Enhancing citizen engagement in urban greening: The potential of large language models in value co-creation

Lita Alita

Technological Forecasting and Social Change, 2025, vol. 216, issue C

Abstract: Urban green spaces are essential for ecosystem functioning, human wellbeing, and social cohesion. Urban greening, as a dynamic process, requires citizen engagement to integrate their lived experiences. The value co-creation approach offers a promising framework to facilitate citizen involvement in shaping urban green spaces. Without successful translation of these experiences into tangible value during the implementation phase, co-creation efforts may revert to mere participatory practices in which citizen involvement is superficial. Since Large Language Models (LLMs) have the potential to streamline user feedback, this study examines whether LLMs can enhance citizen engagement in the co-creation of value for urban greening. Using a survey experiment designed to prompt participation in urban greening initiatives, this study found that the demo of LLM significantly increases citizens' participation at initial stage and their engagement in co-creating value in use. The mechanism analysis identified that the demo of LLMs increases citizen engagement by reducing participation barriers. However, interaction effect analysis reveals a decrease in engagement and enthusiasm among citizens with higher self-efficacy when LLMs are applied. The findings of this study suggest that LLMs can be viable tools in facilitating urban greening initiatives under a co-creation framework.

Keywords: Co-creation; Urban greening; Large language models; Value in use (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162525001659
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:216:y:2025:i:c:s0040162525001659

DOI: 10.1016/j.techfore.2025.124134

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().