The The use of artificial intelligence and big data in advertising
DOI:
https://doi.org/10.18568/cmc.2026.e3133Keywords:
Artificial intelligence, big data, advertising, bibliometric analysis, machine learningAbstract
This work examines the academic research field on the intersection of artificial intelligence (AI) and big data in advertising and explains how scholarly literature has evolved. A bibliometric analysis was performed on 28 articles indexed in Web of Science and Scopus. Co-authorship, citation, keyword co-occurrence and co-citation analyses were used to trace contributors, publications, and emerging themes. Results show that some scholars, like Colin Campbell and Jan Kietzmann, and journals, such as Journal of Advertising and Journal of Consumer Research, are central nodes. Current research seems to focus on AI and machine learning for audience targeting, personalization, and performance measurement. However, there are still some challenges in terms of data privacy and cybersecurity, as well as a shortage of skilled labor. The findings suggest AI-driven advertising research is interdisciplinary and integrates psychology, marketing, and computer science. This research provides a baseline for future studies aimed at developing ethical frameworks and advertising applications.
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AKSOY, F.; ÖCAL, D. The role of big data in advertising strategic planning: a research on advertising agencies. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, v. 15, n. 3, p. 693-717, 2024.
ARIA, M.; CUCCURULLO, C. Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, v. 11, n. 4, p. 959-975, 2017. https://doi.org/10.1016/j.joi.2017.08.007
*BAKKE, N. A.; BARLAND, J. Disruptive innovations and paradigm shifts in journalism as a business: From advertisers first to readers first and traditional operational models to the AI factory. SAGE Open, v. 12, n. 2, 2022. https://doi.org/10.1177/21582440221094819
BLEIER, A.; EISENBEISS, M. Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, v. 37, n. 5, p. 697-726, 2018.
BOERMAN, S. C.; KRUIKEMEIER, S.; ZUIDERVEEN BORGESIUS, F. J. Exploring motivations for online privacy protection behaviour: Privacy concerns and trust in personalized advertising. Computers in Human Behavior, v. 92, p. 44-52, 2019.
CAMPAIGNS OF THE WORLD. M&C Saatchi launches world first artificial intelligence poster ad campaign. Campaigns of the World, 2016. Available at: https://campaignsoftheworld.com/technology/mc-saatchi-launches-world-first-artificial-intelligence-poster-ad-campaign/. Accessed on: Feb. 12, 2025.
*CAMPBELL, C.; PLANGGER, K.; SANDS, S.; KIETZMANN, J. Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, v. 51, n. 1, p. 22-38, 2021. https://doi.org/10.1080/00913367.2021.1909515
*CAMPBELL, C.; PLANGGER, K.; SANDS, S.; KIETZMANN, J. H.; BATES, K. 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, v. 62, n. 3, p. 241-251, 2022. https://doi.org/10.2501/JAR-2022-017
CHENG, X.; FANG, L.; YANG, L.; CUI, S. Mobile big data. Cham: Springer, 2018.
CICHY, P.; WIŚNIEWSKI, P. Privacy by design in programmatic advertising: Empirical insights after GDPR. Journal of Advertising Research, v. 62, n. 2, p. 123-138, 2022.
*COFFIN, J. Asking questions of AI advertising: A maieutic approach. Journal of Advertising, v. 51, n. 5, p. 608-623, 2022. https://doi.org/10.1080/00913367.2022.2111728
*DIWANJI, V. S.; LEE, J.; CORTESE, J. Deconstructing the role of artificial intelligence in programmatic advertising: At the intersection of automation and transparency. Journal of Strategic Marketing, v. 32, n. 7, p. 1005-1018, 2024. https://doi.org/10.1080/0965254X.2022.2148269
*DONG, B.; ZHUANG, M.; FANG, E.; WANG, M. Tales of two channels: Digital advertising performance between AI recommendation and user subscription channels. Journal of Marketing, v. 88, n. 2, p. 141-162, 2024. https://doi.org/10.1177/00222429231190021
EREVELLES, S.; FUKAWA, N.; SWAYNE, L. Big data consumer analytics and the transformation of marketing. Journal of Business Research, v. 69, n. 2, p. 897-904, 2016. https://doi.org/10.1016/j.jbusres.2015.07.001
*FENG, Y.; CHEN, H.; XIE, Q. AI influencers in advertising: The role of AI influencer-related attributes in shaping consumer attitudes, consumer trust, and perceived influencer–product fit. Journal of Interactive Advertising, v. 24, n. 1, p. 25-38, 2024. https://doi.org/10.1080/15252019.2023.2284355
*FORD, J.; JAIN, V.; WADHWANI, K.; GUPTA, D. AI advertising: An overview and guidelines. Journal of Business Research, v. 166, 114124, 2023. https://doi.org/10.1016/j.jbusres.2023.114124
*GARDINER, A.; AASHEIM, C.; RUTNER, P.; WILLIAMS, S. Skill requirements in big data: A content analysis of job advertisements. Journal of Computer Information Systems, v. 58, n. 4, p. 374-384, 2018. https://doi.org/10.1080/08874417.2017.1289354
*GE, T.; WU, X. Accurate delivery of online advertising and the evaluation of advertising effect based on big data technology. Mobile Information Systems, v. 2021, p. 1-10, 2021. https://doi.org/10.1155/2021/1598666
GOUDA, N. K.; BISWAL, S. K.; PARVEEN, B. Application of artificial intelligence in advertising & public relations and emerging ethical issues in the ecosystem. International Journal of Advanced Science and Technology, v. 29, n. 6, p. 7561-7570, 2020.
*GU, C.; JIA, S.; LAI, J.; CHEN, R.; CHANG, X. Exploring consumer acceptance of AI-generated advertisements: From the perspectives of perceived eeriness and perceived intelligence. Journal of Theoretical and Applied Electronic Commerce Research, v. 19, n. 3, p. 2218-2238, 2024. https://doi.org/10.3390/jtaer19030108
*HAJIAN, A.; SADEGHI, R.; PRYBUTOK, V.; KOH, C. Increasing trust and value of mobile advertising in retailing: A survey design, machine learning approach, and blockchain in the trust path. Journal of Retailing and Consumer Services, v. 79, 103794, 2024. https://doi.org/10.1016/j.jretconser.2024.103794
HARVEY, C. Big data pros and cons. Datamation, 2018. Available at: https://www.datamation.com/big-data/big-data-pros-and-cons/. Accessed on: Feb. 28, 2025.
HOGUET, B. Storytelling AI: How artificial intelligence feeds creativity. Canadian Media Fund, 2019. Available at: https://cmf-fmc.ca/now-next/articles/storytelling-ai-how-artificial-intelligence-feeds-creativity/. Accessed on: Feb. 28, 2025.
*HOPP, T.; VARGO, C. Does negative campaign advertising stimulate uncivil communication on social media? Computers in Human Behavior, v. 68, p. 368-377, 2017. https://doi.org/10.1016/j.chb.2016.11.034
JOVANOVIĆ, M.; CAMPBELL, M. Generative artificial intelligence: Trends and prospects. Computer, v. 55, n. 10, p. 107-112, 2022. https://doi.org/10.1109/MC.2022.3192720
KAPUT, M. 20 ways AI could transform PR and communications. Marketing AI Institute, 2021. Available at: https://www.marketingaiinstitute.com/blog/how-ai-could-transform-pr-and-communications. Accessed on: Mar. 5, 2025.
KIETZMANN, J.; PASCHEN, J.; TREEN, E. Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, v. 58, n. 3, p. 263-267, 2018. https://doi.org/10.2501/JAR-2018-035
KOSINSKI, M. Dr. Michal Kosinski on Facebook, big data, and psychographic profiling. YouTube, 2017. Available at: https://www.youtube.com/watch?v=XKNFjVM2SfY. Accessed on: Jan. 20, 2025.
LI, H. Special section introduction: Artificial intelligence and advertising. Journal of Advertising, v. 48, n. 4, p. 333-337, 2019. https://doi.org/10.1080/00913367.2019.1654947
LI, K.; ROLLINS, J.; YAN, E. Web of Science use in published research and review papers 1997–2017. Scientometrics, v. 115, n. 1, p. 1-20, 2018. https://doi.org/10.1007/s11192-017-2622-5
LIU, Y. T.; PAL, N.; MARATHE, A.; LIN, C. T. Weighted fuzzy Dempster-Shafer framework for multi-modal information integration. IEEE Transactions on Fuzzy Systems, v. 26, n. 1, p. 338-352, 2017. https://doi.org/10.1109/TFUZZ.2017.2659764
*LU, S. H.; TRAN, H. T. T.; NGO, T. S. Are we ready for artificial intelligence voice advertising? Comparing human and artificial intelligence voices in audio advertising in a multitasking context. Quality and Quantity: International Journal of Methodology, v. 59, n. 1, p. 1-22, 2024. https://doi.org/10.1007/s11135-024-01967-x
*MALTHOUSE, E. C.; LI, H. Opportunities for and pitfalls of using big data in advertising research. Journal of Advertising, v. 46, n. 2, p. 227-235, 2017. https://doi.org/10.1080/00913367.2017.1299653
MASLOWSKA, E.; SMIT, E. G.; VAN DEN PUTTE, B. It is all in the name: A study of consumers’ responses to personalized communication. Journal of Interactive Advertising, v. 16, n. 1, p. 74-85, 2016. https://doi.org/10.1080/15252019.2016.1161568
*MISHRA, S.; KERN-STONE, R. Smart assistants for smart living: Ideology and mythology in AI-powered smart speaker advertising. Journal of Communication Inquiry, v. 47, n. 4, p. 347-362, 2023. https://doi.org/10.1177/01968599231155612
MURÁR, P.; KUBOVICS, M. Using AI to create content designed for marketing communications. European Conference on Innovation and Entrepreneurship, v. 18, n. 1, p. 660-668, 2023. https://doi.org/10.34190/ecie.18.1.1638
OHLHORST, F. Big data analytics: Turning big data into big money. North Carolina: John Wiley & Sons Inc, 2013.
*PAGET, A. (Synthetic) stump speech: Crafting generative AI disclosure regulations for political advertisements. Fordham Law Review, v. 93, n. 1, p. 321-358, 2024.
PARTIDA, D. 7 Pros and Cons of Big Data. Datamation, 2023. Available at: https://www.datamation.com/big-data/big-data-pros-and-cons/. Accessed on: Mar. 15, 2025.
PLONSKY, L.; DERRICK, D. J. A meta-analysis of reliability coefficients in second language research. The Modern Language Journal, v. 100, n. 2, p. 498-513, 2016.
QIN, X. B.; JIANG, Z. The impact of AI on the advertising process: The Chinese experience. Journal of Advertising, v. 48, n. 4, p. 338-346, 2019. https://doi.org/10.1080/00913367.2019.1652122
*RODGERS, W.; NGUYEN, T. Advertising benefits from ethical artificial intelligence algorithmic purchase decision pathways. Journal of Business Ethics, v. 178, n. 4, p. 1043-1061, 2022. https://doi.org/10.1007/s10551-022-05048-7
SATHI, A. Big data analytics: Disruptive technologies for changing the game. United States: MC Press, 2012.
*SCHULTZ, C. D.; KOCH, C.; OLBRICH, R. Dark sides of artificial intelligence: The dangers of automated decision-making in search engine advertising. Journal of the Association for Information Science and Technology, v. 75, n. 5, p. 550-566, 2024. https://doi.org/10.1002/asi.24798
SHAH, N.; ENGINEER, S.; BHAGAT, N.; CHAUHAN, H.; SHAH, M. Research trends on the usage of machine learning and artificial intelligence in advertising. Augmented Human Research, v. 5, n. 1, e19, 2020. https://doi.org/10.1007/s41133-020-00038-8
*SHI, B.; WANG, H. An AI-enabled approach for improving advertising identification and promotion in social networks. Technological Forecasting and Social Change, v. 188, 122269, 2023. https://doi.org/10.1016/j.techfore.2022.122269
*SHUMANOV, M.; COOPER, H.; EWING, M. Using AI-predicted personality to enhance advertising effectiveness. European Journal of Marketing, v. 56, n. 6, p. 1590-1609, 2022. https://doi.org/10.1108/EJM-12-2019-0941
SMALL, M. Securing big data. In: The chartered institute for it (ed.). Big data: Opportunities and challenges. [S.l.]: BCS, The Chartered Institute for IT, 2014. p. 29-32.
SUNDAR, S. S.; MARATHE, S. S. Personalization versus customization: The importance of agency, privacy, and power usage. Human Communication Research, v. 36, n. 3, p. 298-322, 2010. https://doi.org/10.1111/j.1468-2958.2010.01377.x
SONI, P. What is big data: Advantages and disadvantages of big data. Yuvayana, 2020. Available at: https://er.yuvayana.org/what-is-big-data-advantages-and-disadvantages-of-big-data/. Accessed on: Mar. 14, 2025.
*VAKRATSAS, D.; WANG, X. Artificial intelligence in advertising creativity. Journal of Advertising, v. 50, n. 1, p. 39-51, 2020. https://doi.org/10.1080/00913367.2020.1843090
VAN ECK, N. J.; WALTMAN, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, v. 84, n. 2, p. 523-538, 2010. https://doi.org/10.1007/s11192-009-0146-3
VERA-BAQUERO, A.; COLOMO-PALACIOS, R.; MOLLOY, O. Business process analytics using a big data approach. IT Professional, v. 15, n. 6, p. 29-35, 2013. https://doi.org/10.1109/MITP.2013.60
WANG, L. Heterogeneous data and big data analytics. Automatic Control and Information Sciences, v. 3, n. 1, p. 8-15, 2017. https://doi.org/10.12691/acis-3-1-3
*WANG, L.; HE, S.; SU, S.; LI, Y.; HU, L.; LI, G. Urban neighborhood socioeconomic status (SES) inference: A machine learning approach based on semantic and sentimental analysis of online housing advertisements. Habitat International, v. 124, 102572, 2022. https://doi.org/10.1016/j.habitatint.2022.102572
*WATTS, J.; ADRIANO, A. Uncovering the sources of machine-learning mistakes in advertising: Contextual bias in the evaluation of semantic relatedness. Journal of Advertising, v. 50, n. 1, p. 26-38, 2021. https://doi.org/10.1080/00913367.2020.1821411
*WESCHE, J. S.; SONDEREGGER, A. Repelled at first sight? Expectations and intentions of job-seekers reading about AI selection in job advertisements. Computers in Human Behavior, v. 125, 106931, 2021. https://doi.org/10.1016/j.chb.2021.106931
*WU, L. W.; WEN, T. J. Understanding AI advertising from the consumer perspective: What factors determine consumer appreciation of AI-created advertisements? Journal of Advertising Research, v. 61, n. 2, p. 133-146, 2021. https://doi.org/10.2501/JAR-2021-004
*WU, L. W.; DODOO, N. A.; WEN, T. J. Disclosing AI’s involvement in advertising to consumers: A task-dependent perspective. Journal of Advertising, v. 54, n. 1, p. 20-38, 2024. https://doi.org/10.1080/00913367.2024.2309929
*XIAO, L.; LI, X.; ZHANG, Y. Exploring the factors influencing consumer engagement behavior regarding short-form video advertising: A big data perspective. Journal of Retailing and Consumer Services, v. 70, 2023. https://doi.org/10.1016/j.jretconser.2022.103170
*XIE, W.; LEE, M. H.; CHEN, M.; HAN, Z. Understanding consumers’ visual attention in mobile advertisements: An ambulatory eye-tracking study with machine learning techniques. Journal of Advertising, v. 53, n. 3, p. 397-415, 2024. https://doi.org/10.1080/00913367.2023.2258388
YU, Y. The role and influence of artificial intelligence on advertising industry. In: PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON SOCIAL DEVELOPMENT AND MEDIA COMMUNICATION (SDMC 2021), 2022, Sanya. Proceedings [...]. Sanya: SDMC, 2022. p. 190-194.
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