Power dynamics in the workplace, reflexivity in research, and some thoughts on unicorns

Disclaimer: This is my personal journal and includes rough notes from my PhD journey. Some thoughts may be incomplete or not thoroughly researched. Please do not consider any content in my monthly notes to be definitive or final. If you have insights on any subjects I discuss or would like to start a conversation on a topic, please get in touch!

August has certainly been a productive month. Although work has been busy and having two extra family members in the house has been distracting, I’ve made progress and feel confident that I have found empirical evidence of what I was looking for: the location of ethics in design practice. I am very excited to start writing up this stage of the study and planning out the next stages. But for now, here are some updates from my practice and PhD life, followed by some thoughts on unicorns.

Unable to find the source of this image.

In PhD life…

I am halfway through coding the interviews (8 of 16). At this stage, I decided to pause and refine my codebook before continuing. I then mapped the codes on a Miro board and clustered them into broader code groups. I chose not to wait until all the interviews were coded, as it is good practice to refine and adjust codes as you go.

I am using Reflexive Thematic Analysis (RTA). RTA is a qualitative method that focuses on identifying patterns of meaning across a dataset through the active and reflective involvement of the researcher. I chose it because it is flexible and aligns with my aim of exploring lived experiences while allowing my own positionality to be part of the analysis.

So far, I have written the introduction to RTA, stage 1 (data familiarisation), and stage 2 (initial coding). I am now working on stage 3 – generating initial themes. This stage feels significant, and I am enjoying the process. My interview participants were excellent and shared valuable insights, and I feel confident that writing up this part of the empirical study will be fun. Not easy – nothing in a PhD is easy – but I am very excited!

My son has also decided to go down the route of doing a PhD. Recently, we spoke about imposter syndrome among PhD students. It seems that no matter your experience or career stage, there’s something about starting a PhD that makes you feel less capable than you did before. This may be because academia feels large, complex, and difficult to penetrate or understand. It may also be because creating new knowledge in your area of interest quickly reveals how little you actually know. This is, of course, positive. You cannot know everything – knowledge is vast, and by definition, if you are creating something new, then obviously it does not yet exist.

But this conversation did send me down a rabbit hole…

I have a cognitive bias that leads me to assume everyone else in the room is more intelligent than me. If I know something, I assume everyone else already knows it. This often leads me to skip definitions when presenting, which can make it confusing for the audience. This bias links to imposter syndrome, which I absolutely have in every area of my life.

I then looked for the opposite of imposter syndrome. Google pointed me to the Dunning–Kruger effect: a cognitive bias where people with low competence in a specific area overestimate their abilities, because their lack of knowledge prevents them from recognising their incompetence. In contrast, those with high competence may underestimate their skills, assuming tasks easy for them are also easy for others.

From there, I came across the Peter Principle. This describes how, in hierarchical organisations, high-performing individuals are often promoted until they reach a level where they no longer perform well. To avoid this, organisations should provide training and support, but this rarely happens. The result is managers who struggle to manage, which reduces employee effectiveness, morale, and confidence in leadership. Many promotions also occur because it is the only way to receive a pay rise, even if the individual preferred their previous role. Some organisations also require that certain levels include management responsibilities, regardless of whether someone wants or is suited to managing others.

This links to the Dilbert Principle, a satirical idea from Scott Adams, creator of the comic strip Dilbert. It suggests that companies promote incompetent employees to management to reduce their impact on productivity. Often these individuals also experience Gilbert’s Law, which states that the biggest problem with a job or task is that no one tells you what to do, leaving individuals responsible for working out how best to achieve their goals.

All these ideas highlight unhealthy and unfair power dynamics in the workplace. Outdated hierarchical structures limit psychological safety and turn work into a struggle. This theme is also strongly emerging from my empirical study. Many designers are struggling in their organisations due to power dynamics and systemic blockers.

I find these management theories and principles particularly interesting, not only because of my PhD, where I am observing such tensions, but also because of my work as a practitioner, where I often see them first-hand. I am not sure yet how many of these ideas will feature in my thesis or how prevalent they are across organisations, but it is striking to notice how people use them to make sense of the hierarchical dynamics around them.

In practice life…

I have been supporting some discovery work for a large project at work. My main role was to conduct interviews, though I also joined conversations about method and analysis. One of the tasks we were asked to do was collect quantitative data. At the end of the interviews, we asked two questions on a 1–7 scale about ease and confidence. One stakeholder asked why we used 1–7 and not 1–5. The UX researcher explained that we had chosen a Likert scale because it’s a tool that is often used in UX Design. I found the question interesting because I could see why it felt odd. Personally, I dislike when the volume on the TV or radio does not divide by 5.

I could not find much formal research on this, but I came across many forum comments from people who also prefer round numbers. This might be explained by a psychological preference for round numbers: humans find them easier to process and categorise. It might also be linked to cultural norms: in many cultures, round numbers such as 10, 20, or 15 are common benchmarks and therefore feel more natural. The counting system we grow up with may also play a role. For example, English uses a base-10 (decimal) system, but French combines decimal and base-20 (vigesimal) elements, particularly for numbers above 60.

The answer about the Likert scale also caught my attention. I had heard of it before but never looked closely at its origins. We often use tools we are trained in without questioning why or where they came from. In usability testing, a 7-point Likert scale is often chosen because it offers more precise feedback and a wider distribution of responses. It provides greater sensitivity to nuance than a 5-point scale. A 5-point scale is simpler and quicker, but a 7-point scale allows more gradations and can help researchers detect subtle differences.

Still, I wonder if the choice confuses participants. Several did a double-take when asked to rate on a scale of 1–7 instead of 1–5. And does it truly capture more nuance, or does it simply give more opportunities for people to pick the middle numbers?

The request to collect quantitative data also raises a bigger question. In my experience, people outside design and research tend to prefer quantitative over qualitative findings. Numbers feel clean and easy to understand, while qualitative data is messy, subjective, and harder to digest. I have often added scaled questions to research sessions simply to provide stakeholders with numbers they can trust. It makes them more willing to listen to the qualitative insights.

My son and I debated this over breakfast one morning. He is doing a master’s in computer science, where his work is entirely quantitative. He prefers it because it feels clean and objective. I lean towards qualitative data because I believe numbers alone cannot capture human experiences, motivations, or decision-making.

Yet I am often pushed to extract quantitative data from qualitative research because many people do not see the value of exploring thoughts and feelings. For example, when participants rated task difficulty on the 1–7 scale, some said “between 4 and 5.” We asked them to pick one, but they usually explained why they hesitated. Sometimes they added that their answer might have been different if asked a week earlier. If we had only collected the numbers, we would have missed the rich insight behind the choice.

Being “data-driven” often becomes a numbers game. In today’s fast-paced environment, there is little space for observation and reflection. Yet that space is what helps us understand real thoughts and feelings. Talking about emotions and experiences gives us the stories we need to make sense of data and communicate it to others. Storytelling helps people empathise, which is critical in our hybrid age. Relying on past successes and current numbers alone will not solve present challenges, nor account for today’s context and tomorrow’s needs.

This connects to something Rachel co-wrote about: design fixation. Design fixation is a cognitive bias where designers get “stuck” on a particular idea, approach, or solution, preventing them from exploring alternatives and limiting creativity. It often comes from past experiences, leading people to favour familiar solutions over potentially better ones. To counter design fixation, designers can seek diverse feedback, challenge assumptions, and deliberately test alternative concepts.

Although the idea is usually applied to designers, anyone involved in the process can become fixated. I have seen stakeholders fall in love with an idea to the point where it was hard to move on. This is why discovery and time spent on the problem space before jumping to solutions is so important.

and finally… the unicorn challenge…

Rachel (my study-buddy) challenged me this month to mention unicorns 🦄 in one of my social media posts. At first, I thought I’d simply slip a reference into these monthnotes and then my LinkedIn update. But as I’m working on a little side project (ask me no questions – and I’ll tell you no lies!), looking at corporate jargon, I realised I’d need to research unicorns anyway. So, I started reading.

Unicorns are mythical creatures, usually imagined as horses with a single horn. The earliest reference I’ve found dates back to China around 2700 BC. These unicorns looked very different from the ones we grew up with: they were described as a mix of animals – “the body of a deer, the tail of an ox, a multicoloured or scaly dragon-like coat, and a flesh-covered horn (or horns).” Despite the differences, they were still considered solitary and elusive, much like the modern image.

Unicorns also appear in Christian traditions. Some scholars think this came from a mistranslation: the Hebrew word for ox, Re’em, was translated into Greek as monokeros – changing “ox” into “unicorn.” Medieval art then picked this up, often depicting unicorns with virgin maidens, and even linking Christ himself with a unicorn as a metaphor.

By the 18th century, belief in unicorns faded as explorers failed to find evidence. Yet the idea remains alive today, especially in business. In finance, a “unicorn” is a privately owned start-up valued at over $1 billion – Wikipedia even keeps a list of them. The term has also been stretched to describe a person seen as rare, exceptionally talented, and highly valuable, someone with diverse skills, a visionary mindset, strong leadership qualities, and a willingness to go beyond their role.

The problem, of course, is that this mythical standard is really hurting real people. What one person calls a unicorn, another might see as an average employee. Worse, this search for “mythical talent” often pressures employees to take on more work, well outside their job description or comfort zone. That leads to burnout, disillusionment, and turnover.

And I feel that graphics like the one below should be banned:

Link to a blog post with a perspective I find questionable: https://customers.ai/articles/average-vs-great-employees 

Organisations don’t need mythical creatures. They need balance: people who are generalists (jack-of-all-trades) alongside those who specialise. People who care about their work, their colleagues, their customers, and the organisation. People who are hands-on, practical, and committed. Leave unicorns for Disney to ruin.

And for what it’s worth, I think unicorns exist:

Photo by Casey Allen: https://www.pexels.com/photo/gray-rhinoceros-in-close-up-photography-16040/

It’s just another case of unrealistic expectations.