Enhancing behavioral nudges with large language model-based iterative personalization: A field experiment on electricity and hot-water conservation

TL;DR

This study demonstrates that large language model-based iterative personalization significantly improves behavioral nudging effectiveness in electricity and hot-water conservation through a field experiment.

Contribution

It introduces an LLM agent for iterative personalization in nudging and provides empirical evidence of its superior effectiveness over conventional methods.

Findings

LLM-personalized nudges increased electricity savings by 18.3 percentage points.

The effectiveness of LLM nudges emerged within two rounds and persisted over time.

Hot-water conservation showed similar but smaller and less stable effects.

Abstract

Nudging is widely used to promote behavioral change, but its effectiveness is often limited when recipients must repeatedly translate feedback into workable next steps under changing circumstances. Large language models (LLMs) may help reduce part of this cognitive work by generating personalized guidance and updating it iteratively across intervention rounds. We developed an LLM agent for iterative personalization and tested it in a three-arm randomized experiment among 233 university residents in China, using daily electricity and shower hot-water conservation as objectively measured cases differing in friction. LLM-personalized nudges (T2) produced the largest conservation effects, while image-enhanced conventional nudges (T1) and text-based conventional nudges (C) showed similar outcomes (omnibus p = 0.009). Relative to C, T2 reduced electricity consumption by 0.56 kWh per room-day (p = 0.014), corresponding to an 18.3 percentage-point higher adjusted saving rate. This advantage emerged within the first two intervention rounds, alongside iterative updating of personalized guidance, and persisted thereafter. Hot-water outcomes followed the same direction but were smaller, less precisely estimated, and attenuated over time, consistent with stronger friction in this domain. LLM-personalized nudges emphasized prospective and context-specific guidance and were associated with higher participant engagement. This study provides field evidence that LLM-based iterative personalization can enhance behavioral nudging, with behavioral friction as a potential boundary condition. Larger trials and extension to more behaviors are warranted.