Adopting Artificial Intelligence Technology for Network Operations in Digital Transformation
Seoungkwon Min and
Boyoung Kim (bykim2@assist.ac.kr)
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Seoungkwon Min: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Boyoung Kim: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Administrative Sciences, 2024, vol. 14, issue 4, 1-25
Abstract:
This study aims to define factors that affect Artificial Intelligence (AI) technology introduction to network operations and analyze the relative importance of such factors. Based on this analysis of critical factors, a rational decision-making framework is suggested to promote network operations with AI technology. As affecting factors were derived based on related previous studies, the study model was designed to consist of 22 attribute factors under 6 key factors: relative advantage, compatibility, top management support, organizational readiness, competitive pressure, and cooperative relation. The questionnaire was designed and analyzed using the Delphi method and Analytics Hierarchy Process (AHP) method based on the Technology–Organization–Environment (TOE) framework. To collect data, a survey was conducted among 30 experts in network operations and AI. The importance of attribute factors was in the order of ‘goals and strategies’, ‘commitment of resources’, ‘leadership competency’, ‘financial readiness’, and ‘technology readiness’. As the importance of factors was analyzed comparatively between the demander group and provider group, organizational factors were important in the demander group. In contrast, technological factors were important in the provider group. In conclusion, there was a difference in perspectives between demanders and providers regarding adopting AI technology to network operations.
Keywords: network operation; AI; digital transformation; AHP; TOE framework (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jadmsc:v:14:y:2024:i:4:p:70-:d:1369833
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