Strategic Studies Quarterly

Abstract Introduction
Over the past decade, artificial intelligence (AI) and big data have evolved into groundbreaking paradigms, influencing virtually every aspect of human life. Significant advancements in data processing capabilities, a dramatic drop in storage costs, and the exponential growth of data have paved the way for the development of digital tools, turning digital transformation into a reality. From 2000 to 2017, data processing power increased by a staggering 10,000-fold, while storage costs plummeted by 3,000 times. Furthermore, by 2025, the volume of generated data is projected to grow more than 90-fold. These rapid advancements have made strategic decision-making a key area of focus in management and governance.
Strategic decision-making has always been significant due to its complexity, uniqueness, and long-term, often irreversible, consequences. With the advent of big data and AI, decision-making has transitioned from intuition-based practices to data-driven processes. New tools now allow organizations to reduce biases, enhance accuracy, and manage uncertain environments more effectively. However, many organizations and governments are yet to harness the full potential of these technologies, partly because of the absence of comprehensive theoretical frameworks.
Based on this, the study aims to present an integrated framework for data-driven strategic decision-making by exploring how AI and big data influence this process. By synthesizing previous research findings, it addresses existing gaps in knowledge and provides practical guidance for managers and policymakers. This research emphasizes that data-driven decision-making is not merely a technical tool—it represents a shift in power structures and decision-making mindsets, enabling improved governance and organizational performance.
Methodology
This study uses the meta-synthesis approach, a qualitative method for integrating and interpreting findings from prior research. The method facilitates the identification of patterns, differences, and overlaps, helping to establish cohesive theoretical frameworks. The framework follows the seven-step model of Margarete Sandelowski and Juliet Barroso, which includes formulating research questions, reviewing the literature, identifying and selecting studies, extracting information, analyzing and synthesizing findings, conducting quality control, and presenting results.
Research questions focused on identifying the framework and key elements of data-driven strategic decision-making. Relevant studies were sourced from leading databases, such as Scopus, Web of Science, and Emerald, using keywords including "strategic decision-making," "artificial intelligence," "big data," and "data-driven." The study focused on English-language research from 2010 to 2024. Out of 88 initially identified studies, 36 were selected after removing duplicates and reviewing titles, abstracts, and methodologies. Data extracted from these studies underwent open coding, yielding 102 initial codes. These were categorized into 25 subcategories and four main themes: conditions, features, dimensions, and outcomes. To ensure quality, the CASP tool was used for validity checks, and reliability was measured using the Kappa index, scoring 0.69. This rigorous approach enabled the development of a robust and comprehensive framework.
Discussion and Results
The study's findings are categorized into four main themes:

Conditions: These include prerequisites for effective data-driven decision-making, such as access to high-quality data (characterized by volume, accuracy, timeliness, and variety) and advanced infrastructure (hardware and software). Organizational restructuring is essential to integrate data analytics processes, and cross-functional collaboration is necessary for data collection and interpretation. Building technical expertise to develop AI models and fostering a data-driven culture through employee training are also critical. Regulatory frameworks, including periodic evaluations and risk management, play a vital role in ensuring process quality.
Features: The core characteristics of data-driven decision-making include bias reduction through objective data, the ability to predict trends and behaviors, and the discovery of hidden patterns using AI. High-speed data processing, accuracy, and transparency contribute to reliable decision-making. Additionally, increased resolution—offering a more precise understanding of issues—is a defining feature of this approach.
Dimensions: This theme addresses the structural and contextual elements of the decision-making process. Balancing intuition with data analysis is particularly important in complex, turbulent environments. Structured data significantly enhance the quality of decisions, whereas unstructured data limit the effectiveness of technical tools. Collective intelligence, inspired by natural behaviors, enables the integration of group knowledge. Striking a balance between human creativity and AI computational power, alongside building stakeholder trust through interpretability and user-focused design, are other critical dimensions.
Outcomes: Data-driven decision-making reduces uncertainty, identifies new opportunities and threats, and offers solutions to complex challenges. It improves processes through automation, increases speed and accuracy, enhances organizational performance, and creates sustainable competitive advantages. At a national level, it has the potential to transform governance structures and improve outcomes.

Conclusion
Through the meta-synthesis approach, this research provides a comprehensive framework for data-driven strategic decision-making, organized into four key themes: conditions, features, dimensions, and outcomes. The findings highlight that implementing this approach requires robust infrastructure, high-quality data, a strong data-driven culture, and cross-departmental collaboration. Features such as bias reduction, predictability, speed, and precision differentiate this method from traditional approaches. Structural elements like the balance between human and AI involvement and the role of collective intelligence emphasize the importance of combining human judgment with computational power. The outcomes include reduced uncertainty, enhanced performance, and sustained competitive advantages.
However, the study acknowledges limitations, including its exclusive focus on strategic decision-making, the emerging nature of the topic, and potential biases in study selection. Overall, data-driven strategic decision-making is not optional—it is a necessity for governments and organizations in the digital era. Future research should explore other aspects of strategic management and consider cultural and regional influences to deepen our understanding of this phenomenon. This framework offers managers and policymakers practical tools to harness AI and big data to improve governance and decision-making.

Abstract The main purpose of this article is to evaluate the results and findings of research projects that have been conducted under the common title of "Problems of Progress and Security of the Province". These projects have been carried out in 11 provinces between 2018 and 2020. Methodologically, all these studies have used the method of in-depth interviews, interaction matrix and review of documents such as land management document of the province. Their analysis method has been based on the qualitative research strategy of structural analysis. In this article, the meta synthesis qualitative analysis method has been used to review the findings of this research. the result of this evaluation indicates that on average, each province had 74 main problems and each study identified 10 main problems as the most influential factors. A comparative look at these top 10 problems of the provinces shows that about 63% of the first problems of the provinces are common, and there is the problem of "inefficiency of the provincial management system" is a common challenge of more than 90% of the provinces, followed by corruption is the second major challenge.

Abstract In the past years, developing and implementing policies in the field of whistle-blowing with the aim of realizing public supervision have been pursued by developed countries, which have also provided them with considerable success in controlling administrative corruption. Taking advantage of this capacity and people's participation in dealing with administrative corruption has been favored by the officials of the Islamic Republic of Iran for several years. In this research, an attempt was made to identify the factors affecting the success of the whistle-blowing phenomenon with the meta-combination method and systematic study of 89 articles, including 74 ISI articles from 2000 to 2022 and 15 Persian articles found from internal sources. The final model obtained to recognize and understand the factors affecting the whistle-blowing phenomenon in Iran, in the form of five general categories of organizational factors, human factors, environmental factors, the nature of corruption and possible consequences, in order of importance based on the frequency of factors collected from the subject literature, is introduced. At the end, the factors promoting whistle-blowing in six main axes have been introduced for use in the process of public policy-making in the field of whistle-blowing and its successful implementation in Iran.