Data Driven Computational Framework for Hybrid Bibliometric Journal Ranking

Bibliometric formulas like CiteScore, Source Normalized Impact per Paper (SNIP), and SCImago Journal Rank (SJR) are essential for assessing journal quality and academic impact. Yet, they harbour biases that disadvantage emerging fields with nascent, interdisciplinary citation patterns misaligned with established norms. These metrics undervalue innovation in domains such as entrepreneurship, orange technology (human-centric advancements), white ocean strategies (sustainable ethics), artificial intelligence, blockchain, and green innovation, where evolving networks, low initial citations, and hybrid boundaries skew evaluations toward mature disciplines with dense citation practices. To counter these limitations, this paper proposes the Hybrid Models Ranking (HMR), a composite metric tailored for emerging field journals: HMR = (CiteScore × 0.4) + (SNIP × 0.3) + (SJR × 0.3). This formula prioritizes raw impact via CiteScore to highlight growth, while integrating SNIP's normalization and SJR's prestige for equilibrium, using ScimagoJR 2023 data for validation. Through empirical calculations on 10 entrepreneurship journals, including Aptisi Transactions on Technopreneurship, HMR demonstrates reduced volatility and enhanced equity, elevating scores despite low SJR. A SWOT analysis outlines strengths like fairness, weaknesses such as subjective weights, opportunities for interdisciplinary fostering, and threats like adoption resistance. Results affirm HMR's ability to better capture emerging journals' potential, promoting diverse ecosystems and innovation. Future refinements could employ machine learning for weights or incorporate altmetrics, aligning with DORA for responsible metrics and advocating widespread use in evaluations.