{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T16:44:47Z","timestamp":1776703487740,"version":"3.51.2"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Generative Engine Optimization (GEO) aims to improve content visibility in AI-generated search responses. Prior observational studies have failed to isolate page-level signals because domain identity alone predicts AI citation at AUC = 0.975, confounding every between-domain comparison. We introduce a position-band matching design that controls for Google ranking position, asking: among equally-ranked pages, what page-level features predict AI citation? Using 250 queries across a balanced grid (5 intent types, 10 verticals), we collected citations from ChatGPT, Perplexity, and Google AI Mode and crawled 10,293 unique pages with 66 structural, semantic, and content-quality features. Within position bands, content features and domain identity provide comparable predictive power (content AUC = 0.673, domain AUC = 0.687 with enriched representations, combined AUC = 0.697), a convergence that contrasts sharply with the domain dominance observed without position control (AUC = 0.975). The top actionable predictors are comparison structure (d = 0.43, significant across all five intent types), query-term coverage (d = 0.42), subheading depth, statistical data density, and the absence of first-person\/blog tone. Content structure provides the largest marginal lift beyond rank position (+0.021 AUC). In a second contribution, five domain-level tests reveal that SERP co-occurrence (topical breadth) is the strongest domain trust predictor (rho = 0.341, p = 2.6 x 10^-70), that cited domains are less lexically unique than their SERP competitors, and that a combined domain model achieves AUC = 0.921, with SERP presence accounting for 63% of importance. High-SERP-presence domains are cited more per appearance (2.04 citations per slot for 8+ appearances vs. 0.665 for single-appearance domains), confirming this is not merely an artifact of increased exposure. Data and code are publicly available.<\/jats:p>","DOI":"10.2139\/ssrn.6525578","type":"posted-content","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:50:58Z","timestamp":1776700258000},"source":"Crossref","is-referenced-by-count":0,"title":["I Rank on Page 1 --What Gets Me Cited by AI? Position-Controlled Analysis of Page-Level and Domain-Level Predictors of AI Search Citation"],"prefix":"10.2139","author":[{"given":"Anthony","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:50:58Z","timestamp":1776700258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=6525578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.6525578","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}