Affiliation:
1. Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
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.
Reference70 articles.
1. Studying the characteristics of AIOps projects on GitHub;Aghili;Empirical Software Engineering,2023
2. Al Hleewa, Shahad Omar, and Al Mubarak, Muneer (2023). Technological Sustainability and Business Competitive Advantage, Springer International Publishing.
3. Ambrosch, Wolf-Dietrich, Maher, Anthony, and Sasscer, Barry (1989). The Intelligent Network: A Joint Study by Bell Atlantic, IBM and Siemens, Springer.
4. AIOps–artificial intelligence for IT operations: Todays challenges of new technologies and new methodologies in IT operations;Andenmatten;HMD Praxis der Wirtschaftsinformatik,2019
5. Fairness and explanation in AI-informed decision making;Angerschmid;Machine Learning and Knowledge Extraction,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献