Journal of New Approaches in Management and Marketing

Exploring Artificial Intelligence in the Automotive Industry: A Bibliometric Analysis, Systematic Review, and Future Research Directions

Articles in Press

Document Type : Review Article

Authors

Majid Darvish 1
Seyed Hamid Khodadad Hosseini 2
Fereshteh Mansourimoayyed 1
Gholam Reza Goudarzi 3

1 Department of Business Management, Tarbiat Modares University, Tehran, Iran.

2 Department of Business Management, Tarbiat Modares University, Tehran, Iran

3 Department Management, Imam Sadiq University, Tehran, Iran.

https://doi.org/10.22034/jnamm.2026.579268.1270
Abstract
The aim of this study is to explore artificial intelligence in the automotive industry through a bibliometric analysis, a systematic review, and future research directions. In this study, 179 international articles indexed in the Web of Science and Scopus databases were analyzed. The research methodology consisted of two stages: first, a bibliometric analysis was conducted using VOSviewer software to identify thematic clusters in the literature related to artificial intelligence and the automotive industry. Subsequently, a qualitative systematic review was carried out to provide deeper insights into these clusters.The findings revealed a significant focus on autonomous vehicles, deep learning, and machine learning as key AI technologies. Four thematic clusters were identified, including AI-driven automotive ecosystems, core AI technologies and security, connectivity and resource management, and advanced vehicle technologies. In particular, safety, resource optimization, and regulatory frameworks emerged as key areas within these clusters. The study also identified four emerging research domains that are expected to play a major role in shaping the future of artificial intelligence in the automotive industry, while offering transformative opportunities to address existing knowledge gaps.

Keywords

Artificial Intelligence
Automotive Industry
Resource Management
Machine Learning
Core Technologies
Resource Optimization
Bibliometric Review
Subjects
Journal of New Approaches in Management and Marketing

Articles in Press, Accepted Manuscript
Available Online from 20 June 2026

  • Receive Date 21 January 2026
  • Revise Date 18 April 2026
  • Accept Date 14 May 2026

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