Document Type : Original Article
Authors
1
Associate Professor, Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran
2
Assistant Professor, Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran
3
PhD Student in Business Policy Management, Department of Business Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran
Abstract
This applied–developmental study aims to design and validate the Artificial Spirit model in transforming strategic management and future leadership. The research is grounded in the pragmatist paradigm and follows an inductive–deductive approach. To achieve the study’s objective, an exploratory mixed-method design (qualitative–quantitative) was employed. The qualitative sample consisted of university faculty members along with senior managers and experienced experts in the Vice-Presidency for Science, Technology and the Knowledge-Based Economy. Snowball sampling was used, and theoretical saturation was reached with 15 participants. The quantitative population included managers and experts active in policymaking, technology, and strategic analysis within the Vice-Presidency; based on effect size and statistical power, a sample size of 120 was estimated, and simple random sampling was applied. Data were collected through semi-structured interviews and a researcher-made questionnaire. Qualitative interviews were analyzed using thematic analysis with MAXQDA, and questionnaire data were processed using partial least squares (PLS) with SmartPLS.
The findings revealed that interpretive intelligence and contextual understanding, human–machine cognitive synergy, and intelligent quasi-awareness and ethicality influence the reconfiguration of decision-making processes, deep data orientation and intelligent analytics, and innovation in governance architecture and national strategy. These factors, in turn, foster ethical leadership in the age of deep intelligence and evolutionary leadership in human–technology interaction, ultimately shaping anticipatory and future-oriented leadership.
1. Introduction
The transformation of intelligent technologies in recent years has confronted analytical and macro-level decision-making structures with unprecedented challenges. National organizations—particularly those active in science and technology policymaking—are now faced with waves of complex data, advanced learning models, and emerging futures-oriented requirements (Mazaheri et al., 2024, p. 142). The accelerating pace of technological change has weakened the ability of traditional strategic management approaches at the national level to respond to increasing uncertainty and environmental dynamism. Under such conditions, strategic institutions require deeper and more intelligent analytical mechanisms (Azin et al., 2024, p. 98). The expansion of AI-driven decision-support systems has further intensified the need to redesign macro-level decision-making architectures. This transformation is not merely a technical shift; it reflects a broader pressure to reconfigure the foundations of strategic management (Mandrisel, 2026, p. 201). In parallel, the notion of advanced cognitive capacities in intelligent systems has emerged—capacities that move beyond data analysis and enable contextual understanding. There are also scattered references to deeper layers of intelligence that could provide a conceptual basis for articulating the idea of an Artificial Spirit (Skipper et al., 2024, p. 57).
This study addresses the fundamental question: What is the Artificial Spirit model for transforming strategic management and future leadership in the Vice-Presidency for Science, Technology and the Knowledge-Based Economy?
2. Literature Review
2.1. Artificial Spirit:
In recent literature, the concept of Artificial Spirit has been introduced as an advanced layer of intelligence that extends beyond data processing and encompasses the ability to interpret context, recognize complex patterns, and generate a form of cognitive synergy between humans and intelligent systems (Habibi et al., 2020). This concept is grounded in the development of deep learning models, algorithmic explainability, and advancements in semi-autonomous systems, referring to capacities that enable multidimensional analysis, preliminary ethical reasoning, and situational understanding (McGregor, 2025, p. 39)
2.2. Transformation of Strategic Management:
Strategic management refers to the set of decisions and actions that determine an organization’s long-term direction, align resources with core objectives, and enable responsiveness to environmental changes (Adam et al., 2023, p. 42). The transformation of strategic management is the result of fundamental shifts in technological, data-driven, and governance environments that have rendered traditional models of planning, analysis, and decision-making less effective. This transformation has accelerated with the expansion of artificial intelligence, the emergence of advanced forecasting models, and the rapid pace of technological change (Alheisat et al., 2025, p. 33).
2.3. Future Leadership:
Organizational leadership is the process through which an individual influences, inspires, and guides the behavior of others to shape organizational direction and foster the coordination and motivation necessary to achieve collective goals (Vivek & Chrupalski, 2024, p. 37).
3. Methodology
This study was conducted within the pragmatist paradigm and followed an inductive–deductive reasoning approach. In terms of purpose, it is an applied–developmental study aimed at designing and validating the model of Artificial Spirit in the Transformation of Strategic Management and Future Leadership within the Vice-Presidency for Science, Technology and the Knowledge-Based Economy. From the perspective of data collection, the research falls under non-experimental (descriptive) studies and was implemented using an exploratory mixed-method design to enable both the discovery of conceptual patterns and their quantitative validation.
The qualitative data were analyzed through thematic analysis using MAXQDA. In the quantitative phase, the measurement and structural models were evaluated using the partial least squares (PLS) method with the SmartPLS software.
4. Results
The research findings indicated that interpretive and context-aware intelligence, human–machine cognitive synergy, and intelligent quasi-awareness and ethicality influence the reinvention of decision-making processes, deep data orientation and intelligent analytics, and innovation in governance architecture and national strategy. These factors, through strengthening ethics-driven leadership in the age of deep intelligence and fostering evolutionary leadership within human–technology interaction, ultimately lead to the emergence of future-oriented and anticipatory leadership.
5. Discussion
The present study was conducted with the aim of designing and validating the Artificial Spirit model in the transformation of strategic management and future leadership within the Vice-Presidency for Science, Technology and the Knowledge-Based Economy. The findings revealed that the integration of deep intelligent capacities—namely interpretive and context-aware intelligence, human–machine cognitive synergy, and intelligent quasi-awareness and ethicality—can serve as the foundation for a profound transformation in strategic management at the national science and technology governance level. These three components, by enabling the interpretation of complex situations, multilayered analysis, and responsible decision-making, enhance the quality of decision-making processes and support their redesign into flexible, agile, and environmentally adaptive models.
The pivotal role of deep data orientation and intelligent analytics further advances this transformation by enabling real-time monitoring of technological trends, predictive analysis, and early detection of strategic shifts. Additionally, innovation in governance architecture and national strategy underscores the need for policymaking institutions to restructure decision-support systems, policy processes, and national coordination mechanisms in response to complex and semi-autonomous technologies.
6. Conclusion
The initial model findings indicate that the three pillars of interpretive and context-aware intelligence, human–machine cognitive synergy, and intelligent quasi-awareness and ethicality act as foundational drivers in transforming the logic of strategic management. Through pathways such as the reinvention of decision-making processes, deep data orientation and intelligent analytics, and innovation in governance architecture and national strategy, these drivers steer leadership structures toward ethics-centered leadership in the age of deep intelligence and evolutionary leadership in human–technology interaction, thereby creating the conditions for the emergence of anticipatory and future-oriented leadership.
Thus, the present findings are consistent with the global cutting-edge literature in their emphasis on deepening cognitive capacities, the emergence of quasi-aware states, and the necessity of ethicality in intelligent systems—while extending these ideas into the domain of strategic management and leadership.
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