, R. (2019). Artificial intelligence: distinguishing between types & definitions. Nevada Law Journal, 19(3), 1015-1042.
• Martínez-López, F. J. and Casillas, J. (2013). Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Industrial Marketing Management, 42(4), 489-495. https://doi.org/10.1016/j.indmarman.2013.03.001
• Mustak, M., Salminen, J., Plé, L. and Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389-404. https://doi.org/10.1016/j.jbusres.2020.10.044
• Nguyen, T. H., Waizenegger, L. and Techatassanasoontorn, A. A. (2022). “Don’t neglect the user!” – identifying types of human-chatbot interactions and their associated characteristics. Information Systems Frontiers, 24(3), 797-838. https://doi.org/10.1007/s10796-021-10212-x
• Özdamar, K. (2016). Ölçek ve test geliştirme yapısal eşitlik modellemesi. Nisan Kitabevi.
• Pannu, A. (2015). Artificial intelligence and its application in different areas. International Journal of Engineering and Innovative Technology, 79-84.
• Paschen, J. (2020). Investigating the emotional appeal of fake news using artificial intelligence and human contributions. Journal of Product & Brand Management, 29(2), 223-233. https://doi.org/10.1108/JPBM-12-2018-2179
• Pongiannan, K. (2011). Viewers' perception towards advertisements on TV media as a promotional Strategy in the current marketing scenario. Indian Journal of marketing, 41(4), 39-45.
• Reshetkova, A. (2019). Artificial intelligence in advertising and the consumer journey to purchase. СЕРИЯ ИКОНОМИЧЕСКИ НАУКИ, 145-153.
• Russell, S. J. and Norvig, P. (2010). Artificial intelligence: A modern approach (Third Edition ed.). Pearson Education, Inc.
• Samoili, S., López-Cobo, M., Gómez, E., De Prato, G., Martínez-Plumed, F. and Delipetrev, B. (2020). AI watch. Defining artificial intelligence. Towards an operational definition and taxonomy of artificial intelligence. Publications Office of the European Union.
• Shah, N., Engineer, S., Bhagat, N., Chauhan, H. and Shah, M. (2020). Research trends on the usage of machine learning and artificial intelligence in advertising. Augmented Human Research, 5(19), 1-15. https://doi.org/10.1007/s41133-020-00038-8
• Sharma, S. (1996). Applied multivariate techniques. John Wiley & Sons, Inc.
• Sun, R. (2021). Analysis on the application of artificial intelligence in marketing. nternational Conference on Computer Information Science and Artificial Intelligence (pp. 669-672). Barcelona: IEEE.
• Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74-88. https://doi.org/10.1093/jcmc/zmz026
• Verma, M. (2018). Artificial intelligence and its scope in different areas with special reference to the field of education. International Journal of Advanced Educational Research, 5-10.
• Winter, S., Maslowska, E. and Vos, A. L. (2021). The effects of trait-based personalization in social media advertising. Computers in Human Behavior, 114, 106525. https://doi.org/10.1016/j.chb.2020.106525
• Yaşlıoğlu, M. (2017). Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 74-85.
• Zaichkowsky, J. L. (1986). Conceptualizing involvement. Journal of Advertising, 15(2), 4-34. https://doi.org/10.1080/00913367.1986.10672999
• Zaichkowsky, J. L. (2012). Consumer involvement: Review, update, and links to decision neuroscience. In V. Wells and G. R. Foxall, Handbook of developments in consumer behaviour (pp. 523-546). Edward Elgar Publishing.