Fiber, Protein & Gut Health Claims Credibility Study
Measures how much shoppers trust functional food and beverage claims around fiber, protein, and gut health/probiotics, what evidence makes a claim believable, and where skepticism creeps in. An AI follow-up interview digs into a specific claim the respondent doubted or trusted and reconstructs what actually changed their mind. Built for brand, insights, and regulatory teams evaluating label and marketing language.
샘플 질문
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In the last 30 days, how often have you bought a food or beverage product labeled with a functional claim (e.g., high fiber, added protein, supports gut health, probiotic)?
- Never
- Once
- A few times
- Weekly or more
How credible do you find each of these claim types when you see them on packaging?
- High fiber
- Added or high protein
- Supports gut health / probiotic
- Prebiotic
- Low sugar with a functional benefit
When a package makes a health claim, which of these makes you MOST and LEAST likely to believe it's true?
- A cited clinical or university study
- A specific percentage or daily-value amount listed
- A recognizable third-party certification logo
- Endorsement from a doctor or dietitian
- A short, recognizable ingredient list
- A money-back or satisfaction guarantee
- Reviews from other customers
- The brand's overall reputation
Overall, how trustworthy do you find functional health claims on food and beverage packaging in general?
What makes you doubt a functional claim, if anything? Select all that apply.
- Vague wording (e.g., 'supports' instead of a specific number)
- No amount or percentage listed
- Small print or hard-to-find details
- Product tastes or looks like it can't be healthy
- Past experience where the benefit didn't show up
- Marketing feels exaggerated
- Brand has a history of overpromising
If a product's claimed benefit (more fiber, more protein, better digestion) didn't match what you actually experienced, how likely would you be to buy that brand again?
Ask the respondent to name one specific product with a fiber, protein, or gut-health claim they either strongly believed or strongly doubted, and reconstruct why: what was on the package, what evidence (or lack of it) shaped their reaction, and whether their actual experience with the product confirmed or contradicted the claim. If they mention doubt, probe what single piece of evidence would have changed their mind. If they can't recall a specific product, ask them to walk through their last grocery trip and describe the first functional claim that caught their eye.
Where, if anywhere, do you typically check whether a functional health claim is actually true?
- I search online for the ingredient or study
- I check the nutrition facts panel myself
- I ask a doctor, dietitian, or pharmacist
- I read other customers' reviews
- I look for certification seals or logos
- I don't check — I just trust or ignore the claim
Which age range do you fall into?
- 18-24
- 25-34
- 35-44
- 45-54
- 55-64
- 65+
- Prefer not to say
Which best describes your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Thank you for sharing your honest take on these claims! Your responses will feed into a report on which types of evidence make fiber, protein, and gut-health claims believable — helping shape clearer, more trustworthy labeling.
포함된 기능
AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
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차별화 포인트
- Includes a dedicated AI follow-up interview that asks the respondent to name a specific product with a fiber, protein, or gut-health claim and probes what actually changed their mind about believing or doubting it
- Combines a max-diff exercise on what makes a health claim most/least believable with a matrix rating claim-type credibility and multiple-choice questions on doubt triggers and verification behavior, giving brand, insights, and regulatory teams both breadth and depth
- Every AI probe is built on a transparent, reviewable prompt rather than a hidden script, so teams evaluating label language can see exactly what was asked and why
- Produces an auto-generated report synthesizing closed-ended credibility ratings alongside the qualitative reasoning captured in the follow-up interviews, without requiring a manual coding pass
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