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چکیده 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

چکیده 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, spectacularization, intelligent data analysis, technical capabilities, and smart evaluation and support.

چکیده E-participation has emerged as a central pillar of digital governance, aiming to enhance citizen engagement, strengthen transparency, and improve accountability in public decision-making. Despite the proliferation of conceptual and empirical models, limited attention has been paid to the dynamic interaction between structure and agency as conceptualized in Giddens’ Structuration Theory. This study analyzes existing e-participation models through a structuration lens in order to assess their theoretical grounding and identify conceptual gaps. A Systematic Literature Review (SLR) was conducted in accordance with the PRISMA protocol. Articles were retrieved from high-quality international databases, including Web of Science, Scopus, ScienceDirect, Emerald, and Springer, with a focus on journals ranked Q1 and Q2. After duplicate removal, screening, and quality appraisal using the CASP checklist, 39 eligible studies published up to 2025 were selected for in-depth content analysis. The findings reveal that most models emphasize technological and institutional dimensions, while social, cultural, and reflexive aspects-particularly reflexivity and time-space distanciation- remain underdeveloped. Comparative analysis indicates that existing models can be categorized into structure-oriented, agency-oriented, and integrative approaches. However, only a limited number of studies explicitly operationalize the duality of structure in explaining participation dynamics. This study contributes by (1) synthesizing high-quality interdisciplinary literature, (2) extending Structuration Theory into the domain of digital participation, and (3) proposing a conceptual framework that integrates institutional structures, citizen agency, and reflexive processes within digitally mediated time–space contexts. The findings offer theoretical advancement and practical guidance for designing context-sensitive and sustainable e-participation initiatives.

چکیده 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.

چکیده 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.

چکیده Digital transformation is recognized as a key driver of high-quality development in firms and a critical tool for shaping innovation-oriented models. However, existing studies have not fully examined the specific mechanisms through which it affects firm innovation performance, and perspectives on this matter remain divergent. Some studies argue that digital transformation can improve innovation performance, while others point to its negative effects and fail to clearly explain the precise role of big data capabilities, the Internet of Things (IoT), and organizational agility in this process. Therefore, drawing on the dynamic capabilities theory of the organization and systems engineering theory, this study employs the logical "Strategy–Behavior–Performance" framework to systematically investigate the process through which digital transformation enhances the innovation performance of startups by promoting big data capabilities, IoT capabilities, and organizational agility. Through empirical analysis of data collected from 203 questionnaires completed by CEOs, senior and middle managers, and IT experts from 15 Iranian startups, this study reveals the serial mediation effects of big data capabilities, IoT capabilities, and organizational agility, confirming the key role of all three factors in the digital transformation process. The findings indicate that digital transformation significantly improves the innovation performance of startups, and the triple mediation effects of big data capabilities, IoT capabilities, and organizational agility are important links in its influence mechanism. These results not only provide empirical support for the theoretical model of digital transformation but also offer practical guidance for startups in formulating strategies and optimizing resource allocation in the digital era.

چکیده In recent decades, the expansion of Information and Communication Technology (ICT) and the growth of data volume have transformed data, information, and knowledge management into one of the main pillars of smart business development. This research was conducted with the aim of investigating the role of data, information, and knowledge management on the technological innovation index, with an emphasis on human and financial development, in 15 developing countries. Secondary data for the period 2000 to 2023 were collected from international databases and analyzed using the Panel Autoregressive Distributed Lag (P-ARDL) model. The results indicate that the improvement of data and information management, both in the short and long term, has a positive and significant effect on the technological innovation index. Furthermore, human development and financial development strengthen the relationship between data management and innovation, and countries equipped with stronger human and financial capital benefit the most from data and knowledge management. Based on the findings, it is suggested that policymakers and managers of smart businesses facilitate the enhancement of innovation and productivity by simultaneously investing in data-driven infrastructures and upgrading human and financial capital.

چکیده 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.

چکیده 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.

چکیده Considering the increasing role of knowledge-based enterprises in economic growth, job creation and the development of new technologies, it is of great importance to examine the factors affecting their competitiveness, especially in the field of information technology, in today's economic landscape of Iran. The purpose of this research is to investigate the competitiveness of knowledge-based companies active in the information technology sector using Porter's diamond model. The research is applied in terms of purpose and qualitative and exploratory in terms of methodology. The participants consisted of managers and experts of knowledge-based information technology companies, who were selected by the method of theoretical sampling. Data were collected and then coded through a review of relevant literature and semi-structured in-depth interviews with 28 participants. The findings indicate that the extracted codes are classified in the six main dimensions of related and supporting industries (energy imbalance, quality of technological infrastructure, support for large industries, ...), operational conditions (marketing and branding capabilities, intelligent management, networking between enterprises, ...), strategy-structure-competition (change of brand name to enter governmental and non-governmental and competitive non-governmental and non-governmental markets). . companies, ...), demand conditions (expansion of emerging digital technologies, shortening of product life cycle, domestic market saturation, ...), government (international interactions, access to government data, incentive policies, ...) and luck (sanctions, currency fluctuations, war). Finally, this study provides a practical framework for analyzing and promoting the competitiveness of knowledge-based IT companies in Iran and shows that competitiveness results from the dynamic interaction between environment and strategy.