Executive Summary

• Stigma is not a fixed attribute; it’s a dynamic system that shapes how people talk about and interact with brands.
• Using systems thinking and causal loop modeling helps identify how stigma forms and where it can be interrupted.
• A multi-sample study conducted on Remesh, which included real participants, and synthetic data modeling on ChatGPT, examined five stigmatized brands: Tesla, Tinder, Ozempic, Beyond Meat, and OnlyFans.
• The study revealed distinct types of stigma and self-reinforcing feedback loops that shape consumer behaviors and mindsets.
• Visualizing these loops helps pinpoint intervention strategies that can reduce resistance and reshape public perception.

Rethinking Stigma in the Context of Brand Strategy

Stigma, in its classical sense, refers to a socially discrediting attribute that diminishes one’s value in the eyes of others [3]. In branding, stigma can attach to companies, products, or behaviors—shaping not only consumer judgment but also leading to silence, avoidance, or active rejection. Unlike static reputations, stigma evolves. It is produced and sustained through feedback cycles of public discourse, individual behavior, and institutional norms.

To understand these dynamics, this analysis applies systems thinking; specifically, the concept of feedback loops from Peter Senge’s work on learning organizations [1]. Within this framework, brand stigma is conceptualized not simply as a perception problem but as a self-reinforcing system. Identifying and mapping these causal loops provides strategists with clarity on how stigma persists—and where interventions can be most effective.

From Stigma Typology to Feedback Structures

The study began by defining a typology of stigma grounded in sociological and psychological research [2][3][4][5][7]. This included seven distinct types:

• Self-Stigma: Internalized shame or discomfort about using a brand.
• Perceived Stigma: Anticipation of judgment by peers or the public.
• Public Stigma: Widespread societal disapproval.
• Stigma-by-Association: Negative judgment due to ties to a person or group.
• Label Avoidance: Behavioral attempts to avoid being categorized.
• Structural Stigma: Institutional barriers or biases.
• Professional Stigma: Fear of damage to one’s professional image, sometimes reinforced by professionals themselves through implicit bias [7].

These types were not treated as isolated categories but as components of dynamic feedback systems, referred to here as stigma loops.

Multi-Sample Design and Data Simulation

To investigate brand stigma across both breadth and depth, a multi-sample design was employed combining real and synthetic data sources. A total of 111 participants were recruited via the Remesh FLEX platform (remesh.ai) using an on-demand partnership with Prolific. Participants completed randomized diagnostic tasks designed to elicit perceptions, judgments, and behaviors toward five stigmatized brands. These tasks included polls, rankings, and open-ended prompts aligned to a structured discussion guide focused on stigma typology, severity, and behavioral thresholds.

To extend the analytical utility of this relatively lean sample, over 400 synthetic respondent profiles were generated using a GPT-4-based modeling approach. These profiles replicated key distributions from the original data (across demographics, stigma counts, and behavioral categories) and assigned realistic combinations of attitudes and responses using conditional logic. Each synthetic participant was constrained to maintain internal coherence between their beliefs, segment classification, and language patterns.

This hybrid approach offered several strengths:

• Stabilized subgroup comparisons (e.g., OnlyFans rejectors in Gen Z; Ozempic users facing multi-stigma resistance)
• Reduced simulation volatility (e.g., lowered outcome swing from ±15 to <5 percentage points)
• Surfaced missing behavioral types that were underrepresented in the original sample (e.g., discreet Tinder users, hesitant Beyond Meat adopters)

While synthetic modeling expands exploratory scope, it cannot replicate the unpredictability and nuance of lived experience. These responses mirror patterns rather than generate novel insight. Their primary function in this analysis was to simulate how stigma loops might behave over time when scaled across segments and scenarios.

The combination of human insight and simulated expansion provided the necessary scale and structure to build and test stigma loops, enabling longitudinal forecasting of brand adoption and resistance trajectories.

Loop Dynamics Across Five Brands

Tesla: Stigma-by-Association with Leadership

Tesla occupies a unique space in public consciousness, simultaneously admired for its technological leadership and scrutinized for the behavior of its founder. While the company is celebrated for engineering achievements, its brand identity is tightly bound to Elon Musk’s public persona.

🔁 Stigma Loop:
Association with Musk → Perceived ideological conflict → Emotional discomfort → Brand avoidance → Reinforced association

"The Tesla brand has lost appeal due to its founder’s political involvement."

Tinder: Judgment and Concealment

Tinder is one of the most popular yet publicly muted platforms in the digital landscape. Despite its broad use, it is enveloped in social stigma, often linked to gendered assumptions around respectability, intention, and intimacy [3][4].

🔁 Stigma Loop:
Judgmental discourse → User concealment → Absence of positive narratives → Reinforced stereotype

"All the people I know who use it are just in it for casual sex."

Ozempic: Medical and Moral Shame

Ozempic’s public narrative is shaped by tension between clinical use and cultural judgment. While medically effective for weight management, its use triggers a stigma loop grounded in the suspicion of "shortcuts" [2][5].

🔁 Stigma Loop:
Public discourse on weight loss → Internalized shame → Concealment → Professional gatekeeping → Limited public conversation → Reinforced taboo

"Using Ozempic would mean admitting I can't lose weight through willpower."

Beyond Meat: Cultural Identity and Elitism

Beyond Meat’s brand is often filtered through cultural signaling rather than product utility. Though marketed as sustainable and inclusive, it is frequently interpreted as a lifestyle badge linked to veganism, liberal values, or moral superiority [2].

🔁 Stigma Loop:
Vegan association → Perception of moral posturing → Mainstream rejection → Reinforced niche positioning

"It feels like Beyond Meat is for people who think they’re better than others."

OnlyFans: Sexual Morality and Narrative Suppression

OnlyFans presents the clearest example of narrative suppression driving stigma. Though used by creators across diverse fields, its brand identity is overwhelmingly shaped by its link to sex work [6].

🔁 Stigma Loop:
Adult content association → Shame → Silence → Lack of diverse stories → Reinforced stereotype

"People equate OnlyFans with sex work and judge harshly."

Loop Weights and Intervention Insights

Simulated models were used to evaluate how stigma loops could be disrupted or intensified depending on messaging strategies, stakeholder participation, and user visibility. Examples include:

• Tinder: Amplifying discreet success stories shortens stigma persistence by up to 3 years
• Ozempic: Formal endorsement from healthcare providers reduces self-stigma loop resistance
• OnlyFans: Without visible counter-narratives, stigma may persist for 5+ years

Each loop was assigned a resistance “weight.” The more culturally entrenched the loop, the more layered and sustained the intervention required.

Designing Interventions by Loop Type

If stigma is sustained by feedback loops, then intervention must be surgical, not just symbolic. Effective disruption requires identifying the precise loop mechanics at play and selecting strategies that target the most leveraged points in the system.

Below are four strategic levers, each mapped to a common type of stigma loop:

• Narrative Expansion
  Break the silence that sustains stigma.
  Highlight diverse, counter-stereotypical stories from real users—especially those whose experiences disrupt dominant narratives. This is critical in loops driven by concealment and stereotype reinforcement (e.g., OnlyFans or Tinder).
• Trusted Endorsement
  Borrow credibility to challenge judgment.
  Engage respected experts, institutions, or community figures to validate stigmatized behaviors. This works best in loops involving self-stigma or professional gatekeeping (e.g., Ozempic).
• Decoupling Strategies
  Separate the brand from the stigma source.
  Reposition the brand to reduce associations with stigmatized founders, groups, or symbols. Useful when identity conflict drives the loop, as seen with Tesla.
• Loop Tracking and Measurement
  Make the invisible visible.
  Implement longitudinal tracking to observe how feedback loops evolve in response to interventions. This supports dynamic strategy adjustment and helps avoid reinforcing stigma unintentionally.

Toward a Loop-Literate Brand Strategy

Stigma is best understood not as a reputational problem but as a self-sustaining system. It arises from the interaction of language, judgment, silence, and structural forces, and persists when these elements reinforce one another in feedback loops.

By identifying, modeling, and simulating these loops, brands can move beyond generic messaging toward precise, systemic interventions. Whether the goal is to normalize a behavior, broaden an identity, or disentangle a brand from its baggage, loop literacy offers a roadmap for long-term cultural relevance.

References

1. Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization.
2. Link, B. G., & Phelan, J. C. (2001). Conceptualizing Stigma. Annual Review of Sociology, 27, 363–385.
3. Goffman, E. (1963). Stigma: Notes on the Management of Spoiled Identity.
4. Corrigan, P. W., & Watson, A. C. (2002). Understanding the impact of stigma on people with mental illness. World Psychiatry, 1(1), 16–20.
5. Crocker, J., Major, B., & Steele, C. (1998). Social stigma. In D. Gilbert et al. (Eds.), The Handbook of Social Psychology (Vol. 2, pp. 504–553).
6. Weitzer, R. (2009). Sociology of Sex Work. Annual Review of Sociology, 35, 213–234.
7. Grappone, G. (n.d.). Overcoming Stigma. National Alliance on Mental Illness. https://www.nami.org/depression-disorders/overcoming-stigma/

About the Authors

Yogesh Chavda brings over 25 years of global marketing experience, having led pivotal initiatives at category-defining brands like Spotify, Pinterest, Amway, Kimberly Clark, and WS Audiology. His career also includes a 16-year tenure at Procter & Gamble, where he held senior leadership roles across six countries. He helps brands identify growth blind spots through systems thinking, audience segmentation, and smart research design. At Y2S Consulting, he works with organizations to define their growth roadmap leveraging AI capabilities he’s built. Contact Yogesh at yogesh@y2sconsulting.com.

Suzanne Walsh is an anthropologist with expertise in business and health research. With over 25 years of experience in academia and consulting, she is a research Consultant at Remesh, a SaaS AI-enabled research platform. Suzanne helps clients puzzle through sticky employee and organization research problems, and leverages machine learning, NLP and GPT to understand qualitative data at scale. She has published in high-impact journals, and authors thought leadership for Remesh. Contact Suzanne at s.walsh@remesh.org.