Decision Science in Organizational Strategy - Sustainable Catalyst

Last Updated April 22, 2026

Decision science in organizational strategy examines how firms make consequential choices under uncertainty when competition, capability, cognition, time, and institutional constraints interact. At its deepest level, strategy is not merely about vision or planning. It is about structured judgment: how organizations interpret incomplete information, compare alternatives, allocate scarce resources, commit under uncertainty, and adapt when the environment changes faster than prior assumptions can hold.

Much of mainstream strategy has been taught through frameworks of industry structure, positioning, scale, innovation, or competitive advantage. These remain useful, but they can become static when treated as though strategy were simply a matter of selecting the right framework. Decision science pushes further. It asks how strategic judgments are actually formed, what kinds of uncertainty organizations face, where human reasoning breaks down, how institutions can compensate for those limits, and how strategic resilience can be built when the future cannot be forecast with precision.

This is part of the Decision Science knowledge series.

Corporate leadership team analyzing strategic decisions using data models, scenario planning, and decision frameworks in a modern boardroom — Strategic decision-making under uncertainty integrating data, models, and organizational judgment

Why decision science matters for strategy

Strategic failure is often described after the fact in terms of bad execution, weak leadership, technological disruption, or market misreading. Those explanations are usually incomplete. Beneath them lies a deeper problem: the organization misframed the decision environment. It treated a dynamic system as stable, a contested future as predictable, a temporary advantage as durable, or a model-generated representation as equivalent to reality. Decision science matters because it focuses attention on how these errors emerge before they become visible in performance.

Organizational strategy is fundamentally a sequence of interlocking decisions. Firms decide which customers to serve, which capabilities to build, which technologies to invest in, which risks to absorb, which opportunities to ignore, and which time horizon should govern present trade-offs. Those decisions cannot be reduced to spreadsheet optimization alone because the strategic environment is not fully knowable. It is shaped by rivals, regulators, technologies, consumers, macroeconomic shifts, cultural expectations, and internal institutional behavior, all of which evolve interactively.

That is why strategy belongs naturally inside decision science. A firm does not need perfect foresight to make superior choices. It needs decision processes that remain intelligent when forecasts are incomplete, when assumptions are contestable, and when adaptive learning becomes more important than static plans. This makes strategy less an exercise in predictive certainty than an exercise in disciplined judgment under structured uncertainty.

Decision science matters because the real strategic question is rarely whether an organization can choose. It is whether it can choose coherently when the environment refuses to become simple.

Strategy as an architecture of choice

At a high level, strategy can be defined as the organization of major commitments under conditions of limited knowledge. It identifies priorities, allocates attention, disciplines trade-offs, and links present action to an imagined future. Decision science adds analytical clarity by showing that strategy is not a singular decision but an architecture of choice made up of multiple interacting layers.

Strategic direction

This layer concerns mission, ambition, domain definition, and the question of where the organization intends to create value. It includes choices about market scope, identity, and long-horizon aspiration.

Strategic posture

This layer concerns how the firm behaves under uncertainty. Does it attempt to shape the environment, adapt cautiously, hedge through optionality, or consolidate existing strengths while monitoring the field? Strategic posture becomes especially important when uncertainty cannot be compressed into a stable forecast.

Strategic allocation

This layer covers resource commitment: capital, talent, managerial attention, time, and political will. Every strategy is ultimately revealed by what the organization is willing to fund, sustain, and defend.

Strategic learning

This final layer concerns whether the organization can update. It includes sensing weak signals, revising assumptions, abandoning sunk-cost commitments, and preserving enough institutional flexibility to reconfigure when reality shifts.

Seen this way, strategy is not simply a plan document. It is an ongoing decision system that links foresight, interpretation, resource commitment, and feedback. It is a living architecture in which aspiration, evidence, timing, and institutional capacity must continually be brought back into relation.

Intellectual foundations

The deepest foundations of decision science in organizational strategy are interdisciplinary. Economics contributed formal theories of choice, incentives, market competition, and resource allocation. Strategic management contributed concepts such as industry structure, positional advantage, firm heterogeneity, and capability development. Psychology contributed bounded rationality, heuristics, framing, attention, and bias. Systems thinking contributed feedback, delay, nonlinearity, and emergent outcomes. Organizational theory contributed governance, routines, institutional memory, and the politics of collective choice.

One of the most important turning points came with Herbert Simon’s work on decision-making inside organizations. Simon argued that real decision-makers do not optimize across all possibilities with perfect information. They operate under cognitive constraints, limited information, and finite computational capacity. In organizations, this means that strategy is always shaped by bounded rationality rather than idealized omniscience. The Nobel Prize materials remain especially useful because they make clear that Simon’s contribution was not marginal to business decision-making, but foundational to it. Nobel Prize, 1978 press release; Simon, Nobel lecture.

Later strategy scholarship expanded this foundation. Work on uncertainty emphasized that firms need different strategic responses depending on the character of the unknowns they face. The classic argument in Strategy Under Uncertainty is still powerful because it shows that strategy must vary with the level of uncertainty rather than assuming that all planning problems are structurally alike. Courtney, Kirkland and Viguerie (1997). Meanwhile, dynamic capabilities research argued that sustained advantage in volatile environments depends less on static assets alone than on the organization’s ability to sense change, seize opportunities, and reconfigure capabilities over time. Teece, Pisano and Shuen (1997).

These traditions converge because they all confront the same central problem: how organizations should choose when both the environment and the organization itself are only partially intelligible. Decision science does not dissolve this problem. It gives organizations better languages, models, and procedures for inhabiting it intelligently.

Bounded rationality and strategic judgment

No serious account of organizational strategy can assume that executives survey every option, calculate every consequence, and choose the global optimum. Real organizations are constrained by time, attention, political negotiation, information asymmetries, and cognitive overload. Bounded rationality is therefore not a peripheral caveat. It is the baseline condition of strategic life.

Simon’s insight remains powerful because it reframes strategic decision-making from ideal optimization toward satisficing, search, and procedural intelligence. Organizations often do not find the best possible strategy in a global sense. They find an acceptable strategic path through bounded search, local experimentation, precedent, coalition-building, and iterative revision. This is not necessarily irrational. Under complex conditions, a well-designed satisficing process may outperform the illusion of exhaustive optimization.

The practical importance of bounded rationality in strategy appears in several places:

Attention is scarce. Organizations cannot monitor every signal, so what gets noticed is itself strategic.
Interpretation is selective. Data do not speak for themselves; they are filtered through narratives, incentives, and institutional memory.
Choice sets are socially constructed. The options considered by leaders depend on who is in the room, what assumptions dominate, and what alternatives seem legitimate.
Time pressure compresses analysis. Many strategic choices must be made before exhaustive certainty is possible.

Decision science therefore treats strategic judgment as something that must be designed, not assumed. Better strategy comes not from pretending bounded rationality does not exist, but from building processes that compensate for it. Good judgment at the organizational level is procedural before it is heroic.

Uncertainty, ambiguity, and strategic posture

Strategy becomes especially difficult when the future is not merely risky but ambiguous. It is useful to distinguish among several forms of uncertainty.

Risk

In conditions of risk, probabilities are imperfect but sufficiently tractable that forecasting and optimization can still guide decisions. Mature industries with relatively stable demand patterns often contain many problems of this sort.

Uncertainty

In conditions of uncertainty, decision-makers know the key variables but cannot confidently assign stable probabilities or forecast outcomes with precision. Here, planning must become more adaptive and scenario-sensitive.

Ambiguity

Ambiguity arises when the causal structure itself is contested. It is not simply that the outcome is unknown; it is that the organization may not even agree on what the relevant model of reality is. This is common in technological transitions, shifting consumer norms, geopolitical fragmentation, and emerging regulatory environments.

Deep uncertainty

Under deep uncertainty, actors do not know or cannot agree on the appropriate models, key variables, probability distributions, or long-term system behavior. In these contexts, robust and flexible strategies become more important than precise optimization. This is why the present article should be read alongside Decision-Making Under Deep Uncertainty and Robust Decision-Making.

Different uncertainty conditions require different strategic postures. In relatively stable conditions, firms may focus on efficiency, scale, and selective optimization. In more turbulent environments, they may need portfolios of options, staged commitments, modular investments, and learning-oriented experimentation. Decision science matters because it disciplines the match between the nature of uncertainty and the form of strategic response.

A core error in strategy is not simply underestimating uncertainty. It is using a decision posture built for one uncertainty regime inside another.

Competitive positioning and the limits of static analysis

Traditional strategic analysis often emphasizes competitive positioning: industry structure, relative cost position, differentiation, barriers to entry, and defensibility. These remain essential. Firms still need to understand value chains, market power, competitor behavior, and the economics of substitution. But static positioning has limits when environments become more fluid and interdependent.

A strategy that appears coherent in a static frame may become fragile once dynamics are introduced. Rival adaptation can erode advantage. Technologies can shift customer expectations. Regulatory changes can alter cost structures. Supply-chain dependencies can transform operational assumptions into strategic vulnerabilities. Culture, talent retention, and learning speed can become more important than scale alone.

Decision science expands competitive analysis by asking not just where the firm stands, but how that position evolves under feedback and interaction. Strategic value often depends on second-order questions:

How will competitors respond to the move?
What organizational capabilities are required to sustain the chosen position?
What path dependencies lock the firm into obsolete assumptions?
How sensitive is the position to shocks in regulation, technology, or demand?

Static analysis remains useful, but it is not enough. Strategy becomes more realistic when competitive positioning is embedded inside a broader decision architecture that includes adaptation, learning, and resilience. The point is not to discard structure, but to stop pretending that structure stands still.

Dynamic capabilities and adaptive strategy

One of the most influential responses to the limits of static strategy has been the dynamic capabilities perspective. Rather than asking only what resources a firm possesses, this literature asks whether the organization can renew itself in changing conditions. Teece, Pisano and Shuen’s classic formulation emphasized that firms require the ability to integrate, build, and reconfigure internal and external competences in rapidly changing environments. Strategic Management Journal, 1997.

This matters profoundly for decision science because dynamic capabilities are not just assets. They are decision capacities. They include the ability to sense changes in the environment, seize opportunities through timely commitment, and transform the organization when existing routines become misaligned with external reality. In other words, they are institutionalized forms of strategic judgment.

Dynamic capabilities help explain why some firms survive disruption better than others. They do not merely possess more information. They interpret more effectively, commit more selectively, and reconfigure more coherently. Such capabilities often involve cross-functional coordination, disciplined scanning, scenario thinking, leadership alignment, and the ability to let go of once-successful but now outdated routines.

From a decision-science perspective, adaptive strategy is less about constant change for its own sake than about calibrated reconfiguration. Organizations need enough stability to preserve identity and execution, but enough plasticity to remain viable when the environment shifts. The strategic problem is therefore one of balance: how to preserve coherence without becoming rigid, and how to adapt without dissolving into permanent improvisation.

Behavioral strategy and organizational cognition

Behavioral strategy extends decision science by bringing psychological realism into strategic management. Organizations do not simply analyze markets; they perceive them through the cognition of leaders, teams, boards, and institutional routines. As a result, strategy is shaped by framing effects, status-quo bias, escalation of commitment, overconfidence, groupthink, narrative fixation, and selective interpretation.

Several recurring distortions are especially important:

Overconfidence

Leaders may overestimate the reliability of forecasts, the distinctiveness of their capabilities, or their ability to control external conditions. This can produce oversized commitments to weakly tested assumptions.

Escalation of commitment

Once the organization has invested money, prestige, or identity in a strategy, it may continue despite mounting evidence that the path should be revised. Sunk costs become cognitively and politically sticky.

Framing effects

The same strategic problem looks different depending on whether it is framed as defense, growth, innovation, or survival. Frames structure the perceived choice set.

Groupthink and consensus pressure

Senior teams may confuse agreement with sound judgment, especially when dissent is culturally costly or strategically inconvenient.

Behavioral strategy does not imply that organizations are irrational in a simplistic sense. Rather, it shows that strategic cognition is situated and shaped by context. Good strategy therefore depends on designing decision processes that expose assumptions, surface dissent, and counter known cognitive vulnerabilities. This is one reason decision protocols such as red teaming, premortems, staged investment reviews, and explicit alternative-generation exercises can materially improve strategic quality.

Organizations rarely escape bias through intelligence alone. They do so through structures that make bias easier to detect before it becomes policy.

Systems thinking, feedback, and unintended consequences

Strategy unfolds inside systems, not linear chains. Decisions affect customers, competitors, regulators, employees, suppliers, technologies, and investors, who then react in ways that feed back into the organization. This means that strategic decisions often generate consequences far beyond their immediate target.

Systems thinking strengthens decision science by revealing how structure produces behavior over time. A growth strategy can strain service quality and damage reputation. A cost-reduction program can erode learning capacity and make future adaptation harder. A push for quarterly efficiency can weaken resilience against shocks. A product expansion can create operational complexity that undermines the very advantage it was supposed to generate.

Three system properties are especially important in organizational strategy:

Feedback loops: Success can reinforce investment and scale, but it can also reinforce overconfidence and strategic lock-in.
Delays: The effects of a strategic move may not appear immediately, making it easy to mistake temporary calm for long-term success.
Nonlinearity: Small changes can sometimes produce large strategic shifts, while large investments may sometimes yield little if the surrounding system is misread.

These dynamics connect directly to Decision-Making in Complex Systems, Decision Science and Systems Modeling, and Feedback Loops, Delays, and Policy Resistance. Strategic quality improves when leaders do not mistake immediate outputs for long-run system behavior.

Governance, incentives, and strategic coherence

Even a strategically intelligent leadership team can fail if the surrounding institution rewards contradictory behavior. Strategy is not executed in a vacuum. It is filtered through incentives, reporting lines, budgeting processes, political coalitions, and informal norms. Decision science therefore treats governance as part of the strategy problem, not as an afterthought.

Strategic incoherence often appears when stated priorities diverge from actual incentives. A firm may claim to prioritize innovation while rewarding only short-term efficiency. It may claim to value resilience while underinvesting in redundancy, learning, or workforce stability. It may declare customer-centricity while organizing internally around silos that fragment responsibility. In these cases, the formal strategy document is less important than the effective decision rules embedded in the institution.

Good governance supports better strategy by doing at least four things:

clarifying decision rights and escalation paths;
aligning incentives with the chosen strategic horizon;
creating independent challenge and structured dissent;
ensuring that strategic review includes learning rather than mere performance policing.

Decision science is useful here because it makes visible the difference between declared strategy and operationalized strategy. Organizations are ultimately governed by the decisions they systematically reward, tolerate, and repeat.

Data, AI, and strategic decision support

Contemporary organizations possess more data than ever, but more data does not eliminate the strategic problem. In some cases it intensifies it. Data-rich environments can create false confidence, accelerate local optimization at the expense of broader purpose, and encourage the substitution of measurable proxies for strategic meaning. AI systems add speed and pattern recognition, but they do not remove the need for human judgment, especially when causality is unclear, objectives are contested, or values are at stake.

Recent management writing has increasingly emphasized that older strategic tools often fit poorly in highly dynamic environments and that firms must develop greater adaptability when facing uncertainty. At the same time, MIT’s current AI programming reflects a similar concern: computational capability changes the form of decision-making, but not the underlying need for governance, interpretation, and accountability. Reeves et al. (2022); MIT IDE, 2026.

From a decision-science standpoint, data and AI should strengthen strategy in specific ways:

improving environmental sensing;
supporting scenario generation and pattern recognition;
testing assumptions more rapidly;
revealing operational constraints or opportunities invisible to intuition alone.

But they should not be allowed to collapse strategic reasoning into dashboard dependence. Strategy still requires interpretation, ethical judgment, contextual understanding, and the ability to ask whether the model is solving the right problem.

A decision-science framework for organizational strategy

A practical organizational strategy framework grounded in decision science can be organized in eight stages.

1. Define the real strategic decision

Many organizations confuse symptoms with decisions. They debate tactics before clarifying the core strategic question. The first task is to specify what is actually being decided: market entry, capability renewal, strategic repositioning, innovation commitment, resilience investment, or business-model transformation.

2. Diagnose the uncertainty type

Is the problem one of manageable risk, open uncertainty, ambiguity across competing frames, or deep uncertainty where future states cannot be reliably modeled? The answer shapes the appropriate decision method.

3. Map assumptions explicitly

Every strategy rests on assumptions about customers, competitors, technologies, regulators, costs, time horizons, and internal capabilities. These should be documented rather than left implicit.

4. Generate genuine alternatives

Weak strategy processes often compare one favored option against straw men. Stronger processes construct real alternatives, including phased options, modular commitments, defensive plays, and capability-building paths.

5. Test for system effects

Assess second-order consequences, feedback loops, delays, and hidden interdependencies. A move that appears attractive in isolation may destabilize adjacent parts of the system.

6. Match commitment to reversibility

Irreversible decisions require stronger evidence and broader challenge. Reversible decisions can be used for experimentation and learning. Decision science helps calibrate commitment size to uncertainty level.

7. Build strategic review around learning

Review should not only ask whether targets were met. It should ask which assumptions held, which failed, what changed in the environment, and what the organization now knows that it did not know before.

8. Preserve coherence between aspiration and institution

The most elegant strategy will fail if governance, incentives, capability, and culture make it nonviable. Strategy must be institutionally inhabitable, not merely conceptually impressive.

What this framework offers is not formulaic certainty. It offers a stronger structure for thinking, choosing, and revising. That is the core contribution of decision science to organizational strategy.

Mathematical lens

A simple way to formalize strategic choice is to treat it as selection under uncertainty from a feasible action set:

\[
a^* = \arg\max_{a \in A} \; \mathbb{E}[U(a \mid M, \Theta)]
\]

where \(A\) is the set of available strategic actions, \(U\) is organizational utility, \(M\) is the decision model being used, and \(\Theta\) represents uncertain environmental states. This captures a central point of the article: strategy depends not only on the action chosen, but on the model through which the environment is interpreted.

Under bounded rationality, however, organizations rarely optimize globally. A more realistic formulation is satisficing under procedural constraints:

\[
a^\dagger = \{a \in A : U(a) \geq \tau\}
\]

where \(\tau\) is a threshold of acceptability rather than an ideal maximum. This reflects Simon’s insight that real organizations often search for a viable strategic path rather than a demonstrably optimal one.

Strategic robustness under uncertainty can be represented conceptually as:

\[
R(a) = \min_{\theta \in \Theta} U(a,\theta)
\]

for worst-case robustness, or more broadly as performance stability across scenarios. When deep uncertainty dominates, the strategic task often shifts from maximizing point-estimate returns to selecting actions whose performance degrades gracefully across multiple futures.

A feedback-sensitive view of strategy can also be written as:

\[
x_{t+1} = f(x_t, a_t, e_t)
\]

where \(x_t\) is the organizational state, \(a_t\) is the current strategic action, and \(e_t\) is environmental response. This reminds us that strategy is never a one-shot choice. It is a recursive process in which actions alter the system that later decisions must confront.

Advanced R workflow

The R workflow below builds a stylized comparison of strategic options under uncertainty using expected utility, downside robustness, and scenario dispersion. It is designed to illustrate how different decision rules can produce different “best” strategies.

# Install packages if needed:
# install.packages(c("tidyverse"))

library(tidyverse)

# ------------------------------------------------------------
# R Workflow: Strategic Choice Under Uncertainty
# Purpose:
#   Compare stylized strategic options across scenarios using
#   expected utility, downside robustness, and dispersion.
# ------------------------------------------------------------

strategies <- tibble(
  strategy = c("Scale Existing Core", "Modular Expansion", "High-Risk Market Entry", "Capability Renewal"),
  low_growth = c(72, 68, 40, 62),
  base_case = c(84, 82, 88, 79),
  high_growth = c(91, 89, 118, 92)
)

scenario_probs <- c(low_growth = 0.30, base_case = 0.45, high_growth = 0.25)

results <- strategies %>%
  rowwise() %>%
  mutate(
    expected_value =
      low_growth * scenario_probs["low_growth"] +
      base_case * scenario_probs["base_case"] +
      high_growth * scenario_probs["high_growth"],
    downside_robustness = min(c(low_growth, base_case, high_growth)),
    scenario_sd = sd(c(low_growth, base_case, high_growth))
  ) %>%
  ungroup() %>%
  arrange(desc(expected_value))

print(results)

long_results <- strategies %>%
  pivot_longer(
    cols = c(low_growth, base_case, high_growth),
    names_to = "scenario",
    values_to = "value"
  )

ggplot(long_results, aes(x = scenario, y = value, fill = strategy)) +
  geom_col(position = "dodge") +
  labs(
    title = "Strategic Option Performance Across Scenarios",
    x = "Scenario",
    y = "Outcome",
    fill = "Strategy"
  ) +
  theme_minimal(base_size = 12)

ggplot(results, aes(x = reorder(strategy, expected_value), y = expected_value)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Expected Strategic Value",
    x = "Strategy",
    y = "Expected Value"
  ) +
  theme_minimal(base_size = 12)

write_csv(results, "organizational_strategy_decision_profiles.csv")

Advanced Python workflow

The Python workflow below simulates repeated strategic review cycles under uncertainty. It compares options with different levels of volatility, adaptability, and downside resilience to show why the highest upside strategy is not always the most decision-scientifically attractive.

# Install packages if needed:
# pip install pandas numpy matplotlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# ------------------------------------------------------------
# Python Workflow: Repeated Strategic Review Under Uncertainty
# Purpose:
#   Simulate strategic performance over time using
#   volatility, adaptability, and resilience parameters.
# ------------------------------------------------------------

np.random.seed(42)
time_steps = np.arange(1, 31)

def simulate_strategy(base_return, volatility, adaptability, resilience):
    values = np.zeros(len(time_steps))
    values[0] = 100.0

    for t in range(1, len(time_steps)):
        shock = np.random.normal(0, volatility)
        adjustment = adaptability * 0.6 + resilience * 0.4
        growth = base_return + shock + adjustment
        values[t] = max(40, values[t - 1] * (1 + growth / 100))

    return values

scale_core = simulate_strategy(base_return=1.2, volatility=1.8, adaptability=0.4, resilience=0.8)
modular_expansion = simulate_strategy(base_return=1.4, volatility=2.0, adaptability=0.9, resilience=0.9)
market_entry = simulate_strategy(base_return=2.0, volatility=4.2, adaptability=0.5, resilience=0.3)
capability_renewal = simulate_strategy(base_return=1.5, volatility=2.4, adaptability=1.0, resilience=0.8)

df = pd.DataFrame({
    "time": time_steps,
    "Scale Existing Core": scale_core,
    "Modular Expansion": modular_expansion,
    "High-Risk Market Entry": market_entry,
    "Capability Renewal": capability_renewal
})

print(df.head())

plt.figure(figsize=(10, 6))
for col in df.columns[1:]:
    plt.plot(df["time"], df[col], label=col)

plt.xlabel("Strategic Review Cycle")
plt.ylabel("Strategic Value Index")
plt.title("Organizational Strategy Under Uncertainty")
plt.legend()
plt.tight_layout()
plt.show()

summary = pd.DataFrame({
    "strategy": df.columns[1:],
    "final_value": [df[c].iloc[-1] for c in df.columns[1:]],
    "min_value": [df[c].min() for c in df.columns[1:]],
    "max_value": [df[c].max() for c in df.columns[1:]]
})

print(summary)
summary.to_csv("organizational_strategy_simulation_summary.csv", index=False)

Conclusion

Decision science in organizational strategy reveals that strategic success is not simply a product of foresight or ambition. It depends on how organizations structure judgment under uncertainty. Strategy is best understood not as prediction alone, nor as charismatic vision alone, but as a disciplined process of framing problems, evaluating alternatives, aligning incentives, learning from feedback, and adapting without losing coherence.

The most important strategic organizations are not those that eliminate uncertainty. They are those that become more intelligent in its presence. They know how to distinguish risk from ambiguity, how to avoid false precision, how to resist cognitive and political distortion, how to build adaptive capability, and how to preserve enough institutional integrity to revise course when circumstances demand it.

That is why decision science matters so deeply for organizational strategy. It does not replace classic strategy. It makes it more realistic, more reflective, and more durable in a world defined by complexity, contestation, and change.

References

Courtney, H., Kirkland, J. and Viguerie, P. (1997) ‘Strategy under uncertainty’, Harvard Business Review. Available at: Harvard Business Review.
Grant, R.M. (2021) Contemporary Strategy Analysis. 11th edn. Hoboken, NJ: Wiley.
Harvard Business Review (2022) ‘Strategy in an age of uncertainty’. Available at: Harvard Business Review.
Kahneman, D. (2011) Thinking, Fast and Slow. London: Penguin Books.
Martin, R.L. (2013) Playing to Win: How Strategy Really Works. Boston, MA: Harvard Business Review Press.
MIT IDE (2026) ‘Business implications of AI 2026’. Available at: MIT Initiative on the Digital Economy.
Nobel Prize (1978) ‘The Prize in Economics 1978 – Press release’. Available at: Nobel Prize.
Rumelt, R.P. (2011) Good Strategy/Bad Strategy: The Difference and Why It Matters. New York: Crown Business.
Schoemaker, P.J.H. (1995) Scenario Planning: A Tool for Strategic Thinking. Chicago: Irwin.
Simon, H.A. (1978) ‘Rational decision-making in business organizations’, Nobel Prize lecture. Available at: Nobel Prize.
Simon, H.A. (1997) Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations. 4th edn. New York: Free Press.
Teece, D.J. (2009) Dynamic Capabilities and Strategic Management: Organizing for Innovation and Growth. Oxford: Oxford University Press.
Teece, D.J., Pisano, G. and Shuen, A. (1997) ‘Dynamic capabilities and strategic management’, Strategic Management Journal, 18(7), pp. 509–533. Available at: Wiley Online Library.