Leveraging Large Language Models for Hybrid Workplace Decision Support

TL;DR

This paper explores how Large Language Models can support hybrid workplace decisions by providing suggestions and explanations, improving user experience and decision quality in workspace planning.

Contribution

It introduces a decision support model leveraging LLMs for hybrid workspaces, demonstrating their reasoning ability and influence on user decisions through extensive user studies.

Findings

LLMs can manage trade-offs in workspace suggestions

Participants found the system convenient and helpful

LLMs influence workers' workspace decisions

Abstract

Large Language Models (LLMs) hold the potential to perform a variety of text processing tasks and provide textual explanations for proposed actions or decisions. In the era of hybrid work, LLMs can provide intelligent decision support for workers who are designing their hybrid work plans. In particular, they can offer suggestions and explanations to workers balancing numerous decision factors, thereby enhancing their work experience. In this paper, we present a decision support model for workspaces in hybrid work environments, leveraging the reasoning skill of LLMs. We first examine LLM's capability of making suitable workspace suggestions. We find that its reasoning extends beyond the guidelines in the prompt and the LLM can manage the trade-off among the available resources in the workspaces. We conduct an extensive user study to understand workers' decision process for workspace choices and evaluate the effectiveness of the system. We observe that a worker's decision could be influenced by the LLM's suggestions and explanations. The participants in our study find the system to be convenient, regardless of whether reasons are provided or not. Our results show that employees can benefit from the LLM-empowered system for their workspace selection in hybrid workplace.