Manexia: Journal of Business, Management, and Creative Economy
Manexia publishes research and scholarly insights in business, strategic management, marketing, entrepreneurship, and the creative economy. The journal highlights work that brings clarity to managerial thinking, organizational transformation, and digital-era business practices. Rather than presenting theory in isolation, Manexia prioritizes ideas that connect strategy with real-world application—supporting decision-making, innovation, and sustainable growth in dynamic market environments. Contributions are expected to offer strong conceptual direction, clear analytical perspective, and relevance for contemporary management discourse.
The AI Productivity Paradox Revisited: A Multi-Level Theory of Performance Divergence in SME-Dominated Digital Ecosystems
Artificial intelligence (AI) has intensified debates surrounding the contemporary productivity paradox, where rapid technological progress coexists with uneven improvements in measured productivity. Although growing evidence shows that AI can significantly enhance task-level performance—reducing completion time, improving output quality, and standardizing decision processes—these gains do not always translate into consistent firm-level productivity outcomes, particularly among small and medium-sized enterprises (SMEs) operating in platform-mediated digital markets. This article develops a conceptual framework that revisits the AI productivity paradox through a multi-level theoretical perspective. Integrating insights from productivity paradox research, general-purpose technology theory, task-based technological change, and platform ecosystem scholarship, the study proposes that AI-induced productivity gains propagate unevenly across four analytical layers: tasks, SMEs, platforms, and digital ecosystems. Three generative mechanisms—complement lag, measurement wedge, and compounding learning effects—explain how productivity gains are translated, partially observed, or redistributed across these levels. While SMEs may experience delayed or weakly measured productivity improvements due to complement constraints and measurement limitations, platform infrastructures can accumulate accelerated gains through data-enabled learning and cross-merchant aggregation. The framework introduces productivity divergence as a concept explaining how ecosystem-level efficiency can increase even when individual firms experience uneven productivity outcomes.
Copyright (c) 2026 Manexia: Journal of Business, Management, and Creative Economy
Algorithmic Pricing Intensity and the Curvilinear Reconfiguration of Consumer Fairness Norms
Algorithmic pricing is widely framed as a technological instrument for efficiency and revenue optimization. Yet as pricing decisions become increasingly embedded within autonomous computational systems, their implications extend beyond performance outcomes to the normative foundations of market exchange. This article develops a conceptual framework explaining how algorithmic pricing intensity reshapes consumer fairness norms through curvilinear dynamics. Drawing on justice theory, reference price stability, attribution processes, and institutional legitimacy, the analysis proposes that algorithmic pricing intensity exhibits an inverted-U relationship with normative legitimacy. At low to moderate levels, algorithmic systems enhance procedural objectivity and enable adaptive updating of reference expectations, thereby strengthening fairness norms. Beyond a critical threshold, however, heightened volatility, granular personalization, and causal opacity destabilize reference anchors and intensify exploitative attributions, resulting in legitimacy erosion. By reframing fairness as a dynamic normative constraint rather than a static perception, the article contributes to research on digital market governance and strategic legitimacy, highlighting the bounded nature of algorithmic optimization in competitive digital environments
Demand Atomization and the Erosion of Competitive Coherence: Strategic Implications of Algorithmic Personalization
Algorithmic personalization has been widely conceptualized as a performance-enhancing capability that improves targeting precision and customer alignment. However, its structural consequences for market organization and strategic stability remain under-theorized. This conceptual article advances a market-structure perspective by introducing the constructs of demand atomization and competitive coherence. It argues that increasing algorithmic personalization intensity reduces shared exposure across consumers, dispersing preferences into dynamically reconfigured micro-clusters. Simultaneously, reinforcement mechanisms embedded in digital platforms may concentrate transactional outcomes among highly visible actors. This dual dynamic—fragmentation in preference formation alongside concentration in transaction distribution—creates structural pressures that erode competitive coherence, defined as the firm’s ability to maintain integrative strategic alignment across heterogeneous market contexts. The analysis proposes non-linear effects of personalization intensity and identifies privacy salience and firm size as critical boundary conditions. Small and medium-sized enterprises are theorized to face amplified vulnerability due to limited orchestration capacity. The framework reframes personalization from a tactical optimization tool to a market-structuring force with long-term strategic implications.
Algorithmic Integration Boundaries: Rethinking the Theory of the Firm in Platform Ecosystems
The theory of the firm has long assumed alignment between ownership, coordination authority, and capability execution, conceptualizing firm boundaries as governance solutions that internalize control when markets become inefficient. The diffusion of artificial intelligence (AI) infrastructures within platform ecosystems destabilizes this alignment by relocating decision-relevant intelligence beyond formal ownership domains. Firms increasingly embed externally governed algorithmic systems into pricing, forecasting, visibility management, and workflow coordination, allowing coordination and monitoring to occur without asset transfer or hierarchical integration. This article reconceptualizes firm boundaries as algorithmic integration boundaries defined by infrastructural control over coordination and learning processes. A mechanism-based framework identifies four cumulative processes driving boundary reconfiguration: data dependency intensification, workflow embedding, algorithmic visibility and control redistribution, and capability redistribution. Together, these mechanisms produce algorithmic boundary permeability, a condition in which legal ownership persists while effective coordination authority and adaptive capacity extend into externally governed infrastructures. This reconceptualization refines boundary theory, extends resource-based and dynamic capability perspectives through the notion of infrastructure-dependent capabilities, and identifies algorithmic mediation as a structural source of interorganizational power asymmetry.
Data Extractivism and Strategic Value Appropriation: Rethinking Firm Advantage in AI-Centric SME Ecosystems
Artificial intelligence–enabled platforms are transforming the foundations of competitive advantage in digital market ecosystems. Small and medium-sized enterprises (SMEs) generate substantial transactional and behavioral data through platform participation, yet control over data aggregation and model-training architectures typically resides with platform sponsors. This structural decoupling challenges the core assumption of the resource-based view that ownership and control of valuable resources ensure rent appropriation. Integrating resource-based theory, value appropriation logic, data-enabled learning research, and platform governance scholarship, this article develops a conceptual framework explaining how data extractivism operates as an architecture-mediated mechanism of value capture. The model argues that competitive advantage in AI-centric ecosystems increasingly derives from control over aggregation infrastructures rather than localized data generation. Cross-SME data pooling produces compounding learning rents that disproportionately accrue to actors controlling centralized architectures, especially under conditions of high switching costs, limited data portability, and governance opacity. By reframing advantage as architecture-dependent, the study extends strategic management theory and clarifies how SME performance becomes ecosystem-conditioned in AI-driven markets.
architecture-based competitive advantage
This editorial introduces a collection of conceptual articles examining how algorithmic infrastructures are reshaping the foundations of digital markets. Rather than functioning merely as operational tools, artificial intelligence increasingly acts as a structural coordination mechanism influencing market architecture, organizational boundaries, demand formation, pricing legitimacy, and productivity dynamics. Together, the contributions in this issue highlight how algorithmic governance redistributes control, restructures competitive interaction, and generates new forms of performance divergence within platform-mediated digital ecosystems.
Algorithmic Market Structuring: How AI Reconfigures Competitive Boundaries in Platform-Based Economies
Digital platforms increasingly rely on algorithmic systems to allocate visibility, define evaluative metrics, and recalibrate participation conditions. While prior research has examined platform governance, market shaping, and algorithmic control, strategic management scholarship has yet to fully theorize how algorithmic infrastructures reconfigure the boundaries of competition itself. This article introduces the concept of algorithmic market structuring to explain how competitive boundaries in platform-based economies become endogenous to governance architectures. Integrating boundary theory, competitive dynamics, ecosystem strategy, attention-based theory, performance feedback models, and increasing returns logic, we develop a mechanism-based framework comprising four interrelated processes: visibility-based boundary making, metric re-specification, feedback-loop concentration, and rule volatility–induced adaptation. We argue that algorithmic centrality transforms rivalry from category-based competition to exposure-mediated competition, generating cumulative advantage dynamics and accelerating boundary reconfiguration. Importantly, these effects are not deterministic; their structural consequences depend on differentiation levels, switching costs, and governance automation intensity. By reframing competitive arenas as dynamically curated through algorithmic infrastructures rather than statically defined by industry structure, this study advances strategic management theory and clarifies how digital governance architectures shape the evolution of rivalry in contemporary platform markets.
Compliance as Strategic Capability: Trust, Legitimacy, and Competitive Advantage in Digital Ecosystems
Regulatory intensification in digital ecosystems has repositioned compliance from episodic obligation to persistent organizational condition. Yet compliance remains predominantly framed as a cost of participation rather than a potential source of strategic differentiation. This article advances a capability-based perspective arguing that compliance becomes strategically consequential when embedded as an organizational capability rather than executed as minimal rule adherence. Integrating resource-based theory, dynamic capabilities, trust theory, signaling logic, and legitimacy scholarship, the analysis develops a mediated framework in which compliance capability generates competitive differentiation through relational and institutional mechanisms. Embedded and credible compliance routines function as costly signals of integrity and competence, fostering stakeholder trust under conditions of uncertainty. Stabilized trust accumulates into institutional legitimacy, which enhances ecosystem positioning through preferential partner selection, reduced coordination friction, and reputational resilience. Competitive advantage thus emerges indirectly through credibility-based amplification rather than direct regulatory conformity. The framework contributes a credibility-centered theory of strategic compliance and extends capability research into the governance domain of digitally mediated markets.
Organizational Transformation & Governance
Copyright (c) 2025 Manexia: Journal of Business, Management, and Creative Economy
The Inclusion–Dependency Paradox: Platform-Orchestrated SME Participation and Asymmetric Value Capture in the ASEAN Digital Economy
Platform ecosystems have become central infrastructures for small and medium-sized enterprise (SME) participation in emerging digital economies. While platform-mediated inclusion is widely associated with opportunity expansion, its structural consequences for autonomy and surplus distribution remain under-theorized. This article develops the concept of the Inclusion–Dependency Paradox to explain how platform-orchestrated SME participation simultaneously enhances value creation and intensifies structural dependence. Integrating platform ecosystem theory, resource dependence logic, value creation–value capture distinctions, and embeddedness scholarship, the analysis proposes a mechanism-based framework linking governance centralization, control over critical resources, and asymmetric value capture. Platform inclusion lowers entry barriers and expands market access, thereby increasing SME value creation capacity. However, concentrated control over algorithmic visibility, transactional infrastructure, and data flows reconfigures bargaining asymmetries and shapes surplus allocation. The model specifies a curvilinear relationship between inclusion and autonomy, suggesting that high levels of participation under centralized governance conditions amplify dependency risks. By reframing inclusion as structurally contingent rather than inherently empowering, this study advances a distribution-sensitive perspective on SME participation in digitally mediated markets.
Digital Interoperability and the Reconfiguration of Regional Coordination: A Mechanism-Based Perspective from Emerging Platform Economies
Digital interoperability has emerged as a defining feature of contemporary regional digital economies, yet its role in reshaping cross-border coordination remains under-theorized. While prior research emphasizes regulatory harmonization or platform governance within domestic markets, less attention has been devoted to how interoperable infrastructures reorganize regional coordination in the absence of supranational authority. This article develops a mechanism-based conceptual framework explaining how digital interoperability reconfigures regional institutional alignment in emerging platform economies. Integrating Transaction Cost Economics, ecosystem boundary theory, and regional coordination scholarship, the study identifies four interrelated mechanisms: transaction cost compression, institutional substitution, ecosystem boundary expansion, and coordinated decentralization. Together, these mechanisms demonstrate how infrastructural synchronization can stabilize cross-border exchange without requiring political integration. The framework advances a configurational perspective, arguing that interoperability produces distinct regional outcomes depending on the degree of institutional fragmentation. By reframing interoperability as infrastructural governance rather than technical compatibility, this study contributes to strategy and institutional scholarship and offers a bounded explanation of how digital economies integrate under fragmented governance conditions.
Data Localization and Competitive Reconfiguration: Strategic Adaptation in Emerging Digital Economies
Data localization has emerged as a defining feature of digital governance in emerging economies, reshaping the institutional environment within which firms compete. While existing research largely frames data localization as a compliance burden or trade barrier, its strategic implications for competitive positioning remain underexplored. This article develops a mechanism-based conceptual framework explaining how data localization induces competitive reconfiguration through four interrelated mechanisms: cost asymmetry creation, reinforcement of territorial embeddedness, architectural modularization, and adaptive capability differentiation. Drawing on institutional theory, international business scholarship, dynamic capabilities, and platform ecosystem research, the study argues that localization policies transform data from a globally scalable resource into a jurisdiction-bound strategic asset. This territorialization alters location advantages, redefines scalability logic from global integration to regional clustering, and differentiates firms based on infrastructural flexibility and adaptive capacity. Competitive outcomes are therefore conditional rather than deterministic: firms with strong domestic embeddedness and high dynamic capabilities are better positioned to convert regulatory segmentation into strategic advantage, whereas centrally integrated and rigid architectures face heightened erosion risks. By reframing data localization as a driver of competitive reordering rather than mere regulatory constraint, the article advances understanding of how institutional boundary-making reshapes digital market dynamics in emerging economies.
Platform Power and Institutional Alignment: Strategic Responses to Regulatory Consolidation in Southeast Asia
Regulatory consolidation is increasingly reshaping digital platform ecosystems across emerging economies. While intensified regulation is often assumed to constrain dominant platforms, its structural consequences remain under-theorized. This article develops a mechanism-based framework explaining how regulatory consolidation may recalibrate dependence relations and reinforce platform power under specific conditions. Integrating resource dependence theory, institutional response scholarship, and nonmarket strategy perspectives, the analysis conceptualizes consolidation as an institutional inflection point that raises compliance thresholds and stabilizes oversight regimes. When dominant platforms possess strong bottleneck control and developed political capabilities, they are positioned to convert regulatory stabilization into strategic advantage through institutional alignment. Compliance infrastructure becomes a source of relational leverage, while proactive regulatory engagement enhances legitimacy and competitive shielding. However, reinforcement is conditional. Where institutional complexity remains high or structural centrality is weak, consolidation may redistribute influence rather than entrench dominance. By reframing regulatory consolidation as a structurally contingent driver of ecosystem power, this study advances a power-centered understanding of how institutional stabilization interacts with digital market dominance in emerging platform economies.
Regulatory Architectures and Market Structuring in ASEAN Digital Platform Ecosystems
The rapid maturation of digital platform markets in Southeast Asia has coincided with intensified regulatory development across the region. While existing platform research emphasizes network effects and governance design, limited attention has been given to how regulatory systems actively structure ecosystem configuration. This article develops a mechanism-based framework explaining how regulatory architectures shape digital platform ecosystems in emerging economies. Rather than conceptualizing regulation as an external constraint, the study reframes it as an institutional infrastructure that organizes market participation, redistributes governance control, and reconfigures complementarities among interdependent actors. The framework identifies four architectural dimensions—entry structures, data governance boundaries, interoperability standards, and enforcement intensity—and specifies five structuring mechanisms through which they influence ecosystem outcomes. These mechanisms explain variations in ecosystem concentration, value capture distribution, cross-border scalability, and innovation directionality. By integrating institutional theory, ecosystem strategy, and nonmarket strategy perspectives, the article advances a structurally grounded understanding of how markets are organized in digitally mediated environments. The analysis offers theoretical contributions to institutional and platform scholarship while outlining managerial and policy implications for emerging digital economies navigating regulatory consolidation.
Governing Digital Markets: Institutional Architecture and Structural Power
Digital markets are increasingly structured by institutional architecture and structural power rather than technological novelty alone. Governance devices—regulatory consolidation, data localization, interoperability standards, and compliance infrastructures—reshape ecosystem configuration, dependence relations, and surplus allocation. By integrating institutional theory, ecosystem strategy, and resource dependence logic, this collection advances a governance-centered perspective on digital competition in emerging economies.
Digital Authenticity under Generative AI: Mechanisms of Synthetic Signal Construction and Strategic Legitimacy in Branding
The diffusion of generative artificial intelligence (GenAI) in branding practices challenges a central assumption in authenticity research: that legitimacy derives primarily from indexical continuity between expressive artifacts and experiential origin. As firms increasingly rely on synthetic systems to produce scalable, adaptive, and stylistically coherent brand communications, the structural linkage between expression and embodied authorship becomes attenuated. This article develops a mechanism-based framework to explain how authenticity and strategic legitimacy are reconstructed under conditions of generative intensification. Integrating signaling theory, legitimacy theory, and dynamic capabilities, the analysis conceptualizes authenticity as an emergent property of orchestrated synthetic signal regimes rather than as an intrinsic attribute of origin. The framework specifies a curvilinear relationship between generative AI intensity and perceived authenticity: moderate integration enhances coherence and responsiveness, while uncalibrated intensification risks signal dilution and perceived artificiality. Perceived authenticity mediates the effect of generative intensity on strategic legitimacy, and synthetic signal orchestration capability conditions the threshold at which legitimacy erosion occurs. Contextual salience, including heritage intensity and customer epistemic sensitivity, further shapes these dynamics. By reframing authenticity as governance-dependent under digital abundance, the article extends branding theory and dynamic capabilities scholarship while providing a structured agenda for empirical examination of AI-enabled marketing transformation.
Communicative Agency and Customer Engagement under AI-Generated Marketing Communication
The diffusion of generative artificial intelligence (AI) in marketing communication has intensified personalization, scalability, and message optimization. Existing research largely assumes that such improvements translate proportionally into stronger customer engagement. This study challenges that implicit linearity by introducing communicative agency—the perceived locus of intentional authorship behind a marketing message—as a structurally consequential construct. Drawing on engagement theory, attribution theory, anthropomorphism research, and persuasion models, the article develops a mechanism-based framework explaining how AI-generated communication reshapes engagement through agency attribution processes. As AI intensity increases, customers are more likely to attribute communication to system-based rather than human-intentional sources. This shift enhances cognitive engagement through improved processing fluency and perceived competence, while simultaneously attenuating affective engagement by reducing perceived intentionality and relational warmth. The divergence between cognitive and affective engagement generates non-linear aggregate effects and temporally asymmetric strategic outcomes. In relationally intensive markets, excessive reliance on AI-generated communication may strengthen short-term responsiveness while weakening long-term attachment. By reconceptualizing engagement as compositionally sensitive under system mediation, the study extends engagement theory and reframes the strategic evaluation of AI-enabled marketing communication.
From Content Automation to Capability Trap: Rethinking Marketing Capabilities in the Era of Generative AI
The rapid diffusion of generative artificial intelligence (GenAI) has intensified claims that AI systematically enhances marketing capabilities. While prior research emphasizes efficiency gains, personalization, and agility, it largely assumes a linear relationship between AI integration and strategic performance. This article challenges that assumption by developing a conceptual framework of the Generative AI Capability Trap. Drawing on dynamic capabilities theory, competence trap logic, and emerging research on digital authenticity, the relationship between GenAI intensity and marketing distinctiveness is theorized as inherently non-linear. Moderate AI integration may amplify sensing and seizing capabilities, expand creative throughput, and improve short-term performance. However, excessive reliance may compress symbolic variance, reinforce exploitation bias through KPI-driven optimization, and gradually erode adaptive creative capacity. This erosion dynamic is driven by three mechanisms—pattern convergence, creative deskilling, and algorithmic reinforcement—and is conditioned by boundary factors related to organizational learning orientation, creative governance, and industry dynamism. By reframing generative AI as a dual-edged strategic infrastructure, this study extends capability theory to probabilistic systems and introduces variance preservation as a critical lens for evaluating AI-enabled marketing transformation.
Algorithmic Mediation of Strategic Judgment: Executive Attention, Decision Architecture, and Power Reconfiguration under Generative AI
Strategic management theory has traditionally conceptualized executive strategic judgment as a human-centered process shaped by managerial cognition, attention allocation, and hierarchical authority. The growing integration of generative AI into executive workflows challenges this view by introducing an algorithmic layer that structures how issues are surfaced, options are framed, and decisions are legitimated. This article develops a mechanism-based framework explaining how generative AI mediates executive strategic judgment through four processes: algorithmic attention structuring, probabilistic framing of strategic options, recalibration of epistemic authority, and temporal compression of decision cycles. Integrating the attention-based view of the firm, upper echelons theory, behavioral strategy, and research on algorithmic decision structures, the analysis shows that AI centrality generates non-linear effects on long-term strategic coherence. At moderate levels, algorithmic mediation enhances informational integration and responsiveness; beyond a threshold, convergence of salience, reduced framing diversity, authority centralization, and temporal compression may undermine exploratory capacity and long-term adaptability. Governance architecture, executive cognitive heterogeneity, environmental volatility, and organizational slack emerge as key boundary conditions. By repositioning generative AI as a constitutive element of executive decision architecture rather than a performance tool, this study advances a revised understanding of the microfoundations of corporate strategy in digitally intensive environments.
Synthetic Creativity and the Recomposition of Brand Value: A Dynamic Capability Perspective on Generative AI Intensification
The diffusion of generative AI challenges foundational assumptions in branding and strategic management. Classical brand theory conceptualizes differentiation as the outcome of human-authored creativity, while dynamic capabilities scholarship emphasizes costly exploration and managerial orchestration as the basis of sustained advantage. Generative AI disrupts these premises by enabling large-scale probabilistic recombination of symbolic content at marginal cost. This article advances a conceptual re-theorization of creativity under such conditions by introducing synthetic creativity as an orchestrated dynamic capability through which firms govern probabilistic generative systems to sustain symbolic differentiation. Integrating dynamic capabilities theory, exploration–exploitation logic, organizational learning, and AI research, a mechanism-based model is developed to explain how generative AI intensification produces non-linear effects on brand value. Three interdependent mechanisms are specified: exploration compression, capability substitution versus augmentation, and metric-driven symbolic over-optimization. Together, these mechanisms generate a curvilinear relationship between AI intensification and brand differentiation, moderated by data heterogeneity, governance architecture, and infrastructural control. By shifting analytical focus from AI adoption to orchestration asymmetry, the study reconceptualizes competitive advantage in symbolic markets as dependent on governance of probabilistic infrastructures rather than on ideational originality. The framework establishes theoretical foundations for examining strategic differentiation in the generative economy.
Generative AI as a Dynamic Marketing Capability: Mechanisms of Strategic Reconfiguration
The rapid diffusion of Generative Artificial Intelligence (GenAI) represents a structural inflection point in the evolution of marketing capabilities. While prior research has predominantly examined AI as a performance-enhancing tool, limited attention has been devoted to understanding how generative systems reshape the microfoundations of strategic marketing processes. This study conceptualizes GenAI as a dynamic marketing capability rather than a technological artefact. Drawing on dynamic capabilities theory, marketing capability research, and organizational learning perspectives, we develop a mechanism-based framework explaining how GenAI reconfigures marketing capabilities through three interrelated processes: cognitive amplification, creative recomposition, and decision authority redistribution. We further specify boundary conditions—task complexity, market dynamism, brand positioning, and governance maturity—that moderate whether generative integration enhances adaptive capacity or produces capability erosion. By extending dynamic capabilities theory into probabilistic technological environments, this study advances a socio-technical understanding of marketing capability evolution and clarifies the long-term strategic implications of AI-enabled marketing transformation. The analysis contributes to ongoing debates on capability sustainability, organizational learning, and digital governance in increasingly algorithmic organizational contexts.
Strategic Reconfiguration in the Generative Era
Contemporary organizational environments are marked by shifting patterns of strategic judgment, capability development, and symbolic value creation. Rather than reflecting linear technological progress, recent transformations highlight how mediated decision processes reshape managerial cognition, engagement dynamics, and brand meaning. Emerging perspectives in dynamic capabilities, behavioral strategy, and branding scholarship suggest that competitive advantage increasingly depends on governance, orchestration, and interpretive diversity. This work advances a conceptual view of strategy as an evolving architecture in which agency, authenticity, and organizational learning are continuously reconfigured under conditions of structural change.
Strategic Architecture in Persistent Market Volatility
This editorial articulates a strategic perspective that views organizational action as an architectural process shaped by persistent market volatility and structural uncertainty. It highlights the need to integrate governance, learning, and innovation within a coherent strategic design rather than treating them as isolated managerial responses. By foregrounding interdisciplinary dialogue, the editorial invites conceptual and empirical work that advances adaptive and resilient approaches to contemporary strategic practice.
Dominance, Interruption, and Corrective Strategic Rebalancing Under Market Volatility
Market volatility frequently exposes structural imbalances that accumulate during periods of sectoral or strategic dominance. While prior research has examined diversification and strategic scope largely through structural or performance lenses, limited attention has been given to the endogenous processes through which dominance emerges, stabilizes, and is subsequently disrupted. This conceptual paper develops a process theory explaining how strategic dominance forms through recursive reinforcement, how volatility acts as an interruption mechanism that reveals latent imbalance, and how organizations engage in corrective strategic rebalancing. The framework conceptualizes dominance as an internally reinforced concentration of strategic attention and resource allocation, rather than merely an external market outcome. Volatility functions not simply as exogenous shock but as a revelatory condition that activates managerial interpretation and strategic recalibration. Corrective rebalancing is theorized as a disciplined, governance-mediated response aimed at restoring coherence and long-term viability. By integrating strategic management, managerial cognition, and governance perspectives, the paper advances a dynamic account of how organizations oscillate between dominance and balance. The study contributes to strategy theory by reframing imbalance as an endogenous process and positioning corrective rebalancing as a core mechanism of strategic resilience under persistent uncertainty.
Exposure Symmetry and Volatility Transmission: A Structural Theory of Portfolio Diversification
Persistent market volatility has become a structurally embedded condition of contemporary competitive environments, yet diversification research continues to conceptualize corporate scope primarily in terms of breadth, relatedness, and performance outcomes. Although prior scholarship explains how firms respond to turbulence through dynamic capabilities, governance mechanisms, and organizational learning, it under-theorizes how portfolio structure itself conditions volatility transmission. This paper develops a structural theory of portfolio diversification by introducing the construct of exposure symmetry, defined as a firm-level configuration in which volatility transmission across segments is attenuated through balanced dependency intensity, differentiated covariance structures, and capital redeployability elasticity. We argue that diversification effectiveness under persistent volatility depends not merely on scope expansion but on the architecture through which exposure is distributed and interconnected. The framework specifies three interdependent structural dimensions—exposure concentration intensity, exposure covariance structure, and structural elasticity—and advances propositions explaining how exposure architecture moderates the relationship between sustained volatility and strategic instability. By shifting attention from configurational classification toward volatility transmission mechanisms, this study extends diversification theory and clarifies the structural foundations of strategic stability under sustained turbulence.
Learning Under Persistent Market Volatility: A Conceptual Model of Organizational Learning Cycles and Strategic Renewal
Persistent market volatility has become a structural feature of contemporary competitive environments, yet its relationship with strategic adaptation remains theoretically under-specified. While prior research emphasizes dynamic capabilities, resilience, and structural reconfiguration, comparatively less attention has been devoted to the learning processes that mediate between sustained ambiguity and strategic renewal. This article reconceptualizes volatility not as an automatic trigger of adaptation, but as a generator of learning pressure arising from recurring interpretive discrepancies. Integrating organizational learning theory with strategic renewal scholarship, the paper develops a process model in which adaptation unfolds through recursive cycles of strategic sensemaking, experience codification, deliberate unlearning, and adaptive renewal. The model further identifies learning velocity, memory rigidity, and feedback architecture as critical moderating conditions shaping renewal outcomes. By specifying the micro-processes through which volatility-induced ambiguity is translated into calibrated strategic realignment, the framework extends organizational learning theory into persistently turbulent contexts and provides a process-based articulation of adaptive renewal under sustained market uncertainty.
Strategic Governance Architecture Under Persistent Market Volatility: A Conceptual Framework
This study develops a conceptual framework of strategic governance architecture to explain how corporate governance systems can stabilize strategic decision-making under persistent market volatility. Drawing on corporate governance, strategic management and uncertainty literature, the paper reconceptualizes governance as an integrated architecture composed of oversight depth, temporal orientation, risk framing logic and accountability discipline. Rather than treating governance as a monitoring mechanism designed primarily to mitigate agency costs, the framework positions governance as a systemic stabilizer of managerial discretion under sustained ambiguity. The analysis identifies recurring governance failure patterns – reactive overcorrection, narrative capture, passive oversight and incentive distortion – and explains these as outcomes of architectural misalignment rather than absence of formal control. The study further proposes a governance activation cycle through which persistent market volatility triggers structured recognition, interpretive deliberation, exposure calibration and strategic posture stabilization. By reframing volatility as a constitutive governance condition, the paper extends corporate governance theory beyond agency control and integrates it with strategic management perspectives on uncertainty, resilience and disciplined recalibration in turbulent markets.
Strategic Commitment Architecture Under Persistent Market Volatility
Persistent market volatility has transformed competitive environments from episodic disruption to structural ambiguity, intensifying the risks associated with irreversible strategic commitments. While existing research emphasizes dynamic capabilities, governance oversight, and organizational learning as mechanisms for navigating turbulence, comparatively limited attention has been devoted to how commitments themselves are architected ex ante. This article develops a theory of strategic commitment architecture to explain how firms structure commitment intensity, escalation thresholds, reversibility capacity, and embedded optionality under persistent volatility. We argue that volatility does not directly generate instability; rather, its destabilizing effects are moderated by the architectural configuration of commitments. Highly concentrated intensity, ambiguous continuation thresholds, low reversibility, and absent optionality amplify escalation traps and reactive oscillation. Conversely, calibrated intensity, explicit thresholds, preserved reversibility, and embedded optionality attenuate volatility transmission and enable disciplined recalibration. By integrating insights from strategic commitment theory, escalation research, and uncertainty management, the framework introduces commitment architecture as a structural moderator of stability under sustained turbulence. The study advances strategy scholarship by reframing commitment as systemic configuration rather than discrete strategic act and outlines avenues for empirical examination across industries.
Strategic Diversification as a Managerial Capability Under Persistent Market Volatility
Market volatility has evolved into a structurally embedded condition of contemporary business environments, destabilizing conventional assumptions about strategic planning, risk governance, and long-term value creation. Although diversification is widely regarded as a response to uncertainty, existing scholarship largely conceptualizes it as a structural configuration or portfolio allocation decision, thereby under-theorizing the managerial processes through which diversification is enacted and sustained. This conceptual paper advances a capability-based reconceptualization of strategic diversification, positioning it as a judgment-driven managerial capability rather than a static portfolio outcome. Drawing on strategic management, managerial cognition, and corporate governance perspectives, the study develops a process-oriented framework explaining how executives interpret volatility, balance strategic trade-offs, enforce governance discipline, and integrate diversified activities into a coherent strategic logic. The framework specifies four interdependent dimensions—cognitive framing, strategic balancing, governance orientation, and organizational integration—and identifies mechanisms through which diversification capability fosters strategic resilience, long-term value creation, and organizational coherence. By foregrounding managerial agency in governing strategic scope, this study extends diversification theory and offers a foundation for future research on organizational resilience under persistent uncertainty.