Business Intelligence Management Studies

Abstract Message from the Chief Editor
Dear Researchers and Contributors, It is my pleasure to welcome you to our scholarly community dedicated to advancing knowledge in the field of Business Intelligence Management (BIM). As organizations increasingly rely on data-driven decision-making, the role of Business Intelligence has become more critical than ever in shaping competitive advantage, innovation, sustainability, and organizational performance. To communicate more effectively with you, esteemed researchers, one out of every four issues of the journal will be published in English annually. This issue is the first in this series. Our Mission, The mission of this journal is to promote high-quality, rigorous, and impactful research that advances the theory and practice of Business Intelligence Management. We aim to serve as a global platform for scholars, practitioners, policymakers, and industry leaders to exchange knowledge and develop innovative solutions that transform data into actionable intelligence. Areas of Interest, The journal welcomes contributions in areas including, but not limited to: Business Intelligence Management and Analytics Artificial Intelligence in Business Big Data Management and Analytics Data Mining and Knowledge Discovery Decision Support Systems Predictive and Prescriptive Analytics Machine Learning Applications Explainable and Responsible AI Digital Transformation Data Governance and Quality Performance Management and Business Strategy Intelligent Enterprise Systems Supply Chain and Financial Analytics Customer and Marketing Intelligence Smart Organizations and Innovation Management Expectations from Authors We encourage authors to submit work that demonstrates: Scientific rigor and methodological soundness; Originality and innovation; Practical relevance and managerial implications; Ethical research practices; Clear contribution to knowledge and practice. Closing Remarks As Chief Editor, I invite you to contribute to this collective endeavor of advancing Business Intelligence research and practice. Through your scholarly efforts, we can deepen our understanding of intelligent organizations, support better decision-making, and create meaningful value for businesses and society. We look forward to receiving your high-quality submissions and collaborating with you in shaping the future of Business Intelligence Management. Sincerely, Chief Editor Kamran Faizi, Ph.D Journal of Business Intelligence Management Studies

Abstract A dashboard, as a visual display, plays an effective role in enhancing knowledge and information sharing across various domains. The use of dashboards in the field of policy, equipped with modern data-driven technologies and aligned with the expansion of e-government, creates a platform for achieving smart governance, enabling progress toward transparency, accountability, forward-looking decision-making, and greater participation. In smart cities, participation often occurs through dedicated participation platforms, such as dashboards, through which citizens can vote, discuss, and brainstorm ideas. The aim of the present research is to provide a framework for designing a dashboard in public policy-making to realize smart governance. To this end, through a review conducted in the Scopus and Web of Science databases, 40 articles published between 2012 and 2025 focusing on dashboard design were selected and qualitatively analyzed. In this study, the design of managerial dashboards in various domains was observed, and subsequently, considering the multitude of features and approaches used, the design of dashboards in public policy-making was addressed from a broader perspective. The research findings, using a thematic analysis approach based on the Brown and Clarke (2006) framework, revealed that for designing smart public policy-making dashboards, 55 sub-themes could be categorized into 6 main themes. The main themes are: needs assessment, intelligent data collection and management, visual design, intelligent data analysis, technical capabilities, and intelligent evaluation and support.

Abstract and enhancing digital governance. However, existing models have paid limited attention to the dual interaction between structure and agency. This study aims to analyze the role of Giddens’ Structuration Theory in the development of e-participation models by integrating insights from high-quality international and national studies. The data were derived from articles published in reputable academic databases such as Web of Science, Scopus, and ScienceDirect, focusing on journals ranked Q1 and Q2. After a rigorous quality appraisal using the CASP checklist, 39 eligible papers were selected for content analysis. The findings indicate that while technological and organizational dimensions are dominant in most models, social and cultural aspects—particularly reflexivity and time–space distancing—have received limited attention. A comparative analysis revealed that structure-oriented, agency-oriented, and integrative models each provide complementary perspectives in explaining the dynamics of e-participation. Grounded in the principles of Structuration Theory, this study suggests the development of more comprehensive, context-sensitive, and practice-oriented frameworks that can strengthen theoretical foundations and enhance genuine citizen participation in digital governance.

Abstract This study introduces EmoBERTScr, an intelligent Multilingual BERT-based model for detecting and analyzing emotional and behavioral patterns in Persian and multilingual screenplays. A dataset of 35,800 dialogue samples from 1,700 Persian and English screenplays was manually collected and annotated by linguistics and psychology experts into eight emotional categories according to Plutchik’s (1980) wheel of emotions. The methodology involved data acquisition from cinematic archives, preprocessing (noise removal, normalization, and tokenization), and supervised learning with a fine-tuned Multilingual BERT architecture. The model achieved an overall accuracy of 98.22%, with F1-scores ranging from 95.53% (surprise) to 100% (joy, fear, sadness, trust) across all emotional categories. The primary contribution of this research lies in developing a practical AI assistant for screenwriters, capable of providing real-time feedback to enhance narrative coherence and maintain emotional consistency, which can reduce rewriting and production costs in the film industry. While certain overlapping emotional states, such as anticipation and surprise, remain challenging to distinguish, the proposed approach demonstrates robust performance in both dominant and subtle emotional expressions. This work establishes a foundation for future advancements in intelligent narrative analysis, particularly for low-resource languages like Persian, and highlights the potential for integrating AI-driven emotion recognition into professional screenplay development workflows.

Abstract Digital platforms have become complex structures in which technological architectures, network effects, and multi-sided interactions have created new forms of power at the macro, meso, and micro levels. This study aims to reorganize the scattered dimensions of platform-power governance into a coherent three-level framework. This study used a systematic meta-synthesis design and conducted a guided search in the Scopus database using the PRISMA protocol for the years 2015-2025. After screening 514 articles and applying the Joanna Briggs Institute quality criteria, 44 qualitative studies were selected. Data extraction and coding were conducted through open, axial, and selective stages, and the codes were also reviewed independently to ensure reliability. The reviewed studies included interventions in the domains of regulatory structures, ecosystem architectures, and user-level mechanisms, and the analysis was carried out using qualitative synthesis techniques. The results showed that platform power had been shaped through macro-level institutional and regulatory rules, meso-level infrastructural and algorithmic arrangements, and micro-level design and behavioral mechanisms. The findings also showed that these levels were interconnected and reinforced one another in shaping how platform power functions. Finally, this study showed that understanding platform governance requires simultaneous attention to all three levels and that integrated frameworks are necessary to address the multidimensional nature of power in digital platforms.

Abstract Digital transformation has been widely recognized as a critical driver of high-quality development in firms and a fundamental enabler of innovation-oriented strategies. Despite its growing significance, prior research has not sufficiently clarified the mechanisms through which digital transformation influences innovation performance, and existing findings remain inconclusive. While some studies suggest that digital transformation fosters innovation, others report adverse effects and fail to specify the role of big data capabilities, the Internet of Things (IoT), and organizational agility in this relationship.Building on the dynamic capabilities theory and systems engineering theory, this study adopts a strategy–behavior–performance framework to systematically examine how digital transformation enhances startups’ innovation performance through the development of big data capabilities, IoT, and organizational agility. Empirical evidence was collected from 203 questionnaires completed by CEOs, senior and middle managers, and IT specialists across 15 Iranian startups. The results reveal the chain-mediating effects of big data capabilities, IoT, and organizational agility, highlighting their pivotal role in shaping the outcomes of digital transformation.The findings indicate that digital transformation significantly improves startups’ innovation performance, with the three mediators big data capabilities, IoT, and organizational agility serving as crucial mechanisms in this process. This research not only provides empirical validation for the theoretical model of digital transformation but also offers actionable insights for startups seeking to craft effective strategies and optimize resource allocation in the digital era.

Abstract In recent years, the rapid growth of information and communication technologies and the increasing volume of data have turned data, information, and knowledge management into a core driver of intelligent business development. This study examines the role of data, information, and knowledge management in strengthening technology innovation, with an emphasis on human and financial development, in 15 developing countries. A panel dataset covering the period 2000–2023 was compiled from international sources such as the World Bank and UNDP. The empirical analysis is based on a Panel Autoregressive Distributed Lag (P-ARDL) model, which allows for distinguishing short-run and long-run effects. The results show that improvements in data and information management have a positive and statistically significant impact on technology innovation both in the short and long run. Moreover, human and financial development reinforce this relationship, so that countries with stronger human and financial capital benefit more from data and knowledge management. These findings suggest that policymakers and managers of intelligent businesses should invest simultaneously in data-driven infrastructures and in human and financial capital to enhance innovation and productivity.

Abstract Artificial intelligence, as a principal driver of transformation in recent years and those to come, has redefined the role of universities in global competition and in the provision of scientific and educational services. Aiming to design a strategic roadmap for reconfiguring university culture in this context, this article examines changes in norms, roles, and customary practices in higher education. This transformation rests on three pillars—transformed leadership, faculty readiness, and access to advanced infrastructures—and has been reinforced by the post COVID acceleration of digital transformation in higher education. Methodologically, the study adopts a conceptual synthesis approach and proposes a staged model to explain cultural transformation. The model comprises three successive stages: first, an organizational flexibility stage that emphasizes local meanings and actor networks; second, an institutional consolidation stage in which rules and procedures are stabilized; and third, a technological impetus stage in which data driven infrastructures and emerging information architectures play a decisive role in shaping organizational culture. Across all stages, mediating and moderating mechanisms—including transparent data governance, algorithmic accountability, enhanced digital competence and literacy among faculty members, and equitable access—play a pivotal role. Building on this model, the article proposes an operational, stage based roadmap for policymaking, implementation, and evaluation, organized around three axes: strengthening transparent and effective data governance, enhancing the digital competence of all university actors, and reducing inequalities in access to infrastructures and opportunities in Iranian universities. The roadmap is designed to support the responsible adoption of AI while preventing the reproduction of existing educational inequalities and fostering a culture of accountability, equity, and lifelong learning.

Abstract Family firms face a fundamental challenge at the point of generational transition: How can they facilitate the necessary innovations for the survival and growth of the second generation (G2) simultaneously preserving the legacy and knowledge of the first generation (G1)? This research examines the mechanisms for innovation activation during the intergenerational transition (G1 to G2). By employing a multiple case study approach across five family firms, presented a novel conceptual framework for ‘Two-Generational Innovation.’ This framework is built upon five critical activation levers. The findings indicate success in generational transition is not merely a process of power or ownership transfer, but rather a complex and dynamic process of collaborative innovation requires the sequential and integrated activation of these levers. The study emphasizes overcoming inherent resistance to change and transforming intergenerational conflicts into innovative opportunities is the key to ensuring future organizational resilience. The results show intergenerational conflicts, resource asymmetries, and cultural rigidity hinders innovation. However, targeted interventions in these five areas enable G1 and G2 to create shared value. By reframing succession as a process of collaborative innovation rather than mere replacement, this study advances family business research. Practical implications include strategies for enhancing trust, knowledge transfer, and sustainable governance structures.

Abstract Given the growing role of knowledge-based firms in economic growth, job creation, and the development of new technologies, examining the factors influencing their competitiveness particularly in the field of information technology is of great significance in today’s Iranian economic landscape. This study aims to investigate the competitiveness of knowledge-based firms operating in the information technology sector by employing Porter’s Diamond Model. In terms of purpose, the research is applied in nature, and methodologically, it is qualitative and exploratory. The participants consisted of managers and experts from IT knowledge-based firms who were selected through purposive theoretical sampling. Data were collected through a review of relevant literature and in-depth semi-structured interviews with 28 participants and subsequently coded. The findings indicated that the extracted codes were classified into six main dimensions: Related and Supporting Industries (energy imbalance, quality of technological infrastructure, support from large industries, ...), factor conditions (marketing and branding capabilities, intelligent management, inter-firm networking, ...), strategy–structure–rivalry (rebranding for entry into foreign markets, unfair competition from state-owned and quasi-state-owned firms, ...), demand Conditions (expansion of emerging digital technologies, shortened product life cycles, saturation of the domestic market, ...), government (international interactions, access to governmental data, incentive policies, ...), and chance (sanctions, currency fluctuations, wars). Ultimately, this study presents a practical framework for analyzing and enhancing the competitiveness of IT knowledge-based firms in Iran and highlights that competitiveness results from the dynamic interplay between the environment and strategy.