Leveraging Large Language Models for Hybrid Workplace Decision Support
Yujin Kim Chin-Chia Hsu
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.