In the present information age, methodologies that help structure, combine and interpret information are getting more and more importance as decision support tools. Techniques like Technology Forecasting, Technology Assessment and Technology Foresight are used in order to generate knowledge about current technological developments and derive possible future trajectories. The present report regards these three techniques within the perspective of Strategic Intelligence, which focuses on their role as instruments supporting decision-making processes. The general introduction on the field of Strategic Intelligence in chapter one reveals the general role of Strategic Intelligence in technology-related decision-making processes. Then summarize the main contents of the three individual reports on Technology Forecast, Technology Assessment and Technology Foresight. By focusing on the decision-maker, each report provides an overview of the characteristics and current state of the art, shows their main strengths, and identifies overlaps and synergies. Finally, presents a concluding critique of Strategic Intelligence and reveals how these three techniques could be used for obtaining a complete picture of possible technological developments.

While the concept of cultural intelligence is valid universally، the measure of cultural intelligence will vary by context. It must be fitted in each case to aspects of cultural intelligence that assists capability to manage effectively in culturally diverse settings with that particular cultural context. In this paper، with attend to culture of Iranian social، a measurement tool of cultural intelligence was developed that we call it as Iranian Scale of Cultural Intelligence (ICCI). We also examine the relation between ICCI and strategic entrepreneurship. Data are gathered from 221 managers and experts in oil، gas and petrochemical industries and are tested by factor analysis and regression methods. The results suggest that cultural intelligence is contained three aspects including، perceptual، interactive and confirmative. Moreover، the results indicate that demographic variables such as، gender، age، educational level and experience of cross-cultural interactions are strongly associated with some aspects of cultural intelligence in Iran community. The paper also suggests that cultural intelligence can be recognized as a critical factor in the strategic entrepreneurship; thus، organizations that act in the global contexts must consider managers’ CQ capabilities in their entrepreneurial activities.

BACKGROUND AND OBJECTIVES: In the current competitive and risk-filled environment of the insurance industry, insurance companies need to gather up-to-dated information and knowledge to make appropriate decisions and use this information in their decisions. Decisions that have a great impact on the structure, processes and performance of the organization's members and can fundamentally affect the prediction of the survival of those organizations, profitability and on their nature.  Today, access to correct, effective and up-to-date information in a competitive business environment is one of the power tools for any company in making decisions and adopting competitive strategies of that company.Therefore, the efficient establishment of intelligent systems in the country's insurance industry seems to be a vital issue. In the meantime, the lack of establishment or inefficient induction of the intelligent system will be able to face many companies with challenges. An issue that has a negative impact on the competitiveness of these companies and needs further investigation. The purpose of the current research is to conceptualize the intelligence of businesses with a strategic approach and provide a model for measuring business intelligence in the insurance industry through a qualitative research approach.METHODS: This is a qualitative research and the method of content analysis and in-depth semi-structured interviews with experts, managers and consultants of the insurance industry have been used. Qualitative content analysis can be considered as a method for subjectively interpreting the content of textual data through the processes of systematic classification, coding and creating themes or designing known patterns.In order to extract the codes, an attempt was made to conduct a total of 8 interviews using the theoretical sampling method until reaching theoretical saturation. In order to achieve the validity and reliability of qualitative data, the criteria of credibility, dependability or trustworthiness, acceptability or confirmability, and transferability were considered. To achieve these criteria, researchers carefully select key participants, combine data collection methods (such as interviews and note-taking), allocate sufficient time to conduct interviews, continuously review data and classes in terms of similarities and differences, and review the analyzes performed by project colleagues.FINDINGS: Findings: In this research, 43 codes were extracted as concepts related to intelligence. These codes were placed and categorized in six components and in two general dimensions of intelligence, relative to the specialized business environment and intelligence relative to the general business environment. These components include: intelligence towards the internal business environment, intelligence towards the business market, intelligence towards business competitors, business intelligence in supply, intelligence towards the small business environment and intelligence towards the large business environmentCONCLUSION: Creating an innovative performance requires making real-time strategic decisions based on the information and data obtained from the intelligent system. Based on this, companies are successful if they act intelligently and are able to have proper knowledge of the data and information of their internal and external environment and turn that data into organizational knowledge and intelligence. Upgrading the intelligence system in an organization makes companies analyze the information of their surrounding environment faster and more accurately and save the results and make them available to decision makers when necessary. This topic will speed up the exchange of data, information and knowledge in the company and will be able to improve effectiveness, thinking and decision making. It is suggested that managers use the identified concepts to create and develop insurance companies and increase their intelligence to help achieve the company's goals.

In recent years, the continuous changes of business environment have demanded the organizations to control their internal and external environments and to analyze the information obtained from these changes. Due to this, the organizations are able to seize the opportunities and to deal with the threats. In this regard, providing required information of each department of the organizations, the organization strategic intelligence systems, as new tools, assist the managers to do their best regarding the opportunities and threats. Thus, identifying the different kinds of intelligence based on each department of the organization's information needs to make proper decisions; this study aims at designing an innovative model for measuring organization strategic intelligence from both perspectives of internal and external environments. For the purpose of this study, the required data was collected through delivering a questionnaire to the organizations which used the business intelligence systems. The validity of the provided model was evaluated through using discriminant validity, convergent validity and structural equation modeling. The findings reveal that organization strategic intelligence is measured through internal intelligence and external intelligence. Moreover, the Iranian organizations have reported an average level of strategic intelligence which indicates the necessity of better understanding and optimized implementation of these systems.

Developing competition in business forced companies to have special attention to environmental changes, innovation and its dimensions in business or organization in order to achieve competitive advantage. Competitive intelligence make a basis of innovation process, but lack of strategic Thinking in organizations causes inefficiency and ineffectiveness of this way for achieving organizational innovation. The main goal of this article is checking the mediator role of strategic Thinking of organization level on effect of competitive intelligence on organization organizational innovation. The methodology of the study is quantitative and descriptive and branch of regression. For collecting data, related to variables, we used questionnaires that asessed, organizational innovation for the first time in Iran on the basis of suggested traits by Organization for Economic Co-operation and Development (OECD). Therefore, 100 samples include managers of Qom's insurance companies’ agents’ choosed simple random sample. The results of Pearson regression showed that all features of competitive intelligence on organizational innovation and also strategic Thinking have regression couple by couple. With performing the model of structural equations by minimum trivial square for hypothesis testing, specified that suggested model has a good to assess and also organization strategic Thinking as a mediator has been effective in amount of 66 percent on effect of the competitive intelligence on organizational innovation.

This study aims to explore the role of decision intelligence, combining artificial intelligence (AI) and analytics, in enhancing strategic decision-making processes within organizations. A qualitative research approach was employed, utilizing semi-structured interviews to collect data from 22 participants recruited from online platforms. The study adopted a theoretical saturation approach, with interviews continuing until no new insights were observed. Data were analyzed using NVivo software, and open coding was employed to identify key themes, subcategories, and concepts within the dataset. The analysis revealed four main themes: 1) Strategic Benefits of Decision Intelligence, which emphasized the positive impacts on decision-making accuracy, cost efficiency, and competitive advantage,2) Integration Challenges, identifying barriers such as data integration issues, algorithmic bias, system interoperability, and high implementation costs,3) AI-Driven Analytics Techniques, which highlighted the importance of predictive models, real-time analytics, sentiment analysis, and data visualization in enhancing decision-making,and 4) Future Prospects and Innovations, which pointed to the potential for next-generation AI models and human-centric innovations in shaping future strategic decision-making. The findings suggest that while decision intelligence offers significant strategic advantages, including improved decision speed and accuracy, its successful implementation is hindered by several challenges. Addressing data integration issues, mitigating algorithmic bias, and managing the high costs of implementation are crucial for organizations to fully realize the potential of decision intelligence. Future research should focus on expanding the sample size and exploring the long-term effects of decision intelligence on organizational performance.

The present study examines the role of strategic ethics management in the age of artificial intelligence. With the increasing development of artificial intelligence technologies, strategic decision-making in organizations has faced new challenges in which ethical concepts have become increasingly important. Using a combination of qualitative and quantitative methods, this study attempts to analyze and simulate strategic scenarios using artificial intelligence and ethical analysis. The results show that AI can help managers to more accurately predict scenarios and make strategic decisions, but the use of these technologies also has several ethical issues that need to be managed effectively. Also, combining AI with ethical principles can improve strategic decision-making processes and reduce potential risks. The research recommendations include the need to pay attention to ethical issues in the development of AI and to train managers to use this technology within ethical frameworks.

IntroductionOver 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.MethodologyThis 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 ResultsThe 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.ConclusionThrough 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.

External environment of organizations includes in uncertainty, ambiguity and transformation. In these circumstances, strategic directors of organizations track an approach using the intuitive decision making skills and improvisational capabilities which result in quick decisions in facing challenges and help them improve the performance by responding to external changes quickly. The current research look for designing an organized framework in regards to the concepts of strategic intelligence, strategic improvisation and Agility performance in connection with each other and in Iranian knowledge based companies, trying to take action regarding development of available frontiers of knowledge in this area. This research is an applied-surveying type. The required data for this research has been collected through questioners by random sampling of knowledge based companies which are active in the field of information technology in Iran. Meanwhile, structural equations method is used through making use of Smart PLS ˡ software for data analysis. The obtained results indicate that the strategic intelligence and strategic improvisation are effective on Agility performance of Iranian knowledge based companies. Meanwhile the mediation role of strategic improvisation on effectiveness of strategic intelligence on Agility performance of Iranian Knowledge based companies is verified.