Early-career researcher on generative AI in marketing: personalized ad imagery, generation pipelines, and AI adoption
3
papers here
3
reviewed by Evolve
3
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-16) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Output volume cannot yet be scored: the OpenAlex record shows h-index 0 and i10-index 0, reflecting an early-stage profile rather than a complete publication list. This axis would measure total works and per-year publication consistency.
Impact—
Citation impact is not yet measurable: h-index and i10-index both stand at 0 with no citation aggregate available. This axis would track citation counts, uptake of the 2026 preprints, and influence on marketing practice.
Focus0/100
The focus score is 0 because output is spread evenly across three fields — information systems (generative AI in marketing), human-computer interaction (AI adoption in professional services), and AI & marketing — each holding a 0.33 share. Topic counts reinforce this breadth: Digital Marketing and Social Media (3), AI in Service Interactions (3), Marketing and Advertising Strategies (2), plus single works on image synthesis and digital innovation. The work is thematically cohesive but not yet concentrated in one field.
Collaboration39/100
Collaboration scores 39, a modest level: 6 distinct co-authors appear across the 4 recent-3y works. The recurring partners — Niklas Kühl (listed twice in the frequent-coauthor record, marking the closest tie), Leopold Müller, Claudius Budcke, Felicia Perpina, and Claas Christian Germelmann — form a small, Bayreuth-centered circle bridging information systems and marketing. All three reviewed 2026 papers are first-authored without a corresponding role, consistent with team-based early-career work.
Activity47/100
Activity scores 47 on thin but rising evidence: 4 works in the last three years, with countsByYear showing 1 work in 2025 and 3 in 2026 — all three 2026 items being first-authored arXiv preprints reviewed in the Evolve catalog. The trajectory points upward, but the volume remains early-career; a fuller publication record outside this snapshot could shift the score materially.
Reproducibility32/100
Reproducibility averages 32 across the 3 reviewed papers: the preregistered personalization experiment (N=100) rates medium readiness, while the AdMan pipeline (no baselines, single-run, judge-limited) and the eleven-interview qualitative study rate low. The one quantitative, preregistered study anchors this dimension; systematic release of code, prompts, and interview protocols would lift it most directly.
Research summary
Victor Kolominsky-Rabas is an early-stage researcher at the University of Bayreuth (Germany) working at the intersection of generative AI, information systems, and marketing. The bibliometric snapshot is young but directional: 4 works in the last three years (1 in 2025, 3 in 2026), h-index and i10-index of 0, and 6 distinct co-authors. All three papers reviewed in the Evolve catalog are 2026 arXiv preprints with first-author position: a preregistered N=100 experiment showing that AI-generated advertising imagery performs best at moderate personalization; AdMan, a multi-agent pipeline that turns customer data into personalized ad images (a credible feasibility demo, though without baselines and judge-limited); and an eleven-interview, Gioia-methodology study mapping generative AI use cases and constraints in German real estate marketing. Field distribution splits evenly at 0.33 across information systems, human-computer interaction, and AI & marketing, and the focus score of 0 reflects this breadth rather than incoherence — the topics cluster tightly around generative AI in commercial contexts (Digital Marketing and Social Media, AI in Service Interactions, Marketing and Advertising Strategies). Reproducibility averages 32 across the three reviewed papers, carried by the preregistered experiment. The trajectory suggests a researcher building an empirical program on how GenAI reshapes advertising production and adoption in professional services, with citation impact not yet measurable.
Collaboration pattern
Collaboration is small-scale and institution-centered: 6 distinct co-authors over the recent window, with Niklas Kühl, Leopold Müller, Claudius Budcke, Felicia Perpina, and Claas Christian Germelmann recurring. Kühl appears twice in the frequent-coauthor record, marking the closest tie. All three reviewed 2026 papers are first-authored without corresponding role, consistent with a contributing early-career position in a Bayreuth information-systems/marketing group rather than an independent lab.
Research keywords
Highlights
Moderate personalization works best for AI-generated ad imagery (preregistered N=100)
AdMan: multi-agent pipeline for personalized advertising imagery
Mapping generative AI use cases in German real estate marketing
Timeline
- 2025First work appears in the OpenAlex record: 1 publication indexed for 2025.
- 2026Posts a first-authored arXiv preprint on AdMan, a multi-agent pipeline generating personalized advertising imagery from customer data.
- 2026Releases an eleven-interview, Gioia-methodology study of generative AI adoption and constraints in German real estate marketing.
- 2026Releases the preregistered N=100 study on personalization in AI-generated advertising imagery, closing a three-paper year (3 works indexed in 2026).
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric indicators come from a single OpenAlex snapshot; h-index 0 and i10-index 0 reflect an early-stage record, not a verified complete publication list.
- No ORCID or author-disambiguation identifier was provided; this profile is name-resolved from public bibliographic data.
- Output and impact dimensions lack sufficient underlying data and are intentionally unscored in the radar.
- All reviewed papers are 2026 arXiv preprints; peer-reviewed versions may change findings and metrics.