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CAREER: Computational work design: How networked, intelligent technologies are changing organizational design and worker experience

CAREER: Computational work design: How networked, intelligent technologies are changing organizational design and worker experience
职业:计算工作设计:网络化智能技术如何改变组织设计和员工体验
批准号:
1847091
负责人:
Melissa Valentine
金额:
$47.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-07-15 至 2024-06-30

项目摘要

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中文摘要
翻译
随着组织开发和采用新的工作技术,它们也从根本上改变了工作的组织方式。未来的工作可能会更多地利用计算工作--一种结合了人类和计算机能力的工作,可以完成两者都不能单独完成的任务。新的互联网和数据分析技术正在改变计算工作的设计。这个项目将使用纵向观测现场研究来开发新的理论和对当前在现实世界组织中使用的计算工作设计的基本理解。该项目将强调计算工作如何消除某些类型的工作和任务,创造其他类型的工作,并改变许多剩余的工作。此外,该项目将研究计算工作可能如何影响员工体验和赋权。结果可能会增进人们对机器智能技术对人类和社会福祉影响的理解。研究议程还包括为学生和公民领袖开发和传播教育课程,以更好地了解计算工作设计及其对组织结构和员工体验的影响。新开发的工作系统现在包括未来工作的动态能力。具体地说,机器智能技术正在通过以下方式重塑工作:1)收集每个人正在做什么的数据;2)以算法结合人类的努力或促进直接的人类整合;3)分析分工和整合活动的模式;以及4)建议或干预,以创造新的分工和整合工作的方法。包括此类活动的新工作系统可被理解为从事计算工作设计。该项目将发展对现实世界组织中使用的计算工作设计的基本了解,目的是支持改进其设计和运作,以及相关的社会基础设施。将对三个拥有先进计算工作设计部署的组织进行纵向观察性研究,以发展关于计算工作设计和工人赋权的新理论,并推进定性研究方法。此外,该项目将为政策制定者和管理科学专业的学生创建和传播教育课程。结果应该会促进我们对人类技术前沿工作的未来及其对人类和社会福祉的影响的理解。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
As organizations develop and adopt new technologies for doing work, they also fundamentally change the way work is organized. Future work is likely to make greater use of computational work -- work that combines human and computer capabilities to perform tasks that neither could do alone. New internet and data analytic technologies are transforming the design of computational work. This project will use longitudinal observational field research to develop new theory and fundamental understanding of computational work designs that are currently used in real-world organizations. The project will emphasize how computational work can eliminate some types of jobs and tasks, create others, and transform many jobs that remain. Furthermore, this project will examine how computational work might affect worker experiences and empowerment. Results could advance understanding on the impact of machine-intelligent technologies on human and societal well-being. The research agenda also includes development and dissemination of an educational curriculum for students and civic leaders to better understand computational work design and its effects on organizational structure and worker experience.Newly developed work systems now include dynamic capabilities for future work. Specifically, machine-intelligent technologies are reshaping work by: 1) collecting data on what everyone is doing; 2) algorithmically combining human efforts or facilitating direct human integration; 3) analyzing patterns in work division and integration activities; and 4) recommending or intervening to create new ways of dividing and integrating work. New work systems that include such activities can be understood as being engaged in computational work design. This project will develop fundamental understanding about computational work designs used in real-world organizations, with the aim of supporting improvements to their design and operation, and to the relevant societal infrastructure. Longitudinal, observational studies of three organizations with advanced deployments of computational work design will be conducted to develop new theory about computational work design and worker empowerment, as well as to advance qualitative research methodology. Furthermore, this project will create and disseminate an educational curriculum to policymakers and management science students. Results should advance our understanding of the future of work at the human-technology frontier and its impact on human and societal well-being.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(1)
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会议论文
DOI: 10.5465/annals.2018.0174
发表时间: 2020-01-01
期刊: ACADEMY OF MANAGEMENT ANNALS
影响因子: 21.2
作者: [Kellogg, Katherine C., Valentine, Melissa A., Christin, Angele]
通讯作者: Christin, Angele
国内基金
海外基金
Computational Methods for Analyzing Toponome Data