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利用人工智能(AI)预测组织敏捷性。

Using Artificial Intelligence (AI) to predict organizational agility.

机构信息

Faculty of Science and Technology, Charles Darwin University, Haymarket, NSW, Australia.

Design and Creative Technology, Torrens University Australia, Sydney, NSW, Australia.

出版信息

PLoS One. 2023 May 10;18(5):e0283066. doi: 10.1371/journal.pone.0283066. eCollection 2023.

DOI:10.1371/journal.pone.0283066
PMID:37163532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10171664/
Abstract

Since the pandemic organizations have been required to build agility to manage risks, stakeholder engagement, improve capabilities and maturity levels to deliver on strategy. Not only is there a requirement to improve performance, a focus on employee engagement and increased use of technology have surfaced as important factors to remain competitive in the new world. Consideration of the strategic horizon, strategic foresight and support structures is required to manage critical factors for the formulation, execution and transformation of strategy. Strategic foresight and Artificial Intelligence modelling are ways to predict an organizations future agility and potential through modelling of attributes, characteristics, practices, support structures, maturity levels and other aspects of future change. The application of this can support the development of required new competencies, skills and capabilities, use of tools and develop a culture of adaptation to improve engagement and performance to successfully deliver on strategy. In this paper we apply an Artificial Intelligence model to predict an organizations level of future agility that can be used to proactively make changes to support improving the level of agility. We also explore the barriers and benefits of improved organizational agility. The research data was collected from 44 respondents in public and private Australian industry sectors. These research findings together with findings from previous studies identify practices and characteristics that contribute to organizational agility for success. This paper contributes to the ongoing discourse of these principles, practices, attributes and characteristics that will help overcome some of the barriers for organizations with limited resources to build a framework and culture of agility to deliver on strategy in a changing world.

摘要

自疫情以来,各组织一直被要求提高敏捷性以管理风险、利益相关者参与度、提升能力和成熟度以实现战略目标。不仅需要提高绩效,还需要关注员工参与度,并增加技术的使用,这已成为在新环境中保持竞争力的重要因素。需要考虑战略视野、战略远见和支持结构,以管理战略制定、执行和转型的关键因素。战略远见和人工智能建模是通过对属性、特征、实践、支持结构、成熟度和未来变化的其他方面进行建模,来预测组织未来的敏捷性和潜力的方法。该方法的应用可以支持开发所需的新能力、技能和能力,使用工具并培养适应文化,以提高参与度和绩效,从而成功实现战略目标。在本文中,我们应用人工智能模型来预测组织未来的敏捷性水平,以便主动进行变革,以支持提高敏捷性水平。我们还探讨了提高组织敏捷性的障碍和益处。研究数据来自澳大利亚公共和私营部门的 44 名受访者。这些研究结果以及之前的研究结果确定了有助于组织成功实现敏捷性的实践和特征。本文为这些原则、实践、属性和特征的持续讨论做出了贡献,这些原则、实践、属性和特征将有助于克服资源有限的组织在不断变化的世界中构建敏捷性框架和文化以实现战略目标的一些障碍。

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JMIR Aging. 2022 Oct 7;5(4):e38464. doi: 10.2196/38464.
3
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4
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4
Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic.危机应对中的敏捷与适应性治理:来自新冠疫情的经验教训
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5
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6
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7
SynSys: A Synthetic Data Generation System for Healthcare Applications.SynSys:一种面向医疗保健应用的合成数据生成系统。
Sensors (Basel). 2019 Mar 8;19(5):1181. doi: 10.3390/s19051181.
8
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10
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