Future research directions of generative AI in marketing and logistics
https://doi.org/10.1108/APJML-11-2024-1605
Authors Aw E., Anjomshoae A., Blome C., Cham T., Chan H., Eastman J., Kar A., Ki C., Leong L., Mandal S., Mishra A., Ooi K., Raman R., Rana N., Tan G.
Publication Year 2025
Journal Asia Pacific Journal of Marketing and Logistics 1–15.
DOI 10.1108/APJML-11-2024-1605
Keywords
Artificial intelligence, Generative AI, Marketing, Consumer behavior, Logistics, Supply chain management, Machine learning
Purpose
Generative AI has revolutionized business operations radically, including marketing and logistics. In order to explore the transformative impact of generative AI on these two domains, this article discusses the opportunities, challenges and research agendas in the specific areas of consumer buying behavior, business-to-business marketing, retail marketing, marketing communication, supply chain management, transportation management, and logistic management.
Design/methodology/approach
Building upon an expert-oriented approach, this article examines the current body of literature and practices revolving around the intersection of generative AI and marketing/logistic-related domains.
Findings
The contributors recognize the significant impact of generative AI to reshape marketing and logistic areas, including creating designs, handling customer inquiries and sales processes, optimizing customer experience, and planning and allocating logistic resources. On the other hand, some concerns are highlighted including jeopardizing brand value, resulting in the loss of human touch, discriminating customers, and incurring financial challenges.
Originality/value
This article presents a pioneering approach to understanding the application of generative AI in the field of marketing and logistics. Furthermore, the article charts the research roadmap for future investigations, which includes changes in consumer decision-making under the influence of generative AI, diffusion and ethical consideration of generative AI, and human–generative AI collaboration.
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Future research directions of generative AI in marketing and logistics
https://doi.org/10.1108/APJML-11-2024-1605
Authors Aw E., Anjomshoae A., Blome C., Cham T., Chan H., Eastman J., Kar A., Ki C., Leong L., Mandal S., Mishra A., Ooi K., Raman R., Rana N., Tan G.
Publication Year 2025
Journal Asia Pacific Journal of Marketing and Logistics 1–15.
DOI 10.1108/APJML-11-2024-1605
Keywords
Artificial intelligence, Generative AI, Marketing, Consumer behavior, Logistics, Supply chain management, Machine learning
Purpose
Generative AI has revolutionized business operations radically, including marketing and logistics. In order to explore the transformative impact of generative AI on these two domains, this article discusses the opportunities, challenges and research agendas in the specific areas of consumer buying behavior, business-to-business marketing, retail marketing, marketing communication, supply chain management, transportation management, and logistic management.
Design/methodology/approach
Building upon an expert-oriented approach, this article examines the current body of literature and practices revolving around the intersection of generative AI and marketing/logistic-related domains.
Findings
The contributors recognize the significant impact of generative AI to reshape marketing and logistic areas, including creating designs, handling customer inquiries and sales processes, optimizing customer experience, and planning and allocating logistic resources. On the other hand, some concerns are highlighted including jeopardizing brand value, resulting in the loss of human touch, discriminating customers, and incurring financial challenges.
Originality/value
This article presents a pioneering approach to understanding the application of generative AI in the field of marketing and logistics. Furthermore, the article charts the research roadmap for future investigations, which includes changes in consumer decision-making under the influence of generative AI, diffusion and ethical consideration of generative AI, and human–generative AI collaboration.
Read Article