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AI Persona, Service Consumption, and User Intent Entropy: Field Experimental Evidence from an LLM Platform — arXiv:2609.23274

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23274
License (as archived): CC0-1.0
Archived: 2026-10-09T07:01:50.886Z
SHA-256 of the archived text: 3e25a1a64d30b0e51cc10f58e3ff72bb70c4a3443b47b27f148bf08c3897a253
Stable URL of this page: https://metraipolis.com/documento/dc_bb5d43bd44ee

How to cite

AI Persona, Service Consumption, and User Intent Entropy: Field Experimental Evidence from an LLM Platform — arXiv:2609.23274 — arXiv (en), https://arxiv.org/abs/2609.23274 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb5d43bd44ee (Metraipolis archive dc_bb5d43bd44ee, sha256 3e25a1a64d30b0e51cc10f58e3ff72bb70c4a3443b47b27f148bf08c3897a253)

BibTeX

@misc{dcbb5d43bd44ee,
  title = {AI Persona, Service Consumption, and User Intent Entropy: Field Experimental Evidence from an LLM Platform — arXiv:2609.23274},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23274},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb5d43bd44ee; sha256 3e25a1a64d30b0e51cc10f58e3ff72bb70c4a3443b47b27f148bf08c3897a253},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23274. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Problem definition: Firms deploying large language model services must decide how their AI communicates, not just what it can do. We examine how a relational persona - warmer, more empathetic and more engaging than a non-relational persona - affects service consumption and the evolution of user objectives. Methodology/results: In a randomized field experiment with 9,586 newly registered users, we hold the underlying model and service capabilities constant. The relational persona increases interactions (sessions, +8.1%; duration, +10.6%; chat rounds, +24.2%; intent entropy, +5.8%) and outputs (files, +12.3%; distinct goals, +12.1%). Effects vary by entry intent. First-session effects are insignificant for Task Execution users. Socialization and Knowledge Exploration users show similar increases in chat rounds: Socialization increases intent entropy without more outputs, whereas Knowledge Exploration increases outputs without higher intent entropy. Modeling intent dynamics as a transition process, we find higher intent transition entropy for Socialization (+11.8%) but higher intent continuation probability for Knowledge Exploration (+12.8%), suggesting greater conversational breadth and persistence, respectively. In subsequent use, the relational persona increases aggregate chat rounds and outputs across all entry intents. Session count rises by 12.2% for Task Execution and 49.4% for Socialization, but not significantly for Knowledge Exploration. Effects on session count and intent entropy strengthen over time, whereas output effects remain stable. Managerial implications: AI persona is an operational design lever, not merely a presentation feature. Because more interactions do not uniformly generate more outputs, firms should evaluate interactions and outputs separately and consider matching persona to user intent, especially when added interactions consume costly computing resources.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.23274 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:56:29.219Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).