When a traveler asks an AI search engine to recommend a hotel, which sources get cited -- and does query framing matter? We audit 1,357 grounding citations from Google Gemini across 156 hotel queries in Tokyo and document a systematic pattern we call the Intent-Source Divide. Experiential queries draw 55.9% of their citations from non-OTA sources, compared to 30.8% for transactional queries -- a 25.1 percentage-point gap (p<5×10−20). The effect is amplified in Japanese, where experiential queries draw 62.1% non-OTA citations compared to 50.0% in English -- consistent with a more diverse Japanese non-OTA content ecosystem. For an industry in which hotels have long paid OTAs for demand acquisition, this pattern matters because it suggests that AI search may make hotel discovery less exclusively controlled by commission-based intermediaries.
Figures & tables
Category
T non-OTA%
E non-OTA%
Gap (pp)
95% CI
χ2(1)
p -value
Budget
15.5%
42.5%
27.0
[17.5, 36.5]
26.00
3.4×10−7
Rating/Quality
22.1%
46.7%
24.6
[13.6, 35.5]
16.78
4.2×10−5
Business
40.1%
68.5%
28.4
[18.1, 38.7]
25.81
3.8×10−7
Convenience
40.1%
59.2%
19.0
[9.3, 28.8]
13.29
2.7×10−4
Table 1. Non-OTA Citation Rate by Query Category and Intent
Segment
OTA%
Non-OTA%
N
EN Transactional
68.2%
31.8%
274
EN Experiential
50.0%
50.0%
386
JP Transactional
70.0%
30.0%
333
JP Experiential
37.9%
62.1%
364
Table 2. OTA and Non-OTA Citation Rates by Language and Intent
Source Type
EN
JP
Travel blog
22.9%
1.2%
Editorial curation
22.5%
12.3%
Hotel direct
19.3%
23.6%
Travel agency
0.0%
12.9%
Travel media
8.9%
3.7%
UGC
6.4%
8.6%
Table 3. Non-OTA Source-Type Composition by Language (% of Non-OTA Citations)
Language
T-rate
E-rate
Difference
95% CI
p
English
8.0% ( n =274)
8.3% ( n =386)
+0.3 pp
[ − 4.0, 4.5]
0.90
Japanese
9.3% ( n =333)
12.6% ( n =364)
+3.3 pp
[ − 1.3, 8.0]
0.16
Table 4. Hotel Direct Citation Rate (% of All Citations) by Language and Intent
Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility -- yet what moves their recommendations is undocumented. We conduct a pre-specified algorithm audit using a randomized choice-based conjoint: across personas, prompt templates, and twelve open-weight and proprietary models, assistants choose among five hotels whose guest rating, review volume and recency, management response, chain affiliation, price, eco-certification, and list position are independently randomized. We estimate the average marginal component effect of each signal on the probability of recommendation. Guest rating and price dominate (a top rating raises selection by 31.6 percentage points; a high price lowers it by 30.0), reproducing human valence-and-price primacy but over-weighting eco-certification and ignoring management response. List position -- a content-free artifact -- shifts recommendations causally, worth about $12 per night. Stated reasons track revealed weights imperfectly. The findings ground generative engine optimization and the accountability of AI infomediaries in causal evidence.
Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Asher Ali
Fandaqah, Al Khobar, Saudi Arabia. · Hamdard University, Karachi, Pakistan.
When shopping is delegated to AI agents, it is unclear whether the ranking advantage documented for humans persists. Across 7,000 sessions with five large language models (LLMs) and varying reasoning effort, we compare AI agents with human field data. AI agents search more extensively than human consumers. As with humans, lower-ranked listings are less likely to be inspected, although the effect is smaller for AI agents. Unlike humans, AI agents show a pattern consistent with the lost-in-the-middle effect, whereby middle listings have the lowest probability of inspection. At the choice stage, position effects are concentrated at lower reasoning effort, but higher effort reduces the middle penalty for every LLM that exhibits it. These findings suggest that, when search is delegated, displayed attributes may matter more than placement, and that exposure to position bias depends on how the AI agent is configured.
Davood Wadi, Yu Ma
Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada
Search engines have long allocated attention on the web by routing users from queries to websites. AI search changes this arrangement because information needs can be resolved inside the intermediary. Using URL-level Comscore U.S. desktop clickstream, we compare ChatGPT and Google information-seeking occasions and exploit ChatGPT Search access expansions to estimate traditional search displacement. ChatGPT produces outbound clicks in only 5.2% of conversation sessions, far below Google's referral ratio. The remaining clicks are not a scaled-down Google stream: they skew toward specialized destinations and away from ad-supported sites. Wider access cuts search use by 9.4%, with search-referral losses largest for informational categories. Our findings identify a central economic shift in digital intermediation: AI search might satisfy information needs inside the intermediary while weakening the referral bargain that has linked search, traffic, and content production on the open web.