Mimosa combines energising sativa traits with subtle indica effects, resulting in a versatile strain often called Purple Mimosa due to its striking purple-tinged buds. My personal experience with this strain is only available to paid subscribers below, as I narrate intimate details. But first, let’s explore the strain.
The citrus-forward, award-winning hybrid that turns every morning into a brunch worth savouring.
THC Content
19–27%
Genetics
70% Sativa / 30% Indica
Parentage
Clementine × Purple Punch
Best Time
Daytime use
🏆
High Times Cannabis Cup — 2nd Place, California 2018
Recognised as one of the finest cultivars of its generation among the industry’s most celebrated strains.
Overview
Mimosa, also known as Purple Mimosa, is a sativa-dominant hybrid born from a cross between Purple Punch and Clementine, developed by Symbiotic Genetics in California in 2017. It combines Clementine’s tangy citrus aroma with Purple Punch’s relaxing, berry-like qualities — resulting in a strain comprising 70% sativa and 30% indica genetics, with THC content typically ranging from 19% to 27%.
Mimosa is characterised by bright green buds with orange pistils covered in dense crystal trichomes. In larger doses, the flowers can display striking flecks of purple — particularly pronounced when grown in regions with significant temperature shifts between day and night.
Quick Reference
BreederSymbiotic Genetics
Also Known AsPurple Mimosa
Dominant TerpeneMyrcene
Indica / Sativa Split30% / 70%
Cup Award2nd — High Times CA 2018
Flavour & Aroma
The flavour profile is citrus-forward, often compared to orange zest with sweet fruit notes and a light herbal or earthy edge. The aroma is intensely fruity — strong notes of lemon and sweet citrusy orange, mellowed by earthiness and subtle hints of pine — a profile that lives up to its namesake cocktail of champagne and fresh juice.
In small doses, Mimosa produces happy, level-headed effects that leave users feeling uplifted and motivated, while larger doses can tip into sleepiness and relaxation. Its energising qualities make it popular among those dealing with stress, anxiety, and depression, while its indica genetics bring enough calm to prevent jitteriness — making it an ideal daytime strain for creative work, social situations, or powering through a productive morning.
ⓘ This content is intended for informational purposes only. Cannabis laws vary by jurisdiction. Please consult a qualified medical professional before using cannabis for any health condition. Individual responses may vary.
Obviously, in the UK cannabis is criminalised, and although it is medically acceptable; those who ever had a history of psychosis are rejected from such services as it is assumed that it will lead to psychosis. The prospect of Cannabis becoming legal in the UK are poor at the moment. This has not stopped the population from continuing to consume it. More and more people are using it for therapeutic reasons, including medical reasons. And people like me have many sides. Some sides are experiencing ADHD and I struggle to get things done. Other sides of me are in remission from any psychotic disorder, I have proof of my sanity. I am a complex human being. I cannot be standardised with a blanket rule that dismisses individual differences.
My Experience with Mimosa
Of course, circumstances change, the bio-makeup transforms at the epigenetic level… First of all, let me begin by saying that I’ve never felt more egodystonic than when it comes to having to…
As someone who has spent years studying the hidden corners of the human psyche — including loneliness, rejection, and the pain of feeling unseen — I approach the topic of involuntary celibacy (incel) culture with both clinical curiosity and deep compassion. Incel culture refers to an online subculture of predominantly young, heterosexual men who define themselves by their inability to find romantic or sexual partners despite desiring them. What began as a support forum has evolved into a complex ideological space marked by resentment, misogyny, fatalism, and, in extreme cases, violence. Understanding its psychology is not about excusing harmful beliefs, but about recognising the human suffering that can lead people down such dark paths (Van Brunt and Taylor, 2020).
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The term “incel” was originally coined in the late 1990s by a woman seeking to create a supportive space for those struggling with romantic isolation. Over time, however, certain online communities transformed the label into a rigid identity built around grievance and entitlement. Members often subscribe to the “black pill” worldview — a fatalistic belief that physical attractiveness, genetics, and social hierarchy determine romantic success, rendering self-improvement pointless. This cognitive framework blends elements of evolutionary psychology, nihilism, and social comparison theory, creating a self-reinforcing cycle of despair and anger (Sparks et al., 2022).
At the core of incel psychology lies profound loneliness and rejection sensitivity. Many individuals report repeated experiences of social exclusion, bullying, or romantic rejection during formative years. Research on loneliness shows that chronic social isolation activates the same neural pathways as physical pain, leading to heightened vigilance for threat and emotional dysregulation. When this pain is repeatedly linked to romantic failure, it can crystallise into a core belief: “I am inherently unworthy of love.” This belief fuels defensive anger and externalisation of blame, often directed at women (“Stacys” and “Beckys” in incel terminology) or more conventionally attractive men (“Chads”) (Jaki et al., 2019).
Cognitive distortions play a central role. Incel forums frequently exhibit black-and-white thinking, catastrophising, and overgeneralisation. A single rejection is interpreted as proof of permanent genetic doom. This thinking style shares features with depressive rumination and certain personality disorders, particularly those involving fragile self-esteem. Some researchers have noted overlaps with covert narcissism — a pattern where grandiosity is hidden beneath self-pity and resentment (Sparks et al., 2022).
The internet itself acts as both incubator and amplifier. Echo chambers reinforce extreme beliefs through confirmation bias and group polarisation. What begins as shared frustration can rapidly escalate into dehumanising rhetoric and, in rare but tragic cases, violence. High-profile attacks linked to incel ideology — such as the 2014 Isla Vista killings, the 2018 Toronto van attack, and the 2021 Plymouth shooting— highlight the potential for ideological radicalisation. However, the vast majority of self-identified incels do not commit violence. Most remain trapped in cycles of despair, depression, and social withdrawal.
Importantly, incel culture does not exist in isolation. It reflects broader societal issues: the mental health crisis among young men, the erosion of community, and the commodification of intimacy in the digital age. Research shows rising rates of male loneliness and declining marriage and sexual activity among young adults, particularly in Western countries. These trends create fertile ground for grievance-based identities to flourish (Van Brunt and Taylor, 2020).
From a forensic perspective, understanding incel psychology requires holding two truths simultaneously: acknowledging genuine pain without excusing misogyny or violence. Many incels describe profound despair, social anxiety, and feelings of invisibility. Compassionate interventions — such as addressing underlying depression, building social skills, and challenging cognitive distortions — show promise. Community-based approaches that foster healthy male friendships and purpose beyond romantic validation are also crucial.
In my own work and personal reflections, I see how the fear of never being chosen can mirror deeper fears of never being worthy of existence itself. Healing begins when we separate the pain of loneliness from the toxic narratives that turn that pain outward. For those caught in incel spaces, the path forward is rarely simple, but it starts with recognising that the self is not defined by romantic success or failure.
Ultimately, incel culture is a symptom of our age — a cry from those who feel discarded by a world that celebrates connection but often fails to provide it. By understanding the psychology beneath the ideology, we can respond with both firmness against harm and compassion for the suffering that fuels it. True progress lies not in condemnation alone, but in creating a society where fewer people feel so profoundly unseen.
Hello, wonderful community. It’s Betshy here, your Plymouth-based forensic advocate, writing from my quiet seaside corner. Today I came across something quietly unsettling while browsing a website I sometimes use. A merchant had added a new restrictive policy: they would no longer accept payment cards issued in the United States of America. The notice was polite but firm. Below is a screenshot I recently took.
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I thought that it is interesting because what at first glance appears to be a simple commercial decision is, I believe, a small but telling symptom of something much larger: the growing international fallout from America’s current political direction under President Donald Trump.
This is not an isolated incident. In recent weeks, scattered reports have emerged of online retailers, particularly in Europe and parts of Asia, quietly implementing similar restrictions. Some cite “compliance costs” or “regulatory uncertainty,” but the pattern suggests deeper unease. Merchants are protecting themselves from potential secondary sanctions, payment disruptions, or reputational damage linked to US foreign policy volatility (Reuters, 2025).
At the heart of this trend lies Trump’s distinctive brand of leadership: unpredictable, transactional, and relentlessly self-focused. His second term has been marked by aggressive rhetoric toward Iran, renewed threats of tariffs on European allies, and a willingness to prioritise personal and domestic political goals over traditional alliances (The Guardian, 2025). The administration’s approach often appears less about strategic statecraft and more about immediate optics and leverage. European leaders, once reliable partners, now find themselves publicly criticised for not aligning with Washington’s “America First” demands, even when those demands conflict with their own economic or security interests (BBC News, 2025).
Compounding the unease is the persistent shadow of the Epstein files. Only weeks ago, the release of additional documents renewed intense scrutiny of Trump’s past associations. Rather than addressing the revelations directly, the administration has pursued high-visibility distractions — including the recent military action against Venezuela and the capture of President Maduro (CNN, 2026). The timing is difficult to ignore. When uncomfortable truths surface at home, bold moves abroad can shift the global spotlight. Next, making a lot of countries angry. Many international observers have noted this pattern: domestic vulnerability met with external assertiveness (Washington Post, 2026).
The result is a slow erosion of trust. Allies who once viewed the United States as a stable anchor now see a superpower whose policies can shift dramatically with the mood of one man. Merchants rejecting US cards are not making grand political statements; they are making pragmatic business decisions in an environment where American financial instruments suddenly carry heightened political risk. This is how soft power unravels — not through grand declarations, but through countless small, quiet withdrawals of confidence (Foreign Policy, 2025).
Longer-term, these developments raise serious questions about the future of US foreign policy. Alliances built over decades cannot be sustained on unpredictability alone. When partners begin to insulate themselves from American financial and political volatility, the United States risks isolation at the very moment global challenges — climate, supply chains, security — demand deeper cooperation (Brookings Institution, 2025).
As I sit with this discovery, I am reminded how personal choices and global politics are more intertwined than we often admit. What looks like a minor checkout notice is actually a small thread in a larger tapestry of fracturing relationships. The world is watching, adjusting, and quietly drawing new boundaries. The question now is whether America will notice before those boundaries become walls.
Here I sit. I am currently having a mental health crisis. But it is temporary. Literally, I approximate a period of six hours until I recalibrate myself. I think of son, my dear prince. I have plans some time this year to gift him my current iMac. I would certainly not like it if he saw me in this state. I wish I was not this reckless with my mental health. I don’t want to destroy all the progress I’ve made so far. But due to struggling with life, I had a micro relapse…
As a forensic psychoanalyst, I often reserve my opinion on situations that are frequently misunderstood and which cause great offence to particular communities. However, personally, I cannot, I don’t want to, and I will not tolerate any form of romanticisation of those who harm children sexually – the pederasts. Nowadays, there is plenty of that. There are pederast prophets in some religions, pederast presidents in some countries, and pederast people who migrate.
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I lay in bed staring at the ceiling. Too many thoughts rush through my mind. Too many memories of injustices which might never end. A repertoire of traumas that I can only wish I could shake off. But I cannot; the scar that sexual abuse left in my life cannot be erased. It cannot be healed. It cannot be forgotten. It haunts me every day…
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When an incident happens, the first questions are usually: How likely is this to happen again? and How worried should we be? Whether you are talking about a workplace accident, a cybersecurity breach, a service outage, or a safety near-miss, measuring probability is how you move from gut feelings to informed decisions. (Aven, 2016)
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Probability does not have to mean complicated math. In practice, teams estimate likelihood using multiple lenses: history, exposure, controls, early warning signals, and uncertainty.
Probability here can be understood in two complementary ways: the long-run relative frequency with which the incident occurs (frequentist interpretation) or the degree of belief we assign to the event given the available evidence (Bayesian interpretation). Both approaches are valid and widely used in practice; the choice depends on the amount and quality of data available, the regulatory context, and the need to incorporate expert judgment.
Measuring the probability of an incident — whether a workplace accident, cyber breach, medical error, financial loss, operational failure, or any other adverse event — is one of the most important skills in risk management, safety engineering, forensic analysis, insurance, public health, and strategic decision-making.
1. Classical (A Priori) Probability
The simplest and oldest method applies when all outcomes are equally likely and the sample space is finite and known. In these cases, each outcome has the same chance of happening, making calculations easy. Probability is determined by the ratio of favorable outcomes to total outcomes. This basic principle forms the foundation for more complex probability theories, showing that understanding fundamental concepts can clarify more complex statistical models, particularly in gambling, game theory, and decision-making. Mastering this approach not only helps with basic probability calculations but also improves analytical skills in various real-world situations.
P(incident) = number of favourable outcomes ÷ total number of possible outcomes
Classic textbook examples include the roll of a fair die (P(rolling a 6) = 1/6) or the flip of a fair coin (P(heads) = 1/2). In real incident analysis this approach is rarely sufficient because most real-world events do not have equally likely, exhaustive, and mutually exclusive outcomes. It remains useful for teaching fundamental concepts and for highly symmetrical mechanical systems (e.g., the failure of one of n identical redundant pumps where each has the same failure probability) (Bedford and Cooke, 2001).
2. Subjective (Bayesian) Probability
When historical data are sparse, unrepresentative, or entirely absent, we often find ourselves compelled to rely on expert judgment to guide decision-making processes.
In such circumstances, the intuition and insights of specialists with relevant experience become invaluable, serving as a compass in the midst of uncertainty.
Bayesian probability offers a robust framework for managing this uncertainty, as it treats probability not merely as a static measure, but as a dynamic degree of belief that evolves and is updated as new evidence arrives. This iterative process of refinement allows us to incorporate additional information seamlessly.
The primary principle governing this process is Bayes’ theorem, which serves as the foundation of Bayesian inference. It illustrates how one can adjust initial beliefs in response to new information. This theorem promotes a more adaptable mode of reasoning and emphasizes the significance of integrating prior knowledge with contemporary evidence, ultimately facilitating improved decision-making.
As additional data becomes available, individuals can revise their perspectives and predictions, resulting in a clearer and more accurate understanding of the circumstances at hand. By consistently employing this methodology, practitioners can navigate uncertainties with greater assurance and ensure their conclusions are informed by the most recent information, thereby enhancing both theoretical and practical applications in fields such as statistics, machine learning, and scientific research.
Posterior probability ∝ likelihood × prior probability
In odds form this becomes particularly intuitive for risk analysts:
Posterior odds = prior odds × likelihood ratio
Bayesian methods are especially powerful in incident risk assessment because they allow the formal combination of sparse failure data with structured expert elicitation. Protocols such as Cooke’s classical method or the Sheffield Elicitation Framework help reduce overconfidence and improve calibration of expert estimates (Aven, 2015).
3. Empirical (Frequentist) Probability
When historical data exist, the most common practical method is the empirical (or relative-frequency) estimator:
P(incident) ≈ number of observed incidents ÷ total number of exposure opportunities
“Exposure opportunities” must be clearly defined and relevant — for example:
number of transactions processed for financial systems
kilometres driven for road safety
This estimator is unbiased in the long run, which means that as the number of observations increases, the estimates produced will converge to the true value. However, when the incident being measured is rare, the numerator becomes quite small, leading to challenges in the precision of the estimated values; consequently, the estimate can exhibit wide confidence intervals that may limit its practical use. Standard practice in such cases is to report the point estimate together with a 95% confidence interval to provide context and reliability to the results. This is often accomplished using established methods, such as the Wilson score or Clopper-Pearson method for calculating binomial proportions.
Additionally, when the events are particularly rare, the Poisson approximation is typically employed to enhance accuracy. Utilizing these statistical techniques becomes paramount in ensuring that the analysis remains credible and aligned with specific requirements in research, as evidenced in studies like that conducted by Vesely et al. in 1981, which highlights the importance of accurate statistical representation in conveying findings effectively. (Vesely et al., 1981).
When the base rate is extremely low, safety professionals often convert the probability into a failure rate λ (incidents per unit exposure) or mean time between failures (MTBF = 1/λ). For small probabilities, P(incident in time t) ≈ λ × t.
(π) Exposure-based probability (normalise by opportunity)
A raw count can mislead if activity levels change. Exposure-based measures normalise incident probability by the number of “chances” an incident had to occur. (Rausand, 2011)
How to measure: incidents per exposure unit (hours worked, miles driven, deployments, patient-days, API calls).
Example: “2 incidents per 1,000 deployments.”
Best for: environments where volume fluctuates.
Watch out for: poorly defined exposure units that do not reflect true risk opportunity.
4. Fault Tree Analysis (FTA) – Deductive Quantitative Modelling
Fault Tree Analysis begins with the undesired top event (the incident) and works backwards through logical gates (AND, OR, voting gates, etc.) to identify all combinations of basic events that can cause it. Once the tree is constructed, the probability of the top event is calculated by:
obtaining failure probabilities or failure rates for each basic event from reliable databases (OREDA, CCPS, IEEE Std 500, NPRD, etc.)
identifying the minimal cut sets (the smallest sets of basic events whose simultaneous occurrence causes the top event)
applying the rare-event approximation for low-probability systems: Q(top) ≈ Σ Q(cut set)
FTA explicitly models redundancy, common-cause failures, and human error, making it the industry standard in aerospace, nuclear power, rail, and process safety (NASA, 2011); (Rausand and Høyland, 2004).
5. Event Tree Analysis (ETA) – Inductive Forward Modelling
Event Tree Analysis starts from an initiating event (e.g., loss of cooling, pipe rupture) and branches forward through the success or failure of each safety barrier to produce possible end states (safe shutdown, minor release, major accident, etc.). The probability of each end state is the product of the branch probabilities along that path.
ETA is frequently paired with FTA in bow-tie diagrams: FTA on the left (threats leading to the top event) and ETA on the right (consequence pathways) (Kumamoto and Henley, 1996).
6. Bow-Tie Analysis
Bow-tie diagrams integrate FTA (left side: threats → top event) and ETA (right side: top event → consequences) with preventive and mitigative barriers on each side. Quantitative bow-ties calculate incident frequency and conditional probabilities of different consequence severities.
7. Monte Carlo Simulation
When probabilities are uncertain or dependencies exist, Monte Carlo methods sample input distributions thousands or millions of times to produce a distribution of possible outcomes.
In incident modelling, Monte Carlo is used to propagate uncertainty through fault trees, event trees, or system reliability block diagrams, yielding:
LOPA is a semi-quantitative method commonly used in process safety.
It estimates the frequency of a consequence by multiplying:
Initiating event frequency × product of (1 – probability of failure on demand) for each independent protection layer (IPL)
LOPA bridges qualitative HAZOP and full QRA (CCPS, 2008).
9. Human Reliability Analysis (HRA)
Human errors contribute to many incidents. Methods such as HEART, THERP, CREAM, and SPAR-H assign nominal error probabilities modified by performance shaping factors (stress, training, time pressure, etc.).
10. Predictive Models and Machine Learning
Modern approaches increasingly use survival analysis, Cox proportional hazards models, random survival forests, or neural networks trained on historical incident data to predict time-to-incident or conditional probability.
∞. Confidence and uncertainty scoring (how sure are you?)
Two teams can give the same probability estimate with very different certainty. Tracking confidence prevents false precision. (Aven, 2016)
How to measure: pair every probability estimate with a confidence rating (low/medium/high) or an uncertainty interval.
Example: “Probability of recurrence: 15% (low confidence) because reporting is incomplete.”
Best for: decision-making under uncertainty.
Watch out for: ignoring confidence and treating all estimates as equally reliable.
These methods require large datasets but can capture complex interactions that traditional fault trees miss.
Putting it all together: a simple, practical approach
If you want a lightweight way to use these methods without building a full risk model, try this:
Start with historical and exposure-based rates (Methods 1 to π).
Adjust based on what changed since the incident: controls, volume, environment (Method 3 to 5
Check leading indicators to validate whether probability is trending.
Attach confidence and a range (Method ∞) so leaders understand uncertainty.
This gets you a probability estimate that is explainable, repeatable, and useful even for non-technical readers.
Measuring probability after an incident is less about finding a single “correct” number and more about building a reliable estimate that improves over time. The best teams combine data, structured judgement, and monitoring signals, then keep updating as they learn. (Aven, 2016)
Conclusion
Measuring the probability of an incident is never exact — it is always an informed estimate bounded by uncertainty. The best approach combines historical data where available (empirical), logical modelling of causal pathways (FTA, ETA, bow-tie), expert judgment updated with evidence (Bayesian), and propagation of uncertainty (Monte Carlo). Validation against real outcomes remains essential.
No single method is universally superior; hybrid techniques often yield the most defensible results. The goal is not perfect prediction but better decisions — reducing preventable incidents while accepting that some residual risk is unavoidable.
Kumamoto, H. and Henley, E.J. (1996) Probabilistic Risk Assessment and Management for Engineers and Scientists. 2nd edn. IEEE Press. Available at: https://ieeexplore.ieee.org/book/6267380 (Accessed: 23 February 2026).
Have you noticed this shift that businesses have been doing the past year or so? Well, a few years ago, businesses were loud about it. Everybody had a pledge, a badge, a landing page, and a big statement about saving the planet, then a lot of that energy cooled off. Like, it became “less trendy” if you want to call it that. Well, basically, some companies got quieter because they didn’t want to be accused of greenwashing. Sure, they want to grow their online presence, hence all the PR oriented articles about their grand initiative, but there was no real proof.
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On top of that, though, some got quieter because they realised they didn’t actually have much to say. Some got quieter because, yeah, sure, it’s easier to stop talking than it is to keep improving. There are plenty of brands like this; most of the luxury fashion brands are especially guilty of this, like Chanel. But with all of that said here, there’s a difference between being careful and being vague. Now, you better believe that customers can tell the difference.
And honestly, being vague is starting to feel like a red flag. Well, it’s been a red flag, but it’s even bigger now.
Being Quiet isn’t Automatically “Humble”
Yeah, it’s as plain and as simple as this, honestly. But sure, this is where it gets a little spicy, at the same time, though, because some brands act like silence is this noble move now. Like, “oh, it’s better not talk about it,” and sure, sometimes that’s true if a business is still figuring things out and doesn’t want to overpromise.
But if a business is selling itself as sustainable, and there’s no details anywhere, that’s not humility, that’s just confusing. Think about it here; customers don’t want a scavenger hunt. They don’t want to dig through five pages, a PDF, and a vague Instagram caption just to find out if a company’s claims are real. Oh, and of course, some companies don’t even provide a scavenger hunt; they’ll say they’re active, but there’s literally no proof in any of it.
Now, it makes absolute total sense, though that customers have gotten more sceptical for a reason. Like too many businesses used sustainability as a marketing costume. So now, when a company is vague, people don’t assume it’s being responsible; they assume it’s hiding something. That’s the reality.
It’s Better to be Transparent than Perfectly Sustainable
Well, sure, you should still try and do what you can to be sustainable here, but don’t think it has to be perfection or anything like that. Actually, a lot of small businesses freeze up because they think they need to be perfect before saying anything. Like, if the business can’t claim zero waste or carbon neutral or whatever the big claim is, then it can’t talk about sustainability at all.
But is that all true? Nope, no, not at all. It also sets up a weird dynamic where only huge corporations with big budgets get to “talk sustainability,” while smaller businesses that are actually trying to stay silent. But transparency can be simple. It can be, here’s what’s being done now, here’s what’s still being improved, and here’s what customers can expect.
That kind of honesty is trustworthy because it’s normal. It sounds like a human business, not a marketing machine.
It Wouldn’t Hurt to Audit Competitors
And what exactly would be the reason to do this, though? Just think about it; if competitors are vague, that’s an opportunity. If competitors are making big claims without proof, that’s an opportunity. If competitors have confusing policies or unclear pricing, that’s an opportunity too. Some businesses even use industry tools to see how others communicate offers and policies, especially in operational niches.
Like, a company in the waste space might look at a waste hauler competitor app to understand how other operators present service options and customer communication, then use that insight to create a clearer, more transparent experience. It just helps to spot the gaps they have, so you can fill the gaps for your business.
Customers aren’t Just Buying a Product
And of course, This is what a lot of businesses forget. But sustainability messaging isn’t only about the planet. But it’s also about competence. When a company clearly explains what it does and why, it feels organised. It feels accountable. Well, overall here, it feels like it has standards.
And of course, that matters because customers are constantly making quick trust decisions. Is this business legit? Is it consistent? Is it going to follow through? Is it going to surprise someone with hidden fees, messy policies, or vague claims? Lots of questions here, but the transparency is supposed to answer all of those questions; everything is supposed to be clear right from the get-go. Again, there shouldn’t be some scavenger hunt going on.
It’s Easier to Compete without Racing to the Bottom
Competing was already mentioned, well, in terms of audits and finding gaps, but that’s not the other thing to keep in mind here, though. So, pricing competition is exhausting. You probably already know that here. But competing on “cheapest” usually turns into lower margins, rushed work, and customers who treat the business like it’s interchangeable. Now, clearly, that’s not a sustainable business model, and yeah, that word is doing double duty there.
But go ahead and think about this: transparency gives a business another lane to compete in. It gives a business a way to justify pricing, explain value, and build loyalty with customers who care about responsible practices. And even customers who don’t care deeply about sustainability still like the idea of less waste, fewer problems, and a business that’s honest.
Again, as was mentioned, it helps when competitors are vague. If other businesses are hard to compare because they hide details, then a transparent business stands out. It feels easier to choose. Usually, customers can see what they’re paying for. And again, they don’t like scavenger hunts, and it’s pretty easy to fill in the gaps with how your competitors are messing up.
