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Dec 14, 2023

How to perform thematic analysis to extract meaningful UX insights

Here's how to use thematic analysis to go beyond surface-level insights and start contextualizing user expectations.

Ella Webber

Thematic analysis is all about digging deeper into the subtleties of user feedback, revealing intricate details about user needs, pain points, and preferences that often go unnoticed in conventional data analysis.

This research method sifts through qualitative data and creates contextual themes to derive actionable insights, allowing you to ground UX decisions in authentic feedback.

In this article, we'll explore the thematic analysis research method in detail and explain when and where you can use it. We'll also explain the five crucial steps for performing thematic analysis and share our top tips for success.

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What is thematic analysis?

Let’s start with the basics.

Thematic analysis evaluates data from user interviews , surveys, and other qualitative research methods . The analysis consists of categorizing insights into multiple codes and identifying patterns within the data to create themes.

TL;DR: thematic analysis converts complex and diverse data into a curated set of clear, actionable insights. It simplifies the process of reviewing and analyzing your research, and efficiently identifies the insights necessary to make data-driven decisions.

For example, let’s consider you’re building a project management tool and have interviewed users to gather insights.

When you analyze interview transcripts thematically, you can zoom in on what truly matters for your users, like intuitive design and easy integrations. You can assess all your UX research data as a whole, and categorize the information by:

• Identifying some common topics of interest, called codes
• Determining more nuanced concepts within codes, called themes

What exactly are codes and themes?

Codes are tags or labels used to categorize information. When you work your way through a large dataset, you can use codes to organize and segment all useful insights for further analysis. It’s easier to categorize information using a codebook made up of all relevant codes for your research.

For example, if you're analyzing survey responses for your project management tool, you can create codes like 'design,' 'interface,' 'integrations,' and more to document what users have to say about each topic. Draft initial codes, then refine them as you get more insights.

After organizing all information into these codes, you can start consolidating the insights to create broader themes from your entire data set.

Themes are made from a series of codes. Each theme indicates a core idea gathered from your research data. Themes give meaning to your data and help relate codes to specific experiences with your product or features.

For example, one of the potential themes evident from your user interviews for the project management tool might be 'the importance of seamless integrations with other tools'. The analysis clarifies that users prioritize integrations over other capabilities.

What are the main approaches for thematic analysis?

You can apply the thematic analysis method in two main ways: inductive and deductive. Let’s break down these different approaches.

The inductive approach

The inductive approach to thematic analysis is about discovering ideas and creating themes without preconceptions about the data you're analyzing. This means you don't have any prior expectations or hypotheses about this data.

This approach then draws on the researchers’ awareness of the field—how well they know and understand the research subject—to complete the qualitative study process.

It has four main steps. Using our project management example, this could look like:

• Data collection: Send surveys to 100 project managers
• Initial observation: Find repeat references to 'customization options,' 'ease of use,' 'collaboration'
• Pattern identification: Identify emerging patterns like ‘frustration over tools with a steep learning curve’
• Theory development: Chalk out the theory that a good project management tool should balance simplicity and customization with good integration capabilities

The deductive approach

With the deductive approach, researchers want to test existing theories and validate hypotheses. All the data is collected with the express purpose of validating these theories, gathered by other types of UX research.

When we spoke with Haley Stracher , CEO and Design Director at Iris Design Collaborative , she highlighted the deductive approach as her go-to when conducting thematic analysis.

“Typically, I’ll have a hypothesis on a couple of themes I see in my UX research, which is then either validated or disproven. So, I start the process with a hypothesis instead of doing thematic analysis from scratch.”

Pro tip ✨ You can use deductive analysis alone or alongside inductive analysis, to further delve into themes you’ve uncovered and early hypotheses.

When conducting deductive thematic analysis, your existing theories will come from previous user research and hypotheses relating to your users. Start with these theories to conduct deductive analysis in four steps:

• Theory development: Develop theories like ‘users need feature A to fulfill use case B’
• Hypothesis formulation: Create a hypothesis e.g. ‘feature A will increase retention by X%’
• Data collection: Conduct user interviews and ask research questions aligned with the hypothesis
• Qualitative data analysis: Analyze the data to confirm or nullify the hypothesis and develop new themes based on patterns

What’s the difference between inductive and deductive analysis?

The main difference between the inductive and deductive approaches is that the latter tests existing ideas and theories, while the former is about outlining the scope of your research design from scratch.

In both inductive and deductive analysis, you can approach data using:

• The semantic approach: You interpret all insights at face value and don’t consider the secondary or implicit meaning of the data
• The latent approach: You look at the underlying interpretation of the data instead of considering only the surface-level insights

The semantic and latent approaches are two different ways of approaching your data, and can be used in both inductive and deductive thematic data analysis.

The pros and cons of thematic analysis

Thematic analysis is not a one-size-fits-all solution. Here are the pros and cons you need to consider before adding this research method to your UX research toolkit.

Here are the main benefits of conducting thematic analysis:

• Iterative data: As you analyze more data and find new themes, you can consistently refine your themes until you get a clearer picture of your user's expectations
• In-depth insights: Unlike other qualitative analysis methods, like grounded analysis or discourse analysis, thematic analysis helps identify less obvious information about users, like their motivations, perspectives, and emotions
• Short learning curve: The thematic analysis approach is easy to learn and implement, even if you're a beginner researcher
• Wide applicability: You can use the thematic analysis method for multiple use cases, like market research, customer experience , and competitor benchmarking

Thematic analysis also has its limitations, such as:

• Subjectivity and bias: Cognitive biases of the researcher can negatively influence the analysis process and potentially lead to skewed results
• Time-consuming process: Compared to other qualitative research methods, this process takes more time since it relies heavily on manual effort and understanding (unless you have a tool that can help identify themes)
• Limited reliability: It’s difficult to establish the reliability of thematic analysis data because researchers with varying perspectives can question the validity of findings

When should I use thematic analysis?

Thematic analysis comes from the field of psychology, where researchers actively use this analytical technique to study qualitative data. Given its flexibility and efficiency to identify patterns and find insights from a massive dataset, it’s been widely adopted by UX researchers.

Here are a few common use cases of thematic analysis in UX research :

Evaluate large data sets

Once you’ve collected data from user surveys and interviews, you can systematically organize all the information and extract meaningful insights using codes and themes. It can help you find patterns and document common themes in your data. It’s also useful for diary research spanning over an extended period.

For example, let's consider you rolled out a survey to collect feedback from your entire user base and received over 2,000 detailed responses.

With thematic analysis, you can evaluate these responses and create coded data to fast-track your analysis. For example, allowing you to draw conclusions like 'helpful user interface,' 'seamless onboarding,' and 'inefficient self-serve support,' being priorities for users.

Haley uses thematic analysis to identify large-scale issues or analyze large datasets, and warns against using it for individual feedback:

“Thematic analysis is particularly useful when you’re identifying major problems. For example, if the drop-off point in a section of wireframes is high, analyzing the pattern of why using thematic analysis is really critical.”

"However, when gathering individual experiences or generalized feedback, thematic analysis can be misleading and difficult to create a patterned analysis from, so you’d be better suited to other kinds of analysis.”

Study multi-layered user experiences

Thematic analysis also works well when you're evaluating user behavior or analyzing the transcribed data from speak-aloud usability tests for a complex product. Whether you want to iterate the product experience or introduce new features, this approach can offer behavioral insights to understand your users better.

For example, let's say you conducted usability tests for your new app and received over 50 session recordings. An easier way to study this qualitative data is by transcribing all the sessions and dissecting these transcripts thematically.

You'll discover where users feel confused or get stuck, and it'll also reveal patterns of aha! moments where users are impressed by a particular experience.

Product tip 💡 Maze Interview Studies automatically transcribes and analyzes your session recordings, using AI to sift through the data and highlight key themes in your research.

Contextualize quantitative findings

If you want to know the ‘why’ behind quantitative data, using this analytical method to find users’ first-hand perspectives is a great way to get a more complete picture beyond what can be quantified.

For example, imagine your free trial-to-conversion rate dropped by 8% this month.

You interviewed all free users who didn't convert into paying customers, then analyzed these interviews thematically to find pain points or concerns common for several users. By identifying these themes, you find real context behind performance metrics: giving you deeper insight to inform product decisions, and tangible data to share with stakeholders when looking to solve these blockers but needing buy-in.

Compare responses from a diverse group

Thematic analysis is particularly helpful for studying diverse user groups and their preferences. Thematic findings can highlight ways to tailor your product experience to specific user needs across different regions.

For example, if you want to survey users from different markets like the USA, UK, Singapore, and Japan for your time management app, you can use thematic analysis to compare themes across users in these markets. You can analyze qualitative data to identify lifestyle differences, preferences, and work management.

How does thematic analysis compare to other qualitative methods?

If you’re new to the world of qualitative research , thematic analysis is a great starting point. It can help you build your analytical skills and systemize the data interpretation process. However, it’s important to understand how this technique differs from other qualitative research methods:

• Thematic analysis doesn’t restrict researchers with rigid theoretical frameworks: Researchers across several disciplines or industries can use this method for multiple use cases, research question types , and data types. Other research methods are tied to specific approaches, like grounded theory , which relies on theory generation for analyzing the data.
• Thematic analysis focuses on the data of experience as a whole: This method enables researchers to identify patterns within the data and create themes reflective of users' actual experiences. In comparison, other qualitative methods focus on specific aspects of the participants. For example, narrative analysis focuses on storytelling, and discourse analysis on language.
• Thematic analysis is suited to large amounts of data: While researchers are free to interpret data semantically and latently in thematic analysis, other methods are more structured and limited in their analysis approach. Analyzing data using more specific approaches—such as content analysis , for example—is time-consuming and impractical for vast amounts of data.

Thematic analysis provides strong insights into the overall thoughts, feelings, and pain points of users, while other qualitative research methods tend to be much more rigid, theory-based approaches that leave little room for subjectivity.

How to perform thematic analysis in 5 steps

Braun & Clarke first published these steps for performing thematic analysis in their 2006 study titled ‘ Using thematic analysis in psychology ’. Let’s take a look at these steps and how they apply to UX research.

1. Familiarize yourself with the data

You can’t jump in as soon as you wrap up the data collection process. A crucial step before the actual analysis is simply familiarizing yourself with the data.

Here’s when you listen to all the interview sessions, read the transcripts, or review the survey responses. Remember that you don’t have to document anything at this point, but feel free to make a few notes about recurring themes or obvious patterns visible in the data. You’re essentially reviewing all the data at once with informed curiosity rather than an analytical lens.

Think of this step as getting a lay of the land: what kind of responses you’ve received, what stands out, general thoughts and talking points.

2. Generate your codes

Once you’ve reviewed all responses together, it’s time to revisit the data to create initial codes. These codes will be your foundation for building themes, so you have to selectively decide on the most relevant codes for organizing information.

This step will require re-reading and analyzing the data multiple times to create a long list of potential codes. Then, you can finalize the critical codes and create an order of priority.

The final part of the coding process is grouping insights into different codes. This is where you categorize several aspects of user responses into codes and collate information for each code separately.

3. Review your themes

After putting together insights into different codes, you can start creating potential themes from this information. Not every code will translate to a theme, and you might find a more relevant theme by combining two or more codes.

This is where you have to dig deeper and effectively interpret the data set. Look for connections between different codes and draw mind maps to effectively visualize all this information.

Here are a few factors to watch out for when outlining themes:

• Avoid generalizing ideas or making sweeping conclusions based on a few data points
• Review your themes to prevent biases in the scope of your research process
• Don’t overcomplicate or oversimplify themes; strike a balance to make your research accessible but comprehensive

Pay attention to any outliers in your data, anything that doesn’t fit into your overall themes. While patterns reveal commonly shared thoughts, these outliers could highlight crucial ideas that aren’t as popular or well-known as others. Take a second look at such insights to see if they indicate something bigger.

You can also create sub-themes to support your primary themes, sometimes based on such outliers.

4. Define and refine your themes

Once all themes are outlined, you have to refine and name them properly to aid your research. Properly defining and naming themes can make a huge difference in your findings, and ensure data can add value to a research repository and reused for future research.

For example, if you've generated codes like 'team support,' 'management style,' and 'work-life balance' in your employee wellbeing research, you can create a theme around ‘work-life balance’ and title it 'Work-Life Balance: Ways to support employees to balance personal and professional obligations.'

The title doesn’t have to be a single word or phrase, it can be more detailed, in order to best reflect your research goals and next steps.

At this stage, you want to lay out your themes in more detail and create a narrative that weaves all themes together. For example, the ‘Work-Life Balance’ theme could align with ‘Management Style’ and ‘Policy Changes’ themes to create a holistic narrative.

5. Write your report

The final step is compiling a UX research report communicating your findings from the data analysis process. This report will allow teams to make informed decisions and modify the design and UX strategy whenever necessary.

Make sure your report presents all the codes, themes, and actionable insights in a coherent flow. Begin by outlining the methodology and steps followed so far. Then, elaborate on your interpretive analysis and key findings, highlighting what these findings mean for your team and next steps to take.

It’s equally important to communicate the validity of your analysis by quoting relevant data in the final report. Stakeholders should be able to verify each insight by looking at the exact user responses that led you to it.

6 tips to extract better insights from thematic analysis

Finding and documenting insights can make or break your thematic analysis approach. Without the right research tools and a defined framework to extract insights, you expose your research to data misrepresentation and superficial analysis. What’s more, doing all of the above manually, or without the right resources, is a lot of work.

Here are six tried-and-tested tips to prepare against any issues:

1. Avoid paraphrasing

Good UX research strives to understand and contextualize users' motivations, behaviors, and expectations—in users’ own words and experiences.

When reporting on qualitative research, it can be easy to paraphrase feedback to ‘sum up’ or give ‘the gist’ of what was shared. Instead of simply paraphrasing what your users said in an interview, survey, or focus group, it's important to truly interpret their thoughts and present them accurately in your report.

Avoid paraphrasing and look at the finer details of each user response, then try to contextualize why they said what they said. Dig into the nuance of their response and refine this to provide a quote, explanation, and take away—rather than a summary.

To do this, you might have to re-read their response multiple times and connect the dots to draw your own conclusions. Don’t be afraid to take your time.

2. Look for insights, not data

Go beyond the obvious data to find more meaningful insights. You have to immerse yourself more deeply into the research data and identify patterns that you'd have otherwise missed. Finding and interpreting these subtle patterns are key to extracting richer insights.

For example, surface-level data might tell you that users like your tool's intuitive interface, but digging deeper into your data reveals why —users like the interface because it reduces the training time and fast-tracks onboarding for new members. You can now take this insight with you into future product development , making it an evergreen learning rather than a one-off piece of feedback.

The bottom line: aim to understand the essence of every user response, and what it means going forward, rather than looking at its literal meaning.

3. Base themes on data, not on questions

When analyzing any data thematically, don’t be influenced by the questions posed to participants. These questions can bring researcher bias because they twist the narrative based on your ideas rather than users' perceptions.

Imagine one of the questions in your user interviews is, "What do you think about the task-tracking feature?". One response to this question is, "I like the task-tracking feature because it simplifies communication, along with other capabilities like team chat."

While building out themes, you’d need to focus on 'seamless communication' rather than highlighting the task-tracking feature, as this specific feature was highlighted by the question. Build your themes based only on the responses you’ve received, rather than any pre-provided information—they’re what accurately represent user experiences and expectations.

Sometimes, this means you have to move away from your initial hypothesis and create completely diverse themes. But in doing that, you’re making your research completely user-focused and eliminating bias.

4. Ensure there's enough data to back up your themes

One of the most important things to remember in a thematic analysis process is that you should have enough data to create themes. How many users you need for user research varies on your goal, method, and resources, but broadly-speaking you need enough to generate adequate datasets.

Work to support each theme with recurring patterns in your data and include as many user quotes from your research as possible to validate it.

This exercise ensures you're not creating themes around a few isolated comments or anecdotal responses. It'll also be helpful in building credibility for your report since you're covering a depth of responses in each theme instead of focusing on a few loud voices.

5. Ensure your themes build your narrative

Themes should not exist in isolation. They should collectively build a coherent narrative for your research. You need to stitch the data together to tell a logical story and make a case for your product and design decisions.

You can’t have a few themes connected to each other and a few on completely different topics. Each theme should contribute meaningfully to the overall story and provide relevant, contextual insights.

6. Use thematic analysis software

You can streamline and speed up the entire process using thematic analysis software. These tools use AI-powered natural language processing to identify patterns and themes in your text. They also help transcribe and summarize your interviews and save all transcripts in one place.

One of the best offerings from AI research tools is their ability to handle large quantities of data in an instant. Haley agrees, having used AI to sift through research data:

“AI can be an amazing tool to help you sort through feedback. I’ve put feedback into AI solutions and asked them to identify general themes, which saves so much time and can help me consolidate the feedback without human error.”

With a powerful thematic analysis tool like Maze’s User Interviews , you can also find key insights and automatically fetch the video snippet for that response. The platform then generates ready-to-share reports once you’ve collated insights from different themes—saving you the time and effort of preparing a custom-made, shareable report.

Plus, Maze lets you work with your entire team and organize your data for different research projects, to maintain a repository of user research data and findings.

Easily tag themes and categorize insights with Maze Interview Studies

Getting help with data interpretation and code collection

Thematic analysis is a meticulous process of analyzing qualitative data and uncovering rich insights about your users and product.

While it’s popular among PWDR or beginner-level researchers for its flexible and comparatively less technical approach, the success of thematic analysis relies heavily on a researcher's skill set.

You need to have an analytical mindset and possess keen attention to detail to identify patterns, create meaningful themes, and draw actionable insights. It’s also highly subjective and the insights will depend on your ability to interpret the data. Don’t be afraid to embrace AI-powered tools that can make the job easier and fast-track analysis.

Transform conversations into trusted insights

Automate the theme and insights extraction process and simplify your interview workflow from start to finish.

Frequently asked questions about thematic analysis

1. What is qualitative UX research?

Qualitative UX research consists of collecting and analyzing non-numerical research data to understand participants’ subjective thoughts. It delves into users’ motivations, preferences, needs, and other aspects to find opportunities to improve the user experience.

2. Are thematic analysis and content analysis the same?

No, thematic analysis differs from content analysis. Content analysis focuses on a more surface-level analysis to find trends in the data, whereas thematic analysis creates meaningful themes from the data to better contextualize it.

• UX Researchers
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How to Leverage Thematic Analysis for Better UX

Thematic analysis, an approach used to analyze qualitative data, is central to credible research and can be used to improve UX design by uncovering user needs, motivations, and behaviors.

By Shane Ketterman

Shane comes from a background in architecture, information technology, customer experience, and UX design.

How can we provide better user experiences? One way is to leverage UX research in order to gain a deeper understanding of user needs, motivations, and behaviors. UX research is centered around the analysis of real-life scenarios in order to gain valuable facts, i.e., its aim is not in generating or improving a theory.

Consider a pediatric healthcare UX research study by designer Katie McCurdy . Parents with a child in the hospital aren’t always aware of the resources available to them, such as laundry services, meal options, and sleeping accommodations. Leveraging UX research in the form of user interviews, it was discovered that parents (users) were frustrated and confused because they weren’t properly oriented at the beginning of their child’s stay.

That frustration led to increased anxiety and a feeling of helplessness. A solution was crafted resulting in a half-page booklet that oriented parents with tips, resources, and advice from other parents.

While there are a lot of user research methodologies that can be used to generate both qualitative and quantitative data, it’s the analysis and application of this data that ensures UX designs are better aligned with user needs—a highly flexible framework for analyzing qualitative data is thematic analysis, and it can be leveraged for better UX .

Introducing Thematic Analysis

Thematic analysis is a process user researchers can use to analyze qualitative data. The thematic analysis approach identifies themes (sometimes referred to as patterns) within qualitative data. Themes are used to help researchers answer questions and find meaning within large amounts of data.

The importance of thematic analysis cannot be understated. Though it can be a complex framework to put into practice, it is both flexible and deeply insightful. Because of this, many researchers believe that it should be a fundamental part of the UX design process.

How to Use Thematic Analysis

Proper analysis of qualitative user data is central to credible research. The thematic analysis process follows a systematic approach, resulting in a collection of themes that provide a more accurate representation of user needs, motivations, and behaviors:

• Exploring the UX research data
• Generating initial codes
• Looking for themes
• Reviewing themes
• Defining themes
• Compiling a UX deliverable

Since the results of qualitative data interpretation can be subjective if there are no concrete outcomes established, it’s a good idea to know the questions and goals that are being sought before starting the thematic analysis process.

Becoming Familiar with UX Research Data

Qualitative UX research data is gathered from activities such as card sorts, daily journals, and user interviews. At this beginning stage, the goal is to make early impressions by reading and re-reading the data, not to formulate any conclusions.

If it’s verbal data, transcribe it. If the data is already transcribed, then break it into smaller, digestible chunks. In either case, taking notes is advised. During the exploration stage, it’s good practice to keep an open mind, stay neutral, and resist the urge to formulate anything concrete.

At the end of this stage, there should be a comfortable level of familiarity with the data and some meta ideas should be written down. If anything is unclear, reread it and resist moving on.

Generate Initial Codes

In this phase, the goal is to organize the data in a more meaningful (and systematic) way. If the data is being analyzed manually (without the aid of research software), then highlighting or side notes can be used.

What is actually being highlighted? What are we looking for? Codes are simply highlighted pieces of user data that support the project research questions.

For example, if we are being asked to evaluate video streaming services, then we will “code” or highlight specific instances in the data that support the questions being asked such as “it’s difficult to find something,” or “HBO,” or “Hulu.”

Coding is a fairly complex process because the user researcher needs to keep the project questions and goals in mind at all times. A great idea is to transcribe all of the code snippets onto a spreadsheet which will help with the next phase, looking for themes.

Looking for Themes

The next phase of a thematic analysis is looking for potential themes from the codes/ideas generated in the last stage.

A theme is a recurring pattern of codes that captures something significant about the original research question. For example, we might have a series of codes highlighted such as HBO , Netflix , and Hulu . The research question is based around video streaming so we might pick a theme of “important streaming services” or, more broadly, “services.”

Since finding themes is the core of thematic analysis, the majority of time should be spent in this stage of the process. The most correct themes will be those which align with the research questions.

Reviewing Themes

Once the initial themes have been extracted, the next step is to review them all and make sure they align with the meaning of the data as a whole. It’s tempting at this stage to blindly “accept” all of the themes and move on to the next phase.

A deeper examination is recommended as there are often themes or concepts missed the first time around.

A few questions can serve as a guide during the theme review process:

• Do the themes make sense in context of the research question being asked?
• Are the themes concrete or too general?
• Do any of the themes overlap?
• Are there missed themes?

Defining Themes

After a thorough review, the final set of themes should be documented. It may be helpful at this stage to create a thematic map which shows the relationship between the themes and how they support the overall narrative.

Here’s an example of a thematic map:

UX Deliverable

The final UX deliverable is the last stage of the thematic analysis approach. The analysis should consider the audience. Is this for a team of UX designers? Is it for the client?

Regardless of the audience, a thematic analysis report should be concise, logical, and non-repetitive, and tell an engaging story in support of the findings. It’s also important to provide final recommendations and include examples from the user data to support these recommendations.

A common practice is to include the original qualitative data, codes, and resultant themes so the client can see the how the UX researcher arrived at their conclusions. It also adds validity to the work.

Putting Thematic Analysis into Practice

How can UX designers put thematic analysis into practice in order to help improve UX?

The results of a thematic analysis is insight into user needs that will serve as the foundation for human interaction design decisions, product content, information architecture, and usability design.

Consider a completed thematic analysis with a theme called “customization.” In this case, users want the ability to customize a specific product and have a high degree of autonomy over its features. Having this knowledge and insight will help UX designers during every stage of the design process. Without it, there is a risk of a failed product design that does not work well for the intended audience.

Thematic Analysis Case Study: Telehealth Dashboards

What follows is a UX research case study of thematic analysis that was used for a company providing telehealth products, services, and analytics.

The Question and the Data

The question the client asked was, “How can we improve the user experience of our analytics dashboards?”

The dashboards were not being used, customers were cancelling their subscriptions to the analytics product, and the data team had no UX design experience. In order to decrease churn and improve the overall user experience, UX research was employed to find out what users needed and wanted as well as what their motivations were.

User interviews were performed with the identified customer personas using Google Docs and Zoom for video meetings.

Becoming Familiar with the User Research Data

Once the user interviews were completed, all of the feedback was placed on a separate tab in a spreadsheet. The spreadsheet had three columns:

In the “User Name” column, the qualitative data was placed in the rows. In order to do this, the user feedback was placed in rows by topic, sentence, or a natural place where there was a pause, so the essence of the feedback was kept intact. Non-verbal observations were also included.

Generating Initial Codes

Initial codes were placed in the “codes” column of the spreadsheet. Note that these codes are simply ideas based on the feedback given and the outcome being sought for the project.

Whenever a user discussed something they wanted to have on their analytics dashboard, the code that made the most sense was “analytics story” because each piece of data on a dashboard tells a story.

Here are the final codes that were generated alongside the qualitative user data:

Coding can also be subjective. For example, the user comment of “juggling so many balls” was in reference to being extremely busy, and thus it felt natural to give that a code of “time management.” It could have also been “busy” or “overwhelmed.”

This is one of the most difficult stages of a thematic analysis. For each user, all codes were placed side-by-side on a separate spreadsheet so they could be shown together. Note that when reviewing codes, they will not all be exactly the same word, so look for words and ideas which are similar.

At this stage in the process, we are looking for potential themes that can be pulled out of the codes. For example, “enhancements,” “changes,” and “personalization” of analytics reports were themed as “customization.”

It’s quite possible that a user research project produces a very small amount of data. If this happens, then at this stage final themes could be developed.

The final portion of the thematic analysis was a pared down list of themes which supported the main business objective:

• Customization – Enhancements/opportunities
• Data Usage – Current activities
• Data Stories – Opportunities
• Current Product – Issues, etc.

This may not seem impressive at first; however, consider that this was pulled out of hundreds of pieces of qualitative feedback from hours of interviews.

We learned that customers want to be able to customize their analytics dashboards instead of getting a “one-size-fits-all” report. We also learned several stories the users were looking to tell with their dashboards, such as, “How is my telehealth program performing?”

This offered the company great insight and delivered a clear set of objectives to help the data team turn the underperforming dashboards into a product to which users wanted to stay subscribed.

Showing the executive team (and the data team) the final themes was a huge win because it was something they could both understand and digest; contrast this with an entire spreadsheet of notes taken during the user interviews, without any sort of identification of user needs and behaviors.

The UX deliverable was expanded beyond a simple report. Based on the themes, the report included:

• An expanded version of the themes – This included each theme and the supporting qualitative feedback which tied back to that theme.
• User stories – A set of user stories was created based on the qualitative feedback in the interviews. Each story was given a priority level of importance.
• Wireframes – A preliminary wireframe of the analytics dashboard was created which focused on customization, fixing current issues, and data usage.

Thematic analysis doesn’t end with the deliverable. It can continue to be used throughout an iterative UX design process. For example, a prototype was created based on the above outcomes. The prototype was then used for a new set of user interviews, which resulted in additional qualitative data, and a second thematic analysis was performed with the goal of refining the prototype.

Thematic analysis, a qualitative analysis of data, can improve UX by providing deeper insight into the needs, motivations, and behaviors of users, resulting in improved user experiences.

Further Reading on the Toptal Blog:

• How to Conduct Effective UX Research: A Guide
• The Value of User Research
• Design Talks: Research in Action with UX Researcher Caitria O'Neill
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Understanding the basics

How do you conduct a thematic analysis.

A thematic analysis is conducted using qualitative data and is performed using a six step process: becoming familiar with the data, generating initial codes, looking for themes, reviewing themes, defining themes, and producing a final deliverable.

What is the purpose of thematic analysis?

The purpose of a thematic analysis is to analyze qualitative data and identify themes (sometimes referred to as patterns) within the data. This provides deeper user insights and improves research outcomes.

What is a disadvantage of thematic analysis?

A disadvantage of thematic analysis is the possibility of a subjective bias leading to themes which do not accurately describe user needs, motivations, and behaviors. This could lead to outcomes that do not align with improved user experiences.

How is thematic analysis flexible?

The thematic analysis process is flexible due to the degree of subjectiveness on the part of the user researcher doing the coding and theme generation. Themes are not black and white; rather, they are subject to interpretation and thus provide a high degree of flexibility.

What is thematic analysis method?

Thematic analysis method is the way in which a thematic analysis is performed. The method begins with an exploration of qualitative data following by coding, theme generation, and a deliverable describing the process and conclusions arrived at by the user researcher.

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How to Do a Thematic Analysis of User Interviews

You have been in the field talking to users and you now find yourself with a massive amount of audio, notes, video, pictures, and interesting impressions. All that information can be overwhelming, and it’s difficult to know where to start to make sense of all the data. Here, we will teach you how to go from information chaos to patterns and themes that represent the most interesting aspects of your data and which you can use as the foundation for personas , user scenarios and design decisions.

When you have carried out user interviews , the next step is to analyze what people have told you. Depending on the complexity of your project, this can be a simple or a complex task, but no matter what your project is, it’s important that you follow certain guidelines for how to analyze your interviews. Although you might feel like you have a pretty good idea of what people have told you and you are eager to get started implementing your insights, doing a proper analysis is important for the validity of your results. There is a lot going on in an interview situation, and it’s easy to overlook information that doesn’t fit with your preconceived assumptions of what people were going to say and do during your interviews. A proper analysis will ensure that you go through your data in a systematic and thorough manner. A proper analysis also makes it easy for other people to understand exactly how you reached your various conclusions about your participants and will make your results much more trustworthy . Analyzing your results takes time, especially if your purpose is broad and explorative. But if your time is limited, it’s always better to narrow the scope of your study than to skip steps in the analysis phase and jump straight to acting on your results.

© Highway Agency, CC BY 2.0

It’s important that you properly analyze your interviews, but there is no single right way to perform qualitative data analysis, and the method you choose primarily depends on the actual purpose of your study. Here, we will focus on one of the most common methods for analyzing semi-structured interviews : thematic analysis. A thematic analysis strives to identify patterns of themes in the interview data . One of the advantages of thematic analysis is that it’s a flexible method which you can use both for explorative studies, where you don’t have a clear idea of what patterns you are searching for, as well as for more deductive studies, where you know exactly what you are interested in. An example of an explorative study could be conducting interviews at a technical workplace in order to obtain an understanding of the technicians’ everyday work lives, what motivates them, etc. A more deductive study could be conducting interviews at a technical workplace in order to find out how technicians use a specific technology in order to handle safety-critical situations.

No matter which type of study you are doing and for what purpose, the most important thing in your analysis is that you respect the data and try to represent your interview as honestly as possible. When you share your results with others, you should be transparent about everything in your research process, from how you recruited participants to how you performed the analysis. This will make it easier for people to trust in the validity of your results. People who don’t agree with your conclusion might be critical of your research results, but if you know that you have done everything possible to represent your participants and your research process honestly, you should have no problem defending your results.

Steps in a Thematic Analysis

“Analysis involves a constant moving back and forward between the entire data set, the coded extracts of data that you are analysing, and the analysis of the data that you are producing.” —Virginia Braun and Victoria Clarke, Authors and qualitative researchers in psychology

Thematic analysis describes an iterative process as to how to go from messy data to a map of the most important themes in the data. The process contains six steps:

Familiarize yourself with your data.

Assign preliminary codes to your data in order to describe the content.

Search for patterns or themes in your codes across the different interviews.

Review themes.

Define and name themes.

Produce your report.

• Transcript loading…

In this video, professor of Human-Computer Interaction at University College London and expert in qualitative user studies Ann Blandford provides an overview of what an analysis process can look like.

Thematic analysis is used in many different research fields, but the steps are always the same, and here we build our detailed description of the steps on a famous article, by qualitative researchers in psychology Virginia Braun and Victoria Clarke, called “ Using thematic analysis in psychology ”. We describe the process as you might do it in a business setting; so, if you are conducting interviews for academic purposes, you should look up the original article.

1. Familiarization

During the first phase, you start to familiarize yourself with your data. If you have audio recordings, it’s often necessary to perform some form of transcription, which will allow you to work with your data. In this phase, you go through all your data from your entire interview and start taking notes, and this is when you start marking preliminary ideas for codes that can describe your content. This phase is all about getting to know your data.

How much you need to transcribe will vary depending on your project. If you are performing a broad and exploratory analysis , you may need to transcribe everything that was said and done during the interview, as you don’t know in advance what you are looking for. If you are searching for specific topics, you will probably only need to transcribe those parts of the interview that pertain to that topic. In some cases—e.g., when the interview is a minor part of a larger user test or observation project—writing a detailed summary or summarizing specific themes can be sufficient. When you consider how much to transcribe, take Braun and Clarke’s advice: “What is important is that the transcript retains the information you need, from the verbal account, and in a way which is ‘true’ to its original nature”.

Whether you transcribe it yourself or pay someone to do it for you will depend on your budget and your time. Some researchers prefer to do it themselves because they can start making sense of the data as they transcribe; others feel as though they can use their time more efficiently by reading the finished transcripts that someone else has made.

2. Generating Initial Codes

In phase 2, you assign codes to your data. A code is a brief description of what is being said in the interview; so, each time you note something interesting in your data, you write down a code. A code is a description, not an interpretation. It’s a way to start organizing your data into meaningful groups. As an example, let’s try to code a snippet from an interview about video streaming:

“I: So how did you find something?

Peter: Well, first she [his wife] looked at HBO and suggested that we watch ‘Silicon Valley’, but I’m not really into comedy shows. So, then she went to Netflix and suggested different movies, but there wasn’t really anything that I felt like… but then I remembered that we had been watching ‘Better call Saul’ before the summer holiday, and I couldn’t really remember if we had watched all the episodes, so we looked it up and it turned out that we had stopped in the middle of the season; so, that’s what we watched…”

You can give this section multiple codes (and it’s perfectly fine to give one section multiple codes) depending on your interests. If you are interested in different streaming services, you could use the codes “Netflix” and “HBO”. If your interests are broader or if you are—e.g.—interested in how people collaborate, you could use the code “coming to an agreement”. So, which codes you use depend on what is being said and on the purpose of your research. Your coding also depends on whether you are performing an exploratory analysis, where the themes depend on the data, or a deductive analysis, where you search for specific themes.

© Ditte Hvas Mortensen and Interaction Design Foundation, CC BY-NC-SA 3.0

There is no clear cut-off between phase 1 and phase 2, and initial coding often takes place during the familiarization phase. There is specific software for coding, but you can also code by taking notes on a printed transcript or by using a table in a Word document. The most important thing is that you can match the code to the section of the interview that it refers to. Once you have coded all your data, the next step is to collate all the sections that fit into each code—e.g., collate all sections in your interviews with the code “Netflix”. If you are using pen and paper, you will have to copy sections that have multiple codes so that you can place them in more than one code category. If you are coding digitally, you can just use copy-paste. Braun and Clarke recommend that you code for as many potentially interesting themes as possible and that you keep a little of the data surrounding your coded text when you do the coding; that way, you won’t lose too much of the context.

3. Developing Themes

Whereas codes identify interesting information in your data, themes are broader and involve active interpretation of the codes and the data. You start by looking at your list of codes and their associated extracts and then try to collate the codes into broader themes that say something interesting about your data. As an example, you could combine the codes “Netflix” and “HBO” into a single theme called “Streaming services”. Searching for themes is an iterative process where you move codes back and forth to try forming different themes. Drawing a map of your codes and themes or having codes on sticky notes that you can move around can help you visualize the relationship between different codes and themes as well as the level of the themes. Some themes might be subthemes to others. In this process, not all codes will fit together with other codes. Some codes can become themes themselves if they are interesting, while other codes might seem redundant, and you can place them in a temporary mixed theme. At this point, you shouldn’t throw away codes that don’t seem to fit anywhere, as they may be of interest later.

4. Reviewing Themes

During phase 4, you review and refine the themes that you identified during phase 3. You read through all the extracts related to the codes in order to explore if they support the theme, if there are contradictions and to see if themes overlap. In the words of Braun and Clarke, “Data within themes should cohere together meaningfully, while there should be clear and identifiable distinctions between themes.” If there are many contradictions within a theme or it becomes too broad, you should consider splitting the theme into separate themes or moving some of the codes/extracts into an existing theme where they fit better.

You keep doing this until you feel that you have a set of themes that are coherent and distinctive; then you go through the same process again in relation to your entire data set. You read through all your data again and consider if your themes adequately represent the interesting themes in your interview and if there is uncoded data that should be coded because it fits into your theme. In this process, you might also discover new themes that you have missed. Phase 4 is an iterative process, where you go back and forth between themes, codes, and extracts until you feel that you have coded all the relevant data and you have the right number of coherent themes to represent your data accurately. In this iterative process, you might feel as though you can keep perfecting your themes endlessly, so stop when you can no longer add anything of significance to the analysis.

5. Defining and Naming Themes

During phase 5, you name and describe each of the themes you identified in the previous steps. Theme names should be descriptive and (if possible) engaging. In your description of the theme, you don’t just describe what the theme is about, but you also describe what is interesting about the theme and why it’s interesting. In Braun and Clarke’s words, you “ define the essence that each theme is about ”. As you describe the theme, you identify which story the theme tells and how this story relates to other themes as well as to your overall research question. At this point in the analysis, you should find yourself able to tell a coherent story about the theme, perhaps with some subthemes. It should be possible for you to define what your theme is clearly. Moreover, if you find that the theme is too diverse or complex for you to tell a coherent story, you might need to go back to phase 4 and rework your themes.

6. Producing the Report

What the final report looks like depends on your project; you might want your final delivery to be personas or user scenarios, but there are some commonalities you should always include. When you write up your results, there should always be enough information about your project and process for the reader to evaluate the quality of your research. Given that, you should write up a clear account of what you have done—both when you carried out the research and for your analysis. You already have a description of your themes, and you can use this as a basis for your final report. When you present your themes, use quotes from what the participants said to demonstrate your findings. Video, audio and photo examples are even more convincing, but NEVER use this without the participant’s consent. Remember; you have been talking to these participants. To you, the participants are real humans, each of whom has a set of views and a host of rights you must respect. It is your job to make the participants feel real to the people you report your findings to.

For UX projects, splitting your report up into two parts might be a good idea. Part one contains a summary of your findings in an engaging way—this could be in a presentation, via personas or user scenarios. Part two contains the background information about how you did your research and your full analysis. That way, people who are only interested in your conclusions can stick to those while people who have questions about your research can go to the detailed account of what your work entails. This will ensure the validity of your research and give you a good reference for the future when you have forgotten all the nitty-gritty details of your research project.

You can download our overview of the six steps in thematic analysis here:

Thematic analysis describes a somewhat straightforward process that allows you to get started analyzing interview data; however, obviously, there is a lot of learning-by-doing involved in carrying out the analysis, so it pays to be aware of common pitfalls when doing a thematic analysis. Often an important thing to ensure is that your analysis delivers insights into the areas that you have promised your stakeholders to deliver insights into. If you find interesting information that is off-topic, consider diving into these topics in a follow-up project.

Here, Ann Blandford describes some of the pitfalls she often encounters when her students are learning to analyze interview data.

The Take Away

When it’s time to analyze your interview data, using a structured analysis method will help you make sense of your data. A thematic analysis is something you can use both for deductive and more exploratory interviews.

To analyze your data, follow the steps to analyze your research results to identify themes in your data:

Familiarize yourself with your data . Listen to your recordings and either transcribe or take lots of notes.

Generate initial codes to your data to describe the content . When you encounter a particularly interesting comment or section of the recording, create a descriptor code for it—e.g., “comparing products”. Apply the same code to other comments which you think belong in the same category, and create new codes for aspects that haven’t been discussed before. When you’ve coded all the key sections of your interviews, collate all the interview extracts so they fit into groups under each descriptor code.

Search for patterns or themes in your codes across the different interviews. This is an iterative process where you can move codes around multiple times to form different themes. A good tip is to write your codes on sticky notes to make it easier to move them around and get a better overview.

Review and refine the themes . Read through all the interview extracts in each theme and consider if there is clear coherence inside each one, and also a clear difference between themes. Combine themes you find too similar and split up themes that don’t cohere meaningfully. When you think all your themes work, take a step back and consider if your themes cover what’s most interesting in your data, or if anything is missing. Add and remove themes in an iterative fashion until you’re satisfied your themes suitably represent the insights from your interviews.

Define and name themes . Look at each of your themes. Define what the theme is about and give it an appropriate name.

Produce your report . If you use participant videos, audio or photographs, make sure you seek their consent first.

Here, we have introduced you to all the steps at once… and that might be a bit overwhelming. But don’t worry! Just take one step at a time. The analysis process will become clearer as you progress, thereby affording you a wider scope for making what comes out of your interviews count all the more effectively.

References & Where to Learn More

Learn more about in the course: User Research – Methods and Best Practices

Virginia Braun & Victoria Clarke, Using thematic analysis in psychology, in Qualitative Research in Psychology , Volume 3(2), 2006

Ann Blandford, Dominic Furniss and Stephann Makri , Qualitative HCI Research: Going Behind the Scenes, Morgan & Claypool Publishers, 2016

Hero Image: © Nearsoft Inc, CC BY-NC-SA 2.0

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Thematic Analysis

Thematic analysis is a method used to organize, classify, and discover connections between ideas generated from brainstorming or research by sorting them into categories based on common characteristics and themes.

Thematic Analysis is a qualitative research method that identifies, analyzes, and interprets patterns or themes within data. By coding and categorizing information, researchers uncover insights into underlying meanings, beliefs, and values. Thematic Analysis is used in social sciences, user research, and content analysis, where understanding complex phenomena, human experiences, and cultural dynamics guides theory building, design insights, and interpretive understanding.

Suitable for

• ✓ organizing ideas generated from brainstorming,
• ✓ organizing data from qualitative and quantitative research,
• ✓ discovering connections between individual pieces of information.

Deliverables

Research plan.

A document outlining the objectives, scope, methodology, and timeline for conducting the thematic analysis. It helps to ensure that the research team is aligned and has a clear understanding of the study's goals.

Interview Guide

A list of prepared questions and prompts designed to help the researcher engage participants in a meaningful conversation about the topic at hand. These guides may be adapted or revised as needed throughout the data collection process.

Transcripts

Written or typed records of each interview, focus group, or observation conducted during the study. Transcripts should capture all dialogue and relevant non-verbal cues from participants to provide a comprehensive account of the data for analysis.

A systematic organization of codes, themes, and sub-themes that emerge throughout the data analysis process. Codebooks provide a structured way to manage, categorize, and develop insights from raw data.

Coding Matrix

A visual representation of the codes and themes identified in the data that allows the researcher to see patterns, relationships, and gaps between different concepts. The coding matrix can help establish connections and inform the final thematic structure.

Thematic Map

A visual representation of the main themes and their relationships within the data. Thematic maps help to visualize the structure of the findings and can be used to communicate the results to stakeholders in a clear, accessible format.

Analysis Report

A comprehensive document that details the findings of the thematic analysis, including the main themes, sub-themes, and patterns observed in the data. The report should provide context, interpretation, and implications for the intended audience and may include recommendations for next steps or future research.

Findings Presentation

A presentation that summarizes key findings and insights, visually presents the thematic map, and recommends actionable steps based on the research results. The presentation is tailored to the target audience – whether it be internal teams, stakeholders, or clients – and may include interactive elements to facilitate discussion and collaboration.

Participant Quotes

A compilation of representative quotes from participants that support the themes identified in the analysis. These quotes help to provide evidence, add context, and bring a human voice to the findings, making them more relatable and impactful for stakeholders.

Methodological Reflections

A document reflecting on the research process, challenges encountered, and lessons learned during the study. This can help inform future research projects and improve the overall quality of user experience research within the organization.

1. Data Familiarization

Begin by thoroughly reading and immersing yourself in the data to become familiar with the content. This can include reading transcripts, watching videos, or going through notes multiple times. This step helps in understanding the participants' perspectives, their experiences, and the context surrounding their statements.

2. Generating Initial Codes

Systematically work through the data and generate descriptive codes, labels or tags to define key features or ideas found in the dataset. The codes should be brief, accurate, and energetically descriptive. This process is iterative and should remain flexible as you move through the data, as new insights may require revising or creating new codes.

3. Searching for Themes

Start analyzing the codes and look for potential patterns or relationships that can combine them into larger themes. Themes can be more abstract in nature than codes, capturing concepts or ideas underlying the data. At this stage, you can use visual tools like mind-maps or diagrams to help explore connections and potential thematic structures.

4. Reviewing Themes

Review your identified themes and make sure they accurately represent the data. This includes checking if there are any discrepancies or inconsistencies within the themes, and whether any refinements, merging, or splitting of themes are necessary. Additionally, some themes may be discarded if they do not contribute to the overall understanding of the research topic.

5. Defining and Naming Themes

Further refine and develop a clear definition and name for each theme. This should describe the core essence of what the theme represents and the aspects of the data it covers. A well-defined theme should be able to explain the overall research question and tell the story of the data.

6. Report Writing

Write a detailed report that represents the findings of the thematic analysis. This includes a thorough description of each theme, evidence from the data, and quotes or examples to support your claims. Ensure your report is coherent, logical, and clearly communicates the findings to the reader. Tie your themes back to the research question and provide any relevant insights for your research objectives.

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Thematic Analysis

Thematic analysis is a qualitative research method that can be used to gain insights on UI/UX. It is performed by examining qualitative data (e.g., results from user interviews or feedback forms), identifying patterns within it, categorizing it using codes, and using those codes to determine themes. This results in a clear set of actionable insights to be worked on.

Codes are used to identify common topics of interest, functioning as tags for categorizing data. For example, a thematic analysis of feedback for a messaging app may include codes such as “interface,” “functionality,” and “accessibility,” documenting the comments made about each aspect of the application.

Once the data has been categorized, themes can be determined within the created codes. A theme is a core idea obtained by observing similar concepts within a code, indicating something about user needs or preferences, and providing meaningful insights.

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How to Analyze Qualitative Data from UX Research: Thematic Analysis

Identifying the main themes in data from user studies — such as: interviews, focus groups, diary studies, and field studies — is often done through thematic analysis.

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How to do thematic analysis

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Uncovering themes in data requires a systematic approach. Thematic analysis organizes data so you can easily recognize the context.

• What is thematic analysis?

Thematic analysis is   a method for analyzing qualitative data that involves reading through a data set and looking for patterns to derive themes . The researcher's subjective experience plays a central role in finding meaning within the data.

Streamline your thematic analysis

Find patterns and themes across all your qualitative data when you analyze it in Dovetail

• What are the main approaches to thematic analysis?

Inductive thematic analysis approach

Inductive thematic analysis entails   deriving meaning and identifying themes from data with no preconceptions.  You analyze the data without any expected outcomes.

Deductive thematic analysis approach

In the deductive approach, you analyze data with a set of expected themes. Prior knowledge, research, or existing theory informs this approach.

Semantic thematic analysis approach

With the semantic approach, you ignore the underlying meaning of data. You take identifying themes at face value based on what is written or explicitly stated.

Latent thematic analysis approach

Unlike the semantic approach, the latent approach focuses on underlying meanings in data and looks at the reasons for semantic content. It involves an element of interpretation where you theorize meanings and don’t just take data at face value.

• When should thematic analysis be used?

Thematic analysis is beneficial when you’re working with large bodies of data. It allows you to divide and categorize huge quantities of data in a way that makes it far easier to digest.  

The following scenarios warrant the use of thematic analysis:

You’re new to qualitative analysis

You need to identify patterns in data

You want to involve participants in the process

Thematic analysis is particularly useful when you’re looking for subjective information such as experiences and opinions in surveys , interviews, conversations, or social media posts. 

• What are the advantages and disadvantages of thematic analysis?

Thematic analysis is a highly flexible approach to qualitative data analysis that you can modify to meet the needs of many studies. It enables you to generate new insights and concepts from data. 

Beginner researchers who are just learning how to analyze data will find thematic analysis very accessible. It’s easy for most people to grasp and can be relatively quick to learn.

The flexibility of thematic analysis can also be a disadvantage. It can feel intimidating to decide what’s important to emphasize, as there are many ways to interpret meaning from a data set.

• What is the step-by-step process for thematic analysis?

The basic thematic analysis process requires recognizing codes and themes within a data set. A code is a label assigned to a piece of data that you use to identify and summarize important concepts within a data set. A theme is a pattern that you identify within the data. Relevant steps may vary based on the approach and type of thematic analysis, but these are the general steps you’d take:

1. Familiarize yourself with the data(pre-coding work)

Before you can successfully work with data, you need to understand it. Get a feel for the data to see what general themes pop up. Transcribe audio files and observe any meanings and patterns across the data set. Read through the transcript, and jot down notes about potential codes to create. 

2. Create the initial codes (open code work)

Create a set of initial codes to represent the patterns and meanings in the data. Make a codebook to keep track of the codes. Read through the data again to identify interesting excerpts and apply the appropriate codes. You should use the same code to represent excerpts with the same meaning. 

3. Collate codes with supporting data (clustering of initial code)

Now it's time to group all excerpts associated with a particular code. If you’re doing this manually, cut out codes and put them together. Thematic analysis software will automatically collate them.

4. Group codes into themes (clustering of selective codes)

Once you’ve finalized the codes, you can sort them into potential themes. Themes reflect trends and patterns in data. You can combine some codes to create sub-themes.

5. Review, revise, and finalize the themes (final revision)

Now you’ve decided upon the initial themes, you can review and adjust them as needed. Each theme should be distinct, with enough data to support it. You can merge similar themes and remove those lacking sufficient supportive data. Begin formulating themes into a narrative. 

6. Write the report

The final step of telling the story of a set of data is writing the report. You should fully consider the themes to communicate the validity of your analysis.

A typical thematic analysis report contains the following:

An introduction

A methodology section

Results and findings

A conclusion

Your narrative must be coherent, and it should include vivid quotes that can back up points. It should also include an interpretive analysis and argument for your claims. In addition, consider reporting your findings in a flowchart or tree diagram, which can be independent of or part of your report.  

In conclusion, a thematic analysis is a method of analyzing qualitative data. By following the six steps, you will identify common themes from a large set of texts. This method can help you find rich and useful insights about people’s experiences, behaviors, and nuanced opinions.

• How to analyze qualitative data

Qualitative data analysis is the process of organizing, analyzing, and interpreting non-numerical and subjective data . The goal is to capture themes and patterns, answer questions, and identify the best actions to take based on that data. 

Researchers can use qualitative data to understand people’s thoughts, feelings, and attitudes. For example, qualitative researchers can help business owners draw reliable conclusions about customers’ opinions and discover areas that need improvement. 

In addition to thematic analysis, you can analyze qualitative data using the following:

Content analysis

Content analysis examines and counts the presence of certain words, subjects, and contexts in documents and communication artifacts, such as: 

Text in various formats

This method transforms qualitative input into quantitative data. You can do it manually or with electronic tools that recognize patterns to make connections between concepts.  

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Narrative analysis

Narrative analysis interprets research participants' stories from testimonials, case studies, interviews, and other text or visual data. It provides valuable insights into the complexity of people's feelings, beliefs, and behaviors.

Discourse analysis

In discourse analysis , you analyze the underlying meaning of qualitative data in a particular context, including: 

Historical 

This approach allows us to study how people use language in text, audio, and video to unravel social issues, power dynamics, or inequalities. 

For example, you can look at how people communicate with their coworkers versus their bosses. Discourse analysis goes beyond the literal meaning of words to examine social reality.

Grounded theory analysis

In grounded theory analysis, you develop theories by examining real-world data. The process involves creating hypotheses and theories by systematically collecting and evaluating this data. While this approach is helpful for studying lesser-known phenomena, it might be overwhelming for a novice researcher. 

• Challenges with analyzing qualitative data

While qualitative data can answer questions that quantitative data can't, it still comes with challenges.

If done manually, qualitative data analysis is very time-consuming.

It can be hard to choose a method. 

Avoiding bias is difficult.

Human error affects accuracy and consistency.

To overcome these challenges, you should fine-tune your methods by using the appropriate tools in collaboration with teammates.

Learn more about thematic analysis software

What is thematic analysis in qualitative research.

Thematic analysis is a method of analyzing qualitative data. It is applied to texts, such as interviews or transcripts. The researcher closely examines the data to identify common patterns and themes.

Can thematic analysis be done manually?

You can do thematic analysis manually, but it is very time-consuming without the help of software.

What are the two types of thematic analysis?

The two main types of thematic analysis include codebook thematic analysis and reflexive thematic analysis.

Codebook thematic analysis uses predetermined codes and structured codebooks to analyze from a deductive perspective. You draw codes from a review of the data or an initial analysis to produce the codebooks.

Reflexive thematic analysis is more flexible and does not use a codebook. Researchers can change, remove, and add codes as they work through the data. 

What makes a good thematic analysis?

The goal of thematic analysis is more than simply summarizing data; it's about identifying important themes. Good thematic analysis interprets, makes sense of data, and explains it. It produces trustworthy and insightful findings that are easy to understand and apply. 

What are examples of themes in thematic analysis?

Grouping codes into themes summarize sections of data in a useful way to answer research questions and achieve objectives. A theme identifies an area of data and tells the reader something about it. A good theme can sit alone without requiring descriptive text beneath it.

For example, if you were analyzing data on wildlife, codes might be owls, hawks, and falcons. These codes might fall beneath the theme of birds of prey. If your data were about the latest trends for teenage girls, codes such as mini skirts, leggings, and distressed jeans would fall under fashion.  

Thematic analysis is straightforward and intuitive enough that most people have no trouble applying it.

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A Comprehensive Guide to Thematic Analysis in Qualitative Research

What is Qualitative Data?

What do all the methods above have in common? They result in loads of qualitative data. If you're not new here, you've heard us mention qualitative data many times already. Qualitative data is non-numeric data that is collected in the form of words, images, or sound bites. Qual data is often used to understand people's experiences, perspectives, and motivations, and is often collected and sorted by UX Researchers to better understand the company's users. Qualitative data is subjective and often in response to open-ended questions, and is typically analyzed through methods such as thematic analysis, content analysis, and discourse analysis. In this resource we'll be focusing specifically on how to conduct an effective thematic analysis from scratch! Qualitative data is the sister of quantitative data, which is data that is collected in the form of numbers and can be analyzed using statistical methods. Qualitative and quantitative data are often used together in mixed methods research, which combines both types of data to gain a more comprehensive understanding of a research question.

UX Research Methods

There are many different types of UX research methods that can be used to gather insights about user behavior and attitudes. Some common UX research methods include:

• Interviews: One-on-one conversations with users to gather detailed information about their experiences, needs, and preferences.
• Surveys: Online or paper-based questionnaires that can be used to gather large amounts of data from a broad group of users.
• Focus groups: Group discussions with a moderated discussion to explore user attitudes and behaviors.
• User testing: Observing users as they interact with a product or service to identify problems and gather feedback.
• Ethnographic research: Observing and interacting with users in their natural environments to gain a deep understanding of their behaviors and motivations.
• Card sorting: A technique used to understand how users categorize and organize information.
• Tree testing: A method used to evaluate the effectiveness of a website's navigation structure.
• Heuristic evaluation: A method used to identify usability issues by having experts review a product and identify potential problems.
• Expert review: Gathering feedback from industry experts on a product or service to identify potential issues and areas for improvement.

Introduction to Thematic Analysis of Qualitative Data

Thematic analysis is a popular way of analyzing qualitative data, like transcripts or interview responses, by identifying and analyzing recurring themes (hence the name!). This method often follows a six-step process, which includes getting familiar with the data, sorting and coding the data, generating your various themes, reviewing and editing these themes, defining and naming the themes, and writing up the results to present. This process can help researchers avoid confirmation bias in their analysis. Thematic analysis was developed for psychology research, but it can be used in many different types of research and is especially prevalent in the UX research profession.

When to Use Thematic Analysis

Thematic analysis is a useful method for analyzing qualitative data when you are interested in understanding the underlying themes and patterns in the data. Some situations in which thematic analysis might be appropriate include:

• When you have a large amount of qualitative data, such as transcripts from interviews or focus groups.
• When you want to understand people's experiences, perspectives, or motivations in depth.
• When you want to identify patterns or themes that emerge from the data.
• When you want to explore complex and open-ended research questions.
• When you are interested in understanding how people make sense of their experiences and the world around them.

Some UX research specific questions that could be a good fit for thematic analysis are:

• How do users think about their experiences with a particular product, service or company?
• What are the common challenges that a user might encounter when using a product or service, and how do they overcome them?
• How do users make sense of the navigation of a website or app?
• What are the key drivers of user satisfaction or dissatisfaction with a product or service?
• How do users' experiences with a product or service compare with their expectations?

It is important to keep in mind that thematic analysis is just one of many methods for analyzing qualitative data, and it may not be the most appropriate method for every research question or situation. A key part of a UX researcher's role is being aware of the most appropriate research method to use based on the problem the company is trying to solve and the constraints of the company's research practice.

Types of Thematic Analysis

There are two primary types of thematic analysis, called inductive and deductive approaches. An inductive approach involves going into the study blind, and allowing the results of the data-capture to guide and shape the analysis and theming. Think of it like induction heating-- the data heats your results! (OK, we get it, that was a bad joke. But you won't forget now!) An example of an inductive approach would be parachuting onto a client without knowing much about their website, and discovering the checkout was difficult to use by the amount of people who brought it up. An easy theme! On the flip-side, a deductive approach involves attacking the data with some preconceived notions you expect to find in the qualitative data, based on a theory. For example, if you think your company's website navigation is hard to use because the text is too small, you may find yourself looking for themes like "small text" or "difficult navigation." We don't have a joke for this one, but we tried. To get even more nitty-gritty, there are two additional types of thematic analysis called semantic and latent thematic analysis. These are more advanced, but we'll throw them here for good measure. Semantic thematic analysis involves identifying themes in the data by analyzing the exact wording of the comments made used by participants. Latent thematic analysis involves identifying themes in the data by analyzing the underlying meanings and actions that were taken, but perhaps not necessarily stated by study participants. Both of these methods can be used in user research, though latent analysis is more popular because users often say different things than what they actually do.

Steps in Conducting a Thematic Analysis

Let's jump in! As mentioned before, there are 6 steps to completing a thematic analysis.

Step One: get familiar with your data!

This might seem obvious, but sometimes it's hard to know when to start. This might take the form of listening to the audio interviews or unmoderated studies, or reading the notes taken during a moderated interview. It's important to know the overall ideas of what you're dealing with to effectively theme your study. While you're doing this, pay attention to some big picture themes you can use in step two when you code your data. Break out key ideas from each participant. This might take the form of summarized answers for each question response, or a written review of actions taken for each task given. Just make sure to standardize it across participants.

Step Two: sort & code the data.

Now that you have your standardized notes across your participants, it's time to sort and code the collected qualitative data! Think of the themes from before when you were taking your notes. Think of these codes like metaphorical buckets, and start sorting! Every comment that fits a theme in a box, put it there. Back to our navigation example: some codes could be "small text" or "hard to use." We could put a participant action of "squinting" into the bucket for "small text," or a comment from another mentioning they had trouble finding "tents" in "hard to use."

Step Three: break the codes into themes!

Try to think of each theme as a makeup of three or more codes. For the navigation example, we could put both "small text," and "hard to use" into a theme of "Difficult Navigation."

Step Four: review and name your themes.

Now is the time to clean up the data. Are all your themes relevant to the problem you're trying to solve? Are all the themes coherent and straightforward? Are you comfortable defending your theme choices to teammates? These are all great questions to ask yourself in this stage.

Step Five: Present!!

To have a cohesive presentation of your thematic analysis, you'll need to include an introduction that explains the user problem you were trying to identify and the method you took to study it. Use the terminology from beginning of this resource to identify your research method. Usually for something like this, it will be a user survey or interview. ‍ You also need to include how you analyzed your participant data (inductive, deductive, latent or semantic) to identify your codes and themes. In the meaty section of your presentation, describe each theme and give quotations and user actions from the data to support your points.

Step Six: Insights and Recommendations

Your conclusion should not stop at your presentation of your findings. The best user researchers are valuable for both their insights and recommendations. Since UX researchers spend so much time with participants, they have indispensable knowledge about the best way to do things that make life easy for the company's users. Don't keep this information to yourself! On the final 1-3 slides of your presentation, state the "Next Steps & Recommendations" that you'd like your team and leadership to follow up on. These recommendations could include things like additional qualitative or quantitative studies, UX changes to make or test, or a copy change to make the experience clearer for readers. Your ultimate job is to create the best user experience, and you made it this far-- you got this!

And there you have it! That's everything you need to complete a thematic analysis of qualitative data to identify potential solutions or key concepts for a particular user problem. But don't stop there! We recommend using these principles in the wild to conduct research of your own. Identify a question or potential problem you'd like to analyze on one of your favorite sites. Use a service like Sprig to come up with non-bias questions to ask friends and family to try and gather your own qualitative data. Next, complete and document yourself completing the 6-step analysis process. What do you discover? Be prepared to share on interviews-- hiring managers love to see initiative! Good luck.

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Our Sources: 

Caulfield, J. (2022, November 25). How to Do Thematic Analysis | Step-by-Step Guide & Examples . Scribbr. https://www.scribbr.com/methodology/thematic-analysis/

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Making Sense of User Data: A Guide to Thematic Analysis in UX Research

Thematic analysis explores patterns of meaning within qualitative research data. Thematic analysis is a qualitative research approach that involves the identification of themes in data. Themes are identified through the use of extended codes, which are assigned to research content based on similarities. These thematic codes tend to be more of a phrase than other codes. These themes aim to indicate what a section of data is about and how the researcher is interpreting the data. In UX research, thematic analysis is often used to gain a better understanding of user needs, behaviors, and experiences by analyzing qualitative data such as user interviews or open-ended survey responses.

There is no one way to conduct thematic analysis - different approaches can be used depending on the researcher's preferences and needs. There are several different approaches to thematic analysis, with the two main approaches being categorically and phenomenologically ( Saldana p259 ).

Thematic analysis is often used to explore data that is rich in meaning and context. From Clarke, Braun, and Hayfield , thematic analysis is appropriate for research around: Participants' experiences, participants' perspectives, social factors around specific phenomena, participants' practices, representations of topics and contexts, and social construction of a topic.

The Benefits of a Thematic Analysis

Reflexive approaches to thematic analysis allow for greater flexibility in coding and theme development. Flexibility is a main strength because it can be used with different research approaches. Thematic methods for coding are used by mixed method studies for quantitative data results to compare and relate to themes discovered during qualitative data analysis. Thematic analysis is a good fit to help research teams see related topics across large sets of data.

How to Conduct a Thematic Analysis

Thematic analysis can be conducted with or without a specific method to guide the process. Using a thematic analysis method ensures less subjectivity in coding and pattern identification, which contributes to the trustworthiness of results.

Let's have a look at Brauns' 6 phase approach to reflexive thematic analysis:

• Becoming familiar with the data : Review, and re-read the data to get beyond surface interpretations of the data. Focus on the data and not on researcher's prior conceptions, unless you are using a deductive approach guided by pre-existing theory.
• Generating codes : Explore both overt (semantic) and implicit (latent) meaning in the data and assign codes to later retrieve important data and generate themes. This should be an iterative process, codes can be updated each time the data is reviewed to further refine them.
• Generating initial themes : Review how the codes combine to create overall themes in the research. Themes are going to be more descriptive than codes and are likely in the form of phrases to describe ideas.
• Reviewing themes : It's always good to review your initial themes, you will want to go over the themes to ensure they didn't drift from the data's big picture and that they form coherent patterns. You may find you combine, add, or delete themes. You can compare the themes against the coded data as well as the entire data set.
• Defining and naming themes : This is where you will define and refine your themes for presentation in the final analysis. You can create sub-themes but beware of fragmenting your analysis too much ( Clarke & Braun p84-103 ). Provide a description for each theme.
• Producing the report : Since we are focused on UX research, this would be sharing insights , instead of a "report" since reports are often not the best way to evangelize research results in an organization like a company. These should tie back to your original research questions.

Why Use a Thematic Analysis Approach

Thematic analysis methods are often used by researchers who seek to understand participants' experiences, social factors around specific phenomena, participants' practices, and representations of topics and contexts. It can be used with a variety of research methods, including mixed methods to discover across qualitative and quantitative data. User researchers should consider using thematic analysis for several reasons:

• Provides a deeper understanding of user experiences: Thematic analysis allows researchers to gain insights into the underlying meaning and context of user experiences. This can help researchers to identify patterns, trends, and key themes that may not be apparent through quantitative data alone.
• Helps identify areas for improvement: By analyzing qualitative data using thematic analysis, researchers can identify pain points and areas for improvement in the user experience. This information can be used to inform design decisions and ultimately create a better product or service.
• Complements other research methods: Thematic analysis can be used alongside other research methods such as surveys or usability testing to provide a more complete understanding of user needs and behaviors.
• Enables triangulation of data: Triangulation involves using multiple sources of data to confirm or refute findings from one source. Thematic analysis can be useful in triangulating data because it allows researchers to analyze qualitative data alongside quantitative data, providing a more comprehensive view of the research question.
• Supports iterative design: Thematic analysis helps researchers to identify themes and patterns that can guide iterative design. By analyzing user feedback through thematic analysis, researchers can identify areas for improvement and make changes to the product or service in response.

Overall, thematic analysis is a valuable tool for user researchers because it helps to uncover insights that may not be apparent through other research methods. By using thematic analysis alongside other research methods, researchers can gain a more comprehensive understanding of user needs and behaviors, which can inform design decisions and ultimately create a better user experience.

A High Level Example of Thematic Analysis

Here is an example of how thematic analysis can be used in user research:

Let's say a UX researcher is conducting a study to understand how users feel about a new mobile app that helps people track their fitness goals. As part of the study, the researcher conducts several user interviews and asks participants about their experiences using the app.

After transcribing and reviewing the interview data, the researcher begins to identify common themes that emerge from the interviews. For example, several participants mention feeling overwhelmed by the amount of data presented in the app, while others express frustration with how difficult it is to navigate certain features.

The researcher continues to analyze the data using thematic analysis and identifies several key themes:

• Overwhelming amount of data: Many participants felt that there was too much information presented in the app, making it difficult to focus on their specific fitness goals.
• Confusing navigation: Several participants had difficulty finding certain features within the app or understanding how to use them.
• Motivational aspects: A few participants mentioned that they appreciated certain motivational aspects of the app, such as receiving encouraging push notifications or being able to track progress towards their goals.

Based on these themes, the researcher is able to identify specific areas for improvement in the app's design. For example, the researcher may recommend simplifying the user interface, improving navigation, and adding more motivational features. These recommendations can then be used to guide iterative design and create a better user experience for the app's users.

Overall, this example demonstrates how thematic analysis can be used in user research to uncover insights about user experiences and inform design decisions. By analyzing qualitative data using thematic analysis, researchers can identify patterns and themes that may not be apparent through quantitative data alone.

In conclusion, thematic analysis is a powerful qualitative research method that can be used to uncover patterns and themes within user data. By analyzing qualitative data through thematic analysis, UX researchers can gain a deeper understanding of user experiences and perspectives, identify areas for improvement in product design, and generate insights that may not be apparent through quantitative data alone. Thematic analysis is particularly useful for identifying patterns and trends within open-ended responses from user surveys or interviews. This approach allows researchers to develop a more nuanced understanding of user needs and behaviors, which can ultimately lead to the creation of better products and services that meet the needs of their users.

How to Analyze Qualitative Data from UX Research: Thematic Analysis

Summary:  Identifying the main themes in data from user studies — such as: interviews, focus groups, diary studies, and field studies — is often done through thematic analysis.

Uncovering themes in qualitative data can be daunting and difficult. Summarizing a quantitative study is relatively clear: you scored 25% better than the competition, let’s say. But how do you summarize a collection of qualitative observations?

In the discovery phase , exploratory research is often carried out. This research often produces a lot of qualitative data, which can include:

Qualitative attitudinal data, such as people’s thoughts, beliefs and self-reported needs obtained from...

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Back to methods overview

Thematic analysis, how it works.

Thematic analysis is a systematic method of breaking down and organising data from qualitative research by grouping similar findings and providing them with appropriate names with the goal of identifying themes.

1. Summarise research findings. After conducting research, e.g. through user interviews or usability testing , go through the findings together with the team and ask each team member to note down their top 5 findings.

2. Prepare for documentation. Prepare an easily accessible place to document the analysis, e.g. a Mural board.

3. Go through top 5 findings. Ask each team member to read their top 5 findings out loud one by one and add them to e.g. a Mural board or a wall where all team members can see them.

4. Group similar findings. As each team member presents, ask the others to put similar notes next to the one being presented. This will help save time on grouping.

5. Name the groups. Once all notes are grouped, give them short, descriptive names that focus on the desired outcome. Sometimes there are multiple solutions to reach an outcome. Focusing on the outcome helps us stay open to different solutions.

6. Prioritise the groups , e.g. by running a Prioritisation Canvas workshop. This will help the team understand how the different themes compare in value creation and complexity. We recommend starting with the least complex theme.

7. Ideate on solutions together. Depending on the complexity of the theme and what activity the findings come from, this could be done through a Design Sprint or through a combination of a Design Studio and Usability testing .

• If findings are from different user groups, colour code them. This way you will both be able to identify the themes that matter to your users and which of those themes matter most to your different user groups.
• Save yourself time on documentation by using a digital tool, e.g. Mural, even if you run the workshop on-site. That way you can have lively discussions while still documenting everything in an accessible way right away.
• How to analyse qualitative data from UX Research: Thematic analysis - Nielsen Norman Group (article)
• Thematic analysis of qualitative user research data - Nielsen Norman Group (video)

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The Guide to Thematic Analysis

• What is Thematic Analysis?
• Advantages of Thematic Analysis
• Disadvantages of Thematic Analysis
• Introduction

Thematic analysis example list

Takeaways for qualitative research and data analysis.

• How to Do Thematic Analysis
• Thematic Coding
• Collaborative Thematic Analysis
• Thematic Analysis Software
• Thematic Analysis in Mixed Methods Approach
• Abductive Thematic Analysis
• Deductive Thematic Analysis
• Inductive Thematic Analysis
• Reflexive Thematic Analysis
• Thematic Analysis in Observations
• Thematic Analysis in Surveys
• Thematic Analysis for Interviews
• Thematic Analysis for Focus Groups
• Thematic Analysis for Case Studies
• Thematic Analysis of Secondary Data
• Thematic Analysis Literature Review
• Thematic Analysis vs. Phenomenology
• Thematic vs. Content Analysis
• Thematic Analysis vs. Grounded Theory
• Thematic Analysis vs. Narrative Analysis
• Thematic Analysis vs. Discourse Analysis
• Thematic Analysis vs. Framework Analysis
• Thematic Analysis in Social Work
• Thematic Analysis in Psychology
• Thematic Analysis in Educational Research
• Thematic Analysis in UX Research
• How to Present Thematic Analysis Results
• Increasing Rigor in Thematic Analysis
• Peer Review in Thematic Analysis

Thematic Analysis Examples

Thematic analysis in qualitative research is a widely utilized qualitative research method that provides a systematic approach to identifying, analyzing, and reporting potential themes and patterns within data. Whereas quantitative data often relies on statistical analysis to make judgments about insights, thematic analysis involves researchers conducting qualitative data analysis to interpret various aspects (or themes) of the research topic they are exploring, offering rich, detailed, and complex accounts of the underlying meanings within the data. Thematic analysis is flexible and applicable across a diverse range of disciplines, underscoring its utility in providing insightful interpretations of nuanced datasets.

By focusing on examples of thematic analysis, this article aims to illustrate the practical steps involved in this method and showcase how it can be effectively applied to identify patterns and draw meaningful conclusions from qualitative data . Through this approach, readers will gain an understanding of the systematic nature of thematic analysis and its contribution to a deeper comprehension of research findings.

A typical thematic analysis report conveys researchers' identification of patterns or themes across various domains that answers their research questions . This robust analytical method is particularly valuable in the social sciences, where understanding human behavior, experiences, and societal structures is key. The following sections illustrate how various types of thematic analysis can be applied in different social science fields, each through a hypothetical qualitative research process.

Education: Understanding student motivation

In an educational context, a study was designed to explore the motivational drivers among high school students. Researchers conducted semi-structured interviews with a diverse group of 30 students, probing into their academic experiences, aspirations, and challenges. The thematic analysis of interview transcripts revealed distinct but interconnected themes.

Firstly, 'Teacher Influence' emerged as a critical theme, illustrating how educators' attitudes, feedback, and engagement levels affected student motivation. Positive reinforcement, constructive criticism, and personal attention were highlighted as aspects that fueled students' drive to learn and succeed. Another prominent theme was 'Peer Dynamics,' reflecting the impact of classmates and friends on students' motivation. This theme encompassed both positive influences, such as camaraderie and academic collaboration, and negative aspects like peer pressure and competition.

'Personal Aspirations' was identified as a third theme, indicating how students' goals and perceived future opportunities shaped their current academic engagement. Ambitions related to higher education, career prospects, and personal fulfillment were common motivators. Lastly, 'Learning Environment' emerged, encompassing aspects of the school setting that influenced motivation, including extracurricular activities, school facilities, and the overall educational atmosphere.

Identifying themes such as these underscores the complexity of student motivation, suggesting that multifaceted strategies are needed to enhance educational engagement and achievement.

Healthcare: Patient experiences in chronic disease management

In the healthcare sector, a qualitative study focused on patients with chronic conditions to understand their daily management challenges and support needs. Through interviews and diary entries from patients dealing with diseases like diabetes and hypertension, researchers conducted a thematic analysis to distill the patient experience into core themes.

The 'Healthcare Interaction' theme underscored the importance of patient-provider relationships, highlighting how empathy, communication, and responsiveness from healthcare professionals can significantly impact patient satisfaction and engagement in disease management. Another critical theme was 'Lifestyle Adaptation,' reflecting the ongoing adjustments patients make in diet, exercise, and medication routines. This theme highlighted the emotional and practical challenges of integrating disease management into daily life, as well as the strategies patients employed to cope with these changes.

'Social Support Networks' emerged as a vital theme, illustrating the role of family, friends, and peer support groups in providing emotional encouragement, practical assistance, and motivation. The contrast between strong and lacking support networks provided insights into how social dynamics can influence disease management outcomes. 'Psychological Resilience' was identified as a theme capturing patients' mental and emotional responses to living with a chronic condition. This included coping mechanisms, attitudes toward illness, and the impact on personal identity and life perspective.

These themes offer a comprehensive view of the patient experience in chronic disease management, suggesting areas for improvement in healthcare practices and support systems.

Organizational behavior: Workplace culture and employee satisfaction

A study within the realm of organizational behavior examined how workplace culture influences employee satisfaction and retention. Through thematic analysis of focus group discussions with employees from various sectors, researchers identified key themes that shaped workplace experiences.

The 'Leadership Influence' theme highlighted the critical role of management styles, communication, and decision-making processes in shaping employees' perceptions of their workplace. Leadership approaches that fostered transparency, involvement, and recognition were associated with higher satisfaction levels. 'Work Environment and Resources' was another significant theme, emphasizing the importance of physical workspace, tools, and resources in employee productivity and contentment. Factors such as workspace design, technology access, and resource availability were pivotal.

'Interpersonal Relationships and Team Dynamics' emerged as a theme reflecting the impact of collegial relationships and team cohesion on job satisfaction. Positive interactions, collaborative teamwork, and a supportive atmosphere were key drivers of employee engagement. 'Personal Growth and Development' captured employees' desire for opportunities to learn, advance, and take on new challenges. The availability of training programs, career advancement paths, and feedback mechanisms were crucial to employee fulfillment.

These findings underscore the multifaceted nature of workplace satisfaction, providing valuable insights for organizational development and employee engagement strategies.

Sociology: Social media's role in shaping public opinion

In a sociological study, researchers explored the influence of social media on public opinion regarding environmental issues. Content analysis and narrative analysis of discussions, posts, and comments across various platforms provided a semantic approach for thematic analysis, revealing how online narratives shaped perceptions and discourse. Meanwhile, the resulting themes support discussion for potential applications within social media and other forms of online discourse.

The 'Information Dissemination' theme illustrated the rapid spread of environmental information and misinformation, highlighting the dual role of social media as a tool for awareness and a source of confusion. 'Influencer Impact' emerged as a theme, underscoring the role of prominent figures and opinion leaders in shaping environmental discourse. The credibility and reach of these influencers significantly affected public engagement and perspective.

'Community Engagement' was also identified, showing how online communities mobilized around environmental causes, sharing experiences, organizing actions, and providing mutual support. This theme reflected the potential of social media to foster collective action and advocacy. Lastly, 'Emotional Engagement' captured the affective responses elicited by environmental content, including hope, anger, anxiety, and inspiration. These emotional reactions were pivotal in driving awareness, concern, and action among the public.

Through these themes, the study illustrates the complex dynamics through which social media influences public opinion on critical issues, offering insights into the power of digital platforms in shaping societal discourse.

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While these thematic analysis examples span various fields within the social sciences, they share common methodological threads that highlight best practices in conducting effective thematic analysis. These commonalities can serve as valuable pointers for researchers aiming to employ thematic analysis in their work, regardless of their specific domain of study.

Systematic data engagement : Across all examples, a systematic approach to qualitative analysis is fundamental. Researchers personally engage in data collection and immerse themselves in the resulting data through repeated readings, enabling a deep familiarity to identify themes relevant to the data and research question . This immersion facilitates the initial coding process, where researchers employ data coding to capture data features relevant to the research questions. Researchers should approach their data systematically, ensuring thorough and consistent engagement to capture the depth and breadth of themes.

Iterative theme development : The examples illustrate that identifying themes is an iterative process. Preliminary themes are generated from initial codes that cluster similar data segments. These themes are then reviewed and refined, ensuring they accurately represent the data set. Researchers should be prepared to revisit their data and themes multiple times, refining their themes to ensure they are coherent, distinct, and meaningful. This iterative process is central to the rigor of thematic analysis.

Richness and complexity of themes : Thematic analysis, as demonstrated in these examples, excels in capturing the richness and complexity of qualitative data. Themes are not just surface-level categories; they encapsulate intricate patterns that provide deep insights into the data. Researchers should strive for generating themes that capture the complexity of their data, offering rich, detailed, and nuanced interpretations.

Contextual understanding : Each example underscores the importance of understanding the context from which the data are derived. Context shapes the data and the themes that emerge from them, influencing how researchers interpret and understand the identified patterns. Effective thematic analysis requires researchers to be acutely aware of their data's contextual backdrop, integrating this understanding into their analysis and interpretation.

Transparency and rigor : These examples demonstrate the need for rigor throughout the thematic analysis process. This includes maintaining detailed records of the process of coding data, providing clear definitions for each theme, and offering comprehensive explanations of how themes were derived from the data. Researchers should document their analytic decisions meticulously, ensuring their analysis is transparent and credible.

Triangulation and validation : These hypothetical studies exemplify the value of triangulating thematic analysis findings with other data sources, theories, or methods to enhance credibility. Researchers should consider using additional data sources or analytical methods to validate and enrich their thematic analysis findings, ensuring robust and trustworthy conclusions.

Reflective practice : Finally, these examples highlight the importance of a reflexive thematic analysis. Researchers must continually reflect on their own contexts, assumptions, and perspectives, considering how these might influence their analysis. By engaging in reflective practice, researchers can mitigate potential biases, enhance analytic rigor, and ensure their findings are a credible representation of the data.

These common threads across different thematic analysis examples provide a foundation for conducting robust and insightful thematic analysis. By adhering to these best practices, researchers can leverage thematic analysis to yield meaningful, impactful insights from their qualitative data.

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Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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How to create an affinity map for research

By Crystal Gee and Katie Wofford on August 11, 2022

Affinity maps—sometimes called affinity diagrams—are extremely helpful for any team that has to analyze a lot of research data. But they’re vital for an organization where several cross-functional teams have to look at research findings together. 

At Think Company, we use affinity maps to synthesize our research findings so that anyone on any team can easily see and understand what we’ve uncovered. Then we can all work together to talk about what those findings mean and plan our way forward.

What is an affinity map? 

An affinity map is a visual synthesis tool. You can take large amounts of data gleaned from research and visually organize that information into groups of themes based on commonalities. For example, they can be used when discussing an improvement to the user experience on your website. Everyone places their improvement ideas on the board and then similar ideas are grouped together. Think “better chatbot experience,” “Add a live chat feature,” and “create channels for customer service.”

What are affinity maps used for?

Affinity maps are used to help you quickly and powerfully surface common themes in research findings, and present those themes in a way that almost anyone can interpret. Affinity maps also frequently inform field notes for client updates, streamlining processes and making it easy to communicate what you’ve found so you can decide where to go next.

How to use an affinity map for UX design research

Affinity maps are helpful for many different types of research and analysis—from thematic analysis to assessing qualitative data. But you wouldn’t use it for quantitative research or something like a focus group. (Focus groups tend to have multiple perspectives at one time which allows for thematic conversations to happen at once, which eliminates the need for affinity maps.)

Affinity maps are particularly useful for research with lots of context and taking various experiences into account. Think of research where you’re asking open-ended questions—like in-depth interviews (IDIs). Affinity maps are perfect when you need to synthesize that kind of data. They can also be useful for synthesizing information after an ideation session or workshop.

What should be included in an affinity map?

How to create an affinity diagram.

Once you have the data you want to analyze, how do you start creating an affinity map? There are five key steps:

1. Generate ideas 

Start by generating the ideas you want to gather your findings around. These ideas will help shape your questions and notes.

2. Create notes

Move the process forward by creating notes from your research. This can also be from a team brainstorming session.

3. Look for patterns and themes

Once you have your notes and feedback, put all this information into a spreadsheet or other visual information too l (we often use Miro or FigJam ). This will help you see everything at once and move to the next step.

4. Create and name groups for patterns

Now you can start grouping similar answers and ideas, and begin establishing themes in your findings. Name these groupings by their key identifying factors, but remember that you may need to make slight adjustments as you go.

5. Explore findings from groups

Once all of your data is organized into groups, you can explore your findings and see how each group relates to the others—or doesn’t. You can visualize these connections with grouping, arrows, or other indicators to suggest relationships between themes.

An affinity map example

Let’s get to the good stuff. What does an affinity diagram look like? Here are a few examples:

Here you can see an affinity map from an initial synthesis, where information is clustered around emerging themes and additional questions.

MVP Feedback

After the initial synthesis, you’ll see affinity maps that look like this. MVP feedback pulls out findings specifically related to the product you’re evaluating.

Major themes

This affinity map example builds on the others, summarizing all findings at a high level and making connections between them.

Affinity Mapping for Enhanced Data Analysis 

Here at Think, we heavily focus on research—as a user experience company, our biggest goal is to improve user experience by modernizing digital tools and solutions. We need to understand the user’s actions and pain points to do so. That often means we uncover a lot of data in our research phase. Affinity maps or affinity diagrams are an excellent tool for helping teams analyze a lot of qualitative data. By synthesizing research findings with an affinity map, multiple teams across disciplines can access, analyze, and discuss what the research is saying—and plot a meaningful path forward.

A special shoutout to Kathryn Robbins for her contributions to this blog post and for the creation of our FigJam template .

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Research approach for quantitative vs. qualitative research.

Home » Research Approach for Quantitative vs. Qualitative Research

Research methodologies are crucial in shaping our understanding of phenomena, influencing both academic and practical outcomes. Methodological distinctions between quantitative and qualitative research greatly impact how data is collected, analyzed, and interpreted. Recognizing these differences allows researchers to choose appropriate methods that align with their objectives and target populations.

Quantitative research emphasizes numerical data and statistical analysis, seeking to establish patterns and test hypotheses through measurable variables. In contrast, qualitative research focuses on understanding human experiences and social phenomena through detailed observations and interviews. By grasping the methodological distinctions, researchers can enhance the validity and reliability of their studies, ultimately contributing to deeper insights and informed decision-making.

Quantitative Research: Methodological Distinctions and Approach

Quantitative research is distinguished by its reliance on numerical data and statistical analysis, setting it apart from qualitative methods. Researchers often use structured tools, such as surveys or experiments, to gather quantifiable data. This data can be analyzed using various statistical methods, allowing for the identification of patterns and relationships. Such methodological distinctions are vital in forming clear conclusions based on measurable evidence, contributing to decision-making processes.

In contrast, qualitative research emphasizes understanding human experiences and perspectives through open-ended questions and unstructured approaches. While both methodologies have their strengths, it is essential to recognize the unique contributions of quantitative research. Its focus on quantifiable results helps to ensure objectivity and reliability, providing a solid foundation for further analytical endeavors. Understanding these methodological distinctions enables researchers to select the most appropriate approach for their specific research inquiries.

Data Collection Techniques

Data collection techniques vary significantly between qualitative and quantitative research, reflecting distinct methodological distinctions. In qualitative research, techniques such as interviews, focus groups, and observations enable researchers to gather in-depth insights. These methods allow for open-ended responses, which help in understanding participants' thoughts, behaviors, and experiences.

Conversely, quantitative research relies on structured tools like surveys and experiments, which facilitate the collection of numerical data. This approach aims to quantify variables and ultimately identify relationships, enabling hypothesis testing. By employing both qualitative and quantitative methods, researchers can create a more comprehensive understanding of their study subject. The choice of technique profoundly influences the research outcome, highlighting the importance of selecting the appropriate method based on the research goals.

Statistical Analysis and Interpretation

Statistical analysis and interpretation play pivotal roles in discerning the methodological distinctions between quantitative and qualitative research. Quantitative research relies on statistical methods to process numerical data, enabling researchers to identify patterns and test hypotheses. In contrast, qualitative research emphasizes understanding phenomena through non-numerical data, such as interviews and observations, often requiring thematic or content analysis for interpretation.

The methodological distinctions also dictate the tools employed for analysis. For quantitative approaches, researchers often utilize software for statistical computations and visual representations of data. Qualitative analysis, however, focuses on deriving meaning and insights from textual information, often utilizing coding strategies. Each method’s interpretative framework influences not only how data is collected but also the subsequent conclusions derived, shaping the research output's validity and reliability. This understanding enhances the research's overall impact and informs best practices for conducting robust analyses across different research paradigms.

Qualitative Research: Methodological Distinctions and Approach

Qualitative research focuses on understanding human experiences and the meanings individuals attach to those experiences. Its methodological distinctions set it apart from quantitative approaches, emphasizing depth over breadth. Data collection methods such as interviews, focus groups, and participant observations allow researchers to gather rich narratives that illuminate complex social phenomena. This depth creates a nuanced understanding of participant perspectives, enabling the extraction of themes and patterns inherent in the data.

Moreover, qualitative research prioritizes context and rich descriptions, capturing the variability of human behavior. Unlike quantitative research, which seeks to measure and quantify, qualitative methods emphasize subjective meaning. This approach promotes exploration and discovery, allowing researchers to adapt their inquiries based on emerging findings. Through these methodological distinctions, qualitative research offers valuable insights that inform theory and practice, contributing to a holistic understanding of diverse experiences.

Thematic Analysis and Interpretation

Thematic analysis and interpretation play a crucial role in understanding qualitative data. By identifying patterns and themes, researchers can gain deeper insights into the perspectives and experiences of participants. This process requires careful coding of data, where segments are categorized based on recurring ideas. Methodological distinctions become evident here, as qualitative analysis focuses on context and meaning, contrasting with the more structured approach of quantitative research.

In executing thematic analysis, researchers typically follow several stages. First, they familiarize themselves with the data through thorough reading. Next, they generate initial codes that capture significant features. Following coding, themes are constructed, allowing for interpretation of the results in relation to the research questions. Finally, researchers refine these themes, ensuring they accurately represent the data. Each of these steps underscores the relevance of methodological distinctions in effectively analyzing and interpreting qualitative research.

Conclusion: Synthesizing Methodological Distinctions and Choosing the Right Approach

In conclusion, understanding methodological distinctions between quantitative and qualitative research is essential for effective inquiry. Each approach offers unique insights and caters to different research questions. Quantitative research excels at measuring and analyzing numerical data, establishing patterns and relationships through statistical techniques. Conversely, qualitative research delves into the rich, subjective experiences of individuals, uncovering deeper meanings and nuanced perspectives.

Choosing the right approach hinges on your objectives, context, and the nature of the questions posed. A clear understanding of each methodology's strengths enables researchers to select the most suitable framework. Ultimately, synthesizing these distinctions fosters a more comprehensive understanding of research outcomes and supports informed decision-making in diverse fields.

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• Open access
• Published: 10 August 2024

Mapping biomimicry research to sustainable development goals

• Raghu Raman 1 ,
• Aswathy Sreenivasan 2 ,
• M. Suresh 2 &
• Prema Nedungadi 3  

Scientific Reports volume  14 , Article number:  18613 ( 2024 ) Cite this article

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• Environmental sciences
• Environmental social sciences

This study systematically evaluates biomimicry research within the context of sustainable development goals (SDGs) to discern the interdisciplinary interplay between biomimicry and SDGs. The alignment of biomimicry with key SDGs showcases its interdisciplinary nature and potential to offer solutions across the health, sustainability, and energy sectors. This study identified two primary thematic clusters. The first thematic cluster focused on health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's role in healthcare innovations, sustainable collaboration, and land management. This cluster demonstrates the potential of biomimicry to contribute to medical technologies, emphasizing the need for cross-sectoral partnerships and ecosystem preservation. The second thematic cluster revolves around clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), showcasing nature-inspired solutions for sustainable development challenges, including energy generation and water purification. The prominence of SDG 7 within this cluster indicates that biomimicry significantly contributes to sustainable energy practices. The analysis of thematic clusters further revealed the broad applicability of biomimicry and its role in enhancing sustainable energy access and promoting ecosystem conservation. Emerging research topics, such as metaheuristics, nanogenerators, exosomes, and bioprinting, indicate a dynamic field poised for significant advancements. By mapping the connections between biomimicry and SDGs, this study provides a comprehensive overview of the field's trajectory, emphasizing its importance in advancing global sustainability efforts.

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Introduction.

Biomimicry, which combines 'bio' (life) and 'mimicry' (imitation), uses nature's patterns to solve human problems, aligning with the SDGs by fostering innovations 1 . This discipline studies natural processes to inspire sustainable designs and promote responsible consumption and production 2 . Biomimicry emphasizes sustainability, ideation, and education in reconnecting with nature to achieve the SDGs 3 . Collaboration among designers, technologists, and business experts is vital for translating natural mechanisms into commercial solutions 4 . Biomimetics, which aims for radical innovations by replicating living systems, strives for breakthroughs in economic growth 5 . By promoting systemic change through the emulation of nature's regenerative processes, biomimicry's alignment with the SDGs could enhance sustainability efforts. Merging biomimicry insights with SDGs could exceed sustainability benchmarks.

Integrating biomimicry with sustainable development goals (SDGs) is crucial for addressing global challenges. The SDGs offer a blueprint for global well-being and environmental stewardship by 2030 6 . They aim to protect the environment and foster social and economic development. Biomimicry provides innovative approaches to these objectives, drawing from natural strategies. While SDGs offer clear targets, biomimicry complements these by providing a unique lens for solutions 7 . The investigation of biomimicry in conjunction with the SDGs is based on the understanding that the development of biologically inspired materials, structures, and systems offers a novel and sustainable solution to design problems, particularly in the built environment 8 . By mimicking nature's answers to complicated challenges, biomimicry produces creative, clever, long-lasting, and environmentally responsible ideas.

The SDGs outline a comprehensive sustainability agenda targeting social equity, environmental conservation, and poverty alleviation 9 . The use of biomimicry in research can lead to the development of solutions that mimic natural efficiency 10 , revolutionizing industries with resource-efficient technologies and enhancing sustainability. This synergy could lead to environmentally friendly products, improved energy solutions, and effective waste management systems. Integrating biomimicry into industry and education promotes environmental stewardship and ecological appreciation 11 . Marrying biomimicry research with SDGs has accelerated progress toward sustainable development.

Biomimicry can provide insightful and useful solutions consistent with sustainability ideals by imitating the adaptability and efficiency observed in biological systems 12 . The built environment's use of biomimicry has a greater sustainable impact when circular design features are included 13 . Reusing materials, cutting waste, and designing systems that work with natural cycles are all stressed in a circular design. Combining biomimicry and circular design promotes social inclusion, environmental resilience, resourcefulness, and compassionate governance, all of which lead to peaceful coexistence with the environment. This all-encompassing strategy demonstrates a dedication to tackling the larger social and environmental concerns that the SDGs represent and design challenges 14 . Complementing these studies, Wamane 7 examined the intersection of biomimicry, the environmental, social, and governance (ESG) framework, and circular economy principles, advocating for an economic paradigm shift toward sustainability.

A key aspect of realizing the impact of biomimicry on SDGs is the successful translation and commercialization of biomimicry discoveries. This involves overcoming barriers such as skill gaps, the engineering mindset, commercial acumen, and funding. Insights from the "The State of Nature-Inspired-Innovation in the UK" report provide a comprehensive analysis of these challenges and potential strategies to address them, underscoring the importance of integrating commercial perspectives into biomimicry research.

This research employs bibliometric techniques to assess the integration and coherence within circular economy policy-making, emphasizing the potential for a synergistic relationship between environmental stewardship, economic growth, and social equity to foster a sustainable future.

In addressing the notable gap in comprehensive research concerning the contribution of biomimicry solutions to specific SDGs, this study offers significant insights into the interdisciplinary applications of biomimicry and its potential to advance global sustainability efforts. Our investigation aims to bridge this research gap through a systematic analysis, resulting in the formulation of the following research questions:

RQ1: How does an interdisciplinary analysis of biomimicry research align with and contribute to advancing specific SDGs?

RQ2: What emerging topics within biomimicry research are gaining prominence, and how do they relate to the SDGs?

RQ3 : What are the barriers to the translation and commercialization of biomimicry innovations, and how can these barriers be overcome to enhance their impact on SDGs?

RQ4: Based on the identified gaps in research and the potential for interdisciplinary collaboration, what innovative areas within biomimicry can be further explored to address underrepresented SDGs?

The remainder of this paper is arranged as follows. Section " Literature review " focuses on the literature background of biomimicry, followed by methods (section " Methods ") and results and discussion, including emerging research topics (section " Results and discussion "). Section " Conclusion " concludes with recommendations and limitations.

Literature review

The potential of biomimicry solutions for sustainability has long been recognized, yet there is a notable lack of comprehensive studies that explore how biomimicry can address specific sustainable development goals (SDGs) (Table 1 ). This research aims to fill this gap by investigating relevant themes and building upon the literature in this field.

Biomimicry, with its roots tracing back to approximately 500 BC, began with Greek philosophers who developed classical concepts of beauty and drew inspiration from natural organisms for balanced design 15 . This foundational idea of looking to nature for design principles continued through history, as exemplified by Leonardo Da Vinci's creation of a flying machine inspired by birds in 1482. This early instance of biomimicry influenced subsequent advancements, including the Wright brothers' development of the airplane in 1948 12 , 15 . The term "bionics," coined in 1958 to describe "the science of natural systems or their analogs," evolved into "biomimicry" by 1982. Janine Benyus's 1997 book, “Biomimicry: Innovation Inspired by Nature,” and the founding of the Biomimicry Institute (Biomimicry 16 ) were pivotal, positioning nature as a guide and model for sustainable design. Benyus’s work underscores the potential of biomimicry in tackling contemporary environmental challenges such as climate change and ecosystem degradation 12 , 17 .

In recent years, the call for more targeted research in biomimicry has grown, particularly in terms of architecture and energy use. Meena et al. 18 and Varshabi et al. 19 highlighted the need for biomimicry to address energy efficiency in building design, stressing the potential of nature-inspired solutions to reduce energy consumption and enhance sustainability. This perspective aligns with that of Perricone et al. 20 , who explored the differences between artificial and natural systems, noting that biomimetic designs, which mimic the principles of organism construction, can significantly improve resource utilization and ecosystem restoration. Aggarwal and Verma 21 contributed to this discourse by mapping the evolution and applications of biomimicry through scientometric analysis, revealing the growing significance of nature-inspired optimization methodologies, especially in clustering techniques. Their work suggested that these methodologies not only provide innovative solutions but also reflect a deeper integration of biomimetic principles in technological advancements. Building on this, Pinzón and Austin 22 emphasized the infancy of biomimicry in the context of renewable energy, advocating for more research to explore how nature can inspire new energy solutions. Their work connects with that of Carniel et al. 23 , who introduced a natural language processing (NLP) technique to identify research themes in biomimicry across disciplines, facilitating a holistic understanding of current trends and future directions.

To further illustrate the practical applications of biomimicry, Nasser et al. 24 presented the Harmony Search Algorithm (HSA), a nature-inspired optimization technique. Their bibliometric analysis demonstrated the algorithm's effectiveness in reducing energy and resource consumption, highlighting the practical benefits of biomimicry in technological innovation. Rusu et al. 25 expanded on these themes by documenting significant advancements in soft robotics, showing how biomimicry influences design principles and applications in this rapidly evolving field. Their findings underscore the diverse applications of biomimetic principles, from robotics to building design. Shashwat et al. 26 emphasized the role of bioinspired solutions in enhancing energy efficiency within the built environment, promoting the use of high solar reflectance surfaces that mimic natural materials. This perspective is in line with that of Pires et al. 27 , who evaluated the application of biomimicry in dental restorative materials and identified a need for more clinical studies to realize the full potential of biomimetic innovations in healthcare. Liu et al. 28 explored the application of nature-inspired design principles in software-defined networks, demonstrating how biomimetic algorithms can optimize resource and energy utilization in complex systems. This study builds on the broader narrative of biomimicry's potential to transform various sectors by offering efficient, sustainable solutions. Finally, Hinkelman et al. 29 synthesized these insights by discussing the transdisciplinary applications of ecosystem biomimicry, which supports sustainable development goals by integrating biomimetic principles across engineering and environmental disciplines. This comprehensive approach underscores the transformative potential of biomimicry, suggesting that continued interdisciplinary research and innovation are crucial for addressing global sustainability challenges effectively.

PRISMA framework

This study utilizes the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to structure its analysis, following the established five-step protocol: formulating research questions, defining a search strategy, executing a literature search, screening identified literature, and analyzing the findings (Page et al., 2021). The application of the PRISMA guidelines across various research domains, including the SDGs, is well documented 30 .

To ensure a comprehensive search, we searched the Scopus database, a widely utilized resource for bibliometric studies 31 (Donthu et al. 82 ), which led to the discovery of 46,141 publications from 2013 to 2023. This period marked significant research activity following the introduction of the SDGs at the Rio + 20 summit in 2012. Publications were identified using the following terms in the title and abstract: “ (biomimic* OR biomimetic* OR bioinspired OR bioinsp* OR bionic* OR nature-inspired OR "biologically inspired" OR bioinspiration OR biomimesis OR biognosis).”

During the screening phase, publications lacking complete author details were reviewed, narrowing the field to 46,083 publications for further analysis. The eligibility phase utilized proprietary algorithms to map publications to the 17 SDGs, informed by initiatives such as the University of Auckland (Auckland’s SDG mapping 32 ) and Elsevier's SDG Mapping Initiatives (Elsevier's SDG Mapping 33 ). The selection of the Elsevier SDG Mapping Initiative for this study was based on its seamless integration with Scopus, facilitating the use of predefined search queries for each SDG and employing a machine learning model that has been refined through expert review. This approach has been utilized in various studies to analyze research trends within emerging fields. For example, the exploration of green hydrogen was detailed by Raman et al. 34 , while investigations into Fake News and the Dark Web were conducted by Raman et al. 35 , 36 , 37 and Rama et al. 38 , respectively. These examples demonstrate the efficacy of SDG mapping in elucidating how research outputs align with and contribute to sustainable development goals in these emerging domains. This phase identified 13,287 publications as mapped to SDGs. In the inclusion phase, stringent criteria further filtered the publications to English-language journals and review articles, culminating in 13,271 publications deemed suitable for in-depth analysis. This process ensures a comprehensive and high-quality dataset for the study, reflecting the robust and systematic approach afforded by the PRISMA framework in evaluating literature relevant to SDGs.

Our keyword search strategy, while comprehensive, may capture papers that do not genuinely contribute to the field. To mitigate this, we employed manual verification. After the automated search, the authors conducted a manual review of a subset of the final set of identified papers to assess their relevance and authenticity in the context of biomimicry. The subset was based on 20 highly cited papers from each year. We believe that papers that are frequently cited within the community are more likely to be accurately classified. The authors mainly reviewed the introduction, methodology, and results sections to confirm the relevance and authenticity of the papers. However, we acknowledge that these steps may not fully eliminate the inclusion of irrelevant papers, which could skew the results of our meta-analysis.

SDG framework

The examination of sustainable development goals (SDGs) reveals their interconnected nature, where the achievement of one goal often supports progress in others. Studies by Le Blanc (2015) and Allison et al. (2016) have mapped out the complex web of relationships among the SDGs, identifying both strong and subtle linkages across different objectives. To visualize these connections, we employed a cocitation mapping approach using VOSviewer 39 , which allows us to depict the semantic relationships between SDGs through their cocitation rates in scholarly works. This approach generates a visual map where each SDG is represented as a node, with the node size reflecting the goal's research prominence and the thickness of the lines between nodes indicating the frequency of cocitations among the goals. This visual representation reveals the SDGs as an intricate but unified framework, emphasizing the collaborative nature of global sustainability initiatives.

Topic prominence percentile

The Scopus prominence percentile is a crucial metric indicating the visibility and impact of emerging research topics within the scientific community. High-ranking topics in this percentile are rapidly gaining attention, highlighting emerging trends and areas poised for significant advancements. This tool enables researchers and policymakers to identify and focus on innovative topics, ensuring that their efforts align with the forefront of scientific development 35 , 36 , 37 . Topics above the 99.9th percentile were used in this study.

Results and discussion

Rq1: sdg framework and interdisciplinary research (rq4).

This study evaluates biomimicry research through the framework of SDGs. A cocitation SDG map shows two clusters and provides insights into the interplay between biomimicry themes and SDGs, highlighting the cross-disciplinary nature of this research (Fig.  1 ). The blue box hidden behind the “3 – Good Health and Well-being” and “7 – Affordable and Clean Energy” is “11 – Sustainable cities and Communities”. The blue box hidden behind “15 – Life on Land” is “16 – Peace, Justice and Strong institutions”.

Interdisciplinary SDG network of biomimicry research.

Cluster 1 (Red): Biomimetic innovations for health, partnership, and life on land

This cluster comprises a diverse array of research articles that explore the application of biomimicry across various SDGs 3 (health), 17 (partnership), and 15 (land). The papers in this cluster delve into innovative biomimetic ideas, each contributing uniquely to the intersection of sustainable development and biological inspiration. SDG 3, emphasizing good health and well-being for all, is significantly represented, indicating a global effort to leverage biomimicry for advancements in healthcare, such as new medication delivery systems and medical technologies. Similarly, the frequent citations of SDG 17 underscore the vital role of partnerships in achieving sustainable growth, especially where bioinspired solutions require interdisciplinary collaboration to address complex challenges. Finally, the prominence of 15 SDG citations reflects a commitment to preserving terrestrial ecosystems, where biomimicry is increasingly applied in land management, demonstrating nature's adaptability and resilience as a model for sustainable practices. Table 2 lists the top 5 relevant papers from Cluster 1, further illustrating the multifaceted application of biomimicry in addressing these SDGs.

A unique binary variant of the gray wolf optimization (GWO) technique, designed especially for feature selection in classification tasks, was presented by Emary et al. 40 . GWO is a method inspired by the social hierarchy and hunting behavior of gray wolves to find the best solutions to complex problems. This bioinspired optimization technique was used to optimize SDG15, which also highlights its ecological benefits. The results of the study highlight the effectiveness of binary gray wolf optimization in identifying the feature space for ideal pairings and promoting environmental sustainability and biodiversity. Lin et al. 41 focused on SDG 3 by examining catalytically active nanomaterials as potential candidates for artificial enzymes. While acknowledging the limits of naturally occurring enzymes, this study explores how nanobiotechnology can address problems in the food, pharmaceutical, and agrochemical sectors.

The investigation of enzymatic nanomaterials aligns with health-related objectives, highlighting the potential for major improvements in human health. Parodi et al. 42 used biomimetic leukocyte membranes to functionalize synthetic nanoparticles, extending biomimicry into the biomedical domain. To meet SDG 3, this research presents "leukolike vectors," which are nanoporous silicon particles that can communicate with cells, evade the immune system, and deliver specific payloads. In line with the SDGs about health, this study emphasizes the possible uses of biomimetic structures in cancer detection and treatments. A novel strategy for biological photothermal nanodot-based anticancer therapy utilizing peptide‒porphyrin conjugate self-assembly was presented by Zou et al. 43 . For therapeutic reasons, efficient light-to-heat conversion can be achieved by imitating the structure of biological structures. By providing a unique biomimetic approach to cancer treatment and demonstrating the potential of self-assembling biomaterials in biomedical applications, this research advances SDG 3. Finally, Wang et al. 44 presented Monarch butterfly optimization (MBO), which is a bioinspired algorithm that mimics the migration patterns of monarch butterflies to solve optimization problems effectively. This method presents a novel approach to optimization, mimicking the migration of monarch butterflies, aligning with SDG 9. Comparative analyses highlight MBO's exceptional performance and demonstrate its capacity to address intricate issues about business and innovation, supporting objectives for long-term collaboration and sector expansion.

The publications in Cluster 1 show a wide range of biomimetic developments, from ecological optimization to new optimization techniques and biomedical applications. These varied contributions highlight how biomimicry can advance sustainable development in health, symbiosis, and terrestrial life.

Cluster 2 (green): Nature-inspired solutions for clean water, energy, and infrastructure

Cluster 2, which focuses on the innovative application of biomimicry in sustainable development, represents a range of research that aligns with SDGs 6 (sanitation), 7 (energy), 9 (infrastructure), and 14 (water). This cluster is characterized by studies that draw inspiration from natural processes and structures to offer creative solutions to sustainability-related challenges. The papers in this cluster, detailed in Table 3 , demonstrate how biomimicry can address key global concerns in a varied and compelling manner.

Within this cluster, the high citation counts for SDG 7 underscore the significance of accessible clean energy, a domain where biomimicry contributes innovative energy generation and storage solutions inspired by natural processes. This aligns with the growing emphasis on sustainable energy practices. The prominence of SDG 9 citations further highlights the global focus on innovation and sustainable industry, where biomimicry's role in developing nature-inspired designs is crucial for building robust systems and resilient infrastructure. Furthermore, the substantial citations for SDG 6 reflect a dedicated effort toward ensuring access to clean water and sanitation for all. In this regard, biomimicry principles are being applied in water purification technologies, illustrating how sustainable solutions modeled after natural processes can effectively meet clean water objectives.

The study by Sydney Gladman et al. (2016), which presented the idea of shape-morphing systems inspired by nastic plant motions, is one notable addition to this cluster. This discovery creates new opportunities for tissue engineering, autonomous robotics, and smart textile applications by encoding composite hydrogel designs that exhibit anisotropic swelling behavior. The emphasis of SDG 9 on promoting industry, innovation, and infrastructure aligns with this biomimetic strategy. SDGs 7 and 13 are addressed in the study of Li et al. 45 , which is about engineering heterogeneous semiconductors for solar water splitting. This work contributes to the goals of inexpensive, clean energy and climate action by investigating methods such as band structure engineering and bionic engineering to increase the efficiency of solar water splitting. Li et al. 46 conducted a thorough study highlighting the importance of catalysts for the selective photoreduction of CO2 into solar fuels. This review offers valuable insights into the use of semiconductor catalysts for selective photocatalytic CO2 reduction. Our work advances sustainable energy solutions by investigating biomimetic, metal-based, and metal-free cocatalysts and contributes to SDGs 7 and 13. Wang et al. 47 address the critical problem of water pollution. Creating materials with superlyophilic and superlyophobic qualities offers a creative method for effectively separating water and oil. This contributes to the goals of clean water, industry, innovation, and life below the water. It also correlates with SDGs 6, 9, and 14. Singh et al. 48 also explored the 'green' synthesis of metals and their oxide nanoparticles for environmental remediation, which furthers SDG 9. This review demonstrates the environmentally benign and sustainable features of green synthesis and its potential to lessen the environmental impact of conventional synthesis methods.

Cluster 2 provides nature-inspired solutions for clean water, renewable energy, and sustainable infrastructure, demonstrating the scope and importance of biomimicry. The varied applications discussed in these papers help overcome difficult problems and advance sustainable development in line with several SDGs.

RQ2: Emerging research topics

Temporal evolution of emerging topics.

Figure  2 displays the publication counts for various emerging topics from 2013 to 2022, indicating growth trends over the years. For 'Metaheuristics', there is a notable increase in publications peaking in approximately 2020, suggesting a surge in interest. 'Strain sensor' research steadily increased, reaching its highest publication frequency toward the end of the period, which is indicative of growing relevance in the field. 'Bioprinting' sharply increased over the next decade, subsequently maintaining high interest, which highlights its sustained innovation. In contrast, 'Actuators' showed fluctuating publication counts, with a recent upward trend. 'Cancer' research, while historically a major topic, displayed a spike in publications in approximately 2018, possibly reflecting a breakthrough or increased research funding. 'Myeloperoxidase' has a smaller presence in the literature, with a modest peak in 2019. The number of 'Water '-related publications remains relatively low but shows a slight increase, suggesting a gradual but increasing recognition of its importance. Research on exosomes has significantly advanced, particularly since 2018, signifying a greater area of focus. 'Mechanical' topic publications have moderate fluctuations without a clear trend, indicating steady research interest. 'Micromotors' experienced an initial publication surge, followed by a decline and then a recent resurgence, possibly due to new technological applications. 'Nanogenerators' have shown a dramatic increase in interest, particularly in recent years, while 'Hydrogel' publications have varied, with a recent decline, which may point toward a shift in research focus or maturity of the topic.

Evolution of emerging topics according to publications (y-axis denotes the number of publications; x-axis denotes the year of publication).

Figure  3 presents the distribution of various research topics based on their prominence percentile and total number of publications. Topics above the 99.9th percentile and to the right of the vertical threshold line represent the most emergent and prolific topics of study. Next, we examine the topics within each of the four quadrants, focusing on how each topic has developed over the years in relation to SDGs and the key phrases associated with each topic.

Distribution of research topics based on prominence percentile and total number of publications.

Next, we examine each research topic in four quadrants, assessing their evolution concerning SDGs. We also analyze the keyphrase cloud to identify which keyphrases are most relevant (indicated by their font size) and whether they are growing or not. In the key phrase cloud, green indicates an increasing relevance of the key phrase, grey signifies that its relevance remains constant, and blue represents a declining relevance of the key phrase.

Niche biomimetic applications

These are topics with a lower number of publications and prominence percentiles, indicating specialized or emerging areas of research that are not yet widely recognized or pursued (Quadrant 1—bottom left).

Myeloperoxidase; colorimetric; chromogenic compounds

The inclusion of myeloperoxidase indicates that inflammation and the immune system are the main research topics. The focus on chromogenic and colorimetric molecules suggests a relationship to analytical techniques for identifying biological materials. The evolution of the research is depicted in Fig.  4 a shows an evolving emphasis on various sustainable development goals (SDGs) over time. The research trajectory, initially rooted in SDG 3 (Good Health and Well-being), has progressively branched out to encompass SDG 7 (Affordable and Clean Energy) and SDG 6 (Clean Water and Sanitation), reflecting an expanding scope of inquiry within the forestry sciences. More recently, the focus has transitioned toward SDG 15 (Life on Land), indicating an increased recognition of the interconnectedness between forest ecosystems and broader environmental and sustainability goals. This trend underscores the growing complexity and multidisciplinary nature of forestry research, highlighting the need to address comprehensive ecological concerns along with human well-being and sustainable development.

Evolution of research ( a ) and key phrases ( b ).

The word cloud in Fig.  4 b highlights key phrases such as 'Biocompatible', 'Actuator', and 'Self-healing Hydrogel', reflecting a focus on advanced materials, while terms such as 'Elastic Modulus' and 'Polymeric Networks' suggest an emphasis on the structural properties essential for creating innovative diagnostic and environmental sensing tools. Such developments are pertinent to health monitoring and water purification, resonating with SDG 3 (Good Health and Well-being) and SDG 6 (Clean Water and Sanitation). The prominence of 'Self-healing' and 'Bioinspired' indicates a shift toward materials that emulate natural processes for durability and longevity, supporting sustainable industry practices aligned with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), contributing to the overarching aim of sustainable development.

Next, we analyzed the top 3 cited publications. Catalytically active nanomaterials, or nanozymes, are exciting candidates for artificial enzymes, according to Lin et al. 41 . The authors explore the structural features and biomimetics applications of these enzymes, classifying them as metal-, carbon-, and metal oxide-based nanomaterials. This study emphasizes the benefits of enzymes over natural enzymes, including their high stability, variable catalytic activity, and controlled production. Wang et al. 49 developed biomimetic nanoflowers made from nanozymes to cause intracellular oxidative damage in hypoxic malignancies. Under both normoxic and hypoxic conditions, the nanoflowers demonstrated catalytic efficiency. By overcoming the constraints of existing systems that depend on oxygen availability or external stimuli, this novel technique represents a viable treatment option for malignant neoplasms. Gao et al. 50 investigated the use of a dual inorganic nanozyme-catalyzed cascade reaction as a biomimetic approach for nanocatalytic tumor therapy. This approach produces a high level of therapeutic efficacy by cascading catalytic events inside the tumor microenvironment. This study highlights the potential of inorganic nanozymes for achieving high therapeutic efficacy and outstanding biosafety, which adds to the growing interest in nanocatalytic tumor therapy.

Water; hydrophobicity; aerogels

With an emphasis on hydrophobicity, aerogel use, and water-related features, this topic relates to materials science and indicates interest in cutting-edge materials with unique qualities. From Fig.  5 a, we can see that, initially, the focus was directed toward SDG 6 (Clean Water and Sanitation), which is intrinsically related to the research theme, as biomimetic approaches are leveraged to develop innovative water purification and management solutions. As the research progressed, the scope expanded to intersect with SDG 14 (Life Below Water) and SDG 7 (Affordable and Clean Energy), signifying a broadened impact of biomimetic innovations in marine ecosystem conservation and energy-efficient materials. The gradual involvement with SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action) indicates the interdisciplinary reach of this research, which aims to influence industrial practices and climate change mitigation strategies.

The word cloud in Fig.  5 b reinforces this narrative by showcasing key phrases such as 'Hydrophobic', 'Bioinspired', 'Emulsion', and 'Oil Pollution', which reflect the emphasis on developing materials and technologies that mimic natural water repellency and separation processes. 'Aerogel' and 'polydopamine', along with 'Underwater' and 'Biomimetic Cleaning', suggest a strong focus on creating lightweight, efficient materials capable of self-cleaning and oil spill remediation. These keywords encapsulate the essence of the research theme, demonstrating a clear alignment with the targeted SDGs and the overall aim of sustainable development through biomimicry.

Three highly referenced works that have made substantial contributions to the field of biomimetic materials for oil/water separation are included in the table. The development of superlyophilic and superlyophobic materials for effective oil/water separation was examined by Wang et al. 47 . This review highlights the applications of these materials in separating different oil-and-water combinations by classifying them according to their surface wettability qualities. The excellent efficiency, selectivity, and recyclability of the materials—which present a viable treatment option for industrial oily wastewater and oil spills—are highlighted in the paper. Su et al. 51 explored the evolution of super wettability systems. The studies included superhydrophobicity, superoleophobicity, and undersea counterparts, among other extreme wettabilities. The kinetics, material structures, and wetting conditions related to obtaining superwettability are covered in the article. This demonstrates the wide range of uses for these materials in chemistry and materials science, including self-cleaning fabrics and systems for separating oil and water. Zhang et al. 52 presented a bioinspired multifunctional foam with self-cleaning and oil/water separation capabilities. To construct a polyurethane foam with superhydrophobicity and superoleophobicity, this study used porous biomaterials and superhydrophobic self-cleaning lotus leaves. Foam works well for separating oil from water because of its slight weight and ability to float on water. It also shows exceptional resistance to corrosive liquids. According to the article, multifunctional foams for large-scale oil spill cleaning might be designed using a low-cost fabrication technology that could be widely adopted.

Growing interest in bioinspired healthcare

These topics have a higher prominence percentile but a lower number of publications, suggesting growing interest and importance in the field despite a smaller body of research (Quadrant 2—top left).

Exosomes; extracellular vesicles; MicroRNAs

Exosomes and extracellular vesicles are essential for intercellular communication, and reference to microRNAs implies a focus on genetic regulation. The evolution of this topic reflects an increasing alignment with specific sustainable development goals (SDGs) over the years. The initial research focused on SDG 3 (good health and well-being) has expanded to encompass SDG 9 (industry, innovation, and infrastructure) and SDG 6 (clean water and sanitation), showcasing the multifaceted impact of biomimetic research in healthcare (Fig.  6 a). The research trajectory into SDG 9 and SDG 6 suggests broader application of bioinspired technologies beyond healthcare, potentially influencing sustainable industrial processes and water treatment technologies, respectively.

The word cloud (Fig.  6 b) underscores the central role of 'Extracellular Vesicles' and 'Exosomes' as platforms for 'Targeted Drug Delivery' and 'Nanocarrier' systems, which are key innovations in medical biotechnology. The prominence of terms such as 'Bioinspired', 'Biomimetic', 'Liposome', and 'Gold Nanoparticle' illustrates the inspiration drawn from biological systems for developing advanced materials and delivery mechanisms. These key phrases indicate significant advancements in 'Controlled Drug Delivery Systems', 'Cancer Chemotherapy', and 'Molecular Imaging', which have contributed to improved diagnostics and treatment options, consistent with the objectives of SDG 3.

The work by Jang et al. 53 , which introduced bioinspired exosome-mimetic nanovesicles for improved drug delivery to tumor tissues, is one of the most cited articles. These nanovesicles, which resemble exosomes but have higher creation yields, target cells and slow the growth of tumors in a promising way. Yong et al.'s 54 work presented an effective drug carrier for targeted cancer chemotherapy, focusing on biocompatible tumor cell-exocytosed exosome-biomimetic porous silicon nanoparticles. A paper by Cheng et al. 55 discussed the difficulties in delivering proteins intracellularly. This study suggested a biomimetic nanoparticle platform that uses extracellular vesicle membranes and metal–organic frameworks. These highly cited studies highlight the importance of biomimetic techniques in improving drug delivery systems for improved therapeutic interventions.

Nanogenerators; piezoelectric; energy harvesting

This topic advises concentrating on technology for energy harvesting, especially for those that use piezoelectric materials and nanogenerators. We see a rising focus on medical applications of biomimetics, from diagnostics to energy harvesting mimicking biological systems.

The evolution of this research topic reflects a broader contribution to the SDGs by not only addressing healthcare needs but also by promoting sustainable energy practices and supporting resilient infrastructure through biomimetic innovation (Fig.  7 a). Initially, the emphasis on SDG 3 (Good Health and Well-being) suggested the early application of biomimetic principles in healthcare, particularly in medical devices and diagnostics leveraging piezoelectric effects. Over time, the transition toward SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) indicates an expansion of bioinspired technologies into sustainable energy solutions and industrial applications. Nanogenerators and energy harvesting techniques draw inspiration from biological processes and structures, aiming to optimize energy efficiency and contribute to clean energy initiatives.

The word cloud in Fig.  7 b emphasizes key phrases such as 'Piezoelectric', 'Energy Harvesting', 'Tactile Sensor', 'Triboelectricity', and 'Nanogenerators', highlighting the core technologies that are being developed. These terms, along with 'Bioinspired', 'Wearable Electronic Devices', and 'Energy Conversion Efficiency', illustrate the convergence of natural principles with advanced material science to create innovative solutions for energy generation and sensor technology.

Yang et al.'s 56 study in Advanced Materials presented the first triboelectrification-based bionic membrane sensor. Wearable medical monitoring and biometric authentication systems will find new uses for this sensor since it allows self-powered physiological and behavioral measurements, such as noninvasive human health evaluation, anti-interference throat voice recording, and multimodal biometric authentication. A thorough analysis of the state-of-the-art in piezoelectric energy harvesting was presented by Sezer and Koç 57 . This article addresses the fundamentals, components, and uses of piezoelectric generators, highlighting their development, drawbacks, and prospects. It also predicts a time when piezoelectric technology will power many electronics. The 2021 paper by Zhao et al. 58 examines the use of cellulose-based materials in flexible electronics. This section describes the benefits of these materials and the latest developments in intelligent electronic device creation, including biomimetic electronic skins, optoelectronics, sensors, and optoelectronic devices. This review sheds light on the possible drawbacks and opportunities for wearable technology and bioelectronic systems based on cellulose.

Leading edge of biomimetic sensing and electronics

This quadrant represents topics with both a high number of publications and a prominence percentile, indicating well-established and influential research areas (Quadrant 3—top right).

Strain sensor; flexible electronics; sensor

Figure  8 a highlights the progress of research on bioinspired innovations, particularly in the development of strain sensors and flexible electronics for adaptive sensing technologies. Initially, concentrated on health applications aligned with SDG 3 (Good Health and Well-being), the focus has expanded. The integration of SDG 9 (Industry, Innovation, and Infrastructure) indicates a shift toward industrial applications, while the incorporation of SDG 7 (Affordable and Clean Energy) suggests a commitment to energy-efficient solutions. Additionally, the mention of SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production) reflects the broadening scope to include urban sustainability and eco-friendly manufacturing practices.

Figure  8 b provides insight into the key phrases associated with this research topic, highlighting terms such as 'Bioinspired', 'Self-healing', 'Wearable Electronic Devices', 'Flexible Electronics', and 'Pressure Sensor'. These key phrases speak to the innovative approaches for creating sensors and electronics that are not only inspired by biological systems but also capable of seamlessly integrating human activity and environmental needs. The mention of 'Wearable Sensors' and 'Tactile Sensor' indicates a focus on user interaction and sensitivity, which is crucial for medical applications and smart infrastructure.

The top three articles with the most citations represent the cutting edge of this topic’s study. Chortos et al. 59 investigated how skin characteristics can be replicated for medicinal and prosthetic uses. Kim et al. 60 focused on creating ultrathin silicon nanoribbon sensors for smart prosthetic skin, opening up new possibilities for bionic systems with many sensors. A bioinspired microhairy sensor for ultraconformability on nonflat surfaces was introduced in Pang et al.'s 61 article, which significantly improved signal-to-noise ratios for accurate physiological measurements.

Cancer; photoacoustics; theranostic nanomedicine

Modern technologies such as photoacoustics, theranostic nanomedicine, and cancer research suggest that novel cancer diagnosis and therapy methods are highly needed. Figure  9 a traces the research focus that has evolved across various SDGs over time, commencing with SDG 3 (Good Health and Well-being), which is indicative of the central role of health in biomimetic research. It then extends into SDG 9 (Industry, Innovation, and Infrastructure) and SDG 7 (Affordable and Clean Energy), illustrating the cross-disciplinary applications of biomimetic technologies from healthcare to the energy and industrial sectors.

Figure  9 b provides a snapshot of the prominent keywords within this research theme, featuring terms such as “photodynamic therapy”, “photothermal chemotherapy”, “nanocarrier”, and “controlled drug delivery”. These terms underscore the innovative therapeutic strategies that mimic biological mechanisms for targeted cancer treatment. 'Bioinspired' and 'Biomimetic Synthesis' reflect the approach of deriving design principles from natural systems for the development of advanced materials and medical devices. 'Theranostic nanomedicine' integrates diagnosis and therapy, demonstrating a trend toward personalized and precision medicine.

A study conducted by Yu et al. 62 presented a novel approach for synergistic chemiexcited photodynamic-starvation therapy against metastatic tumors: a biomimetic nanoreactor, or bio-NR. Bio-NRs use hollow mesoporous silica nanoparticles to catalyze the conversion of glucose to hydrogen peroxide for starvation therapy while also producing singlet oxygen for photodynamic therapy. Bio-NR is promising for treating cancer metastasis because its coating on cancer cells improves its biological qualities. Yang et al.'s 63 study focused on a biocompatible Gd-integrated CuS nanotheranostic agent created via a biomimetic approach. This drug has low systemic side effects and good photothermal conversion efficiency, making it suitable for skin cancer therapy. It also performs well in imaging. The ultrasmall copper sulfide nanoparticles generated within ferritin nanocages are described in Wang et al.’s 64 publication. This work highlights the possibility of photoacoustic imaging-guided photothermal therapy with improved therapeutic efficiency and biocompatibility. These highly referenced articles highlight the significance of biomimetic techniques in furthering nanotheranostics and cancer therapy.

Established biomimetic foundations

Here, there are topics with a greater number of publications but a lower prominence percentile, which may imply areas where there has been significant research but that may be waning in influence or undergoing a shift in focus (Quadrant 4—bottom right).

Metaheuristics; Fireflies; Chiroptera

This topic is a fascinating mix of subjects. Using Firefly and Chiroptera in metaheuristic optimization algorithms provides a bioinspired method for resolving challenging issues. The thematic progression of research papers suggests the maturation of biomimetic disciplines that resonate with several SDGs (Fig.  10 a). The shift from initially aligning with SDG 3 (Good Health and Well-being) extends to intersecting with goals such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). This diversification reflects the expansive utility of biomimetic approaches, from health applications to broader environmental and societal challenges.

The top keyphrases, such as 'Swarm Intelligence', 'Global Optimization', 'Cuckoo Search Algorithm', and 'Particle Swarm Optimization', are shown in Fig.  10 b highlights the utilization of nature-inspired algorithms for solving complex optimization problems. These terms, along with the 'Firefly Algorithm' and 'Bat Algorithm', underscore the transition of natural phenomena into computational algorithms that mimic the behavioral patterns of biological organisms, offering robust solutions in various fields, including resource management, logistics, and engineering design.

The three highly referenced metaheuristic publications centered around the “Moth Flame Optimization (MFO),” Salp Swarm Algorithm (SSA),” and Whale Optimization Algorithm (WOA).” The WOA, authored by Mirjalili and Lewis 65 , is a competitive solution for mathematical optimization and structural design issues because it emulates the social behavior of humpback whales. Inspired by the swarming behavior of salps, Mirjalili et al. 66 introduced the SSA and multiobjective SSA. This shows how well they function in optimizing a variety of engineering design difficulties. Finally, Mirjalili 67 suggested the MFO algorithm, which is modeled after the navigational strategy of moths and exhibits competitive performance in resolving benchmark and real-world engineering issues.

Bioprinting; three-dimensional printing; tissue engineering

The emphasis on sophisticated manufacturing methods for biological applications in this field suggests a keen interest in the nexus of biology and technology, especially in tissue engineering. As shown in Fig.  11 a, the topic's evolution encompasses Sustainable Development Goals (SDGs) that have transitioned over the years, including SDG 3 (Good Health and Well-being), which is inherently connected to the advancement of medical technologies and tissue engineering for health applications. This research also touches upon SDG 6 (Clean Water and Sanitation) and SDG 7 (Affordable and Clean Energy), suggesting applications of bioprinting technologies in the environmental sustainability and energy sectors. The progression toward SDG 9 (Industry, Innovation, and Infrastructure) and SDG 15 (Life on Land) reflects a broader impact, where biomimetic principles are applied to foster innovation in industrial processes and contribute to the preservation of terrestrial ecosystems.

Key phrases emerging from the word cloud in Fig.  11 b, such as “Hydrogel”, “Biofabrication”, “Tissue Scaffold”, and “Regenerative Medicine”, highlight the specialized methodologies and materials that are inspired by natural processes and structures. Terms such as 'Three-Dimensional Printing' and 'Bioprinting' underscore the technological advancements in creating complex biological structures, aiming to revolutionize the field of tissue engineering and regenerative medicine.

Three widely referenced papers about advances in 3D printing—particularly in bioprinting, soft matter, and the incorporation of biological tissue with functional electronics—are described next. Truby and Lewis’s 68 review of light- and ink-based 3D printing techniques is ground-breaking. This highlights the technology's capacity to create soft matter with tunable properties and its potential applications in robotics, shape-morphing systems, biologically inspired composites, and soft sensors. Ozbolat, and Hospodiuk 69 provide a thorough analysis of “extrusion-based bioprinting (EBB).” The adaptability of EBB in printing different biologics is discussed in the paper, with a focus on its uses in pharmaceutics, primary research, and clinical contexts. Future directions and challenges in EBB technology are also discussed. Using 3D printing, Mannoor et al. 70 presented a novel method for fusing organic tissue with functioning electronics. In the proof-of-concept, a hydrogel matrix seeded with cells and an interwoven conductive polymer containing silver nanoparticles are 3D printed to create a bionic ear. The improved auditory sensing capabilities of the printed ear show how this novel technology allows biological and nanoelectronic features to work together harmoniously.

RQ3: Translation and commercialization

Biomimicry offers promising solutions for sustainability in commercial industries with environmentally sustainable product innovation and energy savings with reduced resource commitment 71 . However, translating biomimicry innovations from research to commercialization presents challenges, including product validation, regulatory hurdles, and the need for strategic investment, innovative financial models, and interdisciplinary collaboration 71 , 72 , 73 , 74 . Ethical considerations highlight the need for universally applicable ethical guidelines regarding the moral debates surrounding biomimicry, such as motivations for pursuing such approaches and the valuation of nature 75 .

Addressing these barriers requires interdisciplinary collaboration, targeted education, and training programs. Strategic investment in biomimicry research and development is also crucial. Encouraging an engineering mindset that integrates biomimicry principles into conventional practices and developing commercial acumen among researchers is essential for navigating the market landscape 76 . Securing sufficient funding is essential for the development, testing, and scaling of these innovations 76 .

Successful case studies illustrate that the strategic integration of biomimicry enhances corporate sustainability and innovation (Larson & Meier 2017). In biomedical research, biomimetic approaches such as novel scaffolds and artificial skins have made significant strides (Zhang 2012). Architecture benefits through energy-efficient building facades modeled after natural cooling systems (Webb et al. 2017). The textile industry uses biomimicry to create sustainable, high-performance fabrics 77 .

RQ4: Interdisciplinary collaboration

Agricultural innovations (sdgs 1—no poverty and 2—zero hunger).

Environmental degradation, biodiversity loss, poverty, and hunger highlight the need for sustainable agricultural methods to mimic natural ecosystems. This includes computational models for ecological interactions, field experiments for biomimetic techniques, and novel materials inspired by natural soil processes. Research can develop solutions such as artificial photosynthesis for energy capture, polyculture systems mimicking ecosystem diversity, and bioinspired materials for soil regeneration and water retention 28 . These innovations can improve sustainability and energy efficiency in agriculture, addressing poverty and hunger through sustainable farming practices.

Educational models (SDG 4—Quality education)

Integrating sustainability principles and biomimicry into educational curricula at all levels presents opportunities for innovation. Collaborations between educators, environmental scientists, and designers can create immersive learning experiences that promote sustainability. This includes interdisciplinary curricula with biomimicry case studies, digital tools, and simulations for exploring biomimetic designs, and participatory learning approaches for engaging students with natural environments. Designing biomimicry-based educational tools and programs can help students engage in hands-on, project-based learning 10 , fostering a deeper understanding of sustainable living and problem-solving.

Gender-inclusive design (SDG 5—Gender inequality)

Gender biases in design and innovation call for research into biomimetic designs and technologies that facilitate gender equality. This includes participatory design processes involving women as cocreators, studying natural systems for inclusive strategies, and applying biomimetic principles to develop technologies supporting gender equality. Bioinspired technologies can address women's specific needs, enhancing access to education, healthcare, and economic opportunities. Interdisciplinary approaches involving gender studies, engineering, and environmental science can uncover new pathways for inclusive innovation.

Inclusive urban solutions (SDG 11—Sustainable cities and communities)

Rapid urbanization challenges such as housing shortages, environmental degradation, and unsustainable transportation systems require innovative solutions. Methodologies include systems thinking in urban planning, simulation tools for modeling biomimetic solutions, and pilot projects testing bioinspired urban innovations. Research on biomimetic architecture for affordable housing, green infrastructure for climate resilience, and bioinspired transportation systems can offer solutions. Collaborative efforts among architects, urban planners, ecologists, and sociologists are essential 78 .

Peace and justice (SDG 16—Peace, justice and institutions)

Social conflicts and weak institutions necessitate innovative approaches that integrate political science, sociology, and biology. Methods involve case studies, theoretical modeling, and participatory action research to develop strategies for peacebuilding and institutional development.

This research provides a comprehensive exploration of the multifaceted dimensions of biomimicry, SDG alignment, and interdisciplinary topics, demonstrating a clear trajectory of growth and relevance. Interdisciplinary collaboration has emerged as a pivotal strategy for unlocking the full potential of biomimicry in addressing underexplored SDGs.

While answering RQ1, the interdisciplinary analysis underscores the significant alignment of biomimicry research with several SDGs. This reflects the interdisciplinary nature of biomimicry and its ability to generate solutions for societal challenges. The analysis of two thematic clusters revealed the broad applicability of biomimicry across various sustainable development goals (SDGs). The first cluster includes health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's potential in medical technologies, sustainability collaborations, and land management. The second cluster encompasses clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), demonstrating innovative approaches to clean energy generation, sustainable infrastructure, and water purification.

In response to RQ2, this study highlights emerging topics within biomimicry research, such as metaheuristics and nanogenerators, which reflect a dynamic and evolving field that is swiftly gaining attention. These topics, alongside sensors, flexible electronics, and strain sensors, denote evolving research objectives and societal demands, pointing to new areas of study and innovation. This focus on interdisciplinary topics within biomimicry underscores the field’s adaptability and responsiveness to the shifting landscapes of technological and societal challenges.

In addressing RQ3, biomimicry holds potential for sustainable innovation but faces challenges in commercialization. Biomimicry inspires diverse technological and product innovations, driving sustainable advancements (Lurie-Luke 84 ). Overcoming these barriers through strategic investment, training, interdisciplinary collaboration, and ethical guidelines is essential for unlocking their full potential.

For RQ4 , the recommendations are formulated based on underexplored SDGs like 1, 4, 5, and 10 where biomimicry could play a pivotal role.

Future research could apply generative AI models to this dataset to validate the findings and explore additional insights. While our current study did not explore this topic, we see significant potential for this approach. Generative AI models can process extensive datasets and reveal patterns, potentially offering insights into biomimetic research correlations. The interpretation required for context-specific analysis remains challenging for generative AI 36 , 37

Our study provides valuable insights, but some limitations are worth considering. The chosen database might limit the comprehensiveness of the research captured, potentially excluding relevant work from other sources. Additionally, while the combination of cocitation mapping and BERTopic modeling provides a powerful analysis, both methods have inherent limitations. They may oversimplify the complexities of the field or introduce bias during theme interpretation, even with advanced techniques. Furthermore, our use of citations to thematically clustered publications as a proxy for impact inherits the limitations of citation analysis, such as biases toward established ideas and potential misinterpretations 79 , 80 . Another limitation of our study is the potential for missing accurate SDG mappings, as multiple SDG mapping initiatives are available, and our reliance on a single, Scopus-integrated method may not capture all relevant associations. Consequently, this could have resulted in the exclusion of papers that were appropriately aligned with certain SDGs but were not identified by our chosen mapping approach. Given these limitations, this study provides a valuable snapshot for understanding biomimicry research.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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Raman, R., Sreenivasan, A., Suresh, M. et al. Mapping biomimicry research to sustainable development goals. Sci Rep 14 , 18613 (2024). https://doi.org/10.1038/s41598-024-69230-9

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3.1. practical application, 3.2. business benefits, 3.4. customer, 3.5. important business area, 3.6. global trends, 3.7. legal framework, 3.8. data and digitalization, 3.9. health and safety, 3.11. ecology, 3.12. policy, 3.13. cybersecurity, 3.14. strategic advantage, 3.15. business functions, 3.16. ethical aspect, 3.17. commitment of the organization’s authorities, 3.18. other, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Chmielewska-Muciek, D.; Marzec-Braun, P.; Jakubczak, J.; Futa, B. Artificial Intelligence and Developments in the Electric Power Industry—A Thematic Analysis of Corporate Communications. Sustainability 2024 , 16 , 6865. https://doi.org/10.3390/su16166865

Chmielewska-Muciek D, Marzec-Braun P, Jakubczak J, Futa B. Artificial Intelligence and Developments in the Electric Power Industry—A Thematic Analysis of Corporate Communications. Sustainability . 2024; 16(16):6865. https://doi.org/10.3390/su16166865

Chmielewska-Muciek, Dorota, Patrycja Marzec-Braun, Jacek Jakubczak, and Barbara Futa. 2024. "Artificial Intelligence and Developments in the Electric Power Industry—A Thematic Analysis of Corporate Communications" Sustainability 16, no. 16: 6865. https://doi.org/10.3390/su16166865

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