Artificial intelligence marketing usage and firm performance

Jifeng Mu () and Jonathan Z. Zhang ()
Additional contact information
Jifeng Mu: Microfoundation Institute
Jonathan Z. Zhang: College of Business, Colorado State University

Journal of the Academy of Marketing Science, 2025, vol. 53, issue 4, No 8, 1134 pages

Abstract: Abstract Artificial intelligence marketing (AIM) usage has received intense interest yet reports mixed anecdotal performance. The authors clarify AIM usage based on insights from in-depth executive interviews and address three substantive research questions: Why do marketers use AIM? How does AIM usage affect firm performance outcomes, and when does this occur? Employing large-scale data collected from various sources in China involving public and private companies over four years, the authors find that AIM usage positively affects firm profitability, customer satisfaction, and customer acquisition. The findings also suggest that customer acquisition and customer satisfaction mediate AIM usage and firm performance. However, the findings demonstrate that employee resistance to change, technological velocity, and industry competition constraint the effect of AIM usage on performance. The results underscore the complex nature of AIM usage in organizations, suggesting that firms should closely monitor relevant customer metrics, cultivate an adaptive organizational culture, and remain environmentally vigilant and adaptive to fully capitalize on the potential AIM usage performance gains.

Keywords: Artificial intelligence marketing usage; Customer acquisition; Customer satisfaction; Employee resistance; Technological velocity; Industrial competition (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11747-024-01076-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joamsc:v:53:y:2025:i:4:d:10.1007_s11747-024-01076-z

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/11747

DOI: 10.1007/s11747-024-01076-z

Access Statistics for this article

Journal of the Academy of Marketing Science is currently edited by John Hulland, Anne Hoekman and Mark Houston

More articles in Journal of the Academy of Marketing Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().