Conceptual Model Improving Troubleshooting Performance in Enterprise Information Technology Support
Abstract
Effective troubleshooting in enterprise Information Technology (IT) support environments is essential for maintaining business continuity, minimizing downtime, and ensuring optimal end-user productivity. However, escalating system complexity, heterogeneous infrastructures, and the rapid pace of digital transformation have exposed the limitations of traditional, reactive troubleshooting methods. This abstract presents a conceptual model designed to improve troubleshooting performance by integrating intelligent diagnostics, human?machine collaboration, data-driven decision-making, and structured organizational practices. The model emphasizes the interplay between technological enablers, such as AI-assisted monitoring, automation, unified data visibility, and knowledge-centered service systems, and human-centric factors, including technician expertise, collaborative problem-solving, and institutional learning. The conceptual model consists of five core components: intelligent diagnostic frameworks, which automate incident detection and support predictive analysis; a dynamic knowledge management architecture that ensures rapid retrieval and reuse of verified solutions; workflow orchestration mechanisms that standardize troubleshooting procedures and optimize escalation pathways; collaborative support environments that enable cross-tier and cross-team cooperation; and performance feedback engines that reinforce continuous improvement. Together, these components create an integrated ecosystem that enhances diagnostic accuracy, reduces mean time to resolution, and strengthens the overall resilience of IT operations. The model argues that improved troubleshooting performance is driven not only by advanced tools but also by coherent governance structures, user feedback loops, and continuous skill development. Expected outcomes include optimized support efficiency, lower operational costs, reduced system downtime, and higher levels of end-user satisfaction. This conceptual framework provides a foundation for future empirical validation and offers a scalable, adaptable approach to modernizing enterprise IT support functions in increasingly complex digital landscapes.
Keywords
Troubleshooting performance, Enterprise IT support, Intelligent diagnostics, knowledge-centred service (KCS), Automation, Workflow orchestration, Collaborative support, Predictive analytics, IT operations management.
Citations
IRE Journals:
Okeke Obinna ThankGod, Ugwu-Oju Ukamaka Mary, Nwankwo Constance Obiuto "Conceptual Model Improving Troubleshooting Performance in Enterprise Information Technology Support" Iconic Research And Engineering Journals Volume 3 Issue 1 2019 Page 614-627
IEEE:
Okeke Obinna ThankGod, Ugwu-Oju Ukamaka Mary, Nwankwo Constance Obiuto
"Conceptual Model Improving Troubleshooting Performance in Enterprise Information Technology Support" Iconic Research And Engineering Journals, 3(1)
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