Enhancing Academic Publication Decision-Making Quality through Decision Support Systems and Measurement System Analysis (MSA)

Abstract

The academic publication decision-making process is a critical performance driver for both individual researchers and universities. However, the reliance on subjective judgment and the complexity of multi-dimensional publication criteria frequently result in inconsistencies, delays in budget planning, workload estimation, and KPI monitoring. This study aims to develop and evaluate a Decision Support System (DSS) prototype for academic publication decisions, validated through Measurement System Analysis (MSA).

The research followed four systematic stages: (1) pre-improvement baseline analysis, revealing a Total Gage R&R of 55.566% (unacceptable); (2) DSS development based on the Input-Process-Output (IPO) framework, transforming text-based regulations into a numerical computation engine classifying 13 academic output types; (3) post-improvement evaluation using Gage R&R with six appraisers across three datasets; and (4) time-efficiency and utility optimization analysis.

The results demonstrate significant improvement across all MSA indicators. Total Gage R&R decreased from 55.566% to 27.790%, meeting international acceptance criteria. Appraiser Variation (Reproducibility) was reduced to 26.742%, while Part-to-Part Variation reached 96.061%, confirming the system's precision in classifying diverse output types. Bias (0.1078%) and Linearity (8.987%) remained within acceptable thresholds, ensuring reliable outputs across all publication levels. The system enables simultaneous retrieval of funding calculations, workload credits, and quality assessments in a single interface, achieving "Right the First Time" decision accuracy. In conclusion, integrating DSS with MSA effectively transforms academic publication decision-making from subjective interpretation to an empirical, data-driven standard. This approach sustainably enhances research data management efficiency, transparency, and strategic planning within the School of Information and Communication Technology.