Introduction:
In today’s dynamic business environments, strategic management has become a cornerstone of organizational survival. This is particularly salient in Iran’s financial industry, where firms face persistent pressures arising from international sanctions, sharp macroeconomic fluctuations, regulatory shifts, and technological disruptions such as fintech and blockchain. The Economic Policy Uncertainty (EPU) Index for Iran increased from approximately 130 in 2012 to over 450 in 2022, signalling heightened instability associated with sanctions, exchange-rate volatility, and changing banking regulations. Under such conditions, strategic assumptions—the tacit beliefs decision-makers hold about the environment, competitors, resources, and future trajectories—may quickly lose their validity, making strategies built upon them prone to drift and ineffectiveness. Strategic assumption control, defined as the systematic process of identifying, monitoring, and revising the premises underpinning strategy, helps organizations align with environmental change and reduce the likelihood of strategic failure. While foundational models (e.g., Schreyögg & Steinmann, 1987; Preble, 1992; Simons, 1995) position assumption control as a core element of strategic control systems, these frameworks were largely developed in relatively stable Western contexts and provide limited guidance for volatile, sanction-affected economies such as Iran. Likewise, domestic studies have mainly examined general strategic control models or their relationships with performance, leaving a gap in context-sensitive frameworks that explain how strategic assumption control can be structured and operationalized in Iran’s financial industry. Accordingly, developing an indigenous conceptual model that reflects the realities of this environment is both theoretically and practically necessary.
Methodology:
This study aims to develop an indigenous model of strategic assumption control for Iran’s financial industry. It addresses one main research question – what components, relationships, and processes characterize the proposed model? – and three sub-questions concerning the mechanisms of assumption identification, environmental forecasting and monitoring techniques, and the application of monitoring information in decision-making. The research employed an exploratory sequential mixed-methods design, consistent with the pragmatic paradigm, to capture both the cognitive depth of strategic assumptions and the structural relationships among control components. The qualitative phase involved semi-structured interviews with 20 academic and managerial experts in strategic management, finance, and banking, selected through purposive and snowball sampling until theoretical saturation was achieved. Interview protocols were developed based on strategic control literature and covered conceptual layers and practical experiences. Thematic analysis following Braun and Clarke’s (2006) six-step framework was applied, yielding six main components and associated sub-themes. The quantitative phase validated these components and examined causal relationships through a fuzzy Delphi method and fuzzy cognitive mapping (FCM). A 15-member expert panel assessed the importance of each component using a five-point linguistic scale; responses were transformed into triangular fuzzy numbers and defuzzified via the centroid method (threshold: 0.70). A separate 15-expert panel evaluated the direction and intensity of causal relationships among components using a five-level linguistic scale; fuzzy aggregation and defuzzification retained relationships with defuzzified weights exceeding 0.50. The indices of out-degree (influence), in-degree (dependency), and centrality were computed.
Results and Discussion:
Thematic analysis identified six core components for strategic assumption control: (A) Assumption Identification and Articulation, (B) Environmental Scanning and Signal Detection, (C) Continuous Environmental Forecasting and Monitoring, (D) Feedback and Assumption Validity Assessment, (E) Organizational Learning and Institutionalized Memory, and (F) Intelligent Decision-making and Strategy Adaptation. Fuzzy Delphi confirmed all components with defuzzified means above 0.70; "Organizational Learning and Institutionalized Memory" (E) scored highest (0.822), underscoring its foundational role. Fuzzy cognitive mapping validated seven causal relationships >0.50: A→B (0.67), B→C (0.80), C→D (0.78), D→A (0.75), D→F (0.68), E→D (0.70), and F→E (0.78). Centrality analysis revealed "Feedback and Assumption Validity Assessment" (D) as the system's core (centrality: 5.470), connecting the cognitive loop (A→B→C→D→A) and operational-learning loop (D→F→E→D). The unvalidated direct path F→A (<0.50) indicates learning remains predominantly single-loop in Iranian financial organizations, with adaptations targeting programs rather than foundational assumptions. This "semi-cyclical" pattern reflects informal, person-dependent mechanisms and weak double-loop learning institutionalization. The study thus proposes an indigenous "semi-formal learning cycle" model wherein organizational learning (E) mediates between adaptive decisions (F) and assumption reassessment (D), transforming scattered experiences into reusable institutional knowledge. These findings extend Schreyögg-Steinmann and Preble by operationalizing assumption control for sanction-impacted contexts, positioning strategic assumption control as a micro-foundation of dynamic capabilities.
Conclusion:
This research developed a context-specific cyclical model for strategic assumption control in Iran's dynamic financial industry, integrating qualitative insights from expert interviews with quantitative validation via fuzzy Delphi and fuzzy cognitive mapping. The model comprises six validated components, with "Feedback and Assumption Validity Assessment" as the central regulatory hub, linking a cognitive loop (identification→scanning→forecasting→feedback) and an operational-learning loop (feedback→decision→learning→feedback). The absence of a direct decision-to-identification path reveals a "semi-cyclical" pattern, highlighting the prevalence of informal, person-dependent control mechanisms and underdeveloped institutional learning regarding assumptions. Theoretically, the study contributes: (a) an indigenous causal-cyclical framework for strategic assumption control, filling a gap in domestic operational models; (b) empirical grounding of "feedback and validity assessment" as the control core, shifting focus from performance outcomes to foundational premises under high uncertainty; and (c) the "semi-formal learning cycle" concept, bridging formal global models with informal "indigenous intelligence" in Iranian organizations, thereby linking strategic control, assumption-based planning, and organizational learning literatures within Iran's institutional context.