ABSTRACT

Smart office concepts are gaining increasing attention in the built environment domain, as they promise efficient and effective workplaces that cater to user needs. While previous researchers have primarily focused on Internet of Things (IoT) technologies in the office environment and their impacts on office users, few studies have addressed the users’ preferences of smart office concepts. This study aims to fill this gap by investigating users’ preferences for four smart features: smart parking, smart workspace booking, smart temperature control and smart lighting, and exploring how different attributes of these smart features influence users’ preferences. The study conducted a stated choice experiment with 137 valid respondents and analysed the data using both Multinomial Logit (MNL) models and Latent Class (LC) models. The MNL model results indicate that users have varying preferences for different smart features and that there are significant interactive impacts between attribute levels and age, gender, working hours per week and attitudes towards technologies, highlighting the heterogeneity in users’ preferences. The LC model revealed two distinct user classes for each smart feature, which have significantly improved the overall model performance. This study guides the design and implementation of smart office concepts that cater to users’ needs and preferences.

摘要

智能办公概念在建筑环境领域越来越受到关注，因为它们承诺提供满足用户需求的高效工作场所。虽然之前的研究人员主要关注办公环境中的物联网 (IoT) 技术及其对办公用户的影响，但很少有研究涉及用户对智能办公概念的偏好。本研究旨在通过调查用户对四种智能功能的偏好来填补这一空白：智能停车、智能工作空间预订、智能温度控制和智能照明，并探索这些智能功能的不同属性如何影响用户的偏好。该研究对 137 名有效受访者进行了陈述选择实验，并使用多项 Logit (MNL) 模型和潜在类别 (LC) 模型分析了数据。MNL模型结果表明，用户对不同智能特征的偏好不同，属性水平与年龄、性别、每周工作时长和对技术的态度之间存在显着的交互影响，凸显了用户偏好的异质性。LC 模型为每个智能特征揭示了两个不同的用户类别，这显着提高了整体模型性能。本研究指导了满足用户需求和偏好的智能办公概念的设计和实施。LC 模型为每个智能特征揭示了两个不同的用户类别，这显着提高了整体模型性能。本研究指导了满足用户需求和偏好的智能办公概念的设计和实施。LC 模型为每个智能特征揭示了两个不同的用户类别，这显着提高了整体模型性能。本研究指导了满足用户需求和偏好的智能办公概念的设计和实施。