Measuring brand presence in generative engine optimization (geo): a scoping review of metrics and validity
The rise of generative search engines and large language model assistants has shifted digital visibility from clicking a link to being present within synthesized answers, a change that traditional search engine optimization (SEO) metrics fail to capture. This study presents a scoping review, conducted following JBI methodology and reported according to PRISMA-ScR, aimed at mapping how the emerging literature on Generative Engine Optimization (GEO) measures the presence, visibility, and citation of sources and brands. The search combined an AI-assisted discovery tool, citation chasing, and reproducible multilingual queries in OpenAlex, with verification in an indexed database. Twenty-three primary studies were included (2023-2026), mostly preprints, plus two reviews registered as a conceptual framework. Findings reveal a nascent, fragmented field: seven partially overlapping metric families with no integrating framework; a heterogeneous unit of analysis migrating from the source/URL toward the brand and the agent trajectory; insufficient control of engine stochasticity; and, most critically, the absence of psychometrically validated instruments, as measurement relies on ad hoc benchmarks rather than validated tools. We conclude that no integrated, validated instrument for generative brand presence yet exists, and that this gap —in construct validity and documented reliability— justifies developing one. The study also offers a methodological lesson on the under-coverage of indexed databases in this born-digital field.