Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Through the Cognitive Functions Lens : A Socio-Technical Analysis of Predictive Maintenance

Title data

Stohr, Alexander ; O'Rourke, Jamie:
Through the Cognitive Functions Lens : A Socio-Technical Analysis of Predictive Maintenance.
2021
Event: 16. Internationale Tagung Wirtschaftsinformatik , 09.-11.03.2021 , Duisburg/Essen, Germany.
(Conference item: Conference , Speech )

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

The effective use of artificial intelligence promises significant business value. Effective use, however, requires a thorough exploration of its strengths and weaknesses from different perspectives. Information systems research is particularly invested in the management and use of artificial intelligence in organizations. It has proposed the use of cognitive functions to guide this exploration. In this paper, we evaluate the usefulness of such a cognitive functions lens for a relatively mature application of artificial intelligence, predictive maintenance. Our evaluation is informed by the insights we collected from an embedded single-case study. We find that a cognitive functions lens can indeed be a useful tool to explore artificial intelligence. In particular, it can aid the allocation of tasks between human agents and artificial intelligence-based systems and the design of human-AI hybrids. It is particularly helpful for those who investigate the management of artificial intelligence.

Further data

Item Type: Conference item (Speech)
Refereed: Yes
Keywords: Artificial intelligence; Predictive maintenance; Cognitive functions; Embedded single-case study
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 13 Jan 2021 07:59
Last Modified: 13 Jan 2021 07:59
URI: https://eref.uni-bayreuth.de/id/eprint/61679