Strategic Studies Quarterly

Abstract Introduction
Today, social media platforms have become a primary source of news and information for many, rivaling traditional television and satellite channels. In this landscape, coordinated rumors, fake news, deepfakes, and AI-generated content have emerged as powerful tools for undermining the legitimacy of individuals, institutions, and entire political systems. These developments point toward a growing phenomenon known as “computational propaganda”.
Computational propaganda refers to the use of algorithms, automation, and human oversight to manage social media ecosystems or to deliberately spread misleading content across these platforms.
In this context, the rise of computational propaganda presents serious challenges to the soft power of the Islamic Republic of Iran. While some government-affiliated groups have used these tools to defend and promote their image, misuse of such tactics has undermined the credibility of the political system. Meanwhile, domestic and foreign actors alike have increasingly exploited these techniques to weaken Iran’s influence by targeting its internal digital vulnerabilities.
Given the complexity and evolving nature of computational propaganda, it is essential to reassess how Iran can effectively protect its soft power in a competitive and often hostile information environment.
 
Methodology
This study adopts a mixed-methods approach, combining thematic analysis (qualitative) and social network analysis (quantitative).
The research begins by analyzing tweets related to the "Operation True Promise 2", using social network analysis to identify posts influenced or supported by Israeli propaganda. A selection of these tweets is then subjected to thematic analysis to determine which propaganda strategies, as outlined in the theoretical framework, were most commonly used against Iran.
First, tweets containing the hashtags or keywords "Iran", "Israel", "True Promise", and "missile" were collected using data-mining tools. A retweet network graph for the relevant tweets was then created and analyzed using the Force Atlas 2 algorithm. The Gephi software was also used for visualizing and analyzing the graph.
For the thematic analysis, axial coding was applied, based on five key computational propaganda strategies. Each strategy was operationally defined in relation to the Iranian context:
1- Supportive Content: Posts showing support for Israel, advocating war against Iran, or promoting Reza Pahlavi as a viable opposition figure aligned with Israeli interests.
2- Attacks and Exposés: Criticism of the Iranian leadership, the "Operation True Promise 2," and Iran's regional policies, as well as mockery of Iran’s missile capabilities and accusations about intentionally keeping airspace open.
3- Distraction: Attempts to divert public attention away from the "Operation True Promise 2".
4- Polarization and Division: Content aimed at creating rifts between the government and the people of Iran.
5- Harassment and Suppression: Personal attacks or online harassment targeting supporters of Palestine or the "Operation True Promise 2", often through ridicule or mockery.
 
Discussion and Results
The data indicate a clear level of coordination among monarchist users and pro-Israel accounts. This is evidenced by analyzing retweet patterns. On average, there was 1 retweet per 11.55 likes across all users. However, among regular users—who tend to be apolitical—this ratio was 1 retweet per 51.31 likes, suggesting little to no coordination. In contrast, among monarchist users, the ratio was 1 retweet per 9.31 likes, implying higher levels of coordination and deliberate message amplification.
Among the tweets analyzed, humorous content received the most likes. This was followed by posts criticizing the Iranian political system, Supreme Leader, the "Operation True Promise 2", and Iran’s regional policies—especially those mocking the country's missile capabilities. Tweets in support of the government, the resistance axis, or opposing war received the next highest levels of engagement.
 
Conclusion
The findings clearly show that Israeli information operations have been actively used to damage Iran’s credibility and redirect public opinion toward alternative figures. Among the five identified strategies, “attacks and exposés” were the most prevalent. Simultaneously, efforts were made to frame Israel as a hero and Reza Pahlavi as a political alternative.
This study highlights how emerging technologies such as artificial intelligence and social media have opened new avenues for political systems to influence rivals and boost their own power. Computational propaganda, in particular, has become a powerful tool that can directly threaten the political integrity of other nations—and one that even weaker states can deploy against more powerful ones.
Addressing this challenge requires new strategies and a fundamentally different approach to governance, including changes in media, social, cultural, and political policies. Such strategies should aim to enhance the credibility of domestic media, build public trust in political leadership, and reduce societal and political tensions.

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 Artificial intelligence is becoming one of the new dimensions of power. This issue can give more depth to the center-periphery structure of the international system, which is caused by the technological gap. Although this emerging technology is currently used mostly in the field of medicine, agriculture, industry and economy, but its operational scene will soon be expanded to the field of diplomacy and foreign policy of countries. The current research aims to answer this main question: "What effect can artificial intelligence have on the position of the Islamic Republic of Iran in the field of diplomacy?" The results of this research, by using the descriptive-analytical method, indicate that artificial intelligence, as a shaping factor in the diplomatic environment, has the potential to fundamentally change the order and hierarchy of international power. This may threatens the security and national interests of the Islamic Republic of Iran, and the only way to deal with it proactively, is to be present in the process of legalizing and standardizing artificial intelligence and to pay attention to artificial intelligence technology as a diplomatic issue and tool.