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Year 2023, Volume: 5 Issue: 2, 161 - 174, 29.10.2023

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References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, Mass: Addison-Wesley Publishing Company.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

ARTIFICIAL INTELLIGENCE IN ADVERTISEMENTS: A CONCEPTUAL FRAMEWORK BASED ON THE TECHNOLOGY ACCEPTANCE MODEL

Year 2023, Volume: 5 Issue: 2, 161 - 174, 29.10.2023

Abstract

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.

Ethical Statement

Etik Beyana gerek olmamaktadır.

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, Mass: Addison-Wesley Publishing Company.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
There are 44 citations in total.

Details

Primary Language English
Subjects Business Administration, Marketing Management
Journal Section Conceptual Paper
Authors

Serap Sarp 0000-0002-2560-4105

Publication Date October 29, 2023
Submission Date October 16, 2023
Acceptance Date October 26, 2023
Published in Issue Year 2023 Volume: 5 Issue: 2

Cite

APA Sarp, S. (2023). ARTIFICIAL INTELLIGENCE IN ADVERTISEMENTS: A CONCEPTUAL FRAMEWORK BASED ON THE TECHNOLOGY ACCEPTANCE MODEL. Economics Business and Organization Research, 5(2), 161-174.