Rethinking Organizational Performance in a VUCA World : Toward a Relational and Generative Ontology of Governance

1. Introduction

Management sciences are currently confronted with a profound ontological rupture that remains insufficiently theorized despite its far-reaching implications. For decades, the field has built its intellectual legitimacy upon the assumption that organizational phenomena—particularly performance—can be rendered visible, measurable, and ultimately controllable through appropriate analytical frameworks and managerial instruments. This epistemological orientation, deeply rooted in the legacy of neoclassical economics and engineering rationality, presupposes a world characterized by relative stability, linear causality, and bounded uncertainty.

Yet contemporary organizational environments increasingly defy these assumptions. The rise of what has been conceptualized as VUCA conditions—volatility, uncertainty, complexity, and ambiguity—signals not merely a contextual shift, but a transformation in the ontological structure of organizational reality itself (Bennett & Lemoine, 2014; Mack et al., 2016). In such environments, causal relationships become unstable, outcomes emerge through non-linear dynamics, and the future resists predictive modeling. Organizations no longer operate within closed, controllable systems, but within open, evolving ecologies characterized by continuous interaction, feedback, and co-evolution (Stacey, 2011; Tsoukas & Chia, 2002 ; Moleka, 2025a-d, 2026a).

This transformation generates a fundamental tension at the heart of management sciences. On the one hand, prevailing theories and practices continue to privilege measurement, control, and optimization. On the other, the phenomena they seek to govern increasingly exhibit properties—emergence, unpredictability, relationality—that escape such frameworks. The concept of organizational performance thus enters into what may be described as an ontological crisis: it persists as a central category of analysis, yet its underlying assumptions are progressively undermined by empirical reality (Lebas, 1995; Bourguignon, 2005; Pendaries, 2017).

This article argues that addressing this crisis requires more than incremental theoretical refinement. It calls for a paradigmatic shift toward a relational and generative ontology of organizational performance, capable of accounting for the emergent, processual, and situated nature of organizational phenomena. In doing so, it contributes to a broader intellectual project: the re-foundation of management sciences as a discipline concerned not primarily with control and prediction, but with understanding and enabling processes of emergence and transformation.

2. Beyond Instrumental Rationality: A Systemic Critique of Dominant Paradigms

2.1. The Epistemic Limits of Measurement and the Reification of Performance

The dominant paradigms of performance management are grounded in an epistemology of representation, wherein indicators are assumed to faithfully capture underlying organizational realities. However, a growing body of research demonstrates that measurement systems do not merely represent reality; they actively constitute it (Mennicken & Espeland, 2019; Kornberger et al., 2015). Performance indicators shape attention, orient behavior, and redefine organizational priorities, often producing unintended consequences that distort the very phenomena they aim to measure.

Recent scholarship has further highlighted how digitalization and algorithmic governance intensify this dynamic, creating what has been termed “datafication regimes” in which organizational life is increasingly mediated through quantification infrastructures (Leonardi & Treem, 2020; Kellogg et al., 2020). While these systems enhance visibility and coordination, they also risk reinforcing reductionist logics that obscure relational and emergent dimensions of performance.

From this perspective, performance must be understood not as an objective property, but as a performative construct, emerging from socio-technical assemblages that combine metrics, practices, technologies, and institutional logics. This insight necessitates a shift from measurement-centric frameworks toward approaches that account for the generative effects of evaluative infrastructures.

2.2. The Persistence of Control Paradigms and the Illusion of Governability

Despite mounting evidence of complexity and unpredictability, governance theories continue to be dominated by control-oriented paradigms. Agency theory and transaction cost economics remain influential, privileging alignment, monitoring, and contractual design (Jensen & Meckling, 2019; Williamson, 2008 ; Moleka, 2026b). However, these approaches rest on assumptions increasingly at odds with contemporary organizational realities, including stable preferences, bounded rationality, and linear causality.

Recent advances in organizational scholarship emphasize instead the distributed, relational, and emergent nature of coordination (Felin et al., 2020; Puranam et al., 2020). Organizations are increasingly conceptualized as complex adaptive systems, where order arises not from centralized control but from decentralized interactions among heterogeneous actors (Anderson, 1999; Holland, 1992).

In such systems, attempts to impose rigid control structures may not only fail but actively undermine adaptive capacity. Governance thus cannot be reduced to the design of control mechanisms; it must be reconceptualized as the ongoing orchestration of relational dynamics under conditions of uncertainty.

2.3. The Under-Theorization of Generativity in Management Research

While innovation and change have long been central concerns of management research, the deeper concept of generativity—the capacity of systems to produce novel, unanticipated forms—remains insufficiently theorized. Recent work in digital innovation studies highlights generativity as a defining feature of contemporary organizational environments, particularly in platform ecosystems and open systems (Nambisan et al., 2017; Yoo et al., 2012; Wareham et al., 2021).

Generativity implies a shift from a logic of optimization to a logic of possibility. It foregrounds processes of recombination, emergence, and transformation, challenging the assumption that organizational outcomes can be fully specified in advance. Integrating generativity into management theory thus requires a move toward process ontology, where organizations are understood as ongoing accomplishments rather than fixed entities (Tsoukas & Chia, 2002; Langley et al., 2013).

3. Toward a Relational and Generative Ontology of Performance

3.1. From Entities to Relations: Reframing Organizational Reality

A relational ontology challenges the primacy of entities by positing that social phenomena are constituted through relations rather than existing independently of them (Emirbayer, 1997). Applied to organizations, this implies that performance cannot be located within discrete units—individuals, teams, or firms—but emerges from patterns of interaction that continuously reconfigure organizational structures.

Recent developments in network theory and relational sociology reinforce this view, demonstrating that organizational outcomes are deeply shaped by network configurations, relational embeddedness, and interaction dynamics (Borgatti et al., 2020; Powell, 2011). Performance thus becomes a property of relational configurations rather than an attribute of isolated actors.

3.2. Performance as Emergent, Situated, and Processual

Building on complexity theory, performance can be conceptualized as an emergent phenomenon arising from non-linear interactions among system components. This perspective aligns with recent advances in process studies, which emphasize temporality, flow, and becoming as central dimensions of organizational life (Langley et al., 2013; Hernes, 2014).

Moreover, performance is inherently situated, shaped by local contexts, practices, and interpretations (Gherardi, 2019). It is plural, contested, and continuously negotiated among actors with different perspectives and interests.

3.3. Generativity as the Ontological Core of Performance

At its deepest level, performance is generative. It reflects the capacity of organizational systems to produce novelty, adapt to changing conditions, and explore new possibilities. This perspective resonates with recent work on innovation ecosystems, digital platforms, and adaptive organizations (Autio et al., 2021; Nambisan et al., 2017).

Generativity reframes uncertainty not as a problem to be minimized, but as a resource for transformation. It shifts the focus of management from controlling outcomes to enabling conditions for emergence.

4. Generative Governance: A New Paradigm

Generative governance constitutes a profound paradigmatic reorientation in the theory and practice of organizing, marking a decisive shift away from control-centric, equilibrium-based models toward an understanding of governance as an emergent, relational, and processual accomplishment. Rather than conceiving governance as the design and enforcement of formal rules aimed at stabilizing behavior and reducing uncertainty, this perspective reconceptualizes governance as the continuous cultivation of conditions under which coordination, innovation, and performance can emerge through the dynamic interplay of heterogeneous actors, resources, and institutional logics. This repositioning aligns with process-based views of organization, which emphasize becoming over being and highlight the temporality and fluidity of organizational phenomena (Tsoukas & Chia, 2002; Langley et al., 2013).

At its core, generative governance rests on a fundamental ontological premise: organizational order is not pre-given, nor can it be fully specified ex ante; instead, it is continuously enacted through situated interactions, recursive feedback loops, and evolving relational configurations. This perspective resonates strongly with complexity theory, which demonstrates that order in complex adaptive systems arises endogenously from local interactions rather than from centralized design (Holland, 1992; Anderson, 1999), as well as with relational sociology, which posits that social reality is constituted through relations rather than discrete entities (Emirbayer, 1997). Governance, in this sense, is not the imposition of structure upon a passive system, but the active shaping of relational conditions that enable the system to organize itself in adaptive and innovative ways.

This reconceptualization implies a radical transformation of the role of governance. It is no longer primarily about ensuring compliance, minimizing deviation, or aligning incentives through hierarchical control mechanisms, as emphasized in traditional agency-based frameworks (Jensen & Meckling, 2019; Williamson, 2008). Instead, governance becomes a distributed and reflexive process oriented toward fostering generativity—that is, the capacity of the system to produce novel, meaningful, and contextually relevant forms of action and organization (Zittrain, 2006; Nambisan et al., 2017). In this sense, governance shifts from a logic of constraint to a logic of enabling, from predictive control to adaptive orchestration, and from structural design to relational architecture (Felin et al., 2020; Puranam et al., 2020).

Within this framework, generative governance can be understood as a higher-order dynamic capability (Teece, 2007), namely the capacity of organizational systems to continuously reconfigure their own conditions of operation in response to internal dynamics and environmental perturbations. It involves not only responding to change but actively co-producing new trajectories of development through processes of interaction, learning, and experimentation (Autio et al., 2021). As such, generative governance is inherently future-oriented, yet not in the sense of forecasting predefined outcomes; rather, it expands the space of possible futures by maintaining openness, diversity, and adaptive flexibility within the system.

4.1. Recursivity and Continuous Learning

Recursivity constitutes a foundational property of generative governance, referring to the capacity of organizational systems to learn from their own operations through iterative feedback loops. In contrast to linear models of decision-making, where actions are assumed to produce predictable outcomes, recursive systems continuously monitor, interpret, and adjust their behavior in light of emergent consequences (Argyris & Schön, 1996; Stacey, 2011). This process transforms governance into a dynamic learning architecture, where past actions inform present adjustments and future possibilities.

Importantly, recursivity extends beyond simple feedback mechanisms; it involves higher-order learning processes in which the underlying assumptions, norms, and governing logics of the organization themselves become objects of reflection and transformation. This aligns with double-loop learning, where organizations question not only their actions but also the frameworks within which those actions are embedded (Argyris & Schön, 1996). Such reflexivity is increasingly recognized as central to adaptive capacity in complex environments (Duchek, 2020).

Moreover, recursive learning is inherently collective and distributed. It emerges from interactions among actors rather than being centrally orchestrated, and it depends on the circulation of knowledge across organizational boundaries (Nonaka & Takeuchi, 1995; Leonardi & Treem, 2020). Generative governance thus requires the development of infrastructures—both social and technological—that enable feedback, dialogue, and shared sensemaking (Kellogg et al., 2020). In this sense, organizations evolve into learning systems capable of continuously reconfiguring their own modes of governance.

4.2. Non-Linearity and Unpredictability

A second defining property of generative governance is its explicit recognition of non-linearity. In complex systems, the relationship between cause and effect is neither proportional nor predictable; small events may trigger cascading transformations, while large interventions may produce limited or delayed effects (Anderson, 1999; Morin, 2005). This challenges the foundational assumption of traditional governance models that outcomes can be controlled through calibrated inputs.

Generative governance does not attempt to eliminate this unpredictability; instead, it integrates it as a constitutive feature of organizational life. This implies a shift from predictive rationality to adaptive rationality, where the emphasis is placed on responsiveness, flexibility, and the capacity for real-time adjustment (Stacey, 2011; Tsoukas, 2017). Rather than relying on rigid planning, organizations adopt iterative, experimental, and scenario-based approaches that allow them to navigate uncertainty dynamically (March, 1991).

Recent research in digital and platform-based organizations further reinforces this view, showing that innovation often emerges through unpredictable recombination processes within loosely structured systems (Nambisan et al., 2017; Wareham et al., 2021). In such contexts, governance must remain open-ended and adaptive, capable of accommodating unexpected developments without collapsing into disorder.

4.3. Self-Organization and Distributed Agency

Generative governance is fundamentally grounded in the principle of self-organization, whereby coordination and order arise from decentralized interactions among agents rather than from centralized authority (Holland, 1992; Uhl-Bien et al., 2007). This does not imply the absence of structure, but rather a shift in how structure is produced—from top-down imposition to bottom-up emergence.

In self-organizing systems, actors possess a degree of autonomy that allows them to respond to local conditions, experiment with new practices, and contribute to the evolution of the system as a whole. Governance, in this context, is concerned with enabling these processes by creating minimal but enabling structures—rules, norms, and platforms—that provide coherence while preserving flexibility (Felin et al., 2020).

Distributed agency is a critical complement to self-organization. It recognizes that the capacity to act is not concentrated at the top of the hierarchy but dispersed across networks of actors, including technological systems and socio-material arrangements (Orlikowski & Scott, 2008; Leonardi, 2011). This aligns with contemporary views of organizations as socio-technical systems, where human and non-human actors jointly participate in the production of outcomes.

Consequently, leadership within generative governance frameworks is redefined. Leaders are no longer primarily controllers or decision-makers; they become facilitators of distributed intelligence, orchestrators of relational dynamics, and designers of enabling environments (Uhl-Bien et al., 2007; Puranam et al., 2020).

4.4. Co-Evolution and Systemic Adaptation

The final core property of generative governance is co-evolution, referring to the mutual and dynamic adaptation of organizations and their environments. Organizations do not simply react to external conditions; they actively shape and are shaped by them through ongoing interactions (Powell, 2011; Teece, 2007). This recursive coupling generates continuous transformation at both organizational and systemic levels.

Co-evolution challenges the traditional boundary between organization and environment, suggesting instead that both are part of a broader, interconnected ecosystem (Autio et al., 2021). Governance must therefore extend beyond internal coordination to encompass the management of inter-organizational relationships, networks, and ecosystems.

This perspective is particularly salient in the context of grand challenges—such as climate change, digital transformation, and global inequality—where solutions cannot be predefined but must emerge through collaborative, multi-actor processes (George et al., 2020). Generative governance, in this sense, becomes a critical capability for enabling collective adaptation and transformation at scale.

Toward an Integrated Framework of Generative Governance

Taken together, recursivity, non-linearity, self-organization, and co-evolution define generative governance as a fundamentally distinct mode of organizing—one that is adaptive, relational, and oriented toward emergence rather than control. It does not replace structure with chaos; rather, it reconfigures governance as a dynamic process of enabling and sustaining generativity within complex systems.

This framework fundamentally shifts the analytical and practical focus of management—from prediction to adaptability, from optimization to emergence, and from centralized decision-making to distributed relational processes. In doing so, it opens a new horizon for management theory and practice: one in which organizations are understood not as systems to be controlled, but as evolving ecologies of possibility, continuously generating new forms of order, value, and transformation.

5. Managerial Implications: From Control to the Design of Enabling Conditions

The shift toward a relational and generative ontology of organizational performance entails a profound transformation in the role, identity, and practice of management. Within traditional paradigms, managers are primarily conceived as agents of control, responsible for planning, coordinating, and monitoring organizational activities in order to achieve predefined objectives. This model presupposes a relatively stable environment, where outcomes can be predicted and controlled through appropriate interventions. However, in VUCA contexts characterized by non-linearity, emergence, and systemic interdependence, such assumptions no longer hold. As a result, the managerial function must be fundamentally redefined—not as the exercise of control over organizational processes, but as the design of enabling conditions that allow performance, innovation, and coordination to emerge dynamically (Stacey, 2011; Tsoukas & Chia, 2002; Felin et al., 2020).

In this reconceptualization, managers become architects of generative systems. Their primary task is not to dictate outcomes, but to shape the relational, cognitive, and material infrastructures within which organizational dynamics unfold. This perspective aligns with emerging views in strategy and organization theory that emphasize microfoundations, distributed agency, and the role of interaction patterns in shaping organizational outcomes (Felin et al., 2020; Puranam et al., 2020). Management thus becomes an exercise in orchestration rather than control, requiring sensitivity to context, attentiveness to emergent patterns, and the capacity to intervene in ways that enhance rather than constrain system-level generativity.

A first critical dimension of this transformation lies in the structuring of relational architectures. If performance is an emergent property of interaction patterns, then the configuration of relationships within and across organizational boundaries becomes a central object of managerial attention. This involves designing organizational forms that facilitate connectivity, trust, and collaboration, while avoiding excessive rigidity or fragmentation. Network-based forms of organizing, cross-functional teams, and platform-based ecosystems exemplify such architectures, enabling the recombination of knowledge and the co-creation of value across diverse actors (Powell, 2011; Borgatti et al., 2020; Autio et al., 2021). Importantly, relational architectures are not static designs but evolving configurations that must be continuously adjusted in response to changing conditions. Managers, therefore, act as curators of relational spaces, shaping the density, diversity, and quality of interactions that underpin organizational life.

A second key dimension concerns the enablement of knowledge flows. In complex and uncertain environments, the capacity of an organization to generate, circulate, and recombine knowledge becomes a primary driver of adaptability and innovation. Knowledge is no longer confined to individuals or formal repositories; it is distributed across networks of actors and embedded in practices, routines, and technologies (Nonaka & Takeuchi, 1995; Leonardi & Treem, 2020). Managers must therefore create infrastructures—both technological and social—that facilitate the fluid movement of knowledge across boundaries, reduce silos, and support collective sensemaking. Digital platforms, collaborative tools, and open innovation practices play a crucial role in this regard, but their effectiveness depends on the underlying relational conditions, including trust, shared understanding, and psychological safety (Kellogg et al., 2020). Enabling knowledge flows thus requires not only technological investment but also the cultivation of an organizational culture that values openness, dialogue, and continuous learning.

A third dimension relates to the cultivation of diversity and experimentation as sources of generativity. In contrast to traditional models that prioritize efficiency, standardization, and risk minimization, a generative approach recognizes diversity—cognitive, functional, cultural—as a critical resource for innovation and adaptation. Diverse perspectives increase the range of possible interpretations and solutions, thereby enhancing the system’s capacity to respond to complex challenges (Page, 2007; Felin et al., 2020). However, diversity alone is insufficient; it must be coupled with mechanisms that allow for experimentation and variation. This implies creating environments where actors are encouraged to explore alternative approaches, test hypotheses, and learn from failure without fear of sanction. Such environments are characterized by what has been described as “safe-to-fail” conditions, where small-scale experiments generate feedback that informs broader organizational learning (Stacey, 2011; March, 1991). Managers, in this sense, function as designers of experimentation spaces, balancing the need for coherence with the imperative of exploration.

A fourth and equally critical dimension is the support of collective learning processes. In generative systems, learning is not an individual activity but a distributed, social process emerging from interaction, reflection, and shared practice. Organizational learning involves not only the accumulation of knowledge but also the transformation of underlying assumptions, norms, and routines (Argyris & Schön, 1996). Generative governance requires managers to facilitate these processes by creating opportunities for reflection, dialogue, and feedback, as well as by institutionalizing mechanisms for capturing and disseminating insights across the organization. Recent research on organizational resilience underscores the importance of such learning capabilities in enabling organizations to adapt to shocks and disruptions (Duchek, 2020). Managers thus become enablers of collective intelligence, fostering the conditions under which the organization can learn from its own experience and continuously evolve.

Taken together, these dimensions point toward a redefinition of management as a practice of designing conditions of possibility. Rather than seeking to optimize predefined outcomes, managers focus on shaping the generative capacities of organizational systems. This involves embracing uncertainty, cultivating relational richness, and enabling processes of emergence that cannot be fully specified in advance. In doing so, management moves closer to what may be described as a meta-organizing function: the continuous configuration and reconfiguration of the conditions under which organization itself becomes possible.

6. Conclusion: Toward a Science of Organizational Generativity

This article has advanced the argument that contemporary management sciences are confronted with a deep ontological and epistemological misalignment between their dominant analytical frameworks and the evolving nature of organizational reality. In environments characterized by volatility, uncertainty, complexity, and ambiguity, the longstanding emphasis on measurement, control, and optimization proves increasingly insufficient for capturing and guiding organizational phenomena. The concept of organizational performance, in particular, emerges as deeply problematic when reduced to quantifiable outputs, as it fails to account for the relational, processual, and emergent dynamics through which value is actually generated.

In response to this limitation, the article has proposed a paradigmatic shift toward a relational and generative ontology of organizational performance, grounded in insights from complexity theory, relational sociology, and process studies (Emirbayer, 1997; Tsoukas & Chia, 2002; Langley et al., 2013). Within this framework, performance is no longer conceived as a static outcome but as an emergent property of dynamic interaction systems, continuously shaped by relational configurations, contextual conditions, and processes of co-evolution. This reconceptualization opens new analytical possibilities by foregrounding dimensions that have historically been marginalized in management research, including emergence, generativity, and situated practice.

Building on this ontological foundation, the article has introduced the concept of generative governance as a new paradigm for organizing in complex environments. Generative governance shifts the focus from control to enabling, from predictability to adaptability, and from structural design to relational architecture. It conceptualizes governance as a distributed, recursive, and evolving process through which organizations cultivate the conditions necessary for the emergence of innovation, coordination, and performance. In doing so, it aligns with and extends recent developments in organizational theory that emphasize complexity, processuality, and distributed agency (Felin et al., 2020; Puranam et al., 2020).

The implications of this shift are both theoretical and practical. At the theoretical level, it calls for the development of a new research program centered on organizational generativity—the capacity of systems to produce novel and meaningful forms of order under conditions of uncertainty. Such a program would require rethinking core concepts in management sciences, including performance, strategy, and governance, through the lens of emergence and relationality. It would also necessitate methodological innovation, moving beyond static, variable-based models toward approaches capable of capturing dynamics, processes, and interactions over time (Langley et al., 2013; Hernes, 2014).

At the practical level, the shift toward generativity redefines the role of managers as architects of enabling conditions rather than controllers of outcomes. It emphasizes the importance of relational design, knowledge flows, diversity, experimentation, and collective learning as key levers of organizational performance. In this sense, management becomes less about directing action and more about cultivating the environments in which meaningful action can emerge.

Ultimately, this article contributes to a broader reimagining of management sciences as a discipline concerned not merely with efficiency and control, but with the conditions of possibility for continuous transformation. In a world where uncertainty is not an exception but a defining feature, the capacity to generate new forms of organization, knowledge, and value becomes the central challenge. A science of organizational generativity thus represents not only an intellectual advancement but a necessary response to the complexities of contemporary organizational life.