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一个用于协助在建筑中采用感知技术的治理框架。

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction.

机构信息

School of Design and Built Environment, Curtin University, Perth, WA 6102, Australia.

Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang 213300, China.

出版信息

Sensors (Basel). 2021 Dec 30;22(1):260. doi: 10.3390/s22010260.

DOI:10.3390/s22010260
PMID:35009799
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8749552/
Abstract

Sensing technologies present great improvements in construction performance including the safety, productivity, and quality. However, the corresponding applications in real projects are far behind compared with the academically research. This research aims to discover dominate influence factors in the sensing technologies adoption and ultimately develop a governance framework facilitating adoption processes. The framework is dedicated on general sensing technologies rather than single sensor in previous framework studies. To begin with, the influence factors of sensing technologies and other similar emerging technologies are summarised through a review. Then, a mixed methods design was employed to collect quantitative data through an online survey, and qualitative data through semi-structured interviews. Findings of the quantitative method reveal that the most widely implemented sensing technologies are GPS and visual sensing technology, but they're still not adopted by all construction companies. Partial Least Squares Structural Equation Modelling reveals that supplier characteristics have the highest effect in all influence factors. Qualitative method was adopted to investigate perceptions of construction stakeholders on the major decision-making considerations in the adoption process. Ultimately, a triangulation analysis of findings from the literature review, online survey and interviews resulted in the governance framework development. The overarching contribution of this research focus on the general adoption of sensing technologies rather than the adoption of a specific sensor. Therefore, the governance framework can assist with the decision-making process of any sensing technology adoption in construction.

摘要

传感技术在建筑性能方面有了很大的改进，包括安全性、生产力和质量。然而，与学术研究相比，其在实际项目中的相应应用还远远落后。本研究旨在发现传感技术采用中的主导影响因素，并最终开发一个有利于采用过程的治理框架。该框架专注于通用传感技术，而不是以往框架研究中的单一传感器。首先，通过回顾总结了传感技术和其他类似新兴技术的影响因素。然后，采用混合方法设计通过在线调查收集定量数据，并通过半结构化访谈收集定性数据。定量方法的结果表明，GPS 和视觉传感技术是应用最广泛的传感技术，但并非所有建筑公司都采用这些技术。偏最小二乘结构方程模型显示，在所有影响因素中，供应商特征的影响最大。采用定性方法调查了建筑利益相关者对采用过程中主要决策考虑因素的看法。最终，对文献综述、在线调查和访谈结果进行三角分析，开发了治理框架。本研究的主要贡献在于关注传感技术的一般采用，而不是特定传感器的采用。因此，该治理框架可以帮助建筑中任何传感技术采用的决策过程。

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本文引用的文献

1
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Sensors (Basel). 2021 Aug 12;21(16):5439. doi: 10.3390/s21165439.
2
Employee acceptance of wearable technology in the workplace.员工对可穿戴技术在工作场所的接受度。
Appl Ergon. 2019 Jul;78:148-156. doi: 10.1016/j.apergo.2019.03.003. Epub 2019 Mar 13.
3
Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals.工作场所中可穿戴传感器采用的障碍：职业安全与健康专业人员的调查。
Hum Factors. 2018 May;60(3):351-362. doi: 10.1177/0018720817753907. Epub 2018 Jan 10.