Literature Foundations

The problem developed in the preceding section sits near several major traditions in organizational and systems theory. Organizations have long been studied as learning systems, information-processing systems, decision systems, memory systems, adaptive systems, and distributed cognitive systems. Each tradition provides important resources for understanding how organizations sense, interpret, decide, act, remember, and adapt. The purpose of this section is not to displace those traditions. Rather, it is to clarify the specific explanatory gap that motivates Correspondence Theory: existing theories do not explicitly explain how organizations preserve meaningful semantic and causal relationships across continuously evolving networks of organizational representations.

This positioning is important for two reasons. First, Correspondence Theory depends on insights that adjacent theories have already established: cognition is distributed, learning unfolds over time, organizations regulate behavior through feedback, memory persists in artifacts and routines, and actors construct meaning under ambiguity. Second, the existence of these insights does not by itself explain the representational problem introduced in this paper. A theory can explain how organizations learn without explaining whether learning remains connected to prior decisions. A theory can explain feedback without explaining whether the representation of the feedback remains tied to intent, action, and memory. A theory can explain sensemaking without explaining whether the resulting interpretation is preserved across the representation network. The review below therefore treats existing theories as necessary foundations while identifying the residual explanatory gap they leave open.

Cybernetics and systems theory provide the foundations for feedback, regulation, interdependence, and system behavior (Ashby, 1956; Bertalanffy, 1968).

Cybernetics provides one of the deepest foundations for understanding organizational regulation. Cybernetic accounts explain how systems compare current states with reference conditions, detect deviation, and adjust behavior through feedback. This tradition is essential for explaining control, correction, error detection, and adaptive regulation. Yet cybernetic feedback primarily concerns the regulation of system behavior. It can explain how an organization corrects a process, responds to a signal, or adjusts behavior in relation to a goal. It does not, by itself, explain whether the representations through which goals, decisions, actions, outcomes, and memory are expressed remain semantically and causally connected over time. A system may regulate behavior while the organizational representations that explain why behavior is changing become fragmented or detached from prior intent.

This distinction matters because regulation and correspondence are not equivalent. A feedback mechanism may detect variance from a target and correct action, but the correction may not be represented in a way that preserves its relationship to strategic assumptions, decision history, or later learning. The organization may know that behavior changed without preserving why the change occurred, what representation was revised, or which causal assumptions were confirmed or weakened. Cybernetics therefore helps explain correction, but it does not fully account for the representational continuity through which correction becomes organizationally intelligible over time.

Systems theory and systems dynamics similarly provide powerful accounts of interdependence, feedback, delays, accumulations, and dynamic behavior. These perspectives help explain how organizational outcomes arise from interacting structures rather than isolated events. They are especially useful for modeling how variables affect one another over time, how feedback loops amplify or dampen behavior, and how unintended consequences emerge from complex systems. However, the primary objects of these theories are system structures, stocks, flows, feedback relations, and behavioral patterns. They do not directly explain the maintenance of semantic and causal relationships among organizational representations as such. A systems model may describe causal behavior, but the organization may still fail to preserve the representational continuity linking intent, decision, action, outcome, and learning.

The issue is not that systems theories ignore causality. On the contrary, they are among the strongest traditions for explaining causal interdependence. The difference concerns the level of analysis. Systems dynamics may represent causal structure in a model, while Correspondence Theory asks whether the organization's own representations of causality remain meaningfully connected across work. The model may describe a system; the organization may still lose coherence in the representations by which it remembers, communicates, governs, and revises its understanding of that system.

Distributed cognition and boundary-object scholarship establish why cognition can be analyzed across actors, artifacts, tools, and environments (Hutchins, 1995; Star and Griesemer, 1989).

Distributed cognition offers another important foundation. It challenges individualistic accounts of cognition by showing how cognitive activity is distributed across people, artifacts, tools, routines, and environments. This is highly relevant for contemporary organizations, where cognition is rarely contained within individual minds. Organizational work depends on documents, models, decision records, procedures, analytic outputs, governance artifacts, and increasingly AI-generated artifacts. Distributed cognition therefore helps establish why artifacts and environments matter for organizational cognition. Yet its central contribution is to explain the distribution of cognition, not the recursive maintenance of representational coherence. It can show that cognition is spread across actors and artifacts, but it does not fully specify how the resulting representations remain meaningfully and causally connected as they evolve.

In AI-native organizations, this distinction becomes sharper as algorithmic systems alter work, control, and managerial decision-making without eliminating the need for accountable human organizational cognition (Faraj et al., 2018; Kellogg et al., 2020; Raisch and Krakowski, 2021). Cognition may be distributed not only across humans and artifacts, but also across machine-generated outputs, automated recommendations, and persistent organizational memory. Distributed cognition helps make this distribution visible. Correspondence Theory addresses a further question: once cognition is distributed across such representations, what preserves the relationships among them? Distribution explains where cognition occurs. It does not fully explain how the semantic and causal structure of represented cognition is maintained.

Organizational learning scholarship explains error correction, learning from experience, and exploration-exploitation dynamics (Argyris and Schon, 1978; March, 1991).

Organizational learning explains how organizations acquire, retain, interpret, and revise knowledge through experience. Learning theories help explain how experience becomes knowledge, how routines change, how errors are corrected, and how organizations adapt in response to feedback. These accounts are central to any theory of organizational cognition. Still, learning can occur without preserving the relationships that make learning coherent across the organization. A lesson may be recorded without being linked to the decision that produced it. An outcome may be interpreted without revising the assumptions that shaped the original action. A new practice may emerge without updating the strategic or policy representations it affects. Organizational learning explains knowledge change; it does not fully explain whether that change remains connected within the broader representation network.

This limitation is especially visible when learning is local. A team may learn from an execution outcome and alter its behavior, while the organization's strategy, governance records, decision rationale, and memory remain unchanged. From one perspective, the organization has learned. From another, its representation network has become less coherent because the learning is not related to the representations it should modify. Correspondence Theory therefore treats learning events as potentially important representation-network events, but it does not equate learning with correspondence.

Adaptive systems perspectives also contribute significantly. They explain how organizations and other systems adjust to environmental pressure, variation, uncertainty, and internal complexity. Adaptation may involve behavioral change, structural change, learning, experimentation, or reconfiguration. These accounts are particularly useful for understanding organizations under uncertainty. However, adaptation can occur at the level of behavior while representational relationships degrade. An organization may adapt quickly to market or technological change while losing traceability between its new actions and earlier strategic commitments. It may reorganize in response to pressure while leaving policies, decisions, plans, and memory in inconsistent states. Adaptive systems theory explains adaptation; it does not fully explain representational maintenance during adaptation.

The distinction is not merely semantic. Organizations often need to adapt while preserving enough continuity to remain intelligible to themselves. They must know which assumptions were abandoned, which commitments remain, which outcomes motivated the change, and which future decisions should inherit the revised understanding. Without this representational continuity, adaptation can produce local responsiveness but organization-level fragmentation.

Sensemaking scholarship explains how actors construct meaning under ambiguity and enact plausible accounts of organizational situations (Weick, 1995).

Sensemaking theories explain how actors interpret ambiguous cues, construct meaning, and enact plausible accounts of organizational situations. Sensemaking is crucial because organizations do not merely process information; they interpret events, negotiate meaning, and construct shared understandings. This tradition helps explain ambiguity, interpretation, identity, narrative, and collective meaning-making. Yet sensemaking primarily concerns the construction and revision of meaning. It does not fully account for the ongoing maintenance of semantic and causal relationships among persistent organizational representations after interpretation occurs. An organization may make sense of an event locally while failing to update the relationships among strategies, decisions, actions, outcomes, and memory. The residual problem is not only meaning construction, but representational coherence over time.

Sensemaking also tends to foreground interpretive activity. Correspondence Theory accepts the importance of interpretation but asks what happens to the representations after interpretation is stabilized, revised, or contested. Does the new interpretation become connected to the relevant decision records? Does it revise prior assumptions? Does it alter how outcomes are understood? Does it remain linked to the causal chain that produced the event? These questions concern the maintenance of a representation network rather than interpretation alone.

Organizational and transactive memory scholarship explains retention, retrieval, distributed expertise, and remembered organizational experience (Walsh and Ungson, 1991; Wegner, 1987).

Organizational memory addresses how knowledge, experience, routines, and records persist beyond individual actors. It explains retention, retrieval, and the organizational consequences of remembered experience. This tradition is highly relevant because Correspondence Theory also concerns persistent organizational representations. However, memory persistence is not the same as correspondence among representations. An organization may retain decision histories, lessons, policies, and outcome records while the relationships among them become incomplete, outdated, contradictory, or invisible. Memory can persist while causal traceability collapses. Correspondence Theory addresses the relationship integrity of memory representations, not merely their retention.

Knowledge-management scholarship explains knowledge creation, codification, storage, retrieval, and reuse as organizational knowledge practices (Alavi and Leidner, 2001; Nonaka, 1994).

Knowledge management explains how knowledge is captured, stored, shared, retrieved, and reused. It focuses on knowledge assets, repositories, transfer practices, codification, and access. These are important concerns, but knowledge availability is not equivalent to Organizational Correspondence. Knowledge can be available but disconnected. A repository may contain strategy documents, lessons learned, and AI-generated recommendations without preserving how those representations relate to one another semantically or causally. Correspondence Theory therefore addresses a different problem: whether represented knowledge remains meaningfully connected within organizational cognition.

This is a decisive distinction for organizations that are rich in stored information. Accumulated knowledge may create the appearance of cognitive maturity while increasing the burden of relationship maintenance. The more representations an organization retains, the more important it becomes to preserve relationships among them. In this sense, organizational memory can become both a resource and a site of degradation: it preserves representations, but it does not automatically preserve correspondence among them.

Organizational information-processing theory explains how uncertainty, equivocality, and information requirements shape organizational design and communication capacity (Daft and Lengel, 1986; Galbraith, 1974; March and Simon, 1958).

Organizational information processing explains how organizations gather, process, interpret, and distribute information under uncertainty and complexity. It is especially useful for understanding information needs, processing capacity, and the fit between uncertainty and organizational arrangements. Correspondence Theory does not replace this view. It asks a different question. Even when information is processed efficiently, the relationships among resulting representations may degrade. An organization may process a new market signal, generate a decision, and communicate an action while failing to preserve how that signal relates to prior intent, assumptions, outcomes, and learning.

Sociomaterial and boundary-object perspectives support the paper's treatment of artifacts and technologies as entangled with organizing while remaining distinct from the representation-network property theorized here (Orlikowski, 2007; Star and Griesemer, 1989).

Sociomaterial approaches explain how social and material arrangements are entangled in organizational practice. They help show that technologies, artifacts, routines, and actors are not separable in actual organizing. Correspondence Theory is compatible with this insight but focuses on a narrower theoretical target. It does not primarily explain sociomaterial entanglement. It explains whether the representations produced, modified, and stabilized within such entanglements preserve meaningful semantic and causal relationships over time.

Process improvement and decision-cycle models, including PDCA and OODA, add another layer of comparison. PDCA explains iterative improvement through planning, doing, checking, and acting. OODA explains decision and action under changing conditions through observation, orientation, decision, and action. Both are valuable for understanding organizational action under uncertainty and correction over time. But neither is primarily a theory of representation-network maintenance. PDCA can improve a process without ensuring that process changes remain connected to strategic intent, causal assumptions, or organizational memory. OODA can accelerate decision cycles while leaving decision traces, action outcomes, and learning artifacts disconnected. These cycles explain improvement or decision tempo; they do not fully explain the preservation of semantic and causal relationships across evolving organizational representations.

PDCA and OODA also illustrate why the Correspondence Loop should not be understood as another execution loop or decision cycle. The problem addressed in this paper is not how organizations plan, act, check, decide, or orient more effectively. It is how the representations produced by such activities remain connected to the wider network of organizational cognition. A plan, action, check, observation, orientation, decision, or lesson may all become representations. The theoretical question is whether their semantic and causal relationships are preserved as the organization continues to change.

Table 2 - Existing Theory Comparison

Across these traditions, the recurring pattern is complementarity rather than replacement. Existing theories explain regulation, system behavior, distributed cognition, learning, adaptation, meaning construction, memory persistence, knowledge flow, process improvement, and decision cycles. Correspondence Theory builds from these foundations but asks a narrower and underdeveloped question: how does an organization preserve meaningful semantic and causal relationships across the representations through which organizational cognition is expressed, remembered, revised, and acted upon?

This question becomes especially important in AI-native organizations. AI systems can generate, transform, summarize, classify, recommend, and act upon representations at high speed and scale. Existing theories can explain parts of this phenomenon: AI may accelerate learning, distribute cognition across human and machine systems, alter feedback dynamics, support sensemaking, or expand organizational memory. Yet the representational problem remains. As the volume and speed of representations increase, organizations must preserve relationships among intent, decision, action, outcome, memory, policy, and machine-generated artifacts. Without such preservation, cognition may become faster but less coherent.

The residual explanatory gap is therefore specific. Existing organizational and systems theories explain cognition, learning, regulation, memory, adaptation, decision-making, interpretation, and knowledge flow, but none explicitly explain the recursive maintenance of the evolving organizational representation network from which Organizational Correspondence emerges. This gap is ontological before it is mechanistic: the paper must first specify the substrate in which correspondence can be preserved or lost. This motivates the representation-network ontology developed in the next section and the constructs and mechanism that follow.