Business Model Evaluation, Communication and Future Development

Introduction

A business model is not complete when its nine building blocks have been filled in. It is complete when the proposed system of value creation, delivery and capture can withstand evidence, criticism and change. Evaluation therefore moves beyond describing a business model. It asks whether the model can work technically and operationally, generate acceptable returns, adapt to uncertainty, attract resources and communicate a credible investment case.

For entrepreneurs, this stage is consequential. A weak model may waste capital, expose customers to harm or fail because its assumptions were never tested. A strong model does not guarantee success, but it makes assumptions visible, links claims to evidence and establishes mechanisms for learning and adaptation.

This chapter presents an integrated approach to evaluating, communicating and developing business models. It combines feasibility, viability, adaptability and investment-readiness; explains how to build an evidence-based business case and pitch; and examines emerging models influenced by generative artificial intelligence, decentralization, climate transition and regulatory change.

Learning objectives

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

1. Distinguish feasibility, viability, adaptability and investment-readiness.
2. Evaluate a complete business model using evidence, metrics and explicit assumptions.
3. Analyse unit economics, cash requirements, risks and scenarios.
4. Construct an evidence-based business case and an effective pitch.
5. Assess how generative AI, decentralization, climate transition and regulation reshape business models.
6. Design a disciplined process for future model development and governance.

1. From business model description to business model evaluation

A business model describes how an organisation creates, delivers and captures value. Evaluation determines whether the described relationships can operate in practice and remain defensible over time. Four questions provide a useful structure:

• Feasibility: Can the organisation deliver the promised value with available or obtainable resources, capabilities, technology and partnerships?
• Viability: Can revenues, costs, capital requirements and risks support a sustainable economic enterprise?
• Adaptability: Can the model respond to changes in customers, technology, competition, regulation, climate and society?
• Investment-readiness: Is the opportunity sufficiently evidenced, governed and communicated for an external party to allocate resources?

These dimensions overlap but are not interchangeable. A technically feasible product may have no viable customer economics. A viable model may be vulnerable to regulatory change. A promising and adaptable business may still be unready for investment because its claims are unsupported or its governance is weak.

The evidence hierarchy

Evaluation should distinguish between what the team believes, what customers say, what customers do and what the market demonstrates. A practical hierarchy is:

1. Hypotheses: explicit claims about customers, needs, channels, costs or revenue.
2. Qualitative evidence: interviews, observations, expert assessments and usability tests.
3. Behavioural evidence: pre-orders, repeated use, conversion, retention or willingness to pay.
4. Operating evidence: delivery time, defect rates, service costs and partner performance.
5. Financial evidence: actual revenue, gross margin, cash flow and customer acquisition results.
6. External validation: independent research, regulatory opinions, certifications or audited information.

No level is universally sufficient. Early-stage ventures appropriately use qualitative and experimental evidence, whereas a venture requesting substantial capital should normally provide stronger behavioural, operating and financial evidence. Entrepreneurs should record the source, date, sample, method and limitation of every important claim.

Assumptions and tests

A model evaluation begins with an assumption register. Each assumption should identify its consequence if false, confidence level, evidence and next test. High-risk assumptions deserve early testing, particularly those concerning customer willingness to pay, regulatory permission, technical performance, supply access and unit economics.

A useful test is not merely one that produces activity. It must have a decision rule. For example: “If at least 20 of 50 qualified target customers agree to a paid pilot at the proposed price, continue testing the channel; otherwise revise the segment or offer.” The threshold should be established before observing the result to reduce confirmation bias.

2. Feasibility: can the model deliver?

Feasibility concerns the transformation of resources into a reliable customer outcome. It includes four areas.

Technical and product feasibility

The entrepreneur must establish whether the product or service can meet its performance requirements. Relevant evidence may include a prototype, test results, reliability data, interoperability checks, cybersecurity assessment or a documented minimum viable product. Technical feasibility is not the same as technical possibility. A solution may work in a laboratory but fail at the required scale, speed, cost or safety level.

Operational feasibility

Operational questions include:

• What activities must occur before, during and after delivery?
• Which activities are internal and which are outsourced?
• What capacity is required at different demand levels?
• Which inputs have single-source or geographic concentration risk?
• How will quality, support, returns and incidents be managed?

Process mapping can reveal hidden work. A digital marketplace, for example, may appear asset-light but require substantial verification, dispute resolution, trust and safety, payment compliance and customer support.

Organisational feasibility

The founding team needs the skills and decision rights required to execute the model. Gaps may be addressed through hiring, advisers, partners or technology, but they should not be concealed by optimistic role descriptions. Evaluation should consider commercial capability, technical expertise, financial control, legal competence and the ability to learn from evidence.

Ecosystem feasibility

Many models depend on complementary actors: suppliers, logistics providers, distributors, platforms, regulators, data providers or community organisations. An ecosystem is feasible only when partners have incentives, capability and sufficient trust to participate. Dependency on a dominant partner may create bargaining risk even when the partnership is initially attractive.

3. Viability: can the model sustain value capture?

Viability evaluates the economic logic of the model. It includes revenue quality, cost structure, cash timing, capital intensity and risk-adjusted returns.

Revenue logic and cost structure

Revenue should be analysed by customer, product, channel and period. Important questions include whether revenue is recurring or episodic, contracted or discretionary, concentrated or diversified, refundable or irreversible, and dependent on subsidies or promotional pricing.

Costs should be separated into fixed and variable components, while recognising that the distinction depends on the relevant time horizon. A cloud platform may treat server capacity as variable in the long run even if a short-term hosting contract is fixed.

Contribution margin and break-even analysis

For a simple single-product model, contribution margin per unit is:

where CM_u is contribution margin per unit, P is selling price per unit and VC_u is variable cost per unit. The quantities must use the same unit, such as currency per subscription-month or currency per delivered service.

Contribution margin ratio is:

This ratio is meaningful when price and variable cost are defined consistently and when variable cost changes approximately proportionally with volume. It excludes fixed costs, financing costs and taxes.

If fixed costs are F and the unit contribution margin is positive, the accounting break-even volume is:

This is a simplified planning relationship. It assumes a constant price, constant unit variable cost, a single relevant product or a stable sales mix, and no capacity constraint. If CM_u is zero or negative, ordinary volume growth cannot reach break-even.

#### Worked example

An online learning service charges £30 per learner-month. Variable delivery and support cost is £12 per learner-month, and monthly fixed costs are £27,000.

CM_u = £30 − £12 = £18

CMR = £18 ÷ £30 = 0.60, or 60%.

Q_BE = £27,000 ÷ £18 = 1,500 learner-months per month.

At 1,800 learner-months, operating contribution after fixed costs is:

(1,800 × £18) − £27,000 = £5,400 per month, before financing costs, tax and non-cash accounting items. The result is not a forecast; it is a conditional implication of the stated assumptions.

For multiple products, break-even depends on the sales mix. If products have different contribution margins, a weighted average contribution margin can be used only when the assumed mix is stable. Otherwise, scenario analysis is more informative than a single break-even number.

Customer economics

Customer acquisition cost (CAC) is commonly estimated as:

The numerator should include the costs genuinely attributable to acquisition during the chosen period, and the denominator should use a consistent definition of a new customer. CAC is distorted when brand expenditure, sales salaries, free trials or channel commissions are treated inconsistently.

A simplified customer lifetime value estimate for a subscription model is:

This relationship assumes approximately constant revenue, margin and churn, and a stable cohort. Churn must be expressed in the same period as revenue. It is an approximation and ignores discounting, expansion revenue, acquisition cost, refunds, reactivation and changing behaviour. A more complete discounted cash-flow approach is preferable when the customer relationship is long-lived or cash timing is material.

A positive difference between LTV and CAC does not by itself prove viability. The business must also finance the time between acquisition expenditure and cash collection, support growth without service failure and account for overheads and risk.

Cash, runway and funding need

Profit and cash are different. A venture can report accounting profit while cash is tied up in inventory or receivables. Conversely, it can show accounting losses while receiving advance payments.

A simple cash runway estimate is:

It assumes that the burn rate is sufficiently stable for planning. In a growing or seasonal business, a monthly cash forecast is more reliable. Net cash burn should be based on cash inflows and outflows, not depreciation or other non-cash charges.

The funding case should state the amount requested, timing, use of funds, milestones unlocked and the assumptions that will be tested. “Marketing” is weaker than a measurable allocation such as channel experiments, sales hiring, compliance certification or inventory procurement.

4. Adaptability and resilience

Adaptability is the capacity to alter the business model without destroying its value proposition or economic logic. It is not the same as changing direction whenever performance is disappointing. Adaptation should be guided by evidence and strategic coherence.

Sources of change

A model may be challenged by:

• changing customer preferences and demographics;
• new technologies and lower-cost substitutes;
• platform policy or algorithm changes;
• supplier failure and geopolitical disruption;
• climate hazards and resource constraints;
• new laws, standards or enforcement practices; and
• social expectations concerning fairness, privacy and labour.

Real options and modularity

A staged investment can create a real option: the right, but not the obligation, to invest further after uncertainty is reduced. Examples include a pilot before a national rollout, a limited product release before manufacturing scale or a partnership trial before building infrastructure.

Modularity improves adaptability by separating components that may need to change. A firm might use an interchangeable payment provider, maintain portable data structures or design a product with replaceable components. Modularity has costs, including integration complexity and potentially lower short-term efficiency, so it should be applied where uncertainty and switching costs justify it.

Scenario and stress testing

A minimum evaluation should include a base case, upside case and downside case. The scenarios should alter drivers rather than merely apply arbitrary percentage changes. For example, a downside case might combine slower customer conversion, higher fulfilment costs, a delayed regulatory approval and a two-month payment delay.

Sensitivity analysis changes one or more assumptions to identify which variables most affect outcomes. Stress testing examines severe but plausible conditions, such as a key supplier becoming unavailable or a platform commission increasing sharply. The purpose is not to predict precisely but to identify vulnerabilities, triggers and prepared responses.

5. Investment-readiness and the evidence-based business case

Investment-readiness is a condition of decision quality, not a guarantee of funding. A professional business case connects a problem, a market, a solution, an operating system, financial outcomes, risks and a specific request.

Components of a business case

1. Executive proposition: the customer problem, proposed solution and reason it matters now.
2. Target market: defined customer segment, problem evidence, market access and competitive alternatives.
3. Business model: value proposition, channels, relationships, partners, activities, resources, revenue and cost logic.
4. Evidence: experiments, customer behaviour, operating results, external research and limitations.
5. Implementation plan: milestones, responsibilities, dependencies and decision gates.
6. Financial case: assumptions, forecast, unit economics, cash requirement and scenarios.
7. Risk and governance: principal risks, controls, legal duties, data practices and accountability.
8. Funding request: amount, instrument if relevant, use of funds, milestones and expected decision points.

A credible case distinguishes facts, estimates and hypotheses. It should not present a large market statistic as evidence that the venture can obtain a large share. Market size is useful only when linked to a reachable segment, a route to customers and the venture’s capacity.

Communicating the model

Communication should be audience-specific. A customer needs to understand the outcome and trust involved. An employee needs clarity about priorities and responsibilities. A lender focuses on repayment capacity and downside protection. An equity investor may examine growth, defensibility, governance and potential returns. A regulator examines compliance, safety, transparency and public impact.

A useful pitch sequence is:

1. Problem and affected customer: show the cost or consequence of the problem.
2. Insight: explain why existing alternatives are inadequate.
3. Solution and proof: demonstrate the product and present evidence.
4. Market and route to market: define who buys, why and how they are reached.
5. Business model: explain pricing, costs and value capture.
6. Traction and learning: report behaviour and what changed as a result.
7. Competition and defensibility: identify alternatives and the basis for advantage.
8. Operations, risks and responsible practice: show that growth will not create unmanaged harm.
9. Financial outlook and funding ask: connect resources to measurable milestones.

A pitch is not a substitute for due diligence. Visual simplicity should reduce cognitive load without hiding uncertainty. The speaker should be able to answer how each major claim was measured.

Common communication failures

Overclaiming total addressable market, using vanity metrics, presenting forecasts without assumptions, ignoring failed experiments and treating competitors as irrelevant all reduce credibility. So does confusing attention with demand. A million impressions may be valuable for a brand campaign but does not demonstrate willingness to pay or repeat use.

6. A complete evaluation framework

The following scorecard can structure a review. Numerical scores are aids to discussion, not objective truth.

Reviewers should weight dimensions according to the venture’s stage and context. A health technology venture may give greater weight to safety and regulatory evidence than a low-risk consumer experiment. The scorecard should lead to decisions: proceed, test a critical assumption, redesign, pause or stop.

7. Applied case: ReLoop Kitchens (illustrative)

Background

ReLoop Kitchens is an illustrative early-stage venture proposing reusable food containers for independent restaurants in a university district. Customers pay a small deposit through a mobile application, return containers to participating restaurants or collection points, and receive their deposit back. Restaurants pay a service fee per completed circulation.

Problem

Single-use packaging creates material waste and increasing disposal costs. However, restaurants fear that reusable systems will increase labour, create hygiene liabilities and cause container losses. Students may support sustainability but may not return containers consistently.

Initial business model

ReLoop’s value proposition is reduced single-use packaging for customers and a managed service for restaurants. Key resources include containers, washing capacity, software, collection points and compliance expertise. Revenue comes from restaurant service fees and, potentially, institutional contracts. Costs include container procurement, washing, transport, software, customer support, loss and quality control.

Evaluation

Feasibility. A pilot tested 500 containers across six restaurants. The washing partner demonstrated capacity for 1,200 containers per day, but collection routes became inefficient when restaurants were geographically dispersed. Hygiene procedures required documented temperature and handling controls. The model was technically feasible, but route density was a major operational assumption.

Viability. Suppose a restaurant pays £0.45 per completed circulation. Variable washing and transport cost is £0.21, and container loss allowance is £0.06. The contribution margin is therefore £0.18 per circulation. With monthly fixed costs of £9,000, simplified break-even is:

£9,000 ÷ £0.18 = 50,000 circulations per month.

The pilot produced 18,000 circulations, so it had not reached break-even. The result did not prove failure; it indicated that the venture needed higher route density, a higher fee, lower costs or additional revenue. A deposit is not automatically revenue because it may be refundable and may create an obligation to the customer.

Adaptability. The team designed collection zones rather than serving every restaurant immediately. It also used standardised containers compatible with more than one washing provider. This modularity reduced dependence on a single partner. Scenarios tested a 15% loss rate, a 20% increase in washing cost and a temporary closure of one collection point.

Investment-readiness. The team prepared cohort data showing return rates, average circulation time, loss by location and restaurant retention. It separated pilot subsidies from normal pricing and documented hygiene responsibilities. The funding request was tied to a measurable milestone: achieving 60,000 monthly circulations in two dense zones while maintaining a specified return and safety performance.

Outcome and learning points

The illustrative case demonstrates that a sustainability claim is not a complete business model. ReLoop’s evaluation had to connect environmental value with customer behaviour, operational density, unit economics, safety and governance. Its strongest next step was not immediate geographic expansion but a focused experiment designed to resolve the highest-impact assumptions.

Learning points include:

• physical circular models must measure reverse logistics, not only sales;
• deposits, fees and unclaimed balances require careful accounting and legal analysis;
• environmental benefits should be measured against washing, transport and container production impacts;
• investment milestones should describe operating evidence, not only expenditure; and
• a pilot is valuable when it changes a decision, not merely when it generates publicity.

8. Future development of business models

Generative AI

Generative AI can alter value propositions, cost structures and the boundary between human and automated work. New models may sell AI-generated content, copilots, workflow agents, personalised education or synthetic data services. Evaluation must examine model quality, inference cost, latency, data rights, reliability and human oversight.

Important risks include fabricated outputs, biased recommendations, confidential-data leakage, copyright disputes, concentration of compute infrastructure and unclear responsibility when an AI agent acts. A viable AI model needs more than access to a foundation model. Defensibility may arise from proprietary workflow integration, high-quality authorised data, domain expertise, distribution, feedback systems and trust.

A responsible evaluation should specify which decisions AI may support, which require human approval, how outputs are logged, how errors are reported and how affected people can challenge outcomes. Cost assumptions should be tested under realistic usage, including peak demand and repeated generation.

Decentralized and token-mediated models

Decentralization can distribute ownership, governance, verification or transaction settlement across a network. Examples include distributed marketplaces, community-owned infrastructure and token-mediated access systems. Potential benefits include transparency, reduced dependence on an intermediary and new participation mechanisms.

However, decentralization does not remove governance; it changes where governance occurs. Evaluation must ask who can change the rules, resolve disputes, protect users, comply with law and respond to emergencies. Tokens may create speculative incentives, consumer-protection concerns, tax complexity and securities-law exposure depending on their design and jurisdiction. A technically distributed system can still be economically concentrated through ownership of interfaces, developers or infrastructure.

Climate transition and regenerative models

Climate transition reshapes models through carbon pricing, energy constraints, physical risk, customer expectations and regulation. Opportunities include energy efficiency, repair, reuse, low-carbon materials, adaptation services and climate-risk analytics. Claims must avoid vague “green” language. They should define a baseline, system boundary, measurement method, time period and uncertainty.

A model may reduce emissions in one stage while increasing them elsewhere. Life-cycle thinking is therefore important. Entrepreneurs should consider Scope 1, Scope 2 and relevant Scope 3 emissions where applicable, resource use, biodiversity, resilience and distributional effects. Climate value should be integrated into economics rather than treated solely as promotional content.

Evolving regulation

Regulation is not merely a constraint; it can determine market access, cost and trust. Relevant areas may include privacy and data protection, consumer rights, competition, financial services, employment classification, health and safety, environmental claims, product liability, intellectual property and AI-specific obligations. Requirements differ by jurisdiction and change over time, so this chapter does not substitute for legal advice.

A regulatory-ready model maintains a compliance map: obligation, responsible owner, evidence, review date, escalation route and affected process. Entrepreneurs should avoid designing around an assumption that enforcement will not occur. Regulatory change should be treated as a scenario variable, with alternatives such as redesign, licensing, partnership or withdrawal.

9. Ethical governance and professional practice

Evaluation must consider stakeholders who do not control the transaction. A model can be profitable while shifting costs to workers, communities, public institutions or the environment. Ethical analysis asks who benefits, who bears risk, who is excluded and who can seek remedy.

Good governance includes clear accountability, accurate reporting, conflict-of-interest management, secure records and meaningful stakeholder feedback. Data-intensive models should follow data minimisation, purpose limitation, access control and retention principles appropriate to the legal context. Algorithmic systems require monitoring for performance disparities and mechanisms for human review.

Entrepreneurs should document uncertainty rather than conceal it. A transparent limitation can strengthen a business case because it shows that the team understands what must be learned.

10. A disciplined development cycle

Business model development should continue after launch. A practical cycle is:

1. Map the current model and identify assumptions.
2. Rank assumptions by impact and uncertainty.
3. Design the least costly credible test.
4. Define measures, thresholds and decision rights in advance.
5. Run the test while recording context and deviations.
6. Compare results with the model’s predictions.
7. Decide whether to persevere, pivot, partner, pause or stop.
8. Update the model, financial case, risk register and communication.
9. Repeat at a cadence appropriate to the rate of change.

This cycle turns evaluation into organisational learning. It also prevents the common error of treating a business model canvas, pitch deck or forecast as a final answer rather than a decision tool.

Chapter Summary

Business model evaluation tests whether a model can create, deliver and capture value under real conditions. Feasibility concerns technical, operational, organisational and ecosystem capability. Viability concerns revenue quality, cost structure, unit economics, cash and risk. Adaptability concerns resilience, modularity, staged investment and response to uncertainty. Investment-readiness concerns evidence quality, governance, milestones and a clear funding case.

Contribution margin, break-even, CAC, LTV and runway can clarify economic logic when their assumptions and units are made explicit. These measures are not forecasts by themselves. A strong business case links customer evidence, operating capability, financial assumptions, risks and a specific resource request. An effective pitch communicates this logic without replacing due diligence.

Generative AI, decentralized systems, climate transition and evolving regulation create both opportunities and new responsibilities. Future-ready entrepreneurs evaluate technical performance, data and rights, governance, environmental effects, compliance and stakeholder impact. The most credible models are not those that claim certainty; they are those that identify uncertainty, test it systematically and adapt without abandoning accountability.

Key Terms

Adaptability: The capacity of a business model to respond effectively to material change.

Break-even volume: The activity level at which contribution equals fixed costs under stated assumptions.

Business case: An evidence-based argument for pursuing an opportunity and allocating resources.

Business model: The system through which an organisation creates, delivers and captures value.

CAC: Customer acquisition cost attributable to obtaining a new customer.

Cash runway: The estimated period before available cash is exhausted at a stated burn rate.

Contribution margin: Revenue remaining after variable costs, available to cover fixed costs and profit.

Defensibility: The capacity to resist imitation, substitution or loss of advantage.

Feasibility: The extent to which a model can be delivered with available or obtainable capabilities.

Investment-readiness: The condition in which an opportunity has sufficient evidence, governance and clarity for informed resource allocation.

LTV: An estimate of the economic value generated by a customer over the relationship.

Modularity: Designing a system as separable components that can be changed or replaced.

Pilot: A limited implementation designed to test assumptions under realistic conditions.

Regulatory map: A structured record linking legal obligations to owners, evidence and processes.

Scenario analysis: Examination of outcomes under alternative combinations of assumptions.

Sensitivity analysis: Examination of how an outcome changes when selected assumptions vary.

Unit economics: Revenue and cost analysis expressed per customer, transaction or other meaningful unit.

Viability: The capacity of a model to sustain economic value capture and ongoing operation.

Review Questions

Short Answer Questions

1. Distinguish feasibility from viability in business model evaluation.
2. Why should an assumption register include a decision rule for each major test?
3. State the simplified formula for contribution margin per unit and define its variables.
4. Why is a refundable customer deposit not automatically revenue?
5. Identify two governance issues associated with generative AI business models.

Long Answer Questions

1. Explain how a business model can be feasible but not viable. Use an example involving unit economics.
2. Discuss the role of evidence hierarchy in preparing an investment-ready business case.
3. Explain how modularity and staged investment improve adaptability, including their limitations.
4. Analyse the components of an effective entrepreneurial pitch and explain why each matters to a different audience.
5. Evaluate how climate transition and evolving regulation can simultaneously create opportunity and risk.

Analytical / Application Questions

1. A subscription service charges £40 per month, has variable cost of £16 per customer-month and fixed monthly costs of £48,000. Calculate contribution margin per unit, contribution margin ratio and simplified break-even volume. State the assumptions behind your calculation.
2. Design a test for a marketplace’s assumption that independent providers will accept a 15% commission. Specify the sample, evidence, threshold, possible decisions and at least two limitations.
3. Using the ReLoop Kitchens case, recommend whether the venture should expand immediately, run a focused pilot or redesign the model. Support your recommendation with operational, financial, adaptability and governance evidence.

Multiple Choice Questions with Answers

Question 1

Which evaluation dimension asks whether a business can deliver its promised value with available or obtainable capabilities?

A. Feasibility

B. Viability

C. Investment-readiness

D. Market segmentation

Correct Answer: A. Feasibility

Question 2

A product sells for £25 and has a variable cost of £10. What is its contribution margin per unit?

A. £10

B. £15

C. £25

D. £35

Correct Answer: B. £15

Question 3

If fixed costs are £20,000 and contribution margin per unit is £10, what is the simplified break-even volume?

A. 200 units

B. 1,000 units

C. 2,000 units

D. 20,000 units

Correct Answer: C. 2,000 units

Question 4

Which evidence generally provides stronger validation than a customer merely stating that a product is interesting?

A. A larger logo on the pitch deck

B. A general social-media impression count

C. A founder’s personal belief

D. A paid pilot or repeat purchase

Correct Answer: D. A paid pilot or repeat purchase

Question 5

What is a major limitation of the simplified subscription LTV formula using churn?

A. It cannot be used with any subscription model

B. It assumes relatively stable revenue, margin and churn

C. It measures only fixed costs

D. It automatically includes all taxes

Correct Answer: B. It assumes relatively stable revenue, margin and churn

Question 6

Which item is most appropriate in an investment funding request?

A. The amount requested, use of funds and measurable milestones

B. A promise that competitors will disappear

C. An unqualified claim of total market ownership

D. A forecast with no stated assumptions

Correct Answer: A. The amount requested, use of funds and measurable milestones

Question 7

Why is scenario analysis useful in business model evaluation?

A. It guarantees an accurate prediction

B. It removes the need for evidence

C. It examines outcomes under alternative assumptions

D. It replaces all financial planning

Correct Answer: C. It examines outcomes under alternative assumptions

Question 8

Which statement best describes governance in a decentralized business model?

A. Decentralization eliminates the need for governance

B. Governance is relocated or distributed and still requires accountability

C. Only software developers have relevant responsibilities

D. Regulation cannot apply to distributed systems

Correct Answer: B. Governance is relocated or distributed and still requires accountability

Question 9

Which practice strengthens a climate-related business claim?

A. Using the word sustainable without measurement

B. Reporting only the most favourable activity

C. Defining a baseline, system boundary and measurement method

D. Ignoring emissions caused by suppliers

Correct Answer: C. Defining a baseline, system boundary and measurement method

Question 10

What is the main purpose of a decision rule in an experiment?

A. To guarantee a positive result

B. To determine in advance how evidence will affect the next decision

C. To avoid collecting behavioural data

D. To replace customer research with opinion

Correct Answer: B. To determine in advance how evidence will affect the next decision