The Role of Innovation and Technology in Scientometric Governance

Abstract

Purpose: Traditional scientometric tools—such as citation counts, the h-index, journal impact factors, and other quantitative indicators—while useful, have limitations in addressing the complexities of contemporary knowledge networks and rapid scientific developments. These challenges include author name ambiguities, indicator instability, linguistic and regional biases, and difficulties in identifying emerging trends. Given these challenges, integrating innovations and new technologies—such as machine learning, data mining, natural language processing, dynamic network analysis, and artificial intelligence—into the scientometric process has become essential. These technologies can improve accuracy, transparency, fairness, and accountability within the scientific evaluation system. Conversely, the concept of scientometric governance refers to a set of structures, rules, processes, and tools that determine "what and how to measure." Therefore, understanding how technology and innovation influence this governance is a critical issue that has yet to be comprehensively examined. This study aims to achieve several objectives. First, it seeks to ensure that science policymakers and decision-makers rely on scientometric data and indicators to allocate resources and design science development strategies. If these indicators lack sufficient accuracy or transparency, macro-level scientific policies may lead to incorrect or unfair decisions. Second, from a theoretical perspective, this study aims to integrate new technologies with scientific governance theories to enhance the theoretical understanding of how to govern the science evaluation system. Third, with an applied focus, it seeks to promote researchers' trust, reduce errors, and improve the efficiency of scientific evaluation systems through the use of technology in scientometric governance. In summary, this study examines the role of innovation and technology in scientometric governance.Methodology: The present study is quantitative and descriptive-analytical in nature, employing a survey technique. It is applied research in terms of purpose. The statistical population consisted of scientometric experts, data analysts, experts and managers of scientific evaluation units at universities, as well as representatives of scientific databases, reference libraries, and scientometric service companies in Tehran. Due to the researcher's limitations and the absence of reliable statistics regarding the population, a convenience sampling method was used. Consequently, 100 individuals were selected as the sample. Data collection was conducted through fieldwork, with the researcher actively engaging in the research environment. The data collection instrument was a researcher-designed questionnaire based on English-language articles, utilizing a Likert scale ranging from "completely agree" to "completely disagree." Data analysis was performed at two levels: descriptive analysis for demographic variables and inferential analysis using simple regression to test hypotheses. The software used for analysis was SPSS version 26.Findings: The results indicate that the use of new technologies in the scientometric process is positively associated with increased accuracy in scientific evaluation. The coefficient of determination reveals that 73 percent of the variance in accuracy is explained by the use of new technologies. Additionally, technological innovation within scientometric frameworks promotes fairness in scientific evaluation, with 64 percent of the variance in fairness explained by such innovation. Further findings show that the use of new technologies enhances transparency in scientometric governance, accounting for 70.2 percent of the variance in transparency. Finally, the coefficient of determination indicates that 61.1 percent of the variance in responsiveness in scientometric governance is explained by the use of new technologies, demonstrating that new technologies also improve responsiveness in this context.Conclusion: New technologies increase precision by providing tools to measure previously unquantifiable aspects, enabling evaluators to move beyond the limitations of superficial quantitative metrics and gain a deeper understanding of scientific value. Technological innovation advances toward a system of blind scientific evaluation based on merit, replacing subjective judgments or narrow criteria with objective, data-driven measures. These technologies transform scientometric governance from a centralized, opaque process into an open, verifiable, data-consensus-based system, which forms the foundation of trust and credibility within the entire scientific community. Technology shifts accountability from a moral obligation to a structural imperative, where every decision is subject to review, and officials must justify not only the outcomes but also the processes that produced them. Moreover, the adoption of new technologies improves scientometric governance indicators and accelerates the pace of assessments. Such reforms also require clear standards for data quality, model validation, and auditing. Accordingly, integrating advanced technologies into scientometric governance can strengthen evidence-based policymaking and support more adaptive and sustainable scientific evaluation systems.