TY  - JOUR
T1  - ARTIFICIAL INTELLIGENCE IN ADVERTISEMENTS: A CONCEPTUAL FRAMEWORK BASED ON THE TECHNOLOGY ACCEPTANCE MODEL
AU  - Sarp, Serap
PY  - 2023
DA  - October
Y2  - 2023
JF  - Economics Business and Organization Research
PB  - Malton London Yönetim Danışmanlığı ve Organizasyon LTD
WT  - DergiPark
SN  - 2687-5942
SP  - 161
EP  - 174
VL  - 5
IS  - 2
LA  - en
AB  - of influencing their purchase intention. The impact of advertising is important for generating product recognition and sales. With the technological advancement in AI usage in businesses, the integration of Artificial Intelligence in contemporary advertising strategies is impactful. This study aims to explain how Artificial Intelligence (AI) can be used in advertising, underpinned by Technology Acceptance Model (TAM). Using the TAM model, the paper explains how people come to accept and use AI in ads. It is proposed that if people find AI in ads useful and easy to understand, they're more likely to respond positively. Besides, social impact is also considered when explaining consumer attitude and purchase intention.  This research helps advertisers understand how to use AI better in their campaigns to engage consumers and get better results.
KW  - AI
KW  - Advertisement
KW  - TAM Model
KW  - Consumer Attitude
CR  - jstAhn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405-420. https://doi.org/10.1016/j.elerap.2004.05.001
CR  - Aksu, H., Babun, L., Conti, M., Tolomei, G., & Uluagac, A. S. (2018). Advertising in the IoT era: Vision and Challenges. IEEE Communications Magazine, 56(11), 138-144. https://doi.org/10.1109/MCOM.2017.1700871
CR  - Alenezi, H., Tarhini, A., & Sharma, S. K. (2015). Development of quantitative model to investigate the strategic relationship between information quality and e-government benefits. Transforming Government: People, Process and Policy, 9(3), 324-351. https://doi.org/10.1108/tg-01-2015-0004
CR  - Bagozzi, R. P., Wong, N., Abe, S., & Bergami, M. (2000). Cultural and situational contingencies and the theory of reasoned action: Application to fast food restaurant consumption. Journal of Consumer Psychology, 9(2), 97-106. https://doi.org/10.1207/15327660051044187
CR  - Bhattacherjee, A., & Hikmet, N. (2008). Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector. Journal of Computer Information Systems, 48(4), 69-76. https://doi.org/10.1080/08874417.2008.11646036
CR  - Campbell, C., Plangger, K., Sands, S., & Kietzmann, J. (2022). Preparing for an era of Deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 51(1), 22-38. https://doi.org/10.1080/00913367.2021.1909515
CR  - Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312
CR  - Cha, J. (2010). Factors affecting the frequency and amount of social networking site use: Motivations, perceptions, and privacy concerns. First Monday, 15(12). https://doi.org/10.5210/fm.v15i12.2889
CR  - Chao, C. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01652
CR  - Chen, G., Xie, P., Dong, J., & Wang, T. (2019). Understanding programmatic creative: The role of AI. Journal of Advertising, 48(4), 347-355. https://doi.org/10.1080/00913367.2019.1654421
CR  - Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for E-lEarning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003
CR  - Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
CR  - Deng, S., Tan, C., Wang, W., & Pan, Y. (2019). Smart generation system of personalized advertising copy and its application to advertising practice and research. Journal of Advertising, 48(4), 356-365. https://doi.org/10.1080/00913367.2019.1652121
CR  - Donthu, N., Lim, W. M., Kumar, S., & Pattnaik, D. (2022). The Journal of Advertising’s production and dissemination of advertising knowledge: A 50th anniversary commemorative review. Journal of Advertising, 51(2), 153-187. https://doi.org/10.1080/00913367.2021.2006100
CR  - Fang, X., Chan, S., Brezezinski, J., & Xu, S. (2005). Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 22(3), 123-157. https://doi.org/10.2753/mis0742-1222220305
CR  - Farahat, T. (2012). Applying the technology acceptance model to online learning in the Egyptian universities. Procedia - Social and Behavioral Sciences, 64, 95-104. https://doi.org/10.1016/j.sbspro.2012.11.012
CR  - Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, Mass: Addison-Wesley Publishing Company.
CR  - Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-Commerce adoption. Journal of the Association for Information Systems, 1(8), 1-28. https://doi.org/10.17705/1jais.00008
CR  - Huh, J., & Malthouse, E. C. (2020). Advancing computational advertising: Conceptualization of the Field and future directions. Journal of Advertising, 49(4), 367-376. https://doi.org/10.1080/00913367.2020.1795759
CR  - Jin, S., Lin, W., Yin, H., Yang, S., Li, A., & Deng, B. (2015). Community structure mining in big data social media networks with MapReduce. Cluster Computing, 18(3), 999-1010. https://doi.org/10.1007/s10586-015-0452-x
CR  - Kim, G. S., Park, S., & Oh, J. (2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25(8), 769-786. https://doi.org/10.1002/mar.20238
CR  - 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
CR  - Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114-120. https://doi.org/10.1016/j.chb.2016.08.007
CR  - Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
CR  - Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346. https://doi.org/10.1177/0092070305276149
CR  - Panchiwala, S., & Shah, M. (2020). A comprehensive study on critical security issues and challenges of the IoT world. Journal of Data, Information and Management, 2(4), 257-278. https://doi.org/10.1007/s42488-020-00030-2
CR  - Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385. https://doi.org/10.1016/j.tele.2013.11.008
CR  - Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
CR  - Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. (2014). Machine learning for targeted display advertising: Transfer learning in action. Machine Learning, 95(1), 103-127. https://doi.org/10.1007/s10994-013-5375-2
CR  - Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999-1007. https://doi.org/10.1016/j.jbusres.2006.06.003
CR  - Qin, X., & Jiang, Z. (2019). The impact of AI on the advertising process: The Chinese experience. Journal of Advertising, 48(4), 338-346. https://doi.org/10.1080/00913367.2019.1652122
CR  - Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51.
CR  - Ramos-de-Luna, I., Montoro-Ríos, F., & Liébana-Cabanillas, F. (2015). Determinants of the intention to use NFC technology as a payment system: An acceptance model approach. Information Systems and e-Business Management, 14(2), 293-314. https://doi.org/10.1007/s10257-015-0284-5
CR  - Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30. https://doi.org/10.1108/jeim-04-2012-0011
CR  - Shah, N., Engineer, S., Bhagat, N., Chauhan, H., & Shah, M. (2020). Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising. Augmented Human Research, 5, 1-15. https://doi.org/10.1007/s41133-020-00038-8
CR  - Suki, N. M., & Suki, N. M. (2011). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile services. Journal of Information Technology Management, 22(1), 1-7.
 
Tandon, U., Kiran, R., & Sah, A. N. (2016). Customer satisfaction using website functionality, perceived usability and perceived usefulness towards online shopping in India. Information Development, 32(5), 1657-1673. https://doi.org/10.1177/0266666915621106
CR  - Tarhini, A., Arachchilage, N. A., Masa'deh, R., & Abbasi, M. S. (2015). A critical review of theories and models of technology adoption and acceptance in information system research. International Journal of Technology Diffusion, 6(4), 58-77. https://doi.org/10.4018/ijtd.2015100104
CR  - Tarhini, A., Hone, K., & Liu, X. (2013). Factors affecting students’ acceptance of E-LEarning environments in developing Countries:A structural equation modeling approach. International Journal of Information and Education Technology, 3(1), 54-59. https://doi.org/10.7763/ijiet.2013.v3.233
CR  - Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
CR  - Trip, S., Bora, C. H., Marian, M., Halmajan, A., & Drugas, M. I. (2019). Psychological mechanisms involved in radicalization and extremism. A rational emotive behavioral conceptualization. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.00437
CR  - Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal Field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
CR  - Yin, C., Hu, J., Zhang, X., & Xie, X. (2015). Advertising system based on cloud computing and audio watermarking. 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). https://doi.org/10.1109/iih-msp.2015.81
CR  - Yiu, C. S., Grant, K., & Edgar, D. (2007). Factors affecting the adoption of internet banking in Hong Kong—implications for the banking sector. International Journal of Information Management, 27(5), 336-351. https://doi.org/10.1016/j.ijinfomgt.2007.03.002
CR  - Zhang, J., & Mao, E. (2008). Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychology & Marketing, 25(8), 787-805. https://doi.org/10.1002/mar.20239
UR  - https://dergipark.org.tr/tr/pub/ebor/article/1376602
L1  - https://dergipark.org.tr/tr/download/article-file/3477504
ER  -