Details
| Original language | English |
|---|---|
| Title of host publication | AMCIS 2023 Proceedings |
| Publisher | Association for Information Systems |
| ISBN (print) | 978-171389359-2 |
| Publication status | Published - 10 Aug 2023 |
Abstract
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AMCIS 2023 Proceedings. Association for Information Systems, 2023. 1808.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Critical Success Factors for AI-driven Smart Energy Services.
AU - Gerlach, Jana
AU - Lier, Sarah Kristin
AU - Hoppe, Paul
AU - Breitner, Michael H.
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/8/10
Y1 - 2023/8/10
N2 - Artificial Intelligence (AI) can significantly contribute to decentralizing and digitalizing the energy sector and environmental sustainability. Integrating AI-driven smart energy services (SES), such as energy production forecasting, building energy management, and predictive maintenance, remain in an early phase for plant providers or energy utilities. However, the knowledge regarding key factors determining the design of AI-driven SES is limited in the literature. Therefore, we derive critical success factors (CSFs) for the design of AI-driven SES in a design science research (DSR) approach in connection with the design thinking process. We identified ten CSFs and 31 CSF categories by iteratively combining the knowledge of interviewed AI business experts, scientific literature, and results of AI programming projects with students. Based on this, we developed a further research agenda containing six research demands and direct further research questions.
AB - Artificial Intelligence (AI) can significantly contribute to decentralizing and digitalizing the energy sector and environmental sustainability. Integrating AI-driven smart energy services (SES), such as energy production forecasting, building energy management, and predictive maintenance, remain in an early phase for plant providers or energy utilities. However, the knowledge regarding key factors determining the design of AI-driven SES is limited in the literature. Therefore, we derive critical success factors (CSFs) for the design of AI-driven SES in a design science research (DSR) approach in connection with the design thinking process. We identified ten CSFs and 31 CSF categories by iteratively combining the knowledge of interviewed AI business experts, scientific literature, and results of AI programming projects with students. Based on this, we developed a further research agenda containing six research demands and direct further research questions.
UR - http://www.scopus.com/inward/record.url?scp=85187367641&partnerID=8YFLogxK
M3 - Conference contribution
SN - 978-171389359-2
BT - AMCIS 2023 Proceedings
PB - Association for Information Systems
ER -