AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model

As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary.

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Publication Details

Journal
Digital
Published
2024-11-13
DOI
https://doi.org/10.3390/digital4040047
Citations
7
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
2.44
Controls
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article

AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model

Seoungkwon Min, Boyoung Kim
7 citations
Digital
Digital Transformation in Industry
2.44
article

AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model

Seoungkwon Min, Boyoung Kim
article en
7 citations

Abstract

As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary.

DigitalVol. 4(4)
Seoul School of Integrated Sciences and Technologies (KR)
Decent work and economic growth
Openalex Percentile: Top 10%
Digital Transformation in Industry
2.44
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AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model — Seoungkwon Min, Boyoung Kim · Digital (2024) | TGRS Research Map | TGRS