Communication Artificial Intelligence (AI) Advertising
by
Hairong Li
  • LAST REVIEWED: 23 June 2023
  • LAST MODIFIED: 23 June 2023
  • DOI: 10.1093/obo/9780199756841-0291

Introduction

Artificial intelligence (AI) advertising, or intelligent advertising in short, is a generic term for various forms of brand communication powered by AI technologies. It represents the latest development of digital advertising, which has evolved from interactive advertising to programmatic advertising to intelligent advertising since the 1990s. Advertising today is viewed broadly as brand communication that embraces both paid and non-paid, as well as brand-initiated and consumer-initiated communication about products, services, and ideas. AI is a science that studies and develops the theory, methodology, technology, and application systems for simulating, extending, and expanding human intelligence. It has been increasingly used in brand communication recently, resulting in the rise of a new discipline of intelligent advertising.

Special Issue Editorials

Growing research interest in AI issues has motivated advertising and marketing journals to publish special issues. The editorials for these special issues tend to address important topics, define new phenomena and concepts, propose frameworks, and share the insights of these special issue editors. Such editorials can serve as a window to several articles in a special issue on AI in advertising and marketing. This section highlights six special issue editorials, with three from the Journal of Advertising (Huh and Malthouse 2020, Li 2019, and Rodgers 2021), two from the Journal of the Academy of Marketing Science (Grewal, et al. 2020 and Plangger, et al. 2022) and one from Business Horizons (Kietzmann and Pitt 2020).

  • Grewal, Dhruv, John Hulland, Praveen K. Kopalle, and Elena Karahanna. 2020. The future of technology and marketing: A multidisciplinary perspective. In Special issue: Technology and marketing. Edited by Dhruv Grewal, John Hulland, Praveen K. Kopalle, and Elena Karahanna. Journal of the Academy of Marketing Science 48.1: 1–8.

    DOI: 10.1007/s11747-019-00711-4

    Conceptualizes a technology-marketing framework consisting of six technologies—health technology, AI and robotics, the dark web and chatbots, mobile and social, in-store technology, and legacy technology—and reviews major studies on each of these technologies. As for AI and robotics, the editorial argues that personalization strategies can benefit from AI technologies, such that new marketing campaigns can integrate information from various sources.

  • Huh, Jisu, and Edward C. Malthouse. 2020. Advancing computational advertising: Conceptualization of the field and future directions. In Special section: Advances in computational advertising. Edited by Shelly Rodgers. Journal of Advertising 49.4: 367–376.

    DOI: 10.1080/00913367.2020.1795759

    Defines computational advertising as a broad, data-driven advertising approach relying on or facilitated by enhanced computing capabilities, mathematical models/algorithms, and the technology infrastructure to create and deliver messages and monitor/surveil an individual’s behaviors. The study argued that key characteristics of computation advertising include individually addressable, data-driven, interactive, continuous, and measurable.

  • Kietzmann, Jan, and Leyland F. Pitt. 2020. Artificial intelligence and machine learning: What managers need to know. In Special issue: Artificial intelligence and machine learning. Edited by Jan Kietzmann, and Leyland F. Pitt. Business Horizons 62.2: 131–133.

    DOI: 10.1016/j.bushor.2019.11.005

    States that managers should make important decisions about if, where, and how they should be adopting AI and that some articles in this special issue concentrated on the overall properties of AI and machine learning, how they add value to firms, and what managers need to know as they consider adopting these technologies, whereas other articles discuss the role of AI and machine learning in specific contexts of functional areas.

  • Li, Hairong. 2019. Special section introduction: Artificial intelligence and advertising. In Special section: Artificial intelligence and advertising. Edited by Hairong Li. Journal of Advertising 48.4: 333–337.

    DOI: 10.1080/00913367.2019.1654947

    Reviews the evolution of digital advertising from interactive advertising to programmatic advertising to intelligent advertising and defines intelligent advertising as consumer-centered, data-driven, algorithm-mediated brand communication. The editorial also posits that a newer phase of digital advertising always retains the valuable attributes of a previous phase of digital advertising while adding innovative attributes, and it calls for researchers to identify key attributes of intelligent advertising.

  • Plangger, Kirk, Dhruv Grewal, Ko de Ruyter, and Catherine Tucker. 2022. The future of digital technologies in marketing: A conceptual framework and an overview. In Special issue: Creating customer, firm, and social value through cutting-edge digital technologies. Edited by Kirk Plangger, Dhruv Grewal, Ko de Ruyter, and Catherine Tucker. Journal of the Academy of Marketing Science 50.6: 1125–1134.

    DOI: 10.1007/s11747-022-00906-2

    Proposes a framework consisting of strategic resources (financial, human, technological, and customer data), applications of emerging technologies (alternative realities, metaverse, robotics, virtual agents, blockchain, Internet of things, and synthetic content), and strategy outcomes (customer value, brand value, and social value). The framework embeds AI and algorithmic bias among all the components, highlighting the significant roles of AI and algorithmic bias in digital technologies in marketing.

  • Rodgers, Shelly. 2021. Themed issue introduction: Promises and perils of artificial intelligence and advertising. In Special issue: Promises and perils of artificial intelligence and advertising. Edited by Shelly Rodgers. Journal of Advertising 50.1: 1–10.

    DOI: 10.1080/00913367.2020.1868233

    Defines AI advertising as brand communication that uses a range of machine learning functions that learn to carry out tasks with the intent to persuade with input by humans, machines, or both. The editorial recaps the promises and perils of six AI advertising practices—AI influencers, advertising placement using algorithms, use of AI to generate and test creative ideas, detecting image-text mismatch, AI-enabled in-store communication, and analysis of social media posts.

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