Organizations today confront data-saturated markets where exponential growth in digital signals—from social media, IoT devices, customer interactions, and competitive intelligence—creates both unprecedented opportunities and profound challenges for strategic decision-making. Traditional sensemaking processes, rooted in retrospective interpretation of cues, struggle to cope with the velocity, volume, and ambiguity of real-time digital data streams, leading to information overload, signal-noise confusion, and delayed or misguided strategic actions. This theory-development article advances a novel framework of strategic digital sensemaking that explains how organizations can systematically interpret digital signals to reduce uncertainty, filter noise, and translate insights into competitive advantage amid rising market complexity. Drawing on sensemaking theory, dynamic capabilities, and big data analytics literature, the article synthesizes how cognitive, technological, and organizational mechanisms enable effective signal interpretation. It highlights the critical roles of analytics-enabled filtering, collective cognition, and iterative feedback loops in transforming raw digital data into actionable strategic knowledge. Five propositions articulate the relationships among data saturation, interpretation processes, uncertainty navigation, and strategic outcomes. A conceptual model visualizes the dynamic flow from digital signals through interpretation filters and cognition to strategic action, with feedback from outcomes refining future sensemaking. By integrating insights from strategic management, information systems, and organization studies, this manuscript contributes a processual theory that addresses gaps in understanding how firms achieve interpretive agility in data-rich environments. The framework offers actionable implications for managers seeking to build resilient sensemaking capabilities that sustain competitiveness under conditions of high uncertainty and complexity. Ultimately, strategic digital sensemaking emerges not as a static capability but as an ongoing, adaptive organizational practice essential for thriving in data-saturated markets.
Contemporary business landscapes are characterized by data saturation, where organizations generate and encounter vast quantities of digital signals at unprecedented speed and scale [1, 2]. These signals emanate from diverse sources, including real-time customer behaviors on digital platforms, sensor data from IoT ecosystems, social media sentiment, competitive moves tracked via analytics tools, and market intelligence feeds. While such abundance promises richer insights for strategy formulation, it simultaneously amplifies uncertainty and competitive complexity [3], overwhelming traditional decision-making routines [4-8].
Sensemaking, originally conceptualized as the process through which individuals and organizations interpret ambiguous cues to create plausible understandings that guide action [9], has become central to strategic management in volatile environments. However, classical sensemaking models—often retrospective and socially constructed—face limitations when confronted with the continuous, algorithmically mediated flow of digital data [4, 10]. In data-saturated markets, signals arrive asynchronously, exhibit high variability in quality, and are frequently entangled with noise [11], rendering interpretation prone to biases, overload, or paralysis [12]. Organizations must therefore evolve beyond passive cue extraction toward proactive, analytics-augmented sensemaking that actively filters, interprets, and enacts digital signals.
This imperative arises from several interlocking pressures. First, market uncertainty intensifies as digital disruption blurs industry boundaries and accelerates competitive dynamics [5, 13]. Second, information overload risks cognitive and organizational strain, where decision-makers struggle to distinguish weak but strategically vital signals from irrelevant noise [14, 15]. Third, competitive complexity demands rapid yet accurate responses; firms that lag in interpreting digital signals risk being outmaneuvered by agile rivals leveraging real-time analytics [6, 16].
Existing literature has advanced understanding in related domains. Research on market-sensing capabilities underscores the value of detecting and interpreting environmental changes for innovation and performance [17, 18]. Studies in information systems highlight how big data analytics capabilities support decision-making under complexity [7], yet often stop short of theorizing the interpretive mechanisms linking data to strategy [19]. Sensemaking scholarship has explored organizational responses to technological change and uncertainty, but rarely integrates the unique attributes of digital signal streams—volume, velocity, veracity, and variety [4, 10]. A synthesis gap persists: how do organizations develop and deploy strategic sensemaking processes specifically tailored to data-saturated conditions?
This article addresses that gap through theory development. It proposes that effective strategic sensemaking in data-saturated markets relies on three interconnected pillars: (1) technological filters and analytics systems that reduce noise and amplify salient signals; (2) cognitive and collective interpretation processes that imbue data with contextual meaning; and (3) iterative linkages from interpretation to strategic action, supported by feedback loops that refine future sensemaking. These elements enable firms to navigate uncertainty by transforming ambiguous digital inputs into coherent strategic narratives and actions [20, 21].
The manuscript proceeds as follows. The next section synthesizes theoretical foundations from sensemaking, dynamic capabilities, and digital strategy literatures. A dedicated theory development section then articulates core mechanisms and propositions. A conceptual model illustrates the proposed dynamics. The article concludes by outlining pathways for empirical extension while emphasizing practical relevance for managers operating in increasingly data-intensive competitive arenas. Through this contribution, we advance a process-oriented understanding of how organizations can interpret digital signals to sustain strategic advantage amid uncertainty and complexity [1, 5].
Strategic sensemaking draws on foundational insights from seminal work that shows how organizations reduce equivocality by noticing, bracketing, and interpreting cues within ongoing flows of experience [9]. In digital contexts, however, the cue environment shifts dramatically. Digital signals are not discrete events but persistent data streams characterized by high velocity and algorithmic intermediation [4], demanding new interpretive practices [10, 22].
The literature on data-saturated environments documents the challenges of volume-induced overload [11] and the resulting decision complexity [14]. Big data analytics capabilities have been positioned as remedies, enabling pattern detection and predictive insights that support market sensing [7, 19, 23]. Yet, analytics alone do not guarantee strategic value; interpretation requires human and organizational cognition to contextualize outputs, assign meaning, and link them to action [3, 15]. Studies in strategic management emphasize market-sensing capabilities as dynamic routines for detecting opportunities and threats in turbulent settings [17, 18, 24]. These capabilities integrate environmental scanning with internal knowledge creation, yet their operation in purely digital signal ecosystems remains underexplored.
Uncertainty navigation emerges as a recurring theme. Under high ambiguity, firms rely on heuristics, collective dialogue, and enactment to construct plausible realities [9, 20]. In data-rich markets, uncertainty stems not only from environmental volatility but from signal ambiguity—questions of relevance, reliability, and causality [12, 21]. Competitive complexity further compounds this, as rivals simultaneously generate and interpret overlapping digital signals, creating hyper-competitive feedback loops [6, 16].
Synthesis across journals reveals convergence on the need for hybrid human-AI interpretation systems. Analytics tools excel at filtering and scaling data processing [10], while organizational sensemaking supplies the social and contextual layers necessary for strategic judgment [22, 25]. The dynamic capabilities literature complements this by framing sensemaking as a microfoundation for sensing, seizing, and transforming resources in digital transformation [5, 13, 26-28]. However, few works articulate the full processual flow from raw digital signals through multi-layered interpretation to enacted strategy with explicit attention to feedback and adaptation.
This synthesis identifies a theoretical opportunity: to develop an integrated model of strategic digital sensemaking that explicates mechanisms for signal filtering, uncertainty reduction, noise-signal differentiation, and action linkage in data-saturated markets. The following section advances such a theory.
Strategic digital sensemaking is an adaptive, multi-level process in which organizations notice, filter, interpret, and enact digital signals to construct actionable understandings amid uncertainty and competitive complexity. Unlike traditional sensemaking, it foregrounds technology-augmented cognition and iterative loops that continuously calibrate interpretation against outcomes [4, 10, 22].
Core mechanisms include:
Signal detection and filtering: Analytics systems and AI-driven tools serve as initial sieves, reducing volume by identifying anomalies, patterns, or deviations from baselines [7, 19, 23].
Interpretive enrichment: Collective and individual cognition layers contextual meaning, drawing on organizational memory, mental models, and cross-functional dialogue to resolve ambiguity [9, 20, 25].
Enactment and feedback: Interpreted insights translate into strategic experiments or actions, whose results feed back to refine filters and cognitive frames [5, 13, 26].
In data-saturated markets, the effectiveness of strategic sensemaking increases when organizations deploy advanced analytics systems as active filtering mechanisms that prioritize high-fidelity digital signals over noise, thereby reducing cognitive overload and enhancing interpretive accuracy [3, 11, 14].
Collective sensemaking processes moderate the relationship between raw digital signal volume and strategic insight quality; greater cross-functional dialogue and shared mental models enable more robust uncertainty reduction [9, 20, 25].
Organizations that link signal interpretation directly to rapid, low-cost strategic experiments achieve superior navigation of competitive complexity by generating real-time feedback that updates interpretive schemas [5, 16, 26].
The interplay between human cognition and AI-augmented analytics creates hybrid interpretation capabilities that outperform either alone in distinguishing weak strategic signals from ambient digital noise under high uncertainty [7, 10, 22].
Feedback loops from strategic outcomes to sensemaking filters and cognition constitute a self-reinforcing dynamic capability, enabling sustained adaptation in data-saturated environments [13, 23, 26].
Figure 1 shows the conceptual model of strategic digital sensemaking.
Figure 1. Conceptual model of strategic digital sensemaking
These propositions and the model advance theory by identifying and clarifying the underlying mechanisms through which digital data abundance can be translated into strategic outcomes, thereby responding directly to calls for richer processual explanations of organizational adaptation in dynamic digital contexts [1, 2, 6]. Rather than treating digital information simply as an exogenous input to strategy, the model conceptualizes data-saturated environments as interpretive arenas in which meaning is continuously constructed, contested, and revised. In doing so, it explains how organizations move from mere exposure to abundant digital signals toward the development of patterned strategic responses. This is theoretically important because existing work has often acknowledged the strategic relevance of digital data without sufficiently specifying how organizations filter, interpret, socialize, and act on those signals over time. By foregrounding recursive cycles of signal capture, collective interpretation, enactment, and recalibration, the framework shows that competitive outcomes do not emerge from data volume alone, but from the organizational capacity to transform digital stimuli into coherent strategic action. In this sense, the model contributes to theory by bridging digital market complexity with the micro-processes of managerial interpretation and coordinated response, revealing how strategic outcomes are produced through ongoing sensemaking rather than one-time analytical optimization.
Table 1 differentiates major categories of digital signals by linking each to its distinctive interpretive challenge, filtering logic, collective sensemaking requirement, and strategic output pathway.
Table 1. From digital signal categories to strategic interpretation: a signal–meaning–action architecture for data-saturated markets
Digital signal category | Typical data sources | Primary interpretive challenge | Filtering and analytic logic | Collective sensemaking task | Strategic output pathway |
Customer sentiment signals | Social media posts, reviews, chat logs, and service interactions | Distinguishing transient emotional spikes from durable preference shifts | Sentiment clustering, anomaly detection, topic modeling, and trend persistence checks | Interpret whether sentiment reflects service failure, feature dissatisfaction, symbolic backlash, or emerging demand | Product adjustment, communication repositioning, service redesign, and pricing refinement |
Behavioral engagement signals | Clickstream, app usage, dwell time, search behavior, and platform journeys | Separating exploratory behavior from purchase intent or churn risk | Funnel analysis, cohort tracking, conversion sequence mapping, and deviation detection | Determine whether behavior indicates curiosity, friction, confusion, or switching likelihood | Interface redesign, retention intervention, experimentation, and customer journey reconfiguration |
Competitive intelligence signals | Rival price moves, digital campaigns, product launches, and platform ranking shifts | Inferring competitor intent under incomplete information | Benchmark monitoring, pattern comparison, velocity analysis, and response correlation | Assess whether moves reflect offensive positioning, defensive reaction, signaling, or temporary tactical adjustment | Counter-positioning, rapid response, selective imitation, and differentiation strategy |
Operational ecosystem signals | IoT streams, supply chain feeds, logistics events, and system uptime data | Distinguishing routine variation from strategically meaningful disruption | Threshold alerts, predictive maintenance models, anomaly detection, and causal tracing | Interpret whether deviations imply local malfunction, systemic fragility, or emerging demand-supply misalignment | Resource redeployment, process adaptation, resilience investment, and capacity rebalancing |
Market discourse signals | Media narratives, analyst commentary, community forums, and influencer discourse | Detecting narrative shifts before formal market indicators move | Semantic trend analysis, discourse mapping, and issue salience tracking | Evaluate whether discourse signals reputational risk, category transition, legitimacy challenge, or opportunity framing | Narrative reframing, market education, positioning revision, and stakeholder engagement |
Institutional and regulatory signals | Policy updates, legal notices, standards guidance, and industry mandates | Translating formal signals into operational and strategic implications | Rule extraction, compliance screening, and scenario mapping | Interpret whether signals indicate minor compliance adjustment or major strategic constraint/opportunity | Governance redesign, capability investment, market entry/exit recalibration, and risk mitigation |
In data-saturated environments, competitive complexity manifests as rapid, interconnected moves across digital channels where rivals’ signals influence and react to one another in real time [6, 16]. Competitive dynamics in such contexts are no longer shaped solely by periodic market analysis or discrete strategic planning cycles; instead, they unfold through dense streams of digital traces generated simultaneously by consumers, platforms, algorithms, partners, and competitors. Signals emerge from multiple sources—social media reactions, clickstream behavior, search patterns, platform rankings, online reviews, dynamic pricing changes, and digital advertising shifts—and these signals are often ambiguous, transient, and strategically consequential. As a result, firms must operate in environments where market conditions are not simply observed but actively interpreted through ongoing interactions with digitally mediated feedback. Organizations that excel at strategic digital sensemaking gain an advantage by converting fragmented, fast-moving signals into timely, differentiated actions that reshape market positions. This requires not only accurate interpretation but also the willingness to enact plausible understandings before full clarity emerges—a hallmark of high-velocity decision environments [5, 13].
Such enactment is central because competitive advantage in complex digital markets often depends less on perfect prediction than on the ability to move early, learn quickly, and adapt continuously. In these settings, firms rarely possess complete information, and waiting for interpretive certainty may mean losing strategic initiative. Strategic digital sensemaking, therefore, becomes valuable when organizations can formulate provisional interpretations that are credible enough to support action while remaining flexible enough to be revised as new signals emerge. This interpretive agility enables firms to respond to complexity not by eliminating uncertainty, but by organizing around it. Rather than becoming paralyzed by data abundance, strategically capable firms develop routines for converting ambiguity into direction. In this way, sensemaking serves as a mechanism through which uncertainty is rendered actionable, allowing firms to create advantage through speed, responsiveness, and adaptive coherence.
For instance, firms may use interpretable customer sentiment signals from social platforms to dynamically adjust product features or pricing, thereby preempting competitors’ responses. A company monitoring sudden negative sentiment around a product attribute may identify not merely dissatisfaction, but a deeper emerging preference shift that rivals have not yet recognized. Managers may then translate this interpretation into rapid design modifications, targeted communication campaigns, or revised promotional strategies. These responses do not simply react to the environment; they also shape it by influencing how customers, competitors, and other stakeholders respond in turn. Such enactment generates new digital traces (e.g., updated engagement metrics) that loop back, allowing continuous calibration of interpretive frames. Over time, this builds interpretive agility, turning sensemaking into a self-reinforcing source of sustained competitive advantage rather than a one-off exercise [23, 26].
This recursive relationship between interpretation and action is particularly important for understanding how advantage is sustained. Competitive gains do not arise only because a firm correctly reads a market signal once, but because it develops an organizational capability to repeatedly interpret, enact, and learn faster than rivals. Each cycle of action produces additional feedback, which sharpens future interpretation and strengthens shared strategic understanding. As these cycles accumulate, firms develop what may be described as an interpretive memory: a repertoire of frames, cues, and responses that helps them recognize emerging patterns more quickly and act with greater confidence. This process transforms digital sensemaking from an episodic managerial activity into an embedded organizational capability. In highly contested markets, such capability becomes difficult for competitors to imitate because it is rooted not only in technology, but also in shared cognition, cross-functional coordination, and the social processes through which meaning is collectively stabilized and revised.
The proposed theory thus positions strategic digital sensemaking as a higher-order dynamic capability that integrates technological affordances with organizational cognition to address the unique demands of data saturation. This positioning is significant because it extends dynamic capability thinking beyond broad notions of sensing and seizing toward a more granular explanation of how organizations interpret digital complexity in practice. It suggests that technological infrastructure alone does not generate advantage unless it is coupled with interpretive routines, dialogic processes, and decision mechanisms that enable organizations to distinguish meaningful signals from background noise. The theory, therefore, moves beyond static views of market sensing by emphasizing the ongoing, adaptive cycles necessary for thriving amid uncertainty and complexity. In doing so, it reframes competitiveness in digital markets as a function of interpretive orchestration: the ability to align analytical tools, managerial judgment, and collective meaning-making in ways that support timely and strategically consequential action.
Moreover, the framework implies that digital sensemaking does not merely help firms respond to change; it also enables them to shape competitive trajectories. Because enactment feeds back into the digital environment, firms can influence customer expectations, alter rival behavior, and redefine the informational cues circulating in the market. A strategically agile organization may, for example, use early, interpreted insights to launch experimental offerings, adjust platform positioning, or reframe market narratives to trigger new forms of engagement. These moves generate further information advantages, because the organization is then better positioned to observe how the market responds to its interventions. In this formulation, the advantage is therefore not passive or reactive. It is actively produced through recursive cycles in which organizations interpret environments, intervene in them, and then learn from the consequences of those interventions. This makes strategic digital sensemaking especially relevant in markets characterized by velocity, interdependence, and signal overload.
While the core mechanisms apply broadly, several boundary conditions warrant attention. First, the effectiveness of analytics-enabled filtering depends on data quality infrastructure; organizations lacking robust governance may amplify rather than reduce noise [7, 19]. Data abundance becomes strategically useful only when the underlying architecture supports reliability, interoperability, accessibility, and interpretive transparency. In the absence of such foundations, analytics systems may generate misleading outputs, reinforce weak signals, or flood decision-makers with poorly curated information. Under such conditions, the very tools intended to support sensemaking may undermine it by increasing ambiguity and encouraging false confidence in analytically derived patterns. Thus, the theory is most applicable where digital infrastructures are sufficiently mature to support trustworthy signal processing and where governance mechanisms ensure that data inputs are relevant, timely, and interpretable.
Second, collective sensemaking processes are more potent in decentralized structures that encourage psychological safety and diverse perspectives, whereas highly hierarchical cultures may constrain interpretive richness [20, 25]. Because digital signals are often equivocal, their strategic value depends on the quality of collective interpretation surrounding them. Organizations that foster open dialogue, cross-functional interaction, and distributed participation are more likely to surface competing interpretations, challenge premature assumptions, and synthesize richer strategic understandings. By contrast, rigid hierarchies may narrow interpretive participation, reduce dissent, and privilege top-down frames that suppress weak but important emerging signals. In such contexts, digital sensemaking may become overly centralized and less adaptive, limiting the organization’s ability to recognize complexity in a nuanced way. This suggests that the benefits of strategic digital sensemaking are partly contingent on organizational culture and structure, especially the degree to which interpretive work is shared rather than monopolized.
Third, in extremely high-velocity settings (e.g., algorithmic trading or real-time platform ecosystems), the balance between speed and accuracy may require greater reliance on automated enactment loops, raising questions about over-automation and the loss of human judgment [10, 22]. In these environments, the tempo of data generation and competitive interaction may exceed the capacity of human collectives to interpret signals in real time. Organizations may therefore delegate portions of sensemaking and enactment to algorithmic systems that classify signals, initiate responses, and autonomously recalibrate actions. While this can increase responsiveness, it also introduces new tensions related to explainability, accountability, and strategic oversight. Excessive automation may produce rapid but brittle responses if algorithmic systems overfit short-term patterns or fail to capture contextual nuances that human judgment would otherwise detect. Consequently, the theory may require modification in contexts where automated interpretation becomes dominant, particularly regarding the role of human supervision and the design of override mechanisms that preserve strategic discretion.
Additional boundary conditions may also emerge from differences in market maturity, regulatory intensity, and competitive concentration. In relatively stable industries, the strategic value of recursive digital sensemaking may be less pronounced because environmental signals change more slowly and can be interpreted through established routines. Conversely, in turbulent and platform-mediated sectors, the capability may become central to organizational survival. Similarly, heavily regulated environments may constrain the speed or scope of enactment, thereby altering the relationship between interpretation and competitive action. These contextual variations do not invalidate the theory, but they suggest that the strength and form of the proposed mechanisms may vary across institutional and industrial settings.
Future extensions could explore how industry-specific digital signal ecosystems (e.g., healthcare vs. consumer goods) moderate these relationships, or how leadership styles influence the institutionalization of sensemaking rituals. Industries differ not only in the volume of available signals but also in their interpretive characteristics. Healthcare organizations, for example, may confront digital signals shaped by regulatory requirements, clinical risk, and ethical considerations. In contrast, consumer goods firms may operate in more fluid, sentiment-driven environments where rapid experimentation is easier. These differences may influence which signals are considered legitimate, how quickly organizations can act on them, and what forms of collective interpretation are feasible. Investigating such variation would deepen understanding of how digital sensemaking operates under different informational, professional, and institutional regimes.
Leadership is another promising extension because leaders play a critical role in shaping whether sensemaking becomes episodic or institutionalized. Leaders influence the norms governing attention, interpretation, experimentation, and revision. They can create structures that encourage iterative learning, legitimize ambiguity, and support the integration of analytic outputs with contextual judgment. Alternatively, leadership styles that overemphasize certainty, control, or speed may distort interpretive processes by privileging immediate action over reflective understanding. Future research could therefore examine how leadership behaviors affect the durability of sensemaking routines, the openness of interpretive climates, and the organization’s willingness to revise established frames when confronted with contradictory digital evidence.
Cross-cultural variations in uncertainty tolerance may also shape the adoption and outcomes of hybrid human-AI interpretation [4, 11]. Organizations embedded in cultures with higher tolerance for ambiguity may be more willing to rely on provisional interpretations, iterative experimentation, and evolving meaning structures. By contrast, contexts characterized by low tolerance for uncertainty may favor more formalized procedures, greater emphasis on analytical certainty, and slower enactment. These differences could significantly affect how hybrid human-AI systems are designed, trusted, and used in strategic decision processes. Comparative studies across national and organizational cultures would therefore offer valuable insights into the social and institutional contingencies of digital sensemaking capability.
Taken together, these boundary conditions and future extensions reinforce the broader contribution of the theory: strategic digital sensemaking should not be understood as a universal or purely technological solution to competitive complexity, but as a contingent organizational capability shaped by infrastructure, culture, governance, speed, leadership, and institutional context. Its value lies in enabling firms to transform data abundance into strategic responsiveness through recursive cycles of interpretation and enactment. Yet the quality of those cycles depends on organizational conditions that either support or constrain meaning-making in the face of digital complexity. By recognizing these contingencies, the theory becomes more analytically robust and better positioned to guide future empirical inquiry into how organizations navigate data-saturated markets.
For practitioners, the framework offers concrete guidance: invest in layered filtering technologies while simultaneously nurturing cross-functional interpretation forums; design low-risk experimentation protocols to test interpretations quickly; and establish feedback mechanisms that systematically update both algorithms and mental models. Leaders should cultivate a culture that values plausible action over perfect prediction, recognizing that in data-saturated markets, delayed sensemaking often equates to lost opportunity [5, 17, 24]. By treating digital signals as raw material for ongoing strategic narrative construction, managers can build organizations that are not overwhelmed by data but empowered by it.
This theory-development effort synthesizes and extends existing foundations into a cohesive process model tailored to contemporary digital realities. It fills a critical gap by detailing the micro-mechanisms through which organizations interpret signals, navigate uncertainty, filter noise, and link insights to action under competitive pressure. The eight propositions provide testable relationships for future empirical work, while the overall framework advances strategic management and information systems scholarship toward a more dynamic, digitally native understanding of sensemaking.
The proposed theory of strategic digital sensemaking makes three primary contributions to the literature. First, it extends classical sensemaking theory [9] into digital contexts by specifying how analytics-augmented filtering and hybrid human-AI cognition address the unique challenges of volume, velocity, and veracity in data-saturated markets [4, 10, 22]. Unlike retrospective sensemaking, the framework emphasizes prospective, real-time interpretation cycles that match the pace of digital signals.
Second, it bridges dynamic capabilities research with information systems scholarship by positioning interpretive agility as a micro-foundation for sensing and seizing in turbulent environments [5, 13, 26]. The eight propositions delineate testable relationships, such as the moderating role of collective rituals in uncertainty reduction and the self-reinforcing nature of feedback loops, filling a processual gap identified in recent reviews [15, 20].
Future research should empirically test the propositions using longitudinal case studies or mixed-methods designs tracking sensemaking processes across digital transformation initiatives. Cross-level analyses could examine how individual cognitive biases interact with organizational filters, while comparative studies across emerging versus mature markets would illuminate contextual moderators [17, 18, 24].
In data-saturated markets, the ability to interpret digital signals effectively determines which organizations thrive amid uncertainty and competitive complexity. This manuscript has developed a processual theory of strategic digital sensemaking that integrates filtering mechanisms, collective cognition, uncertainty navigation, and enactment cycles into a coherent dynamic capability. By transforming data abundance from a source of overload into a foundation for strategic foresight and action, firms can build interpretive resilience that sustains competitive advantage.
Managers should prioritize investments in layered analytics tools alongside cultural practices that foster shared interpretation and rapid experimentation. As digital signals continue to proliferate, strategic digital sensemaking will evolve from a supportive process to a core organizational competence. The framework offered here provides both theoretical grounding and practical guidance for navigating the interpretive demands of the digital age. Ultimately, organizations that master these mechanisms will not merely survive data saturation—they will shape the markets of tomorrow.
None
None
None
None
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Institute for Management, Business, and Accounting Studies
