Introduction
The proliferation of research information management systems across Iranian universities and scientific institutions has increasingly foregrounded the role of data, indicators, dashboards, and evaluative algorithms in research decision-making. These systems have been developed to organize and manage researchers’ information, evaluate scientific performance, strengthen research governance, and inform science policy. However, their implementation has also raised critical questions concerning fairness in access to, representation within, processing of, and decision-making based on research data. Within this context, research justice extends beyond the equitable distribution of resources or equal technical access to information systems. It encompasses the fair representation and visibility of scholarly activities, meaningful opportunities for stakeholders to participate in the interpretation of research data, transparency and accountability in algorithmic processing, and the equitable consequences of system-generated outputs for institutional decision-making. Furthermore, the interconnectedness of information justice, data justice, and research justice suggests that inequalities embedded throughout the data lifecycle may be reproduced and amplified within the broader scientific system. By focusing on the experiences of professional users of research information management systems in Iran, this study seeks to reconceptualize research justice within a localized institutional context and to elucidate the emergence of a form of “distributed intelligence” that is constituted through the dynamic interaction of human actors, data, algorithms, and institutional structures.
Methodology
In terms of its purpose, this study is applied in nature, while its methodological approach is qualitative, employing thematic analysis based on Braun and Clarke’s six-phase framework. The study population comprised 20 participants, including researchers, mid-level research managers, research experts, and science policymakers, all of whom had direct experience with national and university-based research information management systems. Participants were recruited from among users of systems such as SAMAT, the ISC Scientometric System for Faculty Members, Pazhouheshyar, the University of Tehran Research Portal, the research information systems of Tarbiat Modares University and Iran University of Science and Technology, and the research information systems of the Universities of Medical Sciences in Tehran, Isfahan, and Mashhad. Data were collected through semi-structured interviews. The interview guide was developed based on the IRF framework of information justice, encompassing four dimensions: access, representation, processing, and impact. The IRF framework constitutes a developmental and integrative model derived from the existing literature on data justice and informed by the preliminary analysis of the empirical data. During the analytical process, 174 initial codes were identified. Following multiple rounds of review, consolidation, and refinement, these codes were reduced to 42 conceptual codes, which were subsequently organized into four overarching themes. To enhance the credibility and trustworthiness of the findings, several strategies were employed, including participant validation, the use of verbatim quotations, negative case analysis, and continued sampling until thematic saturation was achieved
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Findings
The findings indicate that justice in research information management systems is a multidimensional, interconnected, and deeply embedded phenomenon that cannot be reduced to a merely technical or managerial concern. Four overarching themes emerged from the analysis: justice in access, justice in data representation, justice in data processing and algorithmic operations, and justice in impact and institutional consequences.
Regarding access, participants reported disparities in their ability to access data, dashboards, and analytical reports. While some researchers were limited to raw or relatively restricted datasets, managers and users occupying higher organizational positions often had access to more comprehensive data and advanced analytical capabilities. These disparities suggest that access to research information is shaped not only by technical availability but also by organizational roles and decision-making authority. With respect to representation, the findings indicate that research information management systems tend to represent scholarly activities associated with international publications and formal quantitative indicators more effectively. By contrast, Persian-language, locally oriented, policy-relevant, interdisciplinary, and non-article-based forms of scholarly activity are comparatively less visible within these systems. This pattern contributes to forms of data underrepresentation and exclusion, particularly in the humanities and in areas of research that are closely connected to local social and policy contexts. In the dimension of processing, participants identified algorithmic errors, author disambiguation and matching problems, dependence on identifiers such as DOIs, automated filtering, and the disproportionate weighting of quantitative indicators as major challenges. Such problems do not remain confined to the technical or data-processing level; in some cases, they propagate into inaccuracies in research rankings, performance scores, evaluations, and ultimately institutional decision-making.
Regarding impact, the findings suggest that research information management systems have significant effects on researchers’ behaviors, scholarly motivations, patterns of academic competition, and institutional management and policy practices. While some participants viewed these systems as enhancing transparency and facilitating research governance, others argued that their score-driven logic encourages researchers to prioritize measurable and easily quantifiable outputs over research that is substantively meaningful, socially relevant, or responsive to real-world problems. Thus, the consequences of these systems extend beyond information management itself, shaping the norms, incentives, and behavioral patterns of the research ecosystem.
Analysis of the relationships among the four themes revealed that the dimensions of justice are not discrete or independent; rather, they are dynamically interconnected and mutually reinforcing. Limited or unequal access may constrain the ability of users to identify and challenge processing errors, while incomplete representation may result from algorithmic filtering, data structures, or the prioritization of particular types of scholarly outputs. In turn, deficiencies in representation and processing may shape managerial evaluations, resource allocation, performance assessments, and even researchers’ perceptions of their scholarly identities. Thus, justice within research information management systems emerges not from any single component but from the interaction of researchers, managers, information technology experts, data, algorithms, dashboards, and institutional policies. This interconnected configuration reflects a form of “distributed intelligence,” in which decision-making capacity and evaluative authority are distributed across human and technological actors. However, the findings suggest that this intelligence is currently characterized by considerable asymmetry, limited participation, and unequal influence among stakeholders. At the same time, the presence of positive and relatively neutral experiences—including adequate access for some users, stronger system performance in certain technical and scientific disciplines, and greater transparency in specific scoring processes—indicates that these systems are not inherently unjust. Rather, their justice-related outcomes are contingent upon how data, algorithms, interfaces, organizational practices, and institutional policies are designed and governed. This finding points to the possibility of reforming and redesigning research information management systems in ways that promote greater transparency, participation, representational inclusiveness, and procedural and distributive justice.
Conclusion
The present study demonstrates that research justice within Iranian research information management systems (RIMS) is not merely a technical, software-related, or administrative concern; rather, it is fundamentally shaped by the ways in which power, data, interpretation, and decision-making are distributed across the research information lifecycle. The injustices identified in this study cannot be attributed solely to isolated technical deficiencies or accidental errors. Instead, they arise from a systemic and interconnected configuration in which human actors, information systems, data structures, algorithms, and institutional arrangements collectively shape the production and consequences of research information. From this perspective, RIMS can be conceptualized as platforms through which a form of distributed intelligence emerges within the scientific system. This intelligence is produced through the interaction of human and technological actors, but its outcomes are not necessarily equitable. When the underlying structures are insufficiently participatory, transparent, and accountable, distributed intelligence may reproduce or amplify existing inequalities rather than mitigate them. The findings therefore suggest that research justice should be understood not as a fixed property of an information system but as an emergent outcome of the relationships among its technological, organizational, and human components. Accordingly, advancing research justice in RIMS requires more than the introduction of additional technical functionalities. It necessitates a fundamental reconsideration and redesign of the relationships among users, data, algorithms, and institutional decision-making structures. Establishing robust mechanisms of algorithmic and data transparency, conducting periodic data audits and reviews, involving researchers in the interpretation and validation of system-generated results, recognizing disciplinary and linguistic diversity, and establishing accessible mechanisms for contesting, correcting, and updating research data are among the measures that can contribute to more equitable and accountable data governance in the research domain. Such measures can help transform RIMS from predominantly evaluative and managerial infrastructures into more participatory and justice-oriented information environments
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Originality and Value
The originality of this study lies in its integration of the concepts of research justice, data justice, and research information management systems (RIMS) within the specific institutional and sociotechnical context of Iranian universities. Drawing on the lived experiences of professional users, the study demonstrates that inequalities within RIMS do not arise solely from technical limitations but emerge through the interaction of organizational structures, data practices and logics, and algorithmic processes. Furthermore, by developing the IRF framework and connecting it to the concept of distributed intelligence, the study provides a conceptual lens for examining how inequalities are produced, reproduced, and potentially addressed across the research information lifecycle. The framework offers a basis for analyzing justice-related challenges in RIMS and for informing more transparent, participatory, inclusive, and accountable approaches to research data governance within Iran’s scientometric and research evaluation infrastructure.