Content Frameworks: Designing Knowledge Architecture for Scalable Content Systems

Last Updated June 2, 2026

Content frameworks appear here not merely as copywriting formulas, but as disciplined structures for making ideas usable. They explain how complex subjects can be organized into pathways, how audiences can move through layered knowledge without becoming lost, how strategic messages can remain coherent across channels, and how large editorial systems can scale without becoming fragmented.

The field matters because complex ideas often fail to spread not because they lack merit, but because they lack structure. Without a framework, sophisticated research can appear fragmented, inaccessible, strategically inert, or difficult to scale. Content frameworks provide the structural logic that allows readers, learners, researchers, decision-makers, and audiences to move through complexity with orientation, continuity, and purpose.

Content Frameworks as a Foundational Discipline

Content frameworks occupy a foundational place within serious communication because they shape how knowledge becomes usable. Information by itself does not guarantee understanding. A large body of research, analysis, or content can remain difficult to use if audiences cannot see sequence, hierarchy, relationship, contrast, purpose, and application. Content frameworks provide the intellectual architecture that gives information shape.

This foundational role does not mean that content frameworks replace research, writing, storytelling, content strategy, curriculum design, policy analysis, or knowledge architecture. Rather, they connect them. Research generates insight. Writing gives insight expression. Storytelling gives insight movement and meaning over time. Content strategy aligns insight with audience, purpose, and channel. Knowledge architecture organizes insight across a larger system. Content frameworks provide reusable structures that allow these activities to become coherent.

The field matters because contemporary knowledge environments are increasingly large, interdisciplinary, and fragmented. A reader may encounter dozens of articles, reports, videos, PDFs, guides, categories, tags, summaries, and links without knowing how they relate. A content framework helps answer a basic but essential question: what path through this complexity should a person follow, and why?

Content Frameworks as Knowledge Architecture in Practice

Content frameworks may be understood as knowledge architecture in practice. Knowledge architecture organizes concepts, categories, taxonomies, hierarchies, semantic relationships, and navigational systems. Content frameworks translate that architecture into communicable forms: article structures, pillar pages, topic clusters, explanatory sequences, message hierarchies, educational pathways, narrative arcs, and decision-support models.

This makes content frameworks different from templates alone. A template tells a writer where to place information. A framework explains why information belongs in a given structure and how each part contributes to understanding. A template may be filled mechanically. A framework must be interpreted, adapted, and evaluated.

The architecture metaphor also clarifies why content frameworks are not neutral containers. A framework shapes what becomes visible, what is emphasized, what is simplified, what is sequenced first, and what is treated as peripheral. A framework can clarify complexity, but it can also distort complexity if used carelessly. Framework literacy therefore requires both practical skill and interpretive judgment.

Content Frameworks as a Quantitative and Computational Practice

Content frameworks are often described qualitatively through models, diagrams, outlines, templates, and editorial guidance. These remain essential. Yet serious content-framework work increasingly benefits from quantitative and computational practice. Large knowledge systems can be mapped, audited, scored, linked, and evaluated through structured data and reproducible workflows.

This does not mean that content frameworks become a purely technical field. A spreadsheet, database, or graph model cannot decide what knowledge should matter, what sequence is pedagogically appropriate, or what ethical context a subject requires. Computation is valuable when it makes the framework visible: the articles, categories, clusters, links, references, metadata, templates, status, gaps, and relationships that support a scalable knowledge system.

For that reason, this series treats SQL schemas, Python workflows, R analysis, metadata models, content-audit scripts, internal-link graphs, article-cluster maps, and open repositories as useful parts of content-framework literacy. Some articles remain primarily conceptual, rhetorical, strategic, or educational. Others naturally require taxonomy models, framework libraries, article maps, link analysis, completeness scoring, editorial dashboards, or reproducible code. The aim is not to reduce content to data, but to make content systems inspectable, maintainable, and coherent.

What Content Frameworks Study

Content frameworks study how ideas are organized for understanding, communication, persuasion, learning, and action. At the cognitive level, they examine sequence, hierarchy, contrast, repetition, scaffolding, mental models, narrative progression, and information load. At the editorial level, they study articles, pillars, clusters, templates, series structures, internal links, metadata, tags, categories, and content governance.

At the strategic level, content frameworks study positioning, message architecture, audience need, value communication, persuasive flow, diagnostic models, environmental analysis, and campaign structure. At the educational level, they study scaffolding, curriculum pathways, concept progression, learning objectives, examples, review loops, and cumulative understanding. At the policy and governance level, they study how complex systems, tradeoffs, institutional processes, and public decisions can be explained without collapsing complexity.

Content frameworks further study the gap between information and use. A report may contain strong research but weak structure. A pillar page may contain many articles but no conceptual pathway. A campaign may have a strong message but no evidence architecture. A curriculum may contain valuable lessons but poor sequencing. Content-framework thinking makes these gaps visible.

What This Pillar Covers

This pillar brings together the major domains through which content frameworks can be understood. It includes pillar pages, topic clusters, narrative pathways, knowledge architecture, educational scaffolding, conceptual models, research communication, policy explanation, governance communication, strategic messaging, AIDA, PAS, BAB, 5W1H, Jobs to Be Done, Value Proposition Canvas, segmentation-targeting-positioning, message house models, SWOT, PESTLE, Porter’s Five Forces, Ansoff Matrix, framework composition, sustainability communication, technology communication, scientific communication, strategic foresight frameworks, editorial systems, digital knowledge platforms, and the limits of framework thinking.

These domains differ in function, scale, and intellectual tradition, but together they form a coherent field: the structured design of communicable knowledge. Content frameworks are therefore not merely marketing devices. They are instruments for organizing thought across research, education, strategy, public reasoning, and digital publishing.

The series also treats content frameworks as a bridge between strategic ideation and knowledge architecture. Strategic ideation generates ideas, concepts, models, and directions. Knowledge architecture organizes those ideas into durable systems. Content frameworks translate both into forms that audiences can understand, navigate, remember, and use.

Mathematics, Computation, and Modeling in Content Frameworks

Mathematics and computation provide useful ways to formalize content frameworks without reducing them to numbers. A content system can be represented as a graph:

where \(V\) is the set of nodes and \(E\) is the set of relationships among them.

A pillar-cluster structure can be represented as:

where \(P\) is the pillar and each \(C_i\) is a topic cluster.

A content-completeness score can be represented conceptually as:

where \(Q_i\) is content quality, \(D_i\) depth, \(S_i\) structure, \(E_i\) evidence, \(L_i\) linking, \(R_i\) references, and \(U_i\) usefulness.

A weighted editorial score can be written as:

Internal-link density can be approximated as:

where \(L_i\) is relevant internal links and \(W_i\) is word count.

A broader model of framework capability can be represented as:

These formulations do not reduce framework design to formulas. They clarify a central point: content frameworks are systems of relationship. They organize concepts, pathways, evidence, audience needs, message sequences, and publishing structures over time.

Computation is especially valuable when content systems grow large. Python can support internal-link graph analysis, article mapping, metadata validation, content-audit workflows, topic-cluster diagnostics, and editorial status tracking. R can support content-library summaries, framework-type comparison, coverage analysis, and visualization. SQL can store articles, pillars, planned articles, references, templates, metadata, links, framework types, and editorial status. These tools make framework systems easier to maintain without replacing human editorial judgment.

Major Domains of Content Frameworks

Content frameworks include a wide range of major domains, each of which organizes communication differently. Knowledge frameworks structure large bodies of material through pillars, clusters, taxonomies, educational pathways, and conceptual models. Persuasive-sequence frameworks structure attention, interest, tension, transformation, and action. Audience and value frameworks clarify needs, jobs, pains, gains, relevance, segmentation, and positioning.

Strategic analysis frameworks diagnose position, environment, competition, growth pathways, and external pressures. Message architecture frameworks organize central claims, supporting pillars, proof points, evidence, and audience-specific language. Policy and governance frameworks explain institutional systems, tradeoffs, incentives, legal environments, public value, and decision structures. Scientific and technical frameworks translate complex evidence into usable explanatory systems.

Framework composition is also a major domain. Complex communication problems rarely require only one model. A team may use PESTLE to scan the environment, SWOT to assess position, Jobs to Be Done to clarify audience need, STP to define audience priority, a message house to organize claims, AIDA to sequence a campaign, and a pillar-cluster model to scale knowledge over time. Framework literacy includes knowing how to compose models without creating confusion.

Why Content Frameworks Matter

Content frameworks matter because complex ideas often fail not from lack of insight, but from lack of structure. Without an organizing framework, even sophisticated research can appear fragmented, inaccessible, strategically inert, or difficult to scale. Frameworks provide the structural logic that allows audiences to move from orientation to understanding, from understanding to evaluation, and from evaluation to action.

Frameworks matter because they serve both cognitive and communicative functions. Cognitively, they reduce confusion by imposing sequence, contrast, hierarchy, relation, and progression on large bodies of material. Communicatively, they improve usability by giving audiences a recognizable path through complexity. A framework can help readers understand not only what something is, but why it matters, how it relates to adjacent ideas, where it fits in a larger system, and what kinds of decisions or interpretations it enables.

They also matter because digital publishing tends toward accumulation. A site, archive, publication, or learning system can grow rapidly while becoming harder to navigate. Content frameworks protect against that drift. They allow knowledge systems to grow while preserving conceptual coherence, internal link logic, series structure, and editorial purpose.

Content Frameworks and Human Understanding

Content frameworks change how human beings understand knowledge because they show that communication is not only expression. It is organization. A reader does not encounter information as an empty container. Readers bring prior knowledge, attention limits, expectations, goals, confusion, memory, and context. Frameworks help create the conditions under which understanding can occur.

The field also changes how people understand persuasion. Persuasion is not only emotional pressure or rhetorical flourish. At its best, persuasion can be structured explanation: a careful ordering of context, need, evidence, value, consequence, and action. At its worst, frameworks can become manipulative devices that exploit tension, simplify harm, or pressure audiences into premature conclusions. A serious content-framework practice must therefore hold usability and ethics together.

For that reason, content frameworks have philosophical as well as practical significance. They raise questions about structure, meaning, attention, persuasion, simplification, knowledge transfer, power, and the relationship between understanding and action. A serious Content Frameworks pillar should therefore not end with templates alone. It should clarify how communicable knowledge is built.

Content Frameworks Pillar Map

The map below organizes the Content Frameworks knowledge series into conceptual domains, moving from foundations and knowledge architecture toward educational scaffolding, classic persuasion models, audience and value frameworks, strategic analysis, policy explanation, digital knowledge systems, framework composition, ethics, and computational editorial systems. Expansion articles are placed inside the sections where they belong once the pillar is complete.

Methods, Measurement, and Framework Practice

One of content-framework practice’s central challenges is that frameworks are often judged by surface familiarity rather than actual usefulness. AIDA, SWOT, PESTLE, Jobs to Be Done, message houses, pillar pages, topic clusters, and value proposition canvases are all recognizable, but recognition is not the same as fit. A framework should be evaluated by the kind of problem it clarifies, the level of abstraction it operates at, the audience it serves, and the decisions it supports.

Framework practice uses several methods. Comparative analysis clarifies what different frameworks are designed to do. Content audits reveal whether a framework is actually organizing material or merely labeling it. User research tests whether audiences can navigate the structure. Editorial review evaluates depth, sequence, evidence, and redundancy. Internal-link analysis examines whether content pathways are visible. Metadata review tests whether articles, categories, references, images, and repositories are consistently governed.

Modern framework practice should combine qualitative judgment with quantitative support. Qualitative work interprets purpose, meaning, ethics, audience, and fit. Quantitative workflows can track coverage, gaps, links, duplicates, article status, reference density, topic clusters, and framework adoption. A serious content-framework practice should not choose between editorial judgment and data systems. It should integrate both.

Content Frameworks, Technology, and the Modern World

Content frameworks have become increasingly important because modern knowledge environments are mediated by digital systems. Search engines, content management systems, recommendation systems, social platforms, learning platforms, AI tools, databases, newsletters, podcasts, video libraries, and institutional websites all shape how knowledge is encountered and understood. Without framework architecture, digital publishing can scale volume while weakening coherence.

Technology can strengthen content-framework practice when it helps map topics, track internal links, identify gaps, organize metadata, audit references, support accessibility, and maintain editorial consistency. It can weaken framework practice when it encourages superficial templates, automated summaries, keyword stuffing, fragmented publishing, or mechanical content production without conceptual depth.

A mature framework approach to technology must therefore ask not only what can be published, but how knowledge should be structured. How are topics related? Which articles serve as conceptual anchors? Which pieces deepen, apply, critique, or extend a pillar? Which frameworks are being used, and why? What pathways should a reader follow? What evidence supports the claims? What metadata makes the system maintainable? These are editorial and architectural questions before they are technical ones.

Content Frameworks, Computation, and Editorial Systems

Computation has become valuable for content frameworks because large publishing systems eventually exceed what memory and manual tracking can manage. A serious knowledge system may contain thousands of posts, hundreds of categories, many content pillars, multiple repositories, image metadata records, references, excerpts, internal links, planned articles, and article-status states. Without structured workflows, even strong content can become difficult to govern.

Editorial systems modeling allows teams to represent content as structured data. Articles can be linked to pillars, clusters, frameworks, references, image records, repositories, excerpts, and review status. Internal links can be represented as a graph. Content gaps can be identified by comparing planned and published articles. Frameworks can be classified by function: persuasion, diagnosis, education, positioning, policy explanation, or knowledge architecture.

For that reason, this pillar treats computation as a supporting discipline of content frameworks, not as a substitute for editorial judgment. The strongest form of computational content-framework practice is auditable editorial architecture: clear metadata, reproducible audits, explicit article relationships, consistent templates, visible gaps, and careful interpretation.

R Section: Modeling a Content Framework Library

The R workflow below models a small content-framework library across framework type, function, complexity level, primary use, audience fit, and ethical risk. It is designed as an evergreen demonstration of how a framework library can be organized for comparison rather than treated as a flat list of acronyms.

# Content Frameworks: Modeling a Framework Library in R
# Educational example only.

# install.packages(c("tidyverse"))
library(tidyverse)

# -------------------------------------------------------------------
# Synthetic framework library.
# -------------------------------------------------------------------

frameworks <- tibble(
  framework = c(
    "Pillar-Cluster Architecture",
    "AIDA",
    "PAS",
    "Jobs to Be Done",
    "Value Proposition Canvas",
    "STP",
    "Message House",
    "SWOT",
    "PESTLE",
    "Porter's Five Forces",
    "Theory of Change"
  ),
  framework_type = c(
    "Knowledge Architecture",
    "Persuasive Sequence",
    "Persuasive Sequence",
    "Audience Need",
    "Value Alignment",
    "Positioning",
    "Message Architecture",
    "Strategic Analysis",
    "Environmental Analysis",
    "Competitive Analysis",
    "Policy and Impact"
  ),
  complexity_level = c(0.82, 0.38, 0.42, 0.70, 0.68, 0.61, 0.72, 0.52, 0.66, 0.74, 0.80),
  transferability = c(0.86, 0.70, 0.62, 0.78, 0.76, 0.72, 0.81, 0.68, 0.75, 0.58, 0.77),
  ethical_risk = c(0.20, 0.46, 0.62, 0.30, 0.28, 0.34, 0.24, 0.22, 0.20, 0.25, 0.36),
  knowledge_depth = c(0.90, 0.42, 0.40, 0.72, 0.68, 0.60, 0.76, 0.62, 0.74, 0.71, 0.86),
  action_support = c(0.78, 0.72, 0.70, 0.76, 0.82, 0.78, 0.80, 0.73, 0.70, 0.66, 0.84)
)

# -------------------------------------------------------------------
# Framework usefulness profile.
# -------------------------------------------------------------------

frameworks <- frameworks |>
  mutate(
    framework_profile =
      0.22 * complexity_level +
      0.20 * transferability -
      0.12 * ethical_risk +
      0.24 * knowledge_depth +
      0.22 * action_support
  )

print(frameworks)

# -------------------------------------------------------------------
# Long format for dimension comparison.
# -------------------------------------------------------------------

frameworks_long <- frameworks |>
  pivot_longer(
    cols = c(
      complexity_level,
      transferability,
      ethical_risk,
      knowledge_depth,
      action_support
    ),
    names_to = "dimension",
    values_to = "value"
  )

ggplot(frameworks_long, aes(x = dimension, y = value, group = framework)) +
  geom_line(aes(linetype = framework)) +
  geom_point() +
  coord_flip() +
  labs(
    title = "Content Framework Library Dimensions",
    x = "Framework dimension",
    y = "Value",
    linetype = "Framework"
  ) +
  theme_minimal(base_size = 12)

# -------------------------------------------------------------------
# Identify frameworks needing ethical caution.
# -------------------------------------------------------------------

caution_flags <- frameworks |>
  mutate(
    high_ethical_risk = ethical_risk > 0.50,
    low_knowledge_depth = knowledge_depth < 0.50,
    requires_caution = high_ethical_risk | low_knowledge_depth
  )

print(caution_flags)

# -------------------------------------------------------------------
# Export outputs.
# -------------------------------------------------------------------

dir.create("outputs", showWarnings = FALSE, recursive = TRUE)

write_csv(frameworks, "outputs/content_framework_library.csv")
write_csv(frameworks_long, "outputs/content_framework_dimensions_long.csv")
write_csv(caution_flags, "outputs/content_framework_caution_flags.csv")

This workflow models a central content-framework principle: frameworks differ by function. A persuasive-sequence framework should not be evaluated in the same way as an environmental analysis framework, a curriculum framework, or a knowledge architecture model.

Python Section: Mapping Pillars, Clusters, and Internal Links

The Python workflow below models a small content system as a graph. It represents pillars, clusters, articles, and internal links, then calculates simple network diagnostics. This demonstrates how content frameworks can become visible as editorial infrastructure.

# Content Frameworks: Mapping Pillars, Clusters, and Internal Links in Python
# Educational example only.

from __future__ import annotations

import pandas as pd
import networkx as nx


nodes = pd.DataFrame({
    "node_id": [
        "content_frameworks",
        "knowledge_architecture",
        "strategic_ideation",
        "storytelling",
        "aida",
        "jtbd",
        "message_house",
        "pillar_clusters",
        "policy_frameworks",
        "digital_knowledge_systems"
    ],
    "node_type": [
        "pillar",
        "related_pillar",
        "related_pillar",
        "related_pillar",
        "article",
        "article",
        "article",
        "article",
        "article",
        "article"
    ],
    "title": [
        "Content Frameworks",
        "Knowledge Architecture",
        "Strategic Ideation",
        "Storytelling",
        "AIDA and Persuasive Sequence",
        "Jobs to Be Done",
        "Message House",
        "Pillar Pages and Topic Clusters",
        "Frameworks for Policy Explanation",
        "Frameworks for Digital Knowledge Systems"
    ]
})

edges = pd.DataFrame({
    "source": [
        "content_frameworks",
        "content_frameworks",
        "content_frameworks",
        "content_frameworks",
        "content_frameworks",
        "content_frameworks",
        "content_frameworks",
        "content_frameworks",
        "strategic_ideation",
        "knowledge_architecture",
        "storytelling"
    ],
    "target": [
        "knowledge_architecture",
        "strategic_ideation",
        "storytelling",
        "aida",
        "jtbd",
        "message_house",
        "pillar_clusters",
        "digital_knowledge_systems",
        "message_house",
        "pillar_clusters",
        "aida"
    ],
    "relationship": [
        "related_topic",
        "parent_context",
        "related_topic",
        "series_article",
        "series_article",
        "series_article",
        "series_article",
        "series_article",
        "supports",
        "supports",
        "supports"
    ]
})


def build_content_graph(nodes_df: pd.DataFrame, edges_df: pd.DataFrame) -> nx.DiGraph:
    """Build a directed content graph from node and edge tables."""
    graph = nx.DiGraph()

    for _, row in nodes_df.iterrows():
        graph.add_node(row["node_id"], node_type=row["node_type"], title=row["title"])

    for _, row in edges_df.iterrows():
        graph.add_edge(row["source"], row["target"], relationship=row["relationship"])

    return graph


graph = build_content_graph(nodes, edges)

centrality = nx.degree_centrality(graph)
in_degree = dict(graph.in_degree())
out_degree = dict(graph.out_degree())

metrics = pd.DataFrame({
    "node_id": list(graph.nodes()),
    "title": [graph.nodes[node]["title"] for node in graph.nodes()],
    "node_type": [graph.nodes[node]["node_type"] for node in graph.nodes()],
    "in_degree": [in_degree[node] for node in graph.nodes()],
    "out_degree": [out_degree[node] for node in graph.nodes()],
    "degree_centrality": [centrality[node] for node in graph.nodes()]
}).sort_values("degree_centrality", ascending=False)

print("Content graph metrics:")
print(metrics)

# -------------------------------------------------------------------
# Identify orphaned or underconnected nodes.
# -------------------------------------------------------------------

metrics["underconnected"] = (
    (metrics["in_degree"] == 0)
    & (metrics["out_degree"] == 0)
)

print("\nUnderconnected nodes:")
print(metrics[metrics["underconnected"]])

# -------------------------------------------------------------------
# Export outputs.
# -------------------------------------------------------------------

nodes.to_csv("content_framework_nodes.csv", index=False)
edges.to_csv("content_framework_edges.csv", index=False)
metrics.to_csv("content_framework_graph_metrics.csv", index=False)

This workflow reinforces a central content-framework distinction. A content system is not only a list of articles. It is a network of relationships. Pillars, clusters, links, categories, templates, and metadata determine whether knowledge becomes navigable or fragmented.

Interpretive Limits and Framework Cautions

Content frameworks are powerful, but they can be misused. A framework can clarify complexity, but it can also oversimplify it. A template can support consistency, but it can also produce formulaic writing. A persuasive model can help structure attention, but it can also manipulate emotion. A strategic framework can organize thinking, but it can also become ritualized, generic, or detached from evidence.

Analysts and practitioners should therefore avoid confusing framework use with understanding. A framework is not useful merely because it is familiar. It is useful when it clarifies a real problem, fits the domain, supports the audience, preserves important complexity, and improves action. AIDA is useful for certain persuasive sequences, but it is not a theory of education. SWOT can help organize strategic position, but it can become shallow if not grounded in evidence. PESTLE supports environmental scanning, but it does not by itself produce strategy.

The field is strongest when frameworks remain flexible, contestable, and purpose-specific. Content-framework literacy means knowing how to use frameworks, combine them, adapt them, and critique them. The goal is not to force every idea into a model. The goal is to create enough structure for ideas to become clear, usable, and responsibly communicated.

Content Frameworks in a Wider Intellectual Context

Content frameworks belong not only to marketing, education, or publishing, but to the broader history of human thought about structure, memory, persuasion, learning, classification, and public understanding. Human beings have always used forms to organize knowledge: lists, taxonomies, dialogues, diagrams, curricula, sermons, legal codes, maps, encyclopedias, scientific papers, policy briefs, and narrative sequences. Content frameworks are the contemporary expression of that older intellectual need.

The field changes the imagination of content. Content is not merely output. It is structured knowledge in circulation. A strong framework helps audiences understand where they are, what they are learning, why it matters, how ideas relate, and what can be done with that understanding. A weak framework turns complex knowledge into disconnected fragments.

For that reason, content frameworks should be understood as both practical tools and intellectual infrastructure. They bring together strategy, pedagogy, rhetoric, knowledge architecture, digital publishing, systems explanation, and ethical communication. They remain indispensable for any serious knowledge environment concerned with research, education, policy, sustainability, technology, governance, storytelling, and long-term public understanding.

Related Reading

• Strategic Ideation
• Knowledge Architecture
• Storytelling
• Design Thinking
• Systems Thinking
• Futures Thinking
• Decision Science

Further Reading

• Barry, T.E. (1987) ‘The development of the hierarchy of effects: an historical perspective’, Current Issues and Research in Advertising, 10(1–2), pp. 251–295.
• Buley, L. (2013) The User Experience Team of One. Brooklyn, NY: Rosenfeld Media.
• Christensen, C.M., Hall, T., Dillon, K. and Duncan, D.S. (2016) Competing Against Luck. New York: HarperBusiness.
• Donella Meadows Project (n.d.) Leverage Points: Places to Intervene in a System. Available at: https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/.
• Harvard Business Review (2008) The Five Competitive Forces That Shape Strategy. Available at: https://hbr.org/2008/01/the-five-competitive-forces-that-shape-strategy.
• Harvard Business School (n.d.) The Five Competitive Forces That Shape Strategy. Available at: https://www.hbs.edu/faculty/Pages/item.aspx?num=34522.
• IDEO (n.d.) Design Thinking. Available at: https://designthinking.ideo.com/.
• Johnson, G., Scholes, K. and Whittington, R. (2008) Exploring Corporate Strategy. 8th edn. Harlow: Financial Times Prentice Hall.
• Kotler, P. and Keller, K.L. (2016) Marketing Management. 15th edn. Harlow: Pearson.
• Organisation for Economic Co-operation and Development (OECD) (n.d.) Strategic Foresight. Available at: https://www.oecd.org/en/about/programmes/strategic-foresight.html.
• Osterwalder, A., Pigneur, Y., Bernarda, G. and Smith, A. (2014) Value Proposition Design. Hoboken, NJ: Wiley.
• Porter, M.E. (2008) ‘The five competitive forces that shape strategy’, Harvard Business Review, 86(1), pp. 78–93.
• Rumelt, R. (2011) Good Strategy/Bad Strategy. New York: Crown Business.
• Stanford d.school (n.d.) Design Thinking Bootleg. Available at: https://dschool.stanford.edu/tools/design-thinking-bootleg.
• Whittington, R., Regnér, P., Angwin, D., Johnson, G. and Scholes, K. (2020) Exploring Strategy. 12th edn. Harlow: Pearson.

References

• Barry, T.E. (1987) ‘The development of the hierarchy of effects: an historical perspective’, Current Issues and Research in Advertising, 10(1–2), pp. 251–295.
• Buley, L. (2013) The User Experience Team of One. Brooklyn, NY: Rosenfeld Media.
• Christensen, C.M., Hall, T., Dillon, K. and Duncan, D.S. (2016) Competing Against Luck. New York: HarperBusiness.
• Donella Meadows Project (n.d.) Leverage Points: Places to Intervene in a System. Available at: https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/.
• Harvard Business Review (2008) The Five Competitive Forces That Shape Strategy. Available at: https://hbr.org/2008/01/the-five-competitive-forces-that-shape-strategy.
• Harvard Business School (n.d.) The Five Competitive Forces That Shape Strategy. Available at: https://www.hbs.edu/faculty/Pages/item.aspx?num=34522.
• IDEO (n.d.) Design Thinking. Available at: https://designthinking.ideo.com/.
• Johnson, G., Scholes, K. and Whittington, R. (2008) Exploring Corporate Strategy. 8th edn. Harlow: Financial Times Prentice Hall.
• Kotler, P. and Keller, K.L. (2016) Marketing Management. 15th edn. Harlow: Pearson.
• Organisation for Economic Co-operation and Development (OECD) (n.d.) Strategic Foresight. Available at: https://www.oecd.org/en/about/programmes/strategic-foresight.html.
• Osterwalder, A., Pigneur, Y., Bernarda, G. and Smith, A. (2014) Value Proposition Design. Hoboken, NJ: Wiley.
• Porter, M.E. (2008) ‘The five competitive forces that shape strategy’, Harvard Business Review, 86(1), pp. 78–93.
• Rumelt, R. (2011) Good Strategy/Bad Strategy. New York: Crown Business.
• Stanford d.school (n.d.) Design Thinking Bootleg. Available at: https://dschool.stanford.edu/tools/design-thinking-bootleg.
• Whittington, R., Regnér, P., Angwin, D., Johnson, G. and Scholes, K. (2020) Exploring Strategy. 12th edn. Harlow: Pearson.