Publicação
Picture perfect
| dc.contributor.author | Heitmann, Mark | |
| dc.contributor.author | Jansen, Tijmen P. J. | |
| dc.contributor.author | Reisenbichler, Martin | |
| dc.contributor.author | Schweidel, David A. | |
| dc.contributor.institution | NOVA School of Business and Economics (NOVA SBE) | |
| dc.contributor.pbl | American Marketing Association | |
| dc.date.accessioned | 2026-06-16T15:09:01Z | |
| dc.date.available | 2026-06-16T15:09:01Z | |
| dc.date.issued | 2026-07 | |
| dc.description | Publisher copyright: © American Marketing Association | |
| dc.description.abstract | Generative artificial intelligence (AI) is poised to transform how brands communicate with consumers. Recent research demonstrates AI's benefits in producing text, but marketing research has not yet explored how marketers can leverage AI to create visual advertising. Despite their impressive capabilities, “off-the-shelf” generative AI models are not aligned with marketing objectives, raising the question of whether it is possible to fine-tune generative AI directly on conventional advertising objectives (e.g., evoking attention, driving interest). In this research, the authors train an open-source generative AI model on marketing mindset metrics and show that the resulting visual content can match and even exceed conventionally produced advertising content in associated performance metrics. The results demonstrate that generative AI can be fine-tuned on multiple communication objectives simultaneously and adapted to specific audiences. In addition to highlighting generative AI's potential in marketing, this article explores the limitations of aligning visual generative AI with marketing objectives. | en |
| dc.description.version | publishersversion | |
| dc.description.version | published | |
| dc.format.extent | 23 | |
| dc.format.extent | 9226498 | |
| dc.identifier.doi | 10.1177/00222429251356993 | |
| dc.identifier.issn | 0022-2429 | |
| dc.identifier.other | PURE: 150525908 | |
| dc.identifier.other | PURE UUID: d11927ec-c623-4de0-ac48-931b6c74edfd | |
| dc.identifier.other | WOS: 001657044000001 | |
| dc.identifier.other | Scopus: 105027195949 | |
| dc.identifier.uri | http://hdl.handle.net/10362/203809 | |
| dc.identifier.url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=nova_api&SrcAuth=WosAPI&KeyUT=WOS:001657044000001&DestLinkType=FullRecord&DestApp=WOS_CPL | |
| dc.identifier.url | https://www.scopus.com/pages/publications/105027195949 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.subject | Advertising | |
| dc.subject | Computer vision | |
| dc.subject | Consumer engagement | |
| dc.subject | Deep learning | |
| dc.subject | Digital marketing | |
| dc.subject | generative AI | |
| dc.title | Picture perfect | en |
| dc.title.subtitle | Engaging customers with visual generative AI | en |
| dc.type | journal article | |
| degois.publication.firstPage | 74 | |
| degois.publication.issue | 4 | |
| degois.publication.lastPage | 96 | |
| degois.publication.title | Journal of Marketing | |
| degois.publication.volume | 90 | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess |
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