AI and Technology in Marketing

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ID: 338363
2026
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Abstract
Distinguished University Chair Professor The emergence of technologies and artificial intelligence (AI) has profoundly reshaped marketing over the past decade. Early foundational work addressed the impact of information technology (IT) on marketing for dynamic customer relationships, while subsequent work on multiple AI intelligences laid the groundwork for understanding AI’s impact on service and marketing and for developing AI-driven marketing strategies. Reflecting this multiple AI intelligences view (mechanical, analytical, intuitive, and empathetic), research on AI and technology in marketing considers AI’s intelligence level, task type, and whether AI is embodied in a robot. The foundational shift from single intelligence to multiple intelligences, and both humans and AI can have multiple intelligences, has also turned research attention to whether AI augments or replaces humans in the marketing context—as in, for example, productivity and quality tensions in service automation, or collaborative AI frameworks for using humans or AI in delivering marketing value. An exemplar development along this tension is the Feeling Economy theory in which humans shift toward feeling tasks and jobs when thinking AI increasingly dominates thinking tasks and jobs. Analytical AI, such as text analysis and text mining, has also been widely used in marketing to extract consumer insights and has been applied to various marketing domains, for example by analyzing real-time social media conversations to track brand reputation. Meanwhile, AI as a computing machine that counts on data also generates data, algorithm, and modeling bias, and researchers are striving to identify the bias, and to design communication and systems to mitigate some of these effects. The multiple AI intelligences view also provides a useful framework for organizing these developments of AI in marketing: mechanical AI provides infrastructure for automation and standardization, thinking AI supports analytics and adaptive personalization, and feeling AI addresses artificial empathy and emotional engagement. Another important research stream is consumer interaction with and responses to robots and chatbots. Robots’ social presence, humanoid design, and chatbot-use disclosure are design-side factors, while consumer experiences and blame attribution following service failures are response-side factors. Finally, GenAI and agentic AI open new directions for marketing, including changes in consumer research, creativity, agentic service robots, and scalable learning from consumer feedback.
Reference Key
openalex_W7225201560 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ming-Hui Huang
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Journal Oxford University Press eBooks
Year 2026
DOI
10.1093/9780197859063.003.0072
URL
https://doi.org/10.1093/9780197859063.003.0072
Keywords Keywords not found

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