Innovation and Emerging Business Models

Introduction

Innovation is the disciplined creation and adoption of new value. It is not restricted to invention, research and development, or the introduction of advanced technology. In business, innovation occurs when an organization creates a valuable improvement in a product, service, process, customer experience, organizational arrangement, or business model and succeeds in implementing it. A technically impressive idea that customers do not value, employees cannot deliver, or the organization cannot appropriate economically is not a successful innovation.

For managers, innovation is both a growth opportunity and a strategic necessity. Digital platforms, artificial intelligence, climate pressures, demographic change, regulatory transformation and shifting customer expectations are altering the basis of competition. Established firms must therefore manage two activities simultaneously: exploiting existing capabilities efficiently and exploring uncertain opportunities. This chapter examines design thinking, experimentation, innovation frameworks and emerging business models as integrated managerial practices.

The central argument is that innovation should be managed as a portfolio of assumptions and evidence rather than as a sequence of large, irreversible commitments. Managers must identify whose problem is being solved, what value is being created, how value will be delivered, and how the organization will capture sufficient value to remain viable.

Learning Objectives

By the end of this chapter, readers should be able to:

1. Distinguish invention, innovation, incremental innovation, disruptive innovation and business-model innovation.
2. Apply design thinking to ambiguous customer and organizational problems.
3. Formulate testable innovation hypotheses and design disciplined experiments.
4. Evaluate business models using value creation, delivery and capture logic.
5. Compare platform, subscription, servitization, sharing, ecosystem and circular business models.
6. Use innovation metrics and portfolio approaches without confusing activity with impact.
7. Assess ethical, regulatory, technological and organizational risks associated with emerging models.

1. The Nature and Strategic Role of Innovation

1.1 Invention, innovation and entrepreneurship

An invention is a novel technical or conceptual possibility. Innovation is the implementation and adoption of that possibility in a context where it creates value. Entrepreneurship concerns the mobilization of resources under uncertainty to pursue an opportunity. These concepts overlap but are not identical. A laboratory invention may never become an innovation; an innovation may combine existing technologies in a new commercial configuration.

Innovation can be classified in several ways:

Incremental innovation improves an existing offer or process. Radical innovation introduces a substantially different technological or organizational logic. Disruptive innovation, in the precise strategic sense associated with disruption theory, begins with a simpler, cheaper or more accessible offer serving overlooked or new-market customers and may later challenge established providers. The term should not be used as a synonym for any dramatic or technologically advanced innovation.

1.2 Exploration and exploitation

Organizations exploit established capabilities through standardization, forecasting, process control and efficiency. They explore through experimentation, search, variation and learning. Exploitation generally produces more predictable short-term returns, whereas exploration creates options whose value is uncertain and delayed. An ambidextrous organization develops structures and leadership practices that permit both.

A common managerial error is to apply exploitation controls to exploration projects. Requiring a new venture to meet the same margin, forecast accuracy and utilization targets as a mature product can eliminate promising opportunities prematurely. The opposite error is to let exploratory projects continue without explicit learning milestones. Effective governance therefore asks not only, “Did the project meet its sales target?” but also, “What uncertainty was reduced, and what decision should follow?”

1.3 Innovation as a system

Innovation depends on interactions among customers, employees, suppliers, regulators, investors, universities, communities and technologies. Open innovation recognizes that valuable knowledge may enter or leave organizational boundaries. Ecosystem thinking extends this idea: a focal firm often creates value only when complementary actors provide infrastructure, content, standards, skills or access.

The system perspective also reveals that innovation may produce negative externalities. A delivery platform can increase convenience while intensifying traffic and precarious work. A generative-AI service can increase productivity while creating risks involving copyright, bias, confidentiality and employment. Managerial evaluation must therefore include affected stakeholders, not only paying customers.

2. Design Thinking for Complex Business Problems

Design thinking is a human-centered approach to addressing problems characterized by ambiguity, incomplete information and competing stakeholder needs. Its frequently used stages are empathize, define, ideate, prototype and test. These stages are not a strictly linear pipeline. Teams often return to earlier stages when evidence changes their understanding of the problem.

2.1 Empathize: understanding lived experience

Empathy work investigates what people do, experience, fear, value and improvise—not merely what they say they want. Methods include contextual observation, semi-structured interviews, diary studies, journey mapping, complaint analysis and frontline-employee inquiry. Sampling should seek variation in behavior and circumstances rather than only convenient or enthusiastic participants.

A useful interview avoids leading questions such as “Would you like an app that automates this?” It instead asks respondents to describe the last time they encountered the problem, what they did, what alternatives they considered, and what consequences followed. Observed behavior is usually stronger evidence than generalized preference statements.

2.2 Define: framing the opportunity

The define stage synthesizes evidence into a focused problem statement. A strong statement identifies a user or stakeholder, a context, an unmet need and the relevant constraint. For example: “Time-pressured outpatient clinics need a reliable way to reduce appointment no-shows without imposing burdens on patients with limited digital access.” This is preferable to “Build an AI scheduling tool,” because it preserves space for multiple solutions.

Teams should distinguish symptoms from root causes. Root-cause analysis, the “five whys,” service blueprints and systems maps can reveal that a visible customer complaint arises from incentives, information gaps or process dependencies elsewhere in the organization.

2.3 Ideate: generating and selecting possibilities

Ideation benefits from separating divergent and convergent activity. During divergence, teams generate alternatives through brainstorming, analogy, forced connections, extreme-user scenarios and “how might we” questions. During convergence, ideas are assessed against strategic fit, desirability, feasibility, viability, compliance and potential impact.

A weighted scoring model can structure discussion, but it does not convert judgment into objective truth. If an idea receives criterion scores sᵢ and weights wᵢ, with weights summing to 1, its overall score can be represented as:

Here, S is the weighted score, sᵢ is the score on criterion i, and wᵢ is the importance assigned to criterion i. Scores must use a consistent scale, such as 1–5. The method is useful for transparency and comparison, but sensitivity analysis should test whether a small change in weights reverses the ranking.

2.4 Prototype: making assumptions visible

A prototype is an artifact that enables learning. It may be a sketch, storyboard, role-play, landing page, spreadsheet, clickable interface, service script or manually delivered “concierge” service. High-fidelity prototypes are not inherently superior. Early prototypes should be inexpensive and fast enough to support several cycles of revision.

Prototyping should expose the most consequential assumptions. If the critical uncertainty is whether customers trust a new service, a polished interface may be less useful than a controlled conversation or simulated transaction. If the uncertainty concerns operational capacity, a process simulation or limited live pilot is more informative than customer focus groups.

2.5 Test: learning from evidence

Testing should specify what would count as support, disconfirmation or an inconclusive result. Teams should record observations rather than retrospectively rationalizing outcomes. A failed prototype is valuable when it prevents a larger investment or reveals a previously hidden need.

Design thinking has limitations. It can become superficial when empathy is reduced to a few interviews, when “ideation” rewards novelty rather than value, or when user desirability is assessed without financial, operational and regulatory analysis. It is best combined with experimentation, strategy and domain expertise.

Figure: Design thinking as an iterative learning cycle rather than a strictly linear process.

3. Experimentation and Evidence-Based Innovation

3.1 From ideas to hypotheses

An innovation hypothesis states a belief that can be tested. A useful format is: “For [defined customer], who experiences [problem], [proposed intervention] will produce [observable behavior or outcome] because [mechanism].” For example: “For small retailers with irregular demand, a demand-forecasting service will increase weekly use of replenishment recommendations because it reduces stock-out uncertainty.”

Hypotheses should distinguish desirability, feasibility and viability:

• Desirability: Do target users experience a meaningful problem and choose the solution?
• Feasibility: Can the organization deliver the solution reliably and at acceptable quality?
• Viability: Can the model generate sufficient economic value relative to its costs and risks?

Additional hypotheses may concern compliance, stakeholder legitimacy, technical performance and ecological impact.

3.2 Minimum viable products and minimum viable evidence

A minimum viable product (MVP) is the smallest version of an offering that permits meaningful learning from real or realistic use. It is not an excuse to release unsafe, misleading or unusable products. In regulated or high-risk contexts, the minimum viable experiment may be a simulation, sandbox, supervised pilot or staged deployment rather than a public release.

The relevant principle is minimum viable evidence: invest only enough to test the highest-risk assumption, but collect evidence of sufficient quality to support the decision. A vanity metric—such as downloads without active use—may look positive while providing little evidence of value.

3.3 Metrics and decision rules

Innovation metrics should be connected to a decision. Common categories include:

For a simple cohort, a retention rate at time t can be expressed as:

where N₀ is the number of users in the starting cohort and Nₜ is the number from that cohort active at time t. This ratio is meaningful only when “active” is defined consistently and the cohort boundary is clear. Retention is not the same as satisfaction, profitability or causal impact.

Contribution margin per transaction can be represented as:

where P is revenue per transaction and V is variable cost per transaction. If fixed costs are relevant, the break-even volume is:

where F is fixed cost and CM must be positive and measured in the same currency per transaction. This simplified calculation assumes constant price and variable cost, a defined period, and no capacity, tax, financing or nonlinear demand effects. It is a screening tool, not a complete financial forecast.

Worked example: evaluating a pilot

A business tests a digital maintenance service with 1,000 initial users. After three months, 420 of those same users are active. The monthly price is £20 and variable servicing cost is £8 per user. Monthly fixed pilot costs are £4,800.

Retention at month three is:

Contribution margin per active user is:

The simplified monthly break-even active-user volume is:

The observed 420 active users exceed this simplified break-even threshold by 20 users. However, the result does not prove commercial viability. The manager must examine acquisition costs, churn, support capacity, payment failure, renewal behavior, customer heterogeneity and whether pilot fixed costs represent the future operating structure.

3.4 Experiment design and causal caution

When decisions concern whether an intervention caused an outcome, randomized controlled experiments are often stronger than simple before-and-after comparisons. In an A/B test, eligible users are randomly assigned to a control condition or treatment condition. The difference in outcomes estimates the treatment effect under assumptions including valid randomization, consistent measurement and limited interference between groups.

A difference in conversion rates is not automatically causal if groups differ systematically or if external events affect only one group. Qualitative evidence remains important because quantitative results may reveal that something happened without explaining why. Mixed-method experimentation combines behavioral data with interviews, observation and process evidence.

4. Business-Model Innovation

A business model explains how an organization creates, delivers and captures value. It is broader than a product description and more operational than a mission statement. A business model normally includes a customer segment, value proposition, channels, relationships, key resources, key activities, partners, cost structure and revenue logic.

4.1 Value creation, delivery and capture

Value creation concerns the benefits produced for customers and other stakeholders. Value delivery concerns the activities and relationships required to make those benefits available. Value capture concerns how the organization obtains revenue, cost advantage, data advantage, access, legitimacy or another return that supports continuity.

These dimensions may conflict. A free service can create substantial user value but lack a sustainable capture mechanism. A profitable model can externalize costs onto workers or communities. A platform can create value for one side while extracting excessive fees from another. Business-model analysis must therefore examine distributional effects and system sustainability.

A business model is also a set of interdependent choices. Changing the revenue model may alter customer behavior, service expectations, cost structure and partner incentives. For example, moving from one-time sales to subscription revenue may improve revenue predictability but create pressure to deliver continuous value and provide cancellation transparency.

4.2 Emerging business-model patterns

Platform models facilitate interactions among two or more interdependent user groups, such as buyers and sellers. Network effects may increase value as participation grows. Direct network effects arise when users benefit from more users of the same type; indirect network effects arise when one group attracts complementary participation from another group. Platforms face challenges involving trust, fraud, governance, multihoming, disintermediation and excessive concentration.

Subscription and servitization models replace or supplement ownership with recurring access, maintenance or outcomes. Industrial firms may sell “uptime” rather than equipment alone. Success depends on usage monitoring, service reliability, renewal value and control of lifecycle costs.

Freemium and usage-based models reduce initial barriers to adoption. Freemium offers a basic service without charge and charges for enhanced features; usage-based pricing charges according to consumption. Both require careful attention to conversion, marginal cost, fairness and customer understanding.

Sharing and access models increase utilization of underused assets or capabilities. Their sustainability depends on whether they genuinely reduce resource use or merely stimulate additional consumption.

Circular models seek to retain products, components and materials at their highest useful value through repair, reuse, remanufacture, refurbishment, resale and recycling. Circularity changes design requirements, reverse logistics, ownership arrangements and revenue timing.

Ecosystem and embedded-finance models integrate complementary services into a broader customer journey. They may improve convenience but increase dependency, data-sharing and regulatory complexity.

4.3 Evaluating a business-model design

A manager should ask:

1. Which customer problem is sufficiently important to change behavior?
2. Who pays, who benefits and who bears the risks?
3. What resources and capabilities are difficult to imitate?
4. What must partners contribute, and how are incentives aligned?
5. What are the marginal and fixed costs as scale increases?
6. Does scale improve economics, quality or network value—or create congestion and risk?
7. How can the model be governed fairly and compliantly?
8. What assumptions are most uncertain and how can they be tested cheaply?

The Business Model Canvas is useful for mapping these elements. However, it is a representation, not a strategy. It may conceal power relationships, competitor responses, dynamic capabilities and regulatory constraints. Managers should supplement it with customer-journey maps, financial models, ecosystem maps and scenario analysis.

5. Managing Innovation Portfolios and Organizations

A portfolio approach allocates resources across different risk and time horizons. A practical portfolio may contain core improvements, adjacent extensions and transformational opportunities. The precise proportions depend on the firm’s financial position, strategic intent and industry dynamics; no universal allocation rule is valid.

Governance should match uncertainty. Early projects need small staged investments, rapid feedback and protection from premature financial targets. As evidence accumulates, the organization can increase investment, establish operating ownership and introduce stronger controls. “Kill criteria” should be defined in advance, while allowing teams to revise assumptions when credible evidence changes the opportunity.

Innovation culture is not simply employee enthusiasm. It includes psychological safety, access to decision makers, cross-functional collaboration, time for exploration, tolerance for intelligent failure and accountability for learning. Senior leaders must resolve conflicts between existing business units and new ventures, especially where a new model threatens current revenue.

Dynamic capabilities help organizations sense opportunities, seize them through investment and reconfigure assets as circumstances change. These capabilities are strengthened by modular technologies, reusable experimentation infrastructure, customer-data governance, partnership competence and leaders who can translate weak signals into strategic choices.

6. Technology, Data and Artificial Intelligence

Technology can enable innovation, but technology-led initiatives often begin with a solution in search of a problem. AI is most valuable when it improves a clearly defined decision, workflow or customer outcome. Examples include demand forecasting, personalization, predictive maintenance, service automation and knowledge retrieval.

AI-enabled business models introduce distinctive requirements. Data quality, representativeness, lineage and consent affect model reliability. Managers must evaluate false positives and false negatives according to the consequences of error. Human oversight is essential where decisions affect credit, employment, health, safety, education or access to essential services.

Generative AI creates opportunities for rapid prototyping, content production, coding assistance and conversational service. It also creates risks of fabricated information, intellectual-property disputes, confidential-data leakage, discrimination and unclear accountability. Responsible implementation requires documented use cases, risk classification, testing, monitoring, incident escalation, access controls and transparent communication with affected users.

Technology governance should be proportional to risk. A low-risk internal brainstorming tool and an automated benefits-eligibility system should not receive identical controls. Nevertheless, every deployment should identify the system owner, purpose, data sources, performance criteria, human responsibilities and procedures for correction or appeal.

7. Ethics, Regulation and Responsible Innovation

Innovation decisions have moral and political consequences. Responsible innovation asks whether an innovation is desirable, sustainable, inclusive and aligned with legitimate social values. Key questions include:

• Are users adequately informed about price, data use, automated decisions and cancellation conditions?
• Are vulnerable groups likely to be excluded or harmed?
• Are workers fairly compensated and protected?
• Can decisions be explained, challenged and corrected?
• Does the innovation increase surveillance or concentration of power?
• Are environmental benefits measured across the lifecycle rather than claimed from a single stage?

Regulatory compliance is a minimum requirement, not a substitute for ethical judgment. Laws vary by jurisdiction and sector, so managers should obtain qualified legal advice for specific deployments. Governance should include privacy-by-design, security controls, accessibility testing, records of decision making, supplier due diligence and impact assessments where appropriate.

8. Illustrative Case: Reimagining Preventive Equipment Maintenance

The following is a fictional illustrative case.

Background

Northstar Foods operates ten production sites. Its refrigeration equipment is reliable but expensive to maintain. Failures cause spoilage, emergency repairs and production delays. The engineering department proposes an AI-enabled subscription service that monitors equipment and recommends maintenance.

Problem and initial assumptions

The proposal assumes that sensors can predict failure, plant managers will trust recommendations, technicians can respond within required windows, and customers will pay a recurring fee. Early discussion focuses heavily on the algorithm, but interviews reveal that plant managers care more about avoiding regulatory violations and unplanned shutdowns than about technical novelty.

Analysis

The team maps the service journey and identifies stakeholders: plant managers, technicians, finance, food-safety officers, equipment manufacturers and insurers. It discovers that some sites have poor connectivity and inconsistent maintenance records. A fully automated intervention would create safety and accountability concerns. The team therefore reframes the offer as “risk-based maintenance coordination” rather than autonomous repair.

The highest-risk assumptions are:

1. Sensor data can identify a useful subset of failure risks.
2. Managers will act on alerts if explanations and recommended actions are clear.
3. The service can reduce costly failures without increasing false alarms.
4. The subscription price exceeds incremental delivery costs and perceived switching costs.

Approach and solution

Northstar runs a supervised pilot on two sites. It uses existing sensor data where possible and manually reviews alerts with technicians. A dashboard reports confidence bands, evidence and recommended inspection steps. The pilot compares alert-supported maintenance with historical failure patterns, while interviews assess trust and workload.

The team defines success as a reduction in unplanned downtime, no increase in safety incidents, acceptable alert precision, and evidence that managers will renew. It deliberately avoids claiming that the system “predicts every failure.”

Outcome

The pilot reduces emergency call-outs, but the first version generates too many low-value alerts at one site. Investigation shows that a particular sensor is poorly calibrated. After improving data quality and changing the alert threshold, the service becomes more useful. Customers accept a subscription only when it includes technician coordination and compliance documentation; they reject a sensor-only proposition.

Northstar launches a tiered model: monitoring and reporting for smaller sites, and monitoring plus coordinated maintenance for larger customers. It retains human review for high-consequence alerts and establishes an audit process for model changes.

Learning points

The case illustrates that innovation was not the algorithm alone. Value emerged from combining data, workflow redesign, human judgment, service coordination and a viable pricing model. The team reduced uncertainty through staged experimentation rather than purchasing a complete technology platform at the outset. It also recognized that trust, accountability and regulatory evidence were part of the customer value proposition.

9. Challenges and Limitations

Innovation frameworks can create false confidence. A canvas may suggest coherence while hiding weak economics. Design thinking may privilege articulate users and neglect non-users or affected communities. Lean experimentation may overemphasize short-term measurable behavior and underinvest in long-term capabilities. Platform strategies may create winner-take-most dynamics that reduce competition. Subscription models may shift risk to customers who pay continuously for uncertain value.

Managers should also guard against innovation theatre: hackathons without implementation authority, pilots without adoption pathways, dashboards filled with activity measures, and AI projects justified by fashionable terminology. The remedy is strategic clarity, explicit assumptions, rigorous evidence, responsible governance and willingness to stop initiatives that no longer merit investment.

Best-Practice Checklist

A disciplined innovation initiative should:

1. Define the problem independently of the proposed technology.
2. Identify users, non-users, partners and negatively affected stakeholders.
3. State desirability, feasibility, viability and responsibility hypotheses.
4. Test the riskiest assumption before scaling expenditure.
5. Use metrics tied to explicit decisions.
6. Combine qualitative insight with behavioral and financial evidence.
7. Separate learning milestones from mature-business performance targets.
8. Design for accessibility, privacy, security and accountability from the beginning.
9. Evaluate the entire business model, not only the product.
10. Establish ownership, governance and a route from pilot to operations.

Chapter Summary

Innovation is the implementation of new value-creating possibilities, not merely invention or technological novelty. Effective managers balance exploitation of current capabilities with exploration of uncertain opportunities. Design thinking provides a human-centered process for understanding problems, framing opportunities, generating alternatives, prototyping and testing. Its iterative nature is essential because evidence may change the problem definition.

Experimentation converts assumptions into testable hypotheses concerning desirability, feasibility, viability and responsible impact. Minimum viable evidence, meaningful metrics and staged investment help organizations learn without committing excessive resources. Basic measures such as retention, contribution margin and break-even volume are useful screening tools when their assumptions are explicit.

Business-model innovation concerns how value is created, delivered and captured. Platform, subscription, servitization, sharing, circular and ecosystem models each offer opportunities but also introduce governance, economic and ethical risks. Technology and AI can enable new offerings and processes, yet responsible deployment requires attention to data quality, privacy, security, bias, human oversight and accountability.

At the master’s level, innovation should be treated as a strategic and organizational capability. The manager’s task is not to predict the future with certainty, but to construct a portfolio of informed experiments, allocate resources progressively, and ensure that emerging models are economically viable, operationally feasible and socially legitimate.

Key Terms

Innovation: Implementation of a new or significantly improved product, process, service, organizational practice or business model that creates value.

Invention: A novel technical, conceptual or scientific possibility that may or may not be implemented commercially.

Incremental innovation: Improvement that extends or enhances an existing product, process or capability.

Radical innovation: Innovation that departs substantially from established technology, practice or market logic.

Disruptive innovation: A market process in which a simpler or more accessible entrant initially serves overlooked or new-market customers and may later challenge established firms.

Exploration: Search and experimentation directed toward uncertain future opportunities.

Exploitation: Refinement and efficient use of existing capabilities and offerings.

Design thinking: A human-centered, iterative approach to addressing ambiguous problems through empathy, framing, ideation, prototyping and testing.

Prototype: A representation or early version of an idea created to generate learning.

Hypothesis: A clearly stated, testable belief about a customer, solution, mechanism or outcome.

Minimum viable product: The smallest responsible version of an offering that can generate meaningful evidence from use.

Business model: The logic through which an organization creates, delivers and captures value.

Platform: An arrangement that enables interactions among two or more interdependent user groups.

Network effect: A change in the value of a service resulting from a change in the number or composition of users.

Servitization: A shift from selling products alone toward selling services, access, performance or outcomes.

Circular business model: A model designed to preserve product, component or material value through reuse, repair, refurbishment, remanufacture or recycling.

Contribution margin: Revenue per unit minus variable cost per unit.

Retention rate: The proportion of an initial cohort that remains active at a specified time.

Responsible innovation: Innovation that considers ethical, social, environmental and governance consequences alongside commercial value.

Ambidextrous organization: An organization capable of exploiting current operations while exploring future opportunities.

Review Questions

5 Short Answer Questions

1. Distinguish invention from innovation.
2. What are the five commonly used stages of design thinking?
3. Explain the difference between desirability, feasibility and viability.
4. What does a business model explain?
5. Why can a minimum viable product be inappropriate in a high-risk context?

5 Long Answer Questions

1. Critically evaluate the claim that design thinking is sufficient for managing innovation.
2. Explain how exploration and exploitation create different managerial and governance requirements.
3. Compare platform, subscription and circular business models in terms of value creation and value capture.
4. Discuss the role of experimentation and metrics in reducing innovation uncertainty.
5. Evaluate the ethical and organizational implications of AI-enabled business models.

3 Analytical / Application Questions

1. A financial-services firm wants to launch an AI-based credit-advice subscription. Formulate at least four hypotheses and design a staged experimentation plan that addresses desirability, feasibility, viability and responsible use.
2. Select an established organization and redesign its business model using a platform, servitization or circular approach. Identify affected stakeholders, critical assumptions, revenue logic and governance risks.
3. A pilot has high user engagement but negative contribution margin and increasing support workload. Analyze possible causes and recommend whether the organization should persevere, pivot or terminate the initiative. State the evidence you would require.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes innovation from invention?

A. Innovation is always based on proprietary technology.

B. Innovation involves implementing a novel or improved possibility that creates value.

C. Innovation occurs only when a new market is created.

D. Innovation is limited to scientific research.

Correct Answer: B. Innovation involves implementing a novel or improved possibility that creates value.

Question 2

Which design-thinking activity is most directly concerned with framing an opportunity independently of a proposed technology?

A. Define

B. Prototype

C. Test

D. Scale

Correct Answer: A. Define

Question 3

A team has 800 users in an initial cohort and 280 of those same users remain active after three months. What is the cohort retention rate?

A. 28%

B. 35%

C. 52%

D. 65%

Correct Answer: B. 35%

Question 4

If price per transaction is £50 and variable cost per transaction is £32, what is the contribution margin per transaction?

A. £18

B. £32

C. £50

D. £82

Correct Answer: A. £18

Question 5

Which question primarily tests business-model viability?

A. Do target users experience a meaningful problem?

B. Can the service be delivered with the required technical quality?

C. Can revenue and costs support a sustainable operation?

D. Can users describe the service concept accurately?

Correct Answer: C. Can revenue and costs support a sustainable operation?

Question 6

What is a defining characteristic of a platform business model?

A. It eliminates the need for partners.

B. It enables interactions among two or more interdependent user groups.

C. It always provides the service free of charge.

D. It sells only physical products.

Correct Answer: B. It enables interactions among two or more interdependent user groups.

Question 7

Why should early innovation projects generally use staged investment?

A. To prevent any possibility of failure.

B. To ensure every project receives equal funding.

C. To increase commitment before evidence is available.

D. To commit additional resources as important assumptions receive support.

Correct Answer: D. To commit additional resources as important assumptions receive support.

Question 8

Which is the strongest example of a vanity metric?

A. Repeat usage by a defined customer cohort

B. Reduction in unplanned downtime

C. Application downloads without evidence of active use

D. Contribution margin per active customer

Correct Answer: C. Application downloads without evidence of active use

Question 9

Which practice is most appropriate for a high-consequence AI-supported decision?

A. Remove human oversight to maximize efficiency.

B. Use undocumented training data to preserve flexibility.

C. Provide monitoring, accountability and a process for correction or appeal.

D. Evaluate the system only by the number of features released.

Correct Answer: C. Provide monitoring, accountability and a process for correction or appeal.

Question 10

A circular business model primarily seeks to do what?

A. Maximize one-time sales of disposable products.

B. Retain products, components or materials at useful value for longer.

C. Replace all physical products with digital advertising.

D. Transfer all lifecycle responsibility to customers.

Correct Answer: B. Retain products, components or materials at useful value for longer.