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A Framework for Collaborative Artificial Intelligence in Marketing

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  • Huang, Ming-Hui
  • Rust, Roland T.

Abstract

We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.

Suggested Citation

  • Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    • Handle: RePEc:eee:jouret:v:98:y:2022:i:2:p:209-223
    DOI: 10.1016/j.jretai.2021.03.001
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    as
    1. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    2. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    3. Ashlee Humphreys & Rebecca Jen-Hui Wang & Eileen FischerEditor & Linda PriceAssociate Editor, 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1274-1306.
    4. Grewal, Dhruv & Roggeveen, Anne L. & Nordfält, Jens, 2017. "The Future of Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 1-6.
    5. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    6. Donna L Hoffman & Thomas P Novak & Eileen FischerEditor & Robert KozinetsAssociate Editor, 2018. "Consumer and Object Experience in the Internet of Things: An Assemblage Theory Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1178-1204.
    7. Seo Young Kim & Bernd H. Schmitt & Nadia M. Thalmann, 2019. "Eliza in the uncanny valley: anthropomorphizing consumer robots increases their perceived warmth but decreases liking," Marketing Letters, Springer, vol. 30(1), pages 1-12, March.
    8. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    9. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    10. Ma, Shaohui & Fildes, Robert, 2021. "Retail sales forecasting with meta-learning," European Journal of Operational Research, Elsevier, vol. 288(1), pages 111-128.
    11. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    12. de Bellis, Emanuel & Venkataramani Johar, Gita, 2020. "Autonomous Shopping Systems: Identifying and Overcoming Barriers to Consumer Adoption," Journal of Retailing, Elsevier, vol. 96(1), pages 74-87.
    13. Shasha Lu & Li Xiao & Min Ding, 2016. "A Video-Based Automated Recommender (VAR) System for Garments," Marketing Science, INFORMS, vol. 35(3), pages 484-510, May.
    14. David Autor, 2014. "Polanyi's Paradox and the Shape of Employment Growth," NBER Working Papers 20485, National Bureau of Economic Research, Inc.
    15. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    16. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    17. Tuck Siong Chung & Roland T. Rust & Michel Wedel, 2009. "My Mobile Music: An Adaptive Personalization System for Digital Audio Players," Marketing Science, INFORMS, vol. 28(1), pages 52-68, 01-02.
    18. Thomas P. Novak & Donna L. Hoffman, 2019. "Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 216-237, March.
    19. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    20. Marchand, André & Marx, Paul, 2020. "Automated Product Recommendations with Preference-Based Explanations," Journal of Retailing, Elsevier, vol. 96(3), pages 328-343.
    21. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
    22. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    23. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    24. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    25. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    26. Dhruv Grewal & John Hulland & Praveen K. Kopalle & Elena Karahanna, 2020. "The future of technology and marketing: a multidisciplinary perspective," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 1-8, January.
    27. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    28. Tuck Siong Chung & Michel Wedel & Roland T. Rust, 2016. "Adaptive personalization using social networks," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 66-87, January.
    29. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    30. Kanishka Misra & Eric M. Schwartz & Jacob Abernethy, 2019. "Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments," Marketing Science, INFORMS, vol. 38(2), pages 226-252, March.
    31. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    32. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    33. Kohei Kawaguchi & Kosuke Uetake & Yasutora Watanabe, 2019. "Effectiveness of Product Recommendations Under Time and Crowd Pressures," Marketing Science, INFORMS, vol. 38(2), pages 253-273, March.
    34. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
    35. Petit, Olivia & Velasco, Carlos & Spence, Charles, 2019. "Digital Sensory Marketing: Integrating New Technologies Into Multisensory Online Experience," Journal of Interactive Marketing, Elsevier, vol. 45(C), pages 42-61.
    36. Pradeep Chintagunta & Dominique M. Hanssens & John R. Hauser, 2016. "Editorial—Marketing Science and Big Data," Marketing Science, INFORMS, vol. 35(3), pages 341-342, May.
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