Purpose: The rapid diffusion of artificial intelligence (AI) in organizational decision environments has intensified concerns about transparency, interpretability, and user trust. As complex machine learning models increasingly support managerial and policy decisions, the need for Explainable Artificial Intelligence (XAI) has become essential to ensure that decision makers understand and appropriately use algorithmic outputs. Decision dashboards have emerged as key interfaces through which AI driven insights are delivered to managers and analysts. Despite this growing importance, the intellectual structure and thematic evolution of XAI within decision dashboard design have not yet been systematically examined. The purpose of this study is therefore to analyze the knowledge structure, conceptual foundations, and thematic development of research on Explainable Artificial Intelligence in decision dashboards. Using scientometric techniques, the study identifies core research themes, emerging trends, and structural relationships shaping this rapidly developing field. It also highlights how technical developments in AI intersect with human centered design, decision support systems, and visualization approaches in explainable decision dashboards.
Methodology: This study adopts a scientometric and bibliometric approach to map the intellectual landscape of XAI research in decision dashboards. Data were collected from the Scopus and Web of Science databases to ensure comprehensive coverage of peer reviewed literature. Following the PRISMA protocol for systematic screening and refinement, an initial dataset of 942 records was identified. After applying inclusion criteria and restricting the corpus to original research articles, 251 records from Scopus and 112 from Web of Science were retained. Duplicate and irrelevant records were removed, resulting in a final dataset of 269 articles. Data standardization and descriptive bibliometric analyses were conducted using the Bibliometrix package and the Biblioshiny interface. These tools enabled the examination of publication trends, leading journals, and author productivity patterns, including Bradford’s and Lotka’s laws. To explore the conceptual structure of the field, keyword co occurrence network analysis was performed using VOSviewer. Network, density, and overlay visualizations were generated to identify thematic clusters, intellectual linkages, and temporal changes in research topics. Additionally, thematic evolution mapping and logistic growth modeling were applied to examine the developmental trajectory of the field.
Findings: The findings show that research on Explainable Artificial Intelligence in decision dashboards is evolving from a purely technical and algorithm centered domain toward a multidimensional and interdisciplinary knowledge structure. Co occurrence analysis reveals that the intellectual core of the field is organized around three central concepts: Explainable AI, decision making, and deep learning. These themes form the primary hub of the conceptual network, indicating that explainability is increasingly recognized as a fundamental component of intelligent decision support systems rather than a supplementary feature of machine learning models. The results also reveal several interconnected thematic clusters, including human ethical considerations, technical methodological development, applied operational contexts, cognitive autonomous systems, and emerging specialized topics. This structure reflects the convergence of two complementary research logics: algorithmic performance and engineering efficiency on the one hand, and human understanding, trust, and accountability on the other. Density visualization confirms that the highest concentration of research lies at the intersection of Explainable AI, decision making, and deep learning, highlighting the central role of deep learning technologies in the field. However, these technologies require integration with interpretability mechanisms and user interface design to be effective in decision dashboards. Overlay visualization further indicates a thematic transition over time. Earlier research focused on classical machine learning techniques such as neural networks and support vector machines, whereas recent studies increasingly emphasize visual deep learning, visualization techniques, trust, and intelligent decision support systems. Another key finding is that concepts such as trust and visualization have moved from peripheral topics to integral components of XAI systems, emphasizing the growing importance of user comprehension and interaction in AI supported decision processes. Logistic growth modeling also indicates that the field remains in a rapid expansion phase and has not yet reached scientific saturation.
Conclusion: The results demonstrate that the research landscape of Explainable Artificial Intelligence in decision dashboards is shifting from a technology centered paradigm toward a human and decision centered paradigm. Explainability is increasingly understood not only as a technical capability that clarifies algorithmic outputs but also as a cognitive bridge between complex AI systems and human decision makers. The findings highlight that the effectiveness of AI enabled decision dashboards depends not only on predictive accuracy but also on the ability of systems to communicate reasoning processes in an interpretable and trustworthy manner. Consistent with previous studies, the practical value of explainable AI emerges when explanations enhance transparency, foster user trust, and support informed decision making in complex environments. By mapping the intellectual structure and thematic evolution of this interdisciplinary domain, the study provides insights into its conceptual foundations and future research directions. In particular, it emphasizes the importance of integrating deep learning technologies with explainability mechanisms, visualization techniques, and human centered interface design to develop effective decision dashboards.