STEEP/PESTLE Analysis¶
Core Idea¶
STEEP/PESTLE Analysis is a systematic framework for scanning and organizing external environmental factors—Social, Technological, Economic, Environmental, Political, (Legal, Ethical)—to detect how each dimension might influence future scenarios, strategic decisions, and organizational vulnerability. The defining commitment is structured external environmental sensing: treating uncertainty about future conditions not as unknowable but as systematically mappable across distinct factor categories, with the insight that most organizations catastrophically underweight some dimensions (typically environmental, ethical, or social) in favor of economic and technological factors, creating strategic blind spots. The framework originated in Aguilar's 1967 work on environmental scanning and has evolved through multiple naming conventions (PEST, PESTLE, STEEP) reflecting different prioritizations of dimensions. The deeper logic is that external factors operate orthogonally—a technically feasible product can fail from political regulation, social resistance, or environmental constraint—so strategic decision-making requires mapping all dimensions simultaneously rather than optimizing within a single frame. STEEP/PESTLE also enables scenario construction: by identifying which factors are changing rapidly (high uncertainty) versus stable (low uncertainty), organizations can construct a 2x2 matrix of plausible futures and stress-test strategy against multiple scenarios rather than betting on a single assumed future. The process also surfaces implicit assumptions: strategy teams often discover they have been collectively assuming particular political stability, technological progress, or social acceptance that is neither inevitable nor well-founded. The power of the framework derives not from perfect prediction but from systematic differentiation of what is known, unknown, and invisible due to disciplinary or cultural bias[1].
How would you explain it like I'm…
Outside-stuff checklist
Outside-world checklist
STEEP/PESTLE scan
Structural Signature¶
- The multi-dimensional environmental-factor categorization scheme parsing external influence into Social, Technological, Economic, Environmental, Political, and Legal/Ethical domains [2]
- The external-versus-internal boundary delineation establishing what organizations can influence versus what they face as given constraints [3]
- The scenario-construction mechanism linking factor trajectories to plausible future states and organizational implications [4]
- The blind-spot-exposure process surfacing dimensions systematically underweighted or ignored in current strategic thinking [5]
- The time-horizon differentiation distinguishing immediate-trend factors from slowly-emerging megatrends from distant wild-cards [4]
- The scenario-stress-testing work identifying which strategies remain viable across multiple futures versus those betting on single outcomes [6]
What It Is Not¶
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Not prediction. STEEP/PESTLE does not predict which specific future will occur; it maps uncertainty and identifies plausible futures. Organizations using it to generate one-point predictions are misapplying the framework and creating false confidence.
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Not purely technical forecasting. While technology is one dimension, STEEP/PESTLE is multivariate; technological advancement absent social acceptance or legal permission produces very different outcomes than technology plus enabling conditions.
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Not analysis of internal capabilities. STEEP/PESTLE focuses on external environmental factors; SWOT analysis adds internal strengths and weaknesses. Organizations confusing the two often miss the environmental constraints that render internal capabilities irrelevant.
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Not normative or prescriptive. The framework names what is changing in the environment; it does not say what should be done in response. Strategic response design requires separate work after STEEP/PESTLE mapping.
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Not comprehensive uncertainty reduction. Some factors remain fundamentally uncertain even with systematic scanning (black swans, genuine unknowns). The framework reduces predictable surprise more than it eliminates uncertainty.
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Not a one-time exercise. STEEP/PESTLE mapping becomes obsolete; organizations that perform it once and store results treat it as prediction rather than continuous environmental sensing. Mature practice revisits it quarterly or when major assumption shifts.
Broad Use¶
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Strategic planning and competitive analysis. Organizations face strategic decisions (enter new market, invest in new product, acquire competitor, divest line) in uncertain environments. STEEP/PESTLE enables scenario-based strategy that remains viable across multiple futures rather than betting on single assumed future.
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Startup and new-venture assessment. Entrepreneurs and venture teams use STEEP/PESTLE to map constraints and enablers across social acceptance, technological feasibility, economic viability, regulatory landscape, and environmental impact to identify whether business model is viable in current environment and likely futures.
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Government and policy planning. Public-sector organizations use STEEP/PESTLE to anticipate implications of infrastructure projects, public programs, and policy changes across multiple environmental dimensions, particularly to surface long-term environmental and social implications that short-term political cycles obscure.
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Organizational transformation and innovation. Teams undergoing significant change use STEEP/PESTLE to understand whether transformation is enabling change in the environment (exploiting emerging opportunity) or blocking inevitable change (resisting environmental force), a distinction determining whether resistance is rational or self-defeating.
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Risk identification and scenario planning. Organizations use STEEP/PESTLE to construct plausible crisis scenarios, identify early-warning indicators, and stress-test business continuity and crisis-response plans against scenarios that push different dimensions to extremes.
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Product and service design. Product teams use STEEP/PESTLE to anticipate how regulatory, environmental, and social factors might constrain or enable product features, market entry, and long-term viability, reducing risk of designing technically excellent products that cannot scale.
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Investment and capital-allocation decisions. Investors use STEEP/PESTLE frameworks to identify emerging opportunities (social/technological alignment creating new markets) and risks (regulatory, environmental, economic constraints threatening current assets).
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Non-profit and advocacy strategy. Advocacy organizations use STEEP/PESTLE to map how political, legal, social, and technological changes create windows of opportunity for campaigns or shift constraints on advocacy effectiveness.
Clarity¶
STEEP/PESTLE clarifies by forcing explicit naming of multiple external factor categories, preventing purely economic or technological thinking from crowding out political, environmental, social, or legal considerations that may determine outcomes. Without the structure, strategy teams often operate within implicit disciplinary bias—engineers optimize for technical feasibility, economists optimize for economic efficiency, lawyers focus only on legal constraints—and arrive at strategies that fail because dimensions outside their discipline were not adequately weighted. The frame makes invisible assumptions visible: the assumption of political stability becomes explicit when mapping political factors; the assumption of social acceptance becomes explicit when naming social factors; the assumption of environmental non-constraint becomes explicit when environmental factors are mapped. Many strategy failures are post-hoc explained as "unforeseen external changes" when the changes were visible in STEEP/PESTLE mapping that was never performed or was ignored.
Manages Complexity¶
STEEP/PESTLE decomposes the overwhelming complexity of environmental uncertainty into six distinct factor categories, making systematic scanning tractable. Rather than trying to anticipate all possible futures (infinite), organizations identify trends and discontinuities within each category and then combine category-specific scenarios into plausible cross-factor futures. The framework also supports prioritization: not all factors matter equally for all decisions. STEEP/PESTLE reveals which factors are most consequential for specific strategic decisions (technological factors may dominate one product's viability while environmental factors dominate another's), enabling focused scenario work rather than equal treatment of all dimensions. Cross-domain transfer is enabled: the STEEP/PESTLE framework applies with equal rigor to pharmaceutical strategy, manufacturing planning, non-profit advocacy, government infrastructure decisions, and technology startups, making it a meta-level framework across specialized strategy contexts.
Abstract Reasoning¶
STEEP/PESTLE training teaches the analyst to ask: What factors in each category are changing, and how fast? Which factors are most consequential for our strategy? What are we assuming about each factor's trajectory? What alternative trajectories are plausible? Which of our current strategic bets depend on particular factor trajectories, and what happens if those trajectories don't materialize? Which factors are we systematically underweighting due to discipline bias or organizational culture? What early-warning indicators could alert us to factor trajectories diverging from assumption? What would we do differently if factor X shifted significantly? The discipline trains habitual skepticism toward single-factor explanations and recognition that failures usually involve multiple factor misalignments rather than single dominant cause.
Knowledge Transfer¶
| Domain | Key factor categories | Scanning method | Common blind spot |
|---|---|---|---|
| Pharmaceutical/biotech | Tech feasibility, regulatory (political/legal), social acceptance, economic | Regulatory tracking, clinical trials, public opinion, health economics | Environmental impact of distribution/disposal, ethical implications of access disparity |
| Renewable energy | Tech maturity, economic viability, political will/incentives, environmental constraint drivers | Patent tracking, cost-curve analysis, policy changes, climate/resource data | Social acceptance of infrastructure (NIMBY), cultural meaning of energy transition |
| Software/SaaS | Tech platform evolution, regulatory (privacy/data/IP), economic market size, social adoption | Platform roadmap tracking, regulatory enforcement patterns, user analytics, social media trends | Environmental impact of data centers, supply-chain ethics, long-term geopolitical constraints on data flow |
| Financial services | Economic regulation, tech innovation, political intervention, social trust | Regulatory filings, fintech funding, legislative activity, consumer sentiment | Environmental factor in climate risk (emerging), social acceptance of inequality (emerging) |
| Consumer goods | Social trends, tech manufacturing/distribution, economic purchasing power, environmental constraint | Consumer research, supply-chain analysis, purchasing-power data, resource availability | Regulatory constraints (safety/labeling), political instability in supply regions, cultural values conflicts |
| Government infrastructure | Political will, environmental constraint, economic viability, social acceptance, tech feasibility | Policy statements, environmental impact assessments, cost-benefit analysis, public consultation, technology readiness | Technological disruption of assumed need (e.g., transit planning facing autonomous vehicle uncertainty) |
| Education/non-profit | Social demand, tech capability, economic funding, political/regulatory environment, environmental feasibility | Enrollment/participation data, platform capabilities, funding landscapes, regulatory tracking, physical constraints | Economic-social interaction (affordability blocking otherwise-demanded service), political constraints on mission freedom |
Transfer pattern: STEEP/PESTLE structure remains constant across rows; factor emphasis and scanning method vary by context. Organizations can import blind-spot patterns across domains—e.g., renewable energy's NIMBY lesson applies to infrastructure generally; biotech's ethical-access issue applies to AI and other powerful technologies; fintech's regulatory-disruption risk applies to any domain facing regulatory innovation.
Examples¶
Formal/abstract¶
A technology startup in the 2010s designed a ride-sharing platform with strong technical architecture (scaling capability, real-time matching, payment integration) and viable economic model (low-cost person-to-person matching). STEEP/PESTLE analysis would have mapped: Social (regulatory backlash from taxi interests and labor advocates predicting disruption); Technological (platform feasibility—adequate, but regulatory capture could constrain API access); Economic (low barriers to entry enabled multiple competitors; price wars likely); Environmental (increased vehicles in cities—emission and congestion implications); Political (incumbent industry influence, labor politics, municipal regulation authority); Legal/Ethical (labor classification, insurance liability, data privacy). Early startup focus on just technology and economic model (venture-capital-encouraged analysis) missed that political-regulatory dimensions would determine viability more than technical excellence. Platforms that mapped STEEP/PESTLE early and negotiated regulatory environments (working with cities, engaging labor concerns) survived; platforms that ignored regulatory dimensions and bet on "move fast and break things" faced existential regulatory threat in multiple jurisdictions. The analysis shows how multiple dimensions operate orthogonally: technical feasibility proved sufficient, but political opposition proved constraining[7].
Mapped back: This instantiates the structural signature directly—multidimensional factor categorization, identification of environmental constraints versus internal capabilities, scenario construction revealing political viability as binding constraint, surfacing of blind spot (regulatory environment), time-horizon differentiation (early political warning signals available to careful observers), and scenario stress-testing that would have revealed political-constraint scenarios.
Applied/industry¶
A pharmaceutical firm developing a novel drug treatment for a chronic disease faces STEEP/PESTLE mapping: Social (patient population willing to try novel treatment; some social resistance based on alternative-medicine preference; aging population increasing patient cohort); Technological (manufacturing feasible; dosing delivery mechanism works; clinical trial data positive); Economic (pricing model viable under insurance reimbursement; manufacturing cost allows profit margin; addressable market size $2B annually); Environmental (manufacturing facilities meet environmental standards; waste disposal protocols adequate); Political (regulatory pathway clear in US and EU; emerging-market regulatory stability uncertain; political pressure for drug-pricing transparency increasing); Legal/Ethical (patent protection ensures market exclusivity for years; ethical concerns about access inequality in developing nations; potential off-label use in vulnerable populations). Scenario construction: (1) Base case: regulatory approval, insurance reimbursement, scaled manufacturing, controlled off-label use—success outcome; (2) Political-constraint scenario: regulatory approval delayed by political appointees responding to drug-pricing opposition, reimbursement denied in some regions due to cost—reduced addressable market; (3) Ethical-reputation scenario: media attention on pricing inequality and developing-nation access drives regulatory and reputational risk, requires assistance programs reducing profitability—success contingent on managing reputation dimension; (4) Economic-scenario: competitor patent expires, generic competition emerges, pricing pressure—need for extended-indication development or superior distribution. Mature practice maps these scenarios not to predict which occurs but to identify which aspects of firm strategy are contingent on scenario assumptions. Strategy dependent on Scenario 1 is fragile; strategy robust across Scenarios 1-3 is more defensible. The process also surfaces implicit assumptions: team assumed regulatory approval timeline and insurance reimbursement as near-certain when political and economic dimensions create genuine uncertainty[8].
Mapped back: Shows how scenario construction reveals multiple plausible futures differing in political, economic, and social factor outcomes; how stress-testing strategy against scenarios identifies fragility; how environmental-dimension (ethical-access) becomes strategically consequential; how blind-spot exposure surfaces underestimated dimensions (pricing politics, access ethics); and how time-horizon differentiation (immediate regulatory decisions, medium-term competitive dynamics, long-term access equity) structures planning.
Structural Tensions¶
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T1: Breadth versus depth. STEEP/PESTLE is broad-brush across six categories; genuine mastery requires deep knowledge within each. Shallow STEEP/PESTLE mapping misses consequential dynamics within factors (e.g., political landscape has local, regional, national, supranational dimensions; superficial treatment misses relevant dynamics). Mature practice either develops cross-functional teams with deep factor expertise or accepts the limitations of shallow mapping and validates with external experts[5].
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T2: Trend-visibility versus signal-to-noise. Scanning produces enormous information; separating genuine trends from noise requires judgment. Early trend signals are ambiguous and can be mistaken for noise; entrenched trends are obvious but often already incorporated in strategy. The window for advantage is detecting trends before consensus but not so early that signal-to-noise ratio is useless. Mature practice uses leading indicators and leading-edge listening (startups, academic research, social media, policy innovation) to detect early signals[4].
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T3: Scenario plausibility versus scenario generation. Generating plausible scenarios requires avoiding both groupthink (all scenarios look like slightly-adjusted base case) and speculative excess (wild-card scenarios so implausible they paralyze rather than inform). The tension is distinguishing "unlikely but possible" (valuable for strategy stress-testing) from "fantasy" (not useful). Mature practice uses structured scenario-generation methods (cross-impact matrices, morphological analysis) and external red-team challenge to distinguish plausible-but-extreme scenarios from merely fanciful ones[6].
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T4: Environmental scanning versus action paralysis. Perfect environmental mapping is impossible; organizations can always scan one more dimension, identify one more uncertainty, wait for clearer signals. But strategic decisions cannot wait indefinitely. Mature practice establishes decision-trigger points: "we will decide when X indicator reaches threshold" or "we will update scan quarterly and decide with that rhythm regardless of information ideal"[9].
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T5: Consistency with organizational assumptions versus challenge to assumptions. STEEP/PESTLE often reveals that organization's current strategy depends on factor assumptions that are neither inevitable nor well-supported. Surfacing this requires leadership willingness to examine strategy legitimacy, not just refine it. Many organizations perform STEEP/PESTLE, recognize that assumptions are fragile, and then simply assert the assumption must hold rather than redesigning strategy. Mature practice treats assumption challenge as STEEP/PESTLE's primary value and uses it to drive strategy redesign[7].
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T6: Factor independence versus factor interaction. STEEP/PESTLE initially treats factors as separate domains; reality is that factors interact. Technological change enables political change, which enables social transformation; environmental constraint creates economic pressure, which creates political pressure. Mapping factors separately then ignoring interactions understates complexity and misses leverage points. Mature practice includes cross-factor interaction mapping, particularly identifying which factors are leading and which are following in specific historical moments[3].
Structural–Framed Character¶
STEEP/PESTLE Analysis is a hybrid on the structural–framed spectrum. Part of it is a bare pattern that means the same thing in any field — parsing a surrounding environment into distinct factor categories so that influences are scanned systematically rather than missed. Part of it is a frame inherited from strategic foresight and futurism, which fixes those categories and supplies the purpose.
The abstract move — carving an external space into mutually exhaustive categories so nothing is overlooked — is a fairly generic organizing structure. But the prime as it travels is not that neutral: it names specific buckets (Social, Technological, Economic, Environmental, Political, and sometimes Legal and Ethical), separates external forces from internal ones, and assumes an organization doing the scanning for the sake of its own decisions and vulnerabilities. Its concrete uses — corporate strategy planning, scenario and risk forecasting, or assessing a market entry — require importing that foresight vocabulary along with its prescriptive aim of better-informed planning. A simple categorization skeleton carries a substantial inherited frame, placing it on the framed side of the middle.
Substrate Independence¶
STEEP/PESTLE Analysis is a narrowly substrate-independent prime — composite 2 / 5 on the substrate-independence scale. It is a strategic-foresight methodology that sorts external factors into fixed buckets — Social, Technological, Economic, Environmental, Political, Legal and Ethical — and that factor-categorization scheme is itself the signature, which is inherently domain-specific. It is used across many industries, but it stays a forecasting and organizational tool rather than a structural pattern, and its spread reflects organizational context rather than genuine cross-substrate reuse. The checklist is useful, but it does not lift off its strategy-planning home.
- Composite substrate independence — 2 / 5
- Domain breadth — 2 / 5
- Structural abstraction — 2 / 5
- Transfer evidence — 2 / 5
Relationships to Other Primes¶
Parents (1) — more general patterns this builds on
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STEEP/PESTLE Analysis is a kind of Environmental Scanning
STEEP/PESTLE Analysis is a kind of environmental scanning specialized by a particular category schema: it organizes the external-factor space into Social, Technological, Economic, Environmental, Political, and Legal (sometimes Ethical) dimensions to ensure scanning coverage across each. It inherits environmental scanning's commitment to deliberate, continuing, structured monitoring of external factors for strategic relevance, and supplies the specific schema that counters organizational tendencies to systematically underweight some factor categories — typically environmental, ethical, or social — relative to economic and technological.
Path to root: STEEP/PESTLE Analysis → Environmental Scanning → Foresight
Neighborhood in Abstraction Space¶
STEEP/PESTLE Analysis sits in a sparse region of abstraction space (62nd percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely rather than landing on a neighbor.
Family — Strategic Foresight & Scanning (15 primes)
Nearest neighbors
- Critical Juncture — 0.79
- Boundary Critique — 0.79
- Stakeholder Analysis — 0.77
- Cultural Diffusion — 0.77
- Decision — 0.77
Computed from structural-signature embeddings · 2026-05-29
Not to Be Confused With¶
STEEP/PESTLE Analysis must be distinguished from Stakeholder Analysis, which operates in a related but distinct space. STEEP/PESTLE scans external environmental factors organized by domain (Social, Technological, Economic, Environmental, Political, Legal/Ethical) to identify how each dimension might shift and create strategic implications. Stakeholder Analysis, by contrast, maps the actors who have stakes in outcomes or decisions—customers, competitors, regulators, employees, communities—and characterizes their interests, power, and influence. The two frameworks are complementary but not synonymous: a STEEP/PESTLE analysis might reveal that environmental regulation is tightening (a Political-Legal factor), while Stakeholder Analysis identifies which specific regulators, environmental advocacy groups, and affected communities have influence over that regulatory evolution. An organization might complete a comprehensive STEEP/PESTLE map and still fail to implement strategy shifts if it has not identified which stakeholders will resist and how to navigate their opposition. Conversely, detailed stakeholder mapping without environmental-factor context fails to distinguish whether stakeholder interests are adapting to genuine external shifts or are simply defending familiar territory against imagined threats. Mature practice uses both: STEEP/PESTLE to understand what is changing in the environment, Stakeholder Analysis to understand who can accelerate or block response to those changes.
Nor is STEEP/PESTLE Analysis identical to Cross-Impact Analysis, though both examine how factors relate. STEEP/PESTLE identifies and categorizes external environmental factors across six domains, surfacing blind spots and structuring scenario development. Cross-Impact Analysis—a methodology originating in futures work—examines the pairwise conditional relationships between factors: "If Factor X occurs, how does it change the probability of Factor Y?" A STEEP/PESTLE analysis might identify that "battery technology maturity" (Technological factor) and "carbon-pricing policy" (Political factor) are both key uncertainties for renewable energy strategy. Cross-Impact Analysis would then ask: "If battery technology matures faster than expected, does this increase or decrease the probability that carbon pricing will be enacted, or does it make carbon pricing less necessary?" The distinction is that STEEP/PESTLE maps the landscape of factors and identifies which matter; Cross-Impact Analysis reveals causal interaction between factors, showing how some factor changes trigger cascades in others. A strategy robust to independent factor variation might be fragile to correlated factor change (technological breakthrough reducing pressure for policy intervention), a vulnerability visible only through cross-impact mapping. Mature practice often combines the two: STEEP/PESTLE to identify consequential factors, Cross-Impact Analysis to understand how those factors mutually condition each other.
STEEP/PESTLE Analysis is also distinct from Three Horizons Analysis, which operates at a different strategic level. STEEP/PESTLE identifies external environmental factors and their trajectories across the six domains, creating a picture of how the external context is shifting. Three Horizons Analysis maps the transition from current to future systems across overlapping time horizons: Horizon 1 is the current dominant system (business-as-usual); Horizon 2 is emerging changes and new entrants destabilizing the current system; Horizon 3 is the desired future system that will replace the current one. While both support future-oriented strategy, they answer different questions. STEEP/PESTLE asks "What factors in the external environment are changing, and how?" Three Horizons asks "What is the trajectory from the current system to the future system, and what is being preserved, transformed, and created along the way?" A technology company might use STEEP/PESTLE to identify that regulatory, environmental, and social factors are shifting demand toward sustainability, and Cross-Impact analysis to show how these factors reinforce each other. Three Horizons would then map: Horizon 1 = profit-maximizing traditional product line; Horizon 2 = emerging green competitors and regulatory pressure; Horizon 3 = fully circular business model with closed-loop supply chains and regenerative practices. STEEP/PESTLE identifies what is changing; Three Horizons structures the journey toward desired futures. Mature practice uses STEEP/PESTLE to ground the Three Horizons framework in external reality rather than allowing it to become purely aspirational.
Solution Archetypes¶
No catalogued solution archetypes reference this prime yet.
Notes¶
STEEP/PESTLE analysis has roots in Aguilar's 1967 foundational work on environmental scanning, with subsequent development by Fahey and Narayanan (1986), Johnson, Scholes, and Whittington's Exploring Strategy framework, and Porter's competitive-analysis extensions. The framework proliferated across strategic-planning consulting (McKinsey, BCG, Deloitte) and became standard in business schools, though often taught mechanically as checklist-filling exercise rather than as discipline for revealing hidden assumptions. The various naming conventions (PEST, PESTLE, STEEP) reflect different regional emphasis and evolving recognition of overlooked dimensions—late addition of Legal and Ethical dimensions reflects growing recognition that law and ethics were often divorced from strategy despite being consequential. Aguilar's original work emphasized that organizations' environmental-scanning capacity varied dramatically and that this variance predicted strategy success; contemporary research confirms that organizations with mature environmental-sensing capabilities (continuous factor tracking, expert interpretation, cross-functional sensemaking) significantly outperform those with episodic scanning or single-factor focus. Related to scenario planning (methodologies for constructing futures), strategic foresight (longer-horizon environmental sensing), systems thinking (understanding cross-factor interaction), and organizational sensemaking (collective interpretation of environmental signals).
References¶
[1] Aguilar, Francis J. (1967). Scanning the Business Environment. Macmillan. [^ansoff-1975]: Ansoff, H. I. (1975). Managing strategic surprise by response to weak signals. California Management Review, 18(2), 21–33. Foundational text on weak-signal management: prescribes governance design (reporting lines, decision authority, planning-cycle integration) that institutionalizes scanning as a strategic capability rather than ad-hoc activity. ↩
[2] Fahey, L., & King, W. R. (1977). "Environmental scanning for corporate planning." Business Horizons, 20(4), 61–71. ↩
[3] Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press. Treats rivalry intensity as a structural property of an industry (the five competitive forces) rather than a matter of individual firms' temperaments. ↩
[4] Ansoff, H. Igor. (1975). "Managing strategic surprise by response to weak signals." California Management Review, 18(2), 21–33. ↩
[5] Hambrick, D. C. (1981). "Strategic awareness within top management teams." Journal of Management Studies, 18(2), 201–221. ↩
[6] Schwanke, D. (2006). "The strategic environmental scan: Clarifying the process and use in planning." The Journal of Strategic Planning and Budgeting, 15(2), 47–59. ↩
[7] Daft, R. L., & Weick, K. E. (1984). "Toward a model of organizations as interpretation systems." Academy of Management Review, 9(2), 284–295. ↩
[8] Marcus, A. A., & Mandl, M. H. (1983). "Bridging the gap between the business and life sciences." The Sloan Management Review, 24(3), 17–26. ↩
[9] Choo, C. W. (2001). "Environmental scanning as information seeking and organizational learning." Information Research, 7(1), e-journal. ↩