TY - JOUR
T1 - No Stone Left Unturned? Toward a Framework for the Impact of Datafication Technologies on Organizational Control
AU - Schafheitle, Simon
AU - Weibel, Antoinette
AU - Ebert, Isabel
AU - Kasper, Gabriel
AU - Schank, Christoph
AU - Leicht-Deobald, Ulrich
N1 - Funding Information:
We thank the anonymous reviewers for their valuable and constructive comments as well as for their helpful advice to improve the quality of the article. We especially thank Claudio Kick, whose remarkable efforts in data collection have contributed to the overall success of the research project. Also, we are truly grateful for the excellent comments and critical thinking of Sim Sitkin and Chet Miller from whom our paper benefited significantly. We are grateful for the dedicated efforts of Giulia Solinas and the entire editorial team of this special issue enabling such a fruitful exchange of ideas during the review and publication process. Finally, we thank our expert sounding board for the valuable insights as well as the Swiss National Science Foundation (NFP75) for the funding supporting this work. 1 Corresponding author.
Publisher Copyright:
© 2020, Academy of Management. All rights reserved.
PY - 2020/9
Y1 - 2020/9
N2 - 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 ex-pand 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 vali-dated 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.
AB - 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 ex-pand 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 vali-dated 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.
KW - n/a OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85095128628&partnerID=8YFLogxK
U2 - 10.5465/amd.2019.0002
DO - 10.5465/amd.2019.0002
M3 - Article
AN - SCOPUS:85095128628
SN - 2168-1007
VL - 6
SP - 455
EP - 487
JO - Academy of Management Discoveries
JF - Academy of Management Discoveries
IS - 3
ER -