Future research directions of generative AI in marketing and logistics

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.

Authors

Institutions

Publication Details

Journal
Asia Pacific Journal of Marketing and Logistics
Published
2025-12-01
DOI
https://doi.org/10.1108/apjml-11-2024-1605
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Future research directions of generative AI in marketing and logistics

Keng‐Boon Ooi, Ali Anjomshoae, Hing Kai Chan, Tat‐Huei Cham et al.
Asia Pacific Journal of Marketing and Logistics
AI in Service Interactions
article

Future research directions of generative AI in marketing and logistics

Keng‐Boon Ooi, Ali Anjomshoae, Hing Kai Chan, Tat‐Huei Cham, Nripendra P. Rana, Arjyalopa Mishra, Garry Wei‐Han Tan, Chung‐Wha Ki, Lai-Ying Leong, Arpan Kumar Kar, Constantin Blome, Eugene Cheng‐Xi Aw, Ramakrishnan Raman, Jacqueline K. Eastman, Subhadeep Mandal
article en

Abstract

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.

Asia Pacific Journal of Marketing and Logistics
Adamson University (PH), University of Jordan (JO), Queen's University Belfast (GB), Hong Kong Polytechnic University (HK), Chulalongkorn University (TH), Stockholm School of Economics (SE), Asia Pacific University of Technology & Innovation (MY), Korea University (KR), Imam Mohammad ibn Saud Islamic University (SA), Symbiosis International University (IN), Jaipuria Institute of Management (IN), Fore School of Management (IN), Florida Gulf Coast University (US), Islamic University (BD), Wenzhou-Kean University (CN), Korea University (JP), Sohar University (OM), UCSI University (MY), City University of Macau (MO), Indian Institute of Technology Delhi (IN), Amrita Vishwa Vidyapeetham (IN), Universiti Tunku Abdul Rahman (MY), Swinburne University of Technology Sarawak Campus (MY)
Openalex Percentile: Top 80%
AI in Service Interactions
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.