Technology Foresight: Anticipating Innovation, Disruption, and Systemic Change

Last Updated June 3, 2026

Technology foresight is the systematic exploration of emerging and established technologies, their possible development pathways, and their implications for society, economy, governance, infrastructure, ecology, labor, security, and complex systems. It helps institutions reason about technological change before adoption, disruption, lock-in, or crisis narrows the available choices.

Technology foresight is not a catalogue of inventions. It is not a prediction market for gadgets, a hype cycle exercise, or a list of promising sectors. It is a disciplined way of asking how technologies evolve within systems of capital, regulation, standards, infrastructure, legitimacy, labor, public trust, environmental constraint, and political power. It examines not only what technologies may emerge, but how they may scale, who may control them, who may benefit, who may bear risk, and what forms of governance may be needed before consequences become irreversible.

Technological innovation is one of the most powerful drivers of long-term change. Advances in artificial intelligence, biotechnology, robotics, energy systems, digital infrastructure, sensing, materials science, automation, quantum technologies, and computational platforms are altering the conditions under which markets operate, states govern, organizations coordinate, and societies imagine possibility. Yet technological change does not unfold automatically. It is shaped by adoption pathways, capital allocation, regulation, procurement, standards, geopolitics, culture, infrastructure readiness, and the distribution of power.

Technology foresight therefore treats innovation not as an isolated event, but as a systemic force that reshapes trajectories of development. It helps decision-makers ask not only which technologies are emerging, but how they may interact, destabilize incumbents, create new dependencies, change institutional capacity, produce unintended consequences, and reorganize collective life.

What Is Technology Foresight?

Technology foresight is the application of futures thinking methods to the identification, interpretation, evaluation, and governance of technological change. It examines how technologies develop, how they interact with wider systems, and how institutions should respond under conditions of uncertainty.

Technology foresight typically addresses questions such as:

• Which technologies are emerging, maturing, converging, or declining?
• Which weak signals suggest possible technological disruption?
• How might technological capabilities develop under different social, political, economic, and ecological conditions?
• What infrastructure, standards, regulation, financing, skills, and public trust would be required for adoption?
• What harms, dependencies, inequalities, or governance failures could emerge?
• Which strategies remain robust across multiple technological futures?

Unlike narrow technology forecasting, which often focuses on prediction, technology foresight explores multiple technological pathways and their systemic implications. It is therefore not just about the technology itself. It is about how technology interacts with institutions, incentives, infrastructure, markets, public services, ecological systems, labor, culture, and human behavior.

Its goal is not to predict one technological future correctly. Its goal is to improve decision-making in environments where innovation is consequential, uncertain, contested, and path dependent.

Technology foresight is strongest when it treats technology as a pathway through systems, not merely as an object moving through markets.

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Technology Forecasting vs Technology Foresight

Technology forecasting and technology foresight overlap, but they are not the same. Forecasting often asks what is likely to happen: when a capability may arrive, how fast adoption may occur, when costs may fall, or which market may grow. Foresight asks a broader question: what could happen, under what conditions, with what consequences, and what choices should be made now?

Forecasting is useful when historical data, measurable trends, and probabilistic assumptions are reasonably stable. Foresight is useful when technological change interacts with uncertainty, values, power, regulation, social legitimacy, ecological constraint, and institutional response. Most important technology questions require both, but foresight is especially important when the consequences of being wrong are large.

Forecasting asks when a technology might arrive. Foresight asks what its arrival could mean, who may be affected, and what choices remain open.

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Technology Foresight as System-Level Analysis

Technology does not evolve in isolation. It is embedded in socio-technical systems that include infrastructures, regulation, governance, capital, culture, standards, user behavior, labor, public legitimacy, and institutional capacity. Energy technologies depend on grids, finance, land use, permitting, supply chains, and political legitimacy. AI depends on data, compute, procurement, labor practices, legal frameworks, institutional incentives, and accountability systems. Biotechnologies depend on scientific capacity, regulatory approval, public trust, ethics, supply chains, and biosecurity governance.

This connects directly to systems modeling, where interaction, feedback loops, and nonlinear dynamics shape outcomes. Technology foresight therefore operates as a form of system-level analysis. It examines how innovation propagates through interconnected structures rather than treating invention as a self-contained event.

The critical question is rarely whether a technology is technically possible. It is whether the surrounding system enables, absorbs, scales, resists, governs, or distorts it.

Technology foresight becomes serious when it asks how technical capability meets system readiness.

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Technology as a Driver of Systemic Change

Technology is not only a driver of incremental improvement. It can function as a structural force that reconfigures entire systems. Artificial intelligence can alter labor markets, knowledge work, administrative capacity, education, surveillance, public services, and decision-making. Renewable energy can transform geopolitics, industrial systems, utility regulation, land use, and infrastructure planning. Digital platforms can reshape coordination, market concentration, labor precarity, public discourse, and institutional accountability. Biotechnology can alter medicine, agriculture, food systems, biosecurity, and moral boundaries around life and intervention.

Technological transformations are not linear. They involve feedback loops, adoption thresholds, network effects, economies of scale, standard-setting, institutional delay, cultural legitimacy, lock-in, and sometimes irreversible transition. A technology may remain marginal for years, then accelerate rapidly when cost, legitimacy, infrastructure, and policy align. Another may appear inevitable but stall because infrastructure, trust, governance, or finance does not materialize.

This is why technology foresight must look beyond novelty. A new capability matters only when it begins to reorganize relationships: between workers and employers, citizens and states, firms and markets, people and machines, economies and ecosystems, or communities and institutions.

Technology matters not only because it changes what can be done, but because it changes what becomes economically, politically, and socially possible.

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Technology Foresight and Socio-Technical Transitions

Technology foresight is closely aligned with socio-technical transition theory, especially the multi-level perspective. In that framework, niches represent spaces where innovations emerge; regimes represent dominant systems of infrastructure, rules, markets, practices, and institutions; and landscape pressures represent wider forces such as climate stress, demographic change, geopolitical competition, economic crises, public values, or ecological limits.

New technologies often begin at the margins. They may be protected by experimentation, research funding, early adopters, mission-driven procurement, public subsidy, or niche demand. Under certain conditions, they scale, destabilize incumbent regimes, and align with broader landscape pressures. But transition is not automatic. Incumbent regimes resist. Standards privilege existing systems. Capital flows toward familiar models. Regulation may lag. Public trust may fail. Infrastructure may constrain adoption.

Technology foresight helps reveal not only which innovations may emerge, but how they may move from niche novelty to regime-level transformation.

Technology foresight is not only about innovation pipelines. It is about the struggle between existing regimes, emerging alternatives, and the pressures that make transformation possible or necessary.

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Core Questions in Technology Foresight

Technology foresight becomes useful when it is anchored in clear questions. These questions should move from technical novelty toward systemic consequence, public value, and strategic choice.

Good technology foresight does not ask only whether a technology is powerful. It asks whether its power can be made accountable to legitimate public purposes.

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Together, these methods help decision-makers reason about technology as possibility, constraint, risk, infrastructure, power, and public choice.

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Technology Readiness and System Readiness

Technology readiness is often measured by whether a technology has moved from concept to demonstration to deployment. But readiness cannot be reduced to technical maturity. A technology may be technically ready while the surrounding system is unready. It may lack governance, standards, workforce capacity, public trust, data infrastructure, financing, maintenance, accountability, or ethical legitimacy.

Technology foresight therefore needs a broader readiness frame. It should ask whether the technology, institution, infrastructure, public, regulatory system, and ecological context are ready together. This is especially important for technologies that affect public services, rights, labor, safety, security, or environmental systems.

A technology can be technically impressive and socially premature at the same time.

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Uncertainty, Nonlinearity, and Path Dependency

Technological change is characterized by deep uncertainty and nonlinear behavior. Some technologies are overhyped and stall. Others appear marginal until conditions align and adoption accelerates rapidly. Timing is uncertain, interaction effects are difficult to model, and broader system conditions can suppress or amplify technological trajectories.

Several concepts are especially important:

• Path dependency: existing systems constrain future options.
• Lock-in effects: dominant technologies resist replacement even when alternatives improve.
• Network effects: the value of adoption increases as more actors adopt.
• Complementary assets: infrastructure, skills, standards, and supply chains shape viability.
• Tipping points: rapid shifts occur once adoption, legitimacy, price-performance, or policy crosses a threshold.
• Hype cycles: expectations may accelerate investment before real capacity is mature.
• Regulatory lag: governance may arrive after deployment has already shaped behavior.

Technology foresight must therefore account not only for innovation itself, but for the constraints that shape its trajectory. The key uncertainty may not be whether the technology improves. It may be whether institutions, infrastructures, markets, law, and public trust can adapt quickly enough.

Technology foresight must be comfortable with uncertainty, but it should never be careless with it.

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The Political Economy of Technological Change

Technological development is never neutral. It is shaped by economic incentives, corporate strategy, financial markets, standards, regulation, industrial policy, labor systems, geopolitical competition, intellectual property, public procurement, and state capacity. This introduces a fundamental insight: technology evolves within systems of power and governance.

A technology may be technically promising yet blocked by incumbent interests. Another may scale quickly not because it is socially optimal, but because it is profitable under existing market structures. Public funding, procurement rules, patent regimes, venture capital, platform dominance, military investment, trade policy, and national strategic priorities all influence which innovations develop and how their benefits and harms are distributed.

Technology foresight must therefore ask not only what is possible, but what becomes likely under real institutional, economic, and political conditions. Without this, foresight risks becoming technically interesting but strategically naive.

Technology foresight is incomplete unless it asks who has the power to shape technology’s path.

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Governance, Regulation, and Public Accountability

Technology foresight should inform governance before harm becomes routine. Many technologies scale faster than institutions can regulate them. This creates a recurring pattern: technological deployment creates facts on the ground, institutions react after harm appears, and regulation struggles to catch up with already entrenched systems.

Anticipatory governance tries to break this pattern. It asks what safeguards, rights, standards, monitoring systems, evaluation criteria, procurement rules, public participation processes, and accountability mechanisms should exist before scaling. This matters especially for technologies that affect public services, civil rights, environmental systems, health, safety, security, labor, or democratic life.

Technology governance should not be reduced to either acceleration or prohibition. Many technology futures require differentiated governance: some uses should be encouraged, some monitored, some redesigned, some delayed, and some rejected. Foresight can help identify which is which.

Responsible technology foresight asks what must be governed before scale makes governance harder.

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Equity, Labor, and Power in Technology Futures

Technological futures are not experienced equally. The same technology can produce convenience for some, displacement for others, profit for owners, risk for workers, surveillance for marginalized communities, and environmental burden elsewhere in the supply chain. Technology foresight must therefore include distributional analysis.

Labor is central. Automation can improve safety and productivity, but it can also displace workers, intensify work, deskill roles, increase surveillance, weaken bargaining power, and shift gains upward. AI can support professionals, but it can also degrade judgment, centralize decision-making, and make opaque systems harder to challenge. Platform technologies can create flexible coordination while also producing dependency, precarity, and market concentration.

Equity also includes geography and ecology. The benefits of clean technology may depend on mining, land use, water demand, or waste burdens elsewhere. Digital infrastructure may depend on energy-intensive data centers. Biotechnology may raise questions of access, consent, ownership, and biosecurity. A justice-oriented technology foresight process asks who is seen, who is counted, who has voice, and who carries the costs.

A technology future is not progressive merely because it is advanced. It is progressive only if its benefits, burdens, governance, and power are made accountable.

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Applications of Technology Foresight

Technology foresight is used across many domains. In each case, it supports anticipatory and adaptive decision-making by helping decision-makers compare technological possibilities against long-term system needs.

Technology foresight is most useful when it connects innovation choices to institutional responsibility.

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Failure Modes and Limitations

Technology foresight can fail in predictable ways. It can overestimate technological potential and underestimate adoption barriers. It can ignore social, legal, ecological, and institutional constraints. It can treat nonlinear systems as if they were smooth and predictable. It can focus too narrowly on the technology while neglecting infrastructure, standards, politics, labor, public legitimacy, or power.

There is also the risk of hype capture. Foresight processes may reproduce the narratives of technology vendors, investors, military planners, dominant firms, or elite institutions. They may treat technological acceleration as inevitable and social consequences as secondary. In such cases, foresight becomes a servant of adoption rather than a discipline of judgment.

Another failure mode is abstraction. A foresight exercise may identify plausible futures without connecting them to organizational capacity, policy authority, budget decisions, procurement rules, or governance action. In such cases, technology foresight becomes descriptive rather than strategic.

The limits of technology foresight are a reminder that foresight is not prediction. It is structured reasoning under uncertainty, and its usefulness depends on analytical rigor, plural perspectives, and practical integration into decision processes.

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Mathematical Lens: Technological Trajectories Under Constraint

A stylized way to represent technological development is as a trajectory shaped by capability growth, institutional support, and systemic constraint:

Competing pathway evaluation can be represented as:

A threshold-based adoption dynamic can be represented conceptually as:

A governance-readiness score can be represented as:

A distributional risk score can be represented as:

These formulas are not predictions. They are transparent representations of relationships that technology foresight should examine: capability, support, constraint, adoption, governance, and distributional consequence.

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Computational Modeling for Technology Foresight

Computational modeling can support technology foresight by organizing signals, scoring readiness, comparing scenarios, testing pathway viability, identifying constraints, simulating adoption, and documenting assumptions. The goal is not to automate judgment. The goal is to make assumptions, inputs, scenarios, and outputs easier to inspect.

A professional technology foresight workflow may include:

• Technology register: technology name, domain, maturity, capability, uncertainty, source notes, and review status.
• Signal register: weak signals, patents, policy changes, research developments, market shifts, worker reports, public controversy, and ecological concerns.
• Readiness matrix: technology readiness, infrastructure readiness, governance readiness, labor readiness, legitimacy, and ecological readiness.
• Scenario set: alternative technology futures shaped by regulation, capital, public trust, geopolitical conditions, ecological stress, and institutional capacity.
• Strategy tests: evaluation of technology adoption, regulation, investment, procurement, or restraint across scenarios.
• Risk register: distributional harm, lock-in, dependency, safety, externalities, and accountability gaps.
• Outputs: ranked profiles, pathway simulations, scenario comparisons, readiness dashboards, and governance recommendations.

Models should be documented carefully. Technology foresight models can easily encode hype, bias, false precision, or narrow institutional assumptions. Reproducible workflows should therefore include data dictionaries, validation checks, assumptions, limitations, and review notes.

Computational technology foresight is useful when it makes judgment more transparent, not when it hides power behind scores.

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Advanced R Workflow: Comparing Technology Foresight Profiles

The R workflow below compares several stylized technological futures across maturity, institutional support, disruption potential, infrastructure readiness, social legitimacy, governance readiness, ecological pressure, and distributional risk. It is designed as an evergreen illustration of how technology foresight can compare trajectories in a multidimensional way rather than through hype or novelty alone.

# ------------------------------------------------------------
# R Workflow: Comparing Technology Foresight Profiles
# Purpose:
#   Compare stylized technology futures using maturity,
#   institutional support, disruption potential, infrastructure
#   readiness, legitimacy, governance readiness, ecological
#   pressure, and distributional risk.
#
# Optional dependency:
#   install.packages(c("tidyverse"))
# ------------------------------------------------------------

library(tidyverse)

technologies <- tibble(
  technology_type = c(
    "AI-Driven Automation",
    "Renewable Energy Acceleration",
    "Biotech Platform Expansion",
    "Distributed Digital Infrastructure",
    "Advanced Materials for Infrastructure",
    "Robotics and Care Systems"
  ),
  maturity = c(0.72, 0.78, 0.54, 0.61, 0.58, 0.52),
  institutional_support = c(0.56, 0.74, 0.48, 0.52, 0.62, 0.46),
  disruption_potential = c(0.88, 0.71, 0.76, 0.69, 0.64, 0.72),
  infrastructure_readiness = c(0.63, 0.68, 0.41, 0.57, 0.55, 0.44),
  social_legitimacy = c(0.46, 0.72, 0.44, 0.58, 0.66, 0.42),
  governance_readiness = c(0.38, 0.66, 0.42, 0.50, 0.56, 0.40),
  ecological_pressure = c(0.62, 0.48, 0.58, 0.64, 0.52, 0.46),
  distributional_risk = c(0.82, 0.54, 0.70, 0.68, 0.46, 0.76)
)

technologies <- technologies %>%
  mutate(
    enabling_score =
      0.18 * maturity +
      0.18 * institutional_support +
      0.18 * infrastructure_readiness +
      0.18 * social_legitimacy +
      0.18 * governance_readiness +
      0.10 * (1 - ecological_pressure),

    risk_score =
      0.35 * disruption_potential +
      0.25 * distributional_risk +
      0.20 * ecological_pressure +
      0.20 * (1 - governance_readiness),

    foresight_priority =
      0.55 * risk_score +
      0.45 * enabling_score,

    profile_class = case_when(
      foresight_priority >= 0.70 ~ "High-priority foresight concern",
      foresight_priority >= 0.60 ~ "Moderate-priority foresight concern",
      TRUE ~ "Monitor"
    )
  ) %>%
  arrange(desc(foresight_priority))

print(technologies)

technologies_long <- technologies %>%
  select(
    technology_type,
    maturity,
    institutional_support,
    infrastructure_readiness,
    social_legitimacy,
    governance_readiness,
    ecological_pressure,
    distributional_risk
  ) %>%
  pivot_longer(
    cols = -technology_type,
    names_to = "dimension",
    values_to = "value"
  )

ggplot(technologies_long, aes(x = dimension, y = value, fill = technology_type)) +
  geom_col(position = "dodge") +
  labs(
    title = "Technology Foresight Dimensions",
    x = "Dimension",
    y = "Value",
    fill = "Technology Type"
  ) +
  theme_minimal(base_size = 12) +
  coord_flip()

ggplot(technologies, aes(x = reorder(technology_type, foresight_priority), y = foresight_priority)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Technology Foresight Priority Scores",
    x = "Technology Type",
    y = "Foresight Priority"
  ) +
  theme_minimal(base_size = 12)

dir.create("outputs", showWarnings = FALSE)
write_csv(technologies, "outputs/technology_foresight_profiles.csv")

This workflow does not claim to predict technological futures. It demonstrates how a foresight team can compare technologies using multiple dimensions rather than relying on novelty, investment hype, or capability alone.

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Advanced Python Workflow: Simulating Technological Pathways Under Uncertainty

The Python workflow below simulates stylized technological pathways under different assumptions about capability, institutional support, governance readiness, public legitimacy, constraint, and disruption. It is useful for showing why similar technologies can diverge sharply depending on surrounding system conditions.

# ------------------------------------------------------------
# Python Workflow: Simulating Technological Pathways
# Purpose:
#   Compare stylized technology trajectories under different
#   capability, support, legitimacy, governance, and constraint
#   conditions.
#
# Optional dependencies:
#   pip install pandas numpy matplotlib
# ------------------------------------------------------------

from pathlib import Path

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

OUTPUT_DIR = Path("outputs")
OUTPUT_DIR.mkdir(exist_ok=True)

time_steps = np.arange(1, 41)

technologies = [
    {
        "technology": "Supported Transition Technology",
        "capability": 0.72,
        "institutional_support": 0.76,
        "governance_readiness": 0.70,
        "public_legitimacy": 0.74,
        "constraint": 0.34,
        "distributional_risk": 0.42
    },
    {
        "technology": "Constrained Disruptive Technology",
        "capability": 0.78,
        "institutional_support": 0.42,
        "governance_readiness": 0.36,
        "public_legitimacy": 0.40,
        "constraint": 0.68,
        "distributional_risk": 0.78
    },
    {
        "technology": "High-Capability Low-Governance AI",
        "capability": 0.86,
        "institutional_support": 0.58,
        "governance_readiness": 0.30,
        "public_legitimacy": 0.38,
        "constraint": 0.52,
        "distributional_risk": 0.84
    },
    {
        "technology": "Public-Interest Infrastructure Technology",
        "capability": 0.64,
        "institutional_support": 0.72,
        "governance_readiness": 0.76,
        "public_legitimacy": 0.80,
        "constraint": 0.40,
        "distributional_risk": 0.36
    }
]

def simulate_technology(
    capability,
    institutional_support,
    governance_readiness,
    public_legitimacy,
    constraint,
    distributional_risk,
    initial_state=1.0
):
    state = np.zeros(len(time_steps))
    state[0] = initial_state

    for t in range(1, len(time_steps)):
        enabling_force = (
            0.22 * capability
            + 0.20 * institutional_support
            + 0.18 * governance_readiness
            + 0.16 * public_legitimacy
        )

        friction = (
            0.18 * constraint
            + 0.16 * distributional_risk
            + 0.10 * (1 - governance_readiness)
        )

        shock = 0.03 if (t + 1) % 9 != 0 else 0.10
        legitimacy_penalty = 0.04 if public_legitimacy < 0.45 and t > 12 else 0.00

        state[t] = state[t - 1] + enabling_force / 4 - friction / 4 - shock / 5 - legitimacy_penalty
        state[t] = np.clip(state[t], 0, 2.0)

    return state

rows = []

for tech in technologies:
    path = simulate_technology(
        capability=tech["capability"],
        institutional_support=tech["institutional_support"],
        governance_readiness=tech["governance_readiness"],
        public_legitimacy=tech["public_legitimacy"],
        constraint=tech["constraint"],
        distributional_risk=tech["distributional_risk"]
    )

    for t, value in zip(time_steps, path):
        rows.append({
            "technology": tech["technology"],
            "time": t,
            "trajectory_strength": value
        })

df = pd.DataFrame(rows)

summary = (
    df.groupby("technology")
    .agg(
        final_trajectory_strength=("trajectory_strength", "last"),
        mean_trajectory_strength=("trajectory_strength", "mean"),
        minimum_trajectory_strength=("trajectory_strength", "min"),
        maximum_trajectory_strength=("trajectory_strength", "max")
    )
    .reset_index()
    .sort_values("final_trajectory_strength", ascending=False)
)

print(summary)

plt.figure(figsize=(10, 6))
for tech_name in df["technology"].unique():
    subset = df[df["technology"] == tech_name]
    plt.plot(subset["time"], subset["trajectory_strength"], label=tech_name)

plt.xlabel("Time Step")
plt.ylabel("Trajectory Strength")
plt.title("Technological Pathways Under Uncertainty")
plt.legend()
plt.tight_layout()
plt.savefig(OUTPUT_DIR / "technology_trajectory_paths.png", dpi=150)
plt.close()

df.to_csv(OUTPUT_DIR / "technology_trajectory_paths.csv", index=False)
summary.to_csv(OUTPUT_DIR / "technology_trajectory_summary.csv", index=False)

This workflow shows why similar technologies can diverge. Capability matters, but so do governance readiness, legitimacy, institutional support, constraint, distributional risk, and shock conditions.

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GitHub Repository

The companion repository for this article contains computational examples for technology foresight profiles, readiness scoring, technology pathway simulation, governance readiness, distributional risk, adoption uncertainty, scenario comparison, and reproducible technology foresight workflows.

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Why This Matters

Technology foresight is essential for understanding one of the most powerful forces shaping modern systems. It helps decision-makers anticipate innovation, prepare for disruption, design governance before harm becomes normalized, and build strategies that remain viable under uncertainty rather than being trapped by short-term reaction or hype cycles.

The stakes are high because technological change reorganizes more than markets. It reorganizes labor, knowledge, public services, infrastructures, ecological pressures, social trust, legal responsibilities, geopolitical dependencies, and the distribution of power. A society that only reacts after technological systems have scaled will often inherit choices already made by vendors, investors, incumbents, or crisis conditions.

In a world defined by rapid technological change, the ability to think systemically about technology becomes a critical strategic capability. Technology foresight is therefore not simply about watching innovation. It is about understanding how technological change reorganizes the conditions of collective life—and what choices remain open before those reorganizations harden into new structures.

The central question is not whether technology will shape the future. It will. The question is whether institutions, communities, and publics can shape technology before it shapes them.

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Related Articles

• Futures Thinking
• What Is Futures Thinking?
• Forecasting, Foresight, and Futures Studies
• Horizon Scanning
• Weak Signals and Early Indicators
• Trend Analysis and Megatrends
• Scenario Planning
• Strategic Foresight Methods
• Uncertainty Matrices and Driver Mapping
• Foresight Data Systems and Reproducible Workflows
• Artificial Intelligence Systems
• Data Systems & Analytics

Further Reading

• Georghiou, L., Cassingena Harper, J., Keenan, M., Miles, I. and Popper, R. (eds) (2008) The Handbook of Technology Foresight: Concepts and Practice. Cheltenham: Edward Elgar.
• Government Office for Science (2024) The Futures Toolkit: Tools for Futures Thinking and Foresight Across UK Government. London: Government Office for Science. Available at: https://www.gov.uk/government/publications/futures-toolkit-for-policy-makers-and-analysts/the-futures-toolkit-html.
• Geels, F.W. (2002) ‘Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study’, Research Policy, 31(8–9), pp. 1257–1274.
• Kemp, R., Schot, J. and Hoogma, R. (1998) ‘Regime shifts to sustainability through processes of niche formation: The approach of strategic niche management’, Technology Analysis & Strategic Management, 10(2), pp. 175–198.
• Martin, B.R. (2010) ‘The origins of the concept of “foresight” in science and technology: An insider’s perspective’, Technological Forecasting and Social Change, 77(9), pp. 1438–1447.
• Organisation for Economic Co-operation and Development (OECD) (2025) Strategic Foresight Toolkit for Resilient Public Policy. Paris: OECD Publishing. Available at: https://www.oecd.org/en/publications/foresight-toolkit-for-resilient-public-policy_bcdd9304-en.html.
• Organisation for Economic Co-operation and Development (OECD) (2025) OECD Science, Technology and Innovation Outlook 2025. Paris: OECD Publishing. Available at: https://www.oecd.org/en/publications/2025/10/oecd-science-technology-and-innovation-outlook-2025_bae3698d/full-report.html.
• Schot, J. and Steinmueller, W.E. (2018) ‘Three frames for innovation policy: R&D, systems of innovation and transformative change’, Research Policy, 47(9), pp. 1554–1567.
• Stilgoe, J., Owen, R. and Macnaghten, P. (2013) ‘Developing a framework for responsible innovation’, Research Policy, 42(9), pp. 1568–1580.
• European Commission (2023) 2023 Strategic Foresight Report: Sustainability and People’s Wellbeing at the Heart of Europe’s Open Strategic Autonomy. Brussels: European Commission. Available at: https://commission.europa.eu/strategy-and-policy/strategic-foresight/2023-strategic-foresight-report_en.

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References

• European Commission (2023) 2023 Strategic Foresight Report: Sustainability and People’s Wellbeing at the Heart of Europe’s Open Strategic Autonomy. Brussels: European Commission. Available at: https://commission.europa.eu/strategy-and-policy/strategic-foresight/2023-strategic-foresight-report_en.
• Geels, F.W. (2002) ‘Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study’, Research Policy, 31(8–9), pp. 1257–1274.
• Georghiou, L., Cassingena Harper, J., Keenan, M., Miles, I. and Popper, R. (eds) (2008) The Handbook of Technology Foresight: Concepts and Practice. Cheltenham: Edward Elgar.
• Government Office for Science (2024) The Futures Toolkit: Tools for Futures Thinking and Foresight Across UK Government. London: Government Office for Science. Available at: https://www.gov.uk/government/publications/futures-toolkit-for-policy-makers-and-analysts/the-futures-toolkit-html.
• Kemp, R., Schot, J. and Hoogma, R. (1998) ‘Regime shifts to sustainability through processes of niche formation: The approach of strategic niche management’, Technology Analysis & Strategic Management, 10(2), pp. 175–198.
• Martin, B.R. (2010) ‘The origins of the concept of “foresight” in science and technology: An insider’s perspective’, Technological Forecasting and Social Change, 77(9), pp. 1438–1447.
• Organisation for Economic Co-operation and Development (OECD) (2025) Strategic Foresight Toolkit for Resilient Public Policy. Paris: OECD Publishing. Available at: https://www.oecd.org/en/publications/foresight-toolkit-for-resilient-public-policy_bcdd9304-en.html.
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