Responsible, Global and Future Brand Management

Introduction

Brand management is no longer limited to creating distinctive names, symbols and communications. Brands operate within complex social, technological, environmental and regulatory systems. A brand may be judged not only by product quality and advertising creativity, but also by its treatment of workers, use of consumer data, representation of social groups, environmental impact, political associations and ability to communicate truthfully.

Globalisation increases both opportunity and responsibility. A brand can reach multiple markets rapidly, yet a message, image or product practice that is acceptable in one culture may be ineffective, offensive or unlawful in another. Digital platforms also allow content to travel beyond its intended audience. At the same time, artificial intelligence (AI) is changing research, targeting, content production, customer service and marketing automation. These capabilities create efficiency, but also risks involving bias, surveillance, misinformation, manipulation and accountability.

Responsible brand management therefore requires an integrated approach. Managers must protect brand meaning and commercial performance while respecting people, communities, cultures, democratic institutions and ecological limits.

Learning Objectives

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

1. Explain how brands can be adapted across cultures without losing core identity.
2. Apply principles of sustainability, inclusion, consumer protection, privacy and ethical influence.
3. Identify and manage risks involving misinformation, greenwashing and purpose-washing.
4. Evaluate AI-enabled research, personalisation, content and automation.
5. Explain AI bias, transparency, human oversight and governance requirements.
6. Assess emerging developments including immersive experiences, synthetic media and purpose-led branding.
7. Develop a responsible brand-management plan for a contemporary organisation.

1. The Responsible Brand

A responsible brand makes decisions with regard to several stakeholder groups rather than only short-term sales. Relevant stakeholders may include customers, employees, suppliers, communities, regulators, investors and the natural environment. Responsibility does not mean abandoning commercial objectives. Rather, it means recognising that long-term brand value depends on trust, legitimacy and sustainable relationships.

A useful distinction is between brand promise and brand proof. The promise is what the brand claims to deliver; proof is the evidence that its behaviour supports that claim. A company promoting fairness while using discriminatory recruitment practices creates a gap between promise and proof. Such gaps can produce reputational damage, regulatory action and consumer scepticism.

Responsible brand management commonly involves five tests:

• Truthfulness: Are claims accurate, clear and supported by evidence?
• Fairness: Could the offer, price, communication or algorithm disadvantage vulnerable groups?
• Respect: Does the brand respect privacy, dignity, culture and autonomy?
• Impact: What are the social and environmental consequences of the brand’s operations?
• Accountability: Can the organisation explain decisions and provide redress when harm occurs?

Ethical influence differs from manipulation. Ethical influence provides relevant information, allows meaningful choice and avoids exploiting vulnerability. Manipulation may conceal material information, create artificial urgency, exploit anxiety or use personal data in ways that consumers would not reasonably expect.

2. Global Brand Management and Cultural Adaptation

Global branding involves deciding what should remain consistent across markets and what should be adapted. A brand needs enough consistency to be recognisable, but enough flexibility to be relevant and respectful.

Standardisation and adaptation

Standardisation uses a substantially similar brand identity, product proposition and communication approach across countries. Its advantages include economies of scale, consistent recognition and easier coordination. Its risks include cultural insensitivity, weak local relevance and non-compliance with local requirements.

Adaptation modifies products, packaging, language, channels, imagery or promotional appeals for local contexts. Its advantages include cultural resonance and improved market fit. Its risks include higher costs, fragmented identity and inconsistent customer experiences.

The strongest approach is often strategic consistency with tactical adaptation. Core values, visual assets and quality standards may remain stable, while language, occasions, media, service practices and product features change.

Cultural analysis

Managers should avoid treating a country as culturally uniform. Differences may exist by region, age, ethnicity, religion, class, language and digital access. Cultural research should therefore combine secondary research, local expertise, qualitative interviews, observation and controlled market testing.

Cultural adaptation requires attention to:

• Language: Translation should capture meaning, tone and connotations, not merely words. Transcreation may be required for slogans and humour.
• Symbols: Colours, gestures, animals, numbers and images can carry different meanings.
• Values and social norms: Appeals to individual achievement, family, community, status or security may vary in relevance.
• Religion and ritual: Product timing, imagery, ingredients and promotional practices may require sensitivity.
• Gender and representation: Imported stereotypes may conflict with local expectations or inclusion goals.
• Regulation: Claims, comparative advertising, packaging, endorsements and data practices differ by jurisdiction.

A practical process is to identify the non-negotiable identity core, map cultural risks, involve local stakeholders, test interpretations and establish approval responsibilities. Localisation should not be used to excuse harmful practices. A company should not claim that unethical conduct is merely a cultural difference.

Cultural intelligence and semiotics

Cultural intelligence is the capability to interpret and act effectively across cultural settings. It includes knowledge, motivation, behavioural flexibility and reflective learning. Semiotics examines how signs create meaning. A logo, colour, celebrity or product ritual becomes meaningful through shared cultural codes. Semiotic analysis can reveal unintended meanings before a campaign is launched.

3. Sustainability and Purpose-Led Branding

Sustainability concerns the ability to meet present needs without undermining the ability of future generations to meet their needs. In branding, it involves the environmental and social consequences of product design, sourcing, production, distribution, use and disposal.

A credible sustainability strategy begins with operational change. Communication follows evidence; it does not replace it. Relevant actions may include reducing emissions, improving durability, using safer materials, supporting decent work, reducing waste and designing take-back or repair systems.

Green claims and greenwashing

Greenwashing occurs when environmental benefits are exaggerated, vague, irrelevant or unsupported. Common examples include:

• “Eco-friendly” without defining the benefit;
• highlighting a small recyclable component while concealing major environmental harm;
• presenting legal compliance as exceptional environmental performance;
• using natural imagery to imply sustainability without evidence;
• advertising future ambitions without a credible pathway.

Responsible claims should specify the object, comparison, time period and evidence. For example, a claim that packaging uses “30% less virgin plastic than the previous design” is more assessable than “planet-positive packaging.” Claims must also consider the full life cycle, including production, transport, use and disposal.

Purpose-led branding

Purpose is the organisation’s reason for creating value beyond immediate transactions. Purpose can strengthen employee commitment, differentiation and stakeholder trust, but it creates expectations. A brand that adopts social causes only for visibility risks purpose-washing.

A purpose-led brand should demonstrate:

1. A clear relationship between the social issue and the organisation’s capabilities.
2. Measurable commitments and public progress reporting.
3. Alignment between marketing claims and internal policies.
4. Willingness to accept trade-offs and criticism.
5. Participation by affected communities rather than speaking over them.

Purpose should guide decisions about products, suppliers, employment, investment and governance—not only advertising themes.

4. Inclusion, Representation and Accessibility

Inclusive branding recognises the diversity of people and seeks to avoid exclusion in products, communications and experiences. It includes representation, accessibility and fair treatment.

Representation is not achieved simply by placing a diverse person in an advertisement. Managers should ask whether people are portrayed with agency, complexity and dignity, whether the campaign reinforces stereotypes, and whether the organisation’s actual practices support the message.

Accessibility includes readable typography, colour contrast, captions, audio description, alternative text, keyboard navigation, clear language and accessible physical environments. Accessibility benefits people with disabilities and often improves usability for everyone.

Inclusive research should recruit participants who reflect the intended market, compensate participants fairly, protect privacy and provide culturally appropriate methods. Data categories should not be treated as perfect descriptions of identity. Gender, ethnicity, disability and other characteristics can be sensitive, fluid or self-defined.

A useful inclusion audit examines:

• who is represented and who is absent;
• who can afford, access and use the product;
• whether targeting excludes or exploits groups;
• whether automated decisions produce unequal outcomes;
• whether complaints are easy to make and resolve;
• whether diverse employees participate in decision-making.

5. Privacy, Consumer Protection and Ethical Influence

Personal data can improve relevance and service, but collection creates obligations. Privacy is not merely a legal issue; it is part of the relationship between a brand and its customers.

Good practice includes purpose limitation, data minimisation, informed consent where required, secure storage, retention limits, access and correction rights, and clear explanation of automated decisions. Consent should not be hidden in confusing interfaces or obtained through coercive design.

Dark patterns are interface designs that steer users toward choices they may not otherwise make. Examples include preselected add-ons, difficult cancellation, confusing privacy settings, hidden costs and misleading countdown timers. Such designs may increase short-term conversion while weakening trust and attracting regulatory scrutiny.

Consumer protection requires that marketing communications be identifiable, claims be substantiated and material conditions be disclosed. Influencer marketing should clearly identify commercial relationships. Reviews should not be fabricated, selectively suppressed or purchased without disclosure.

Ethical persuasion respects informed autonomy. Before using behavioural targeting, a manager should consider:

• What is known about the individual?
• Would the person reasonably expect this use of data?
• Does the message exploit fear, grief, addiction, financial stress or another vulnerability?
• Can the individual decline or opt out?
• Is the offer equally fair to people outside the target segment?

6. Misinformation, Disinformation and Brand Safety

Misinformation is false or inaccurate information shared without necessarily intending harm. Disinformation is deliberately created or distributed to deceive or cause harm. A brand can be affected when its advertisements appear beside harmful content, when false claims about the brand spread, or when the brand itself amplifies unreliable information.

Brand safety is broader than avoiding offensive words. It involves understanding the context in which a brand appears, the funding of media environments and the risks of adjacency to extremist, fraudulent or misleading content.

A response system should include:

1. Monitoring of credible sources and social discussion.
2. Verification before public response.
3. A risk classification based on reach, severity, evidence and vulnerability of affected audiences.
4. Clear escalation routes for legal, security and communications teams.
5. Corrections that are prompt, factual and proportionate.
6. Documentation of decisions and lessons learned.

Brands should avoid repeating false claims unnecessarily, attacking critics without evidence or using automated replies during sensitive events. Prebunking—explaining common deceptive techniques before exposure—can improve resilience, but communication must remain factual and non-partisan.

7. AI-Enabled Brand Management

AI tools can support four broad activities: research, content, personalisation and automation.

AI-enabled research

Machine learning can identify patterns in reviews, search behaviour, customer service records and social conversation. Natural-language systems can classify themes and summarise large datasets. However, automated analysis may misinterpret sarcasm, dialect, code-switching and minority language. Online data also represents only people who are visible and willing to participate.

AI outputs should therefore be treated as evidence requiring validation, not as objective truth. Research teams should document data sources, sampling limitations, model version, prompts or settings, validation procedures and human interpretation.

AI-generated content and synthetic media

Generative AI can create text, images, audio and video. It can accelerate ideation and adaptation, but it may reproduce stereotypes, infringe intellectual property, invent facts or imitate real people without permission. Synthetic media can also make it difficult for audiences to distinguish authentic testimony from fabrication.

Controls include approved tools, fact checking, rights clearance, disclosure where material, secure handling of confidential data, human review and records of source materials. A synthetic representation of a person should require appropriate consent and contractual clarity about use, duration and compensation.

Personalisation and automation

Personalisation may improve relevance, but excessive personalisation can feel intrusive. Automated pricing, recommendations or eligibility decisions can create unequal treatment. Managers should distinguish harmless contextual adaptation from high-impact decisions affecting access to essential goods, credit, employment or insurance.

Automation should have a defined purpose, performance threshold, escalation route and stop condition. Customers should be able to reach a human when an automated process fails or causes significant difficulty.

Bias and fairness

AI bias can enter through historical data, sampling, labels, proxy variables, model design, deployment context or feedback loops. Removing a sensitive attribute does not necessarily remove bias because other variables may act as proxies.

A simple group comparison can identify possible disparities. If group A receives a favourable outcome at rate p_A and group B at rate p_B, the selection-rate ratio is:

where R is the selection-rate ratio. A ratio substantially below 1 may indicate that group B receives favourable outcomes less often, but it is not by itself proof of unlawful discrimination. Interpretation depends on the decision context, legitimate criteria, sample size, measurement quality and applicable law. Fairness metrics can conflict; improving one may worsen another. Human and legal review is essential.

AI governance

A practical AI governance framework includes:

Governance should be risk-based. A creative brainstorming tool may require relatively modest controls, while an automated system that determines access to a financially important service requires rigorous testing, documentation, security and human review. Organisations should comply with applicable privacy, consumer-protection, equality, intellectual-property and AI regulations in each market.

8. Emerging Brand Environments

Immersive experiences

Virtual reality, augmented reality and persistent digital environments can create interactive brand experiences. Applications include virtual product trials, training, events, retail exploration and community building. Risks include motion discomfort, biometric data collection, harassment, exclusion of people without advanced devices and unclear boundaries between entertainment and advertising.

Immersive design should provide safety information, age-appropriate experiences, accessible alternatives, moderation and clear commercial disclosure. Brands should collect only necessary data and explain how behavioural or biometric information is used.

Synthetic media and virtual influencers

Virtual influencers and AI-generated spokespeople can offer creative control and global scalability. They may also blur authenticity and accountability. Audiences should not be misled about whether a testimonial reflects a real person’s experience. Disclosure is especially important when synthetic content could affect beliefs, purchases or political opinions.

Community and creator economies

Creators can provide cultural insight and credibility, but brands remain responsible for claims made in sponsored communication. Contracts should define disclosure, content approval, intellectual-property rights, safety expectations, data use and procedures for misconduct or crisis situations.

Purpose, participation and trust

Contemporary audiences increasingly expect brands to demonstrate values through behaviour. Yet consumers are not a single political or cultural bloc. A responsible brand should avoid opportunistic activism, listen to affected stakeholders and accept that credible purpose may attract disagreement. Trust is built through consistency, competence, openness and repair after failure—not through slogans alone.

9. An Integrated Responsible Brand-Management Framework

Managers can use the following cycle when planning a campaign, product or AI-enabled initiative.

1. Define the objective. State the commercial and stakeholder outcomes.
2. Map affected parties. Identify customers, non-customers, employees, communities and vulnerable groups.
3. Assess context. Examine cultural, legal, environmental, technological and media conditions.
4. Identify harms and benefits. Consider foreseeable direct and indirect effects.
5. Design alternatives. Compare standardisation and adaptation; human and automated processes; low-impact and high-impact options.
6. Test with evidence. Use local research, accessibility testing, claim substantiation, privacy review and bias evaluation.
7. Approve and document. Record owners, assumptions, evidence, limitations and escalation routes.
8. Launch responsibly. Provide disclosures, consent mechanisms, support and monitoring.
9. Measure outcomes. Track commercial performance alongside trust, complaints, inclusion, environmental and fairness indicators.
10. Learn and repair. Correct errors, compensate where appropriate, update controls and communicate lessons.

A balanced scorecard can prevent managers from judging success only by reach or conversion. Possible indicators include repeat purchase, complaint resolution time, claim accuracy, accessibility defects, opt-out rates, supplier performance, emissions, representation, disparity measures and stakeholder trust. Metrics should be interpreted carefully: high engagement can reflect controversy, and low complaints can reflect barriers to reporting.

10. Applied Case: Lumina Mobile’s Global AI Campaign

Background

Lumina Mobile, an illustrative international smartphone brand, planned a campaign for a new device in four markets: Canada, India, Brazil and Kenya. Its central promise was “technology that gives people more control.” The marketing team proposed AI-generated advertisements, personalised offers based on browsing behaviour and an augmented-reality product demonstration.

Problem

Early review identified several risks. The draft advertisements used a hand gesture that was positive in one market but offensive in another. The personalisation model offered premium financing more often to customers with high-value browsing histories, but the data also correlated with neighbourhood and income. The AI-generated copy made an unsupported claim that the device had a “zero-impact” environmental profile. The augmented-reality experience collected facial and movement data by default.

Analysis

The campaign had a coherent global proposition but confused consistency with uniform execution. Cultural research showed that the gesture and humour required adaptation. The financing model created a potential fairness and consumer-protection issue because customers were not told how their data influenced offers. The environmental claim failed the truthfulness and substantiation tests. Default biometric collection was disproportionate to the purpose of a product demonstration.

Approach and solution

Lumina retained the core promise but commissioned local cultural review and replaced the gesture with market-appropriate demonstrations. It changed “zero-impact” to a specific, evidence-based packaging claim and published the comparison basis. The company removed browsing-history variables from financing eligibility, conducted disparity testing, added a human review route and provided a clear explanation of offer factors. The augmented-reality experience was redesigned to function without facial tracking; optional data collection required separate consent and had a short retention period.

All AI-generated content was reviewed by trained staff, checked for intellectual-property and factual risks, and labelled where synthetic media could reasonably be mistaken for real testimony. Local accessibility testing added captions, text alternatives and a non-immersive product demonstration.

Outcome

The revised campaign launched later than planned but achieved stronger completion rates for the product demonstration and fewer privacy complaints than Lumina’s previous campaign. The company also found that transparent offer explanations increased customer-service confidence, even though some customers received fewer premium offers. Internal review concluded that the campaign’s long-term trust benefits outweighed the short-term cost of redesign.

Learning points

• A global promise can remain stable while expression and execution are localised.
• AI efficiency does not remove the need for evidence, cultural knowledge or human accountability.
• Privacy and fairness should be designed into the customer journey rather than added after launch.
• Specific, limited claims are more credible than absolute sustainability language.
• Responsible innovation may require rejecting data collection that is technically possible but not necessary.

Challenges and Limitations

Responsible brand management involves genuine tensions. Local adaptation can increase cost and create inconsistency. Sustainability improvements may increase prices or reduce product variety. Personalisation can improve relevance while increasing surveillance concerns. Fairness metrics may conflict, and available data may be too incomplete to measure impacts accurately. Purpose-led positioning can become politicised, while neutrality may also communicate a value position.

Ethical frameworks cannot eliminate judgement. Laws differ across markets and may lag technological change. Stakeholders may disagree about what counts as harm or acceptable representation. Managers should therefore avoid claiming that a checklist guarantees ethical behaviour. Governance must include organisational culture, incentives, expertise, independent challenge and willingness to stop profitable activities when risks are unacceptable.

Best-Practice Checklist

Before approving a responsible global or AI-enabled brand initiative, ask:

• Is the promise accurate, specific and supported by evidence?
• What remains globally consistent, and what must be locally adapted?
• Have local experts and affected communities been involved?
• Is the experience accessible and inclusive?
• What data is collected, why, for how long and with what consent?
• Could the activity exploit vulnerability or create unequal outcomes?
• Are synthetic content, endorsements and commercial relationships disclosed?
• Has the environmental and social impact been assessed across the life cycle?
• Who is accountable for errors, complaints and remediation?
• What evidence will determine whether the initiative should continue, change or stop?

Chapter Summary

Responsible, global and future brand management combines commercial capability with cultural intelligence, ethical judgement and governance. Global brands should protect a coherent identity while adapting language, symbols, products and experiences to local contexts. Sustainability and purpose are credible only when supported by operational evidence, measurable commitments and transparent reporting. Inclusion requires meaningful representation, accessible design and fair treatment throughout the customer journey.

Privacy, consumer protection and ethical influence require clear claims, respectful data practices, meaningful choice and avoidance of dark patterns. Brands must prepare for misinformation through monitoring, verification, escalation and correction. AI can improve research, content, personalisation and automation, but its outputs require validation, documentation, human oversight and risk-based governance. Bias may arise from data, design and deployment, and fairness cannot be reduced to one metric.

Immersive experiences and synthetic media create new opportunities as well as risks involving disclosure, accessibility, safety, identity and data. The responsible manager integrates stakeholder mapping, cultural review, impact assessment, testing, monitoring and redress into the entire brand-management cycle. Long-term brand value depends not only on being distinctive, but also on being trustworthy, accountable and capable of learning.

Key Terms

Responsible brand management: Managing a brand while considering commercial, social, environmental, cultural and ethical consequences.

Standardisation: Using a substantially consistent brand strategy across markets.

Adaptation: Modifying a brand’s offer or communication for a particular market or audience.

Cultural intelligence: The capability to understand and act effectively across cultural settings.

Semiotics: The study of how signs and symbols create meaning.

Sustainability: Meeting present needs without undermining future generations’ ability to meet theirs.

Greenwashing: Misleading or unsupported communication about environmental performance.

Purpose: An organisation’s reason for creating value beyond immediate transactions.

Purpose-washing: Presenting social purpose without meaningful organisational commitment.

Inclusion: Designing representation, access and treatment so that diverse people can participate fairly.

Dark pattern: An interface design that steers users toward choices against their interests or expectations.

Misinformation: Inaccurate information shared without necessarily intending harm.

Disinformation: Deliberately deceptive or harmful information.

Synthetic media: AI-generated or AI-manipulated text, image, audio or video.

Personalisation: Adapting content, offers or experiences to an individual or segment.

AI bias: Systematic unfairness introduced through data, design, modelling or deployment.

Human oversight: The ability of responsible people to review, challenge, pause or reverse automated decisions.

Redress: Remedy or correction provided when a person experiences harm or unfair treatment.

Brand safety: Managing the risk that brand activity appears in harmful, unsuitable or misleading contexts.

Review Questions

Short Answer Questions

1. What is the difference between a brand promise and brand proof?
2. Give two reasons why a global campaign may require local adaptation.
3. What is greenwashing?
4. Identify two risks associated with AI-generated brand content.
5. Why is human oversight important in AI-enabled marketing?

Long Answer Questions

1. Compare standardisation and adaptation in global brand management, including their advantages and risks.
2. Explain how a brand can demonstrate that its purpose is genuine rather than purpose-washing.
3. Discuss the relationship between privacy, consumer protection and ethical influence.
4. Explain how bias can enter an AI-enabled marketing system and describe appropriate controls.
5. Evaluate the opportunities and risks of immersive experiences and synthetic media.

Analytical / Application Questions

1. A clothing brand wants to use one sustainability slogan in ten countries. Develop a process for testing the claim, adapting it where necessary and preventing greenwashing.
2. An online retailer’s recommendation system gives premium offers mainly to customers from high-income neighbourhoods. Analyse possible causes, ethical risks and steps for investigation and remediation.
3. Design a responsible launch plan for a culturally adapted, AI-assisted campaign. Include stakeholder mapping, privacy safeguards, inclusion testing, content governance, performance measures and a response plan for misinformation.

Multiple Choice Questions with Answers

Question 1

Which approach best describes strategic consistency with tactical adaptation?

A. Using exactly the same message, product and imagery in every market

B. Abandoning the global brand identity in favour of entirely local brands

C. Maintaining core identity while adapting local expression and execution

D. Allowing an algorithm to decide all cultural differences automatically

Correct Answer: C. Maintaining core identity while adapting local expression and execution

Question 2

Which statement is the strongest environmental claim?

A. Our packaging is planet-positive

B. Our packaging is natural and guilt-free

C. Our packaging is better for the Earth

D. Our packaging uses 30% less virgin plastic than the previous design

Correct Answer: D. Our packaging uses 30% less virgin plastic than the previous design

Question 3

What is a dark pattern?

A. A visual identity using dark colours

B. An interface design that steers users toward choices against their interests

C. A privacy law applying to online advertising

D. A method for testing colour contrast

Correct Answer: B. An interface design that steers users toward choices against their interests

Question 4

Which is an example of disinformation?

A. An accidental error in a customer review

B. A customer misunderstanding product instructions

C. A deliberately fabricated claim distributed to deceive audiences

D. A legitimate disagreement about product quality

Correct Answer: C. A deliberately fabricated claim distributed to deceive audiences

Question 5

Why should AI-generated research findings be validated by people?

A. AI cannot process any customer data

B. Automated analysis may misinterpret context and reflect biased or incomplete data

C. Human review always produces statistically larger samples

D. Validation removes the need for documentation

Correct Answer: B. Automated analysis may misinterpret context and reflect biased or incomplete data

Question 6

In the selection-rate formula R = p_B ÷ p_A, what do p_A and p_B represent?

A. The prices of two products

B. The favourable-outcome rates for two groups

C. The number of advertising impressions

D. The environmental impacts of two campaigns

Correct Answer: B. The favourable-outcome rates for two groups

Question 7

Which practice best supports responsible synthetic media?

A. Hiding all use of generation tools

B. Using a real person’s likeness without permission

C. Combining disclosure, rights clearance and human review

D. Allowing synthetic testimonials to appear as verified customer experiences

Correct Answer: C. Combining disclosure, rights clearance and human review

Question 8

Which action best reflects data minimisation?

A. Collecting every available behavioural signal for possible future use

B. Collecting only data necessary for a defined and legitimate purpose

C. Keeping personal data indefinitely

D. Sharing data with partners before explaining its use

Correct Answer: B. Collecting only data necessary for a defined and legitimate purpose

Question 9

What is the main weakness of relying on one fairness metric?

A. Fairness metrics cannot be calculated

B. Different metrics may conflict and context affects interpretation

C. Fairness metrics measure only advertising creativity

D. A single metric always proves discrimination

Correct Answer: B. Different metrics may conflict and context affects interpretation

Question 10

Which response is most appropriate when a brand discovers an unsupported claim in a live campaign?

A. Ignore it unless competitors complain

B. Repeat the claim more frequently to dominate discussion

C. Remove or correct it, investigate the cause and provide proportionate disclosure

D. Blame the advertising agency without reviewing internal approval

Correct Answer: C. Remove or correct it, investigate the cause and provide proportionate disclosure