领域
控制(管理)
知识管理
口译(哲学)
新兴技术
计算机科学
过程管理
业务
政治学
人工智能
程序设计语言
法学
作者
Simon Daniel Schafheitle,Antoinette Weibel,Isabel Ebert,Gabriel Kasper,Christoph Schank,Ulrich Leicht‐Deobald
出处
期刊:Academy of Management discoveries
[Academy of Management]
日期:2020-05-15
被引量:26
标识
DOI:10.5465/amd.2019.0002
摘要
The goal of this article is to develop an empirically grounded framework to analyze how new technologies, particularly those used in the realm of datafication, alter or expand traditional organizational control configurations. Datafication technologies for employee-related data-gathering, analysis, interpretation, and learning are increasingly applied in the workplace. Yet there remains a lack of detailed insight regarding the effects of these technologies on traditional control. To convey a better understanding of such datafication technologies in employee management and control, we used a three-step, exploratory, multi-method morphological analysis. In step 1, we developed a framework based on 26 semi-structured interviews with technological experts. In step 2, we refined and redefined the framework in [...] and redefined the framework in four workshops with scholars specializing in topics that emerged in step 1. In step 3, we evaluated and validated the framework using potential and actual users of datafication technology controls. As a result, our refined and validated “Datafication Technology Control Configuration” (DTCC) framework comprises 11 technology control dimensions and 36 technology control elements, offering the first insights into how datafication technologies can change our understanding of traditional control configurations.
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