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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Examining Practical, Everyday Theory Use in Design Research

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This paper discusses how theories (as objects) are used in articles published in Design Studies. While theory and theory construction have been given time and attention in the literature, less is known about how researchers put theories to work in their written texts—about “practical, everyday” theory use. In the present paper, we examine 32 articles and synthesize six models of “theory use” based on our examination.

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• General Economics, Econometrics and Finance
• Management of Technology and Innovation

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• everyday theory Social Sciences 100%
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• research planning Social Sciences 44%
• Theory Construction Business & Economics 14%
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AB - This paper discusses how theories (as objects) are used in articles published in Design Studies. While theory and theory construction have been given time and attention in the literature, less is known about how researchers put theories to work in their written texts—about “practical, everyday” theory use. In the present paper, we examine 32 articles and synthesize six models of “theory use” based on our examination.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

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Research Design Considerations

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Editor's Note: The online version of this article contains references and resources for further reading and the authors' professional information.

The Challenge

“I'd really like to do a survey” or “Let's conduct some interviews” might sound like reasonable starting points for a research project. However, it is crucial that researchers examine their philosophical assumptions and those underpinning their research questions before selecting data collection methods. Philosophical assumptions relate to ontology, or the nature of reality, and epistemology, the nature of knowledge. Alignment of the researcher's worldview (ie, ontology and epistemology) with methodology (research approach) and methods (specific data collection, analysis, and interpretation tools) is key to quality research design. This Rip Out will explain philosophical differences between quantitative and qualitative research designs and how they affect definitions of rigorous research.

What Is Known

Worldviews offer different beliefs about what can be known and how it can be known, thereby shaping the types of research questions that are asked, the research approach taken, and ultimately, the data collection and analytic methods used. Ontology refers to the question of “What can we know?” Ontological viewpoints can be placed on a continuum: researchers at one end believe that an observable reality exists independent of our knowledge of it, while at the other end, researchers believe that reality is subjective and constructed, with no universal “truth” to be discovered. 1,2 Epistemology refers to the question of “How can we know?” 3 Epistemological positions also can be placed on a continuum, influenced by the researcher's ontological viewpoint. For example, the positivist worldview is based on belief in an objective reality and a truth to be discovered. Therefore, knowledge is produced through objective measurements and the quantitative relationships between variables. 4 This might include measuring the difference in examination scores between groups of learners who have been exposed to 2 different teaching formats, in order to determine whether a particular teaching format influenced the resulting examination scores.

In contrast, subjectivists (also referred to as constructionists or constructivists ) are at the opposite end of the continuum, and believe there are multiple or situated realities that are constructed in particular social, cultural, institutional, and historical contexts. According to this view, knowledge is created through the exploration of beliefs, perceptions, and experiences of the world, often captured and interpreted through observation, interviews, and focus groups. A researcher with this worldview might be interested in exploring the perceptions of students exposed to the 2 teaching formats, to better understand how learning is experienced in the 2 settings. It is crucial that there is alignment between ontology (what can we know?), epistemology (how can we know it?), methodology (what approach should be used?), and data collection and analysis methods (what specific tools should be used?). 5

Key Differences in Qualitative and Quantitative Approaches

Use of theory.

Quantitative approaches generally test theory, while qualitative approaches either use theory as a lens that shapes the research design or generate new theories inductively from their data. 4

Use of Logic

Quantitative approaches often involve deductive logic, starting off with general arguments of theories and concepts that result in data points. 4 Qualitative approaches often use inductive logic or both inductive and deductive logic, start with the data, and build up to a description, theory, or explanatory model. 4

Purpose of Results

Quantitative approaches attempt to generalize findings; qualitative approaches pay specific attention to particular individuals, groups, contexts, or cultures to provide a deep understanding of a phenomenon in local context. 4

Establishing Rigor

Quantitative researchers must collect evidence of validity and reliability. Some qualitative researchers also aim to establish validity and reliability. They seek to be as objective as possible through techniques, including cross-checking and cross-validating sources during observations. 6 Other qualitative researchers have developed specific frameworks, terminology, and criteria on which qualitative research should be evaluated. 6,7 For example, the use of credibility, transferability, dependability, and confirmability as criteria for rigor seek to establish the accuracy, trustworthiness, and believability of the research, rather than its validity and reliability. 8 Thus, the framework of rigor you choose will depend on your chosen methodology (see “Choosing a Qualitative Research Approach” Rip Out).

View of Objectivity

A goal of quantitative research is to maintain objectivity, in other words, to reduce the influence of the researcher on data collection as much as possible. Some qualitative researchers also attempt to reduce their own influence on the research. However, others suggest that these approaches subscribe to positivistic ideals, which are inappropriate for qualitative research, 6,9,10 as researchers should not seek to eliminate the effects of their influence on the study but to understand them through reflexivity . 11 Reflexivity is an acknowledgement that, to make sense of the social world, a researcher will inevitably draw on his or her own values, norms, and concepts, which prevent a totally objective view of the social world. 12

Sampling Strategies

Quantitative research favors using large, randomly generated samples, especially if the intent of the research is to generalize to other populations. 6 Instead, qualitative research often focuses on participants who are likely to provide rich information about the study questions, known as purposive sampling . 6

How You Can Start TODAY

• Consider how you can best address your research problem and what philosophical assumptions you are making.
• Consider your ontological and epistemological stance by asking yourself: What can I know about the phenomenon of interest? How can I know what I want to know? W hat approach should I use and why? Answers to these questions might be relatively fixed but should be flexible enough to guide methodological choices that best suit different research problems under study. 5
• Select an appropriate sampling strategy. Purposive sampling is often used in qualitative research, with a goal of finding information-rich cases, not to generalize. 6
• Be reflexive: Examine the ways in which your history, education, experiences, and worldviews have affected the research questions you have selected and your data collection methods, analyses, and writing. 13

How You Can Start TODAY—An Example

Let's assume that you want to know about resident learning on a particular clinical rotation. Your initial thought is to use end-of-rotation assessment scores as a way to measure learning. However, these assessments cannot tell you how or why residents are learning. While you cannot know for sure that residents are learning, consider what you can know—resident perceptions of their learning experiences on this rotation.

Next, you consider how to go about collecting this data—you could ask residents about their experiences in interviews or watch them in their natural settings. Since you would like to develop a theory of resident learning in clinical settings, you decide to use grounded theory as a methodology, as you believe asking residents about their experience using in-depth interviews is the best way for you to elicit the information you are seeking. You should also do more research on grounded theory by consulting related resources, and you will discover that grounded theory requires theoretical sampling. 14,15 You also decide to use the end-of-rotation assessment scores to help select your sample.

Since you want to know how and why students learn, you decide to sample extreme cases of students who have performed well and poorly on the end-of-rotation assessments. You think about how your background influences your standpoint about the research question: Were you ever a resident? How did you score on your end-of-rotation assessments? Did you feel this was an accurate representation of your learning? Are you a clinical faculty member now? Did your rotations prepare you well for this role? How does your history shape the way you view the problem? Seek to challenge, elaborate, and refine your assumptions throughout the research.

As you proceed with the interviews, they trigger further questions, and you then decide to conduct interviews with faculty members to get a more complete picture of the process of learning in this particular resident clinical rotation.

What You Can Do LONG TERM

• Familiarize yourself with published guides on conducting and evaluating qualitative research. 5,16–18 There is no one-size-fits-all formula for qualitative research. However, there are techniques for conducting your research in a way that stays true to the traditions of qualitative research.
• Consider the reporting style of your results. For some research approaches, it would be inappropriate to quantify results through frequency or numerical counts. 19 In this case, instead of saying “5 respondents reported X,” you might consider “respondents who reported X described Y.”
• Review the conventions and writing styles of articles published with a methodological approach similar to the one you are considering. If appropriate, consider using a reflexive writing style to demonstrate understanding of your own role in shaping the research. 6

Supplementary Material

An Anthology of Theories and Models of Design pp 1–45 Cite as

Theories and Models of Design: A Summary of Findings

• Amaresh Chakrabarti 3 &
• Lucienne T. M. Blessing 4  
• First Online: 01 January 2014

This chapter introduces the goals of the book and provides a historic overview of theoretical developments in design. The main focus of the chapter is an attempt to answer the three main questions addressed in this book: (1) What is a theory or model of design? What is its purpose: what should it describe, explain or predict? (2) What are the criteria it must satisfy to be considered a design theory or model? (3) How should a theory or model of design be evaluated or validated?. The answers are derived from the contributions of the various authors in this book and from the results of the International Workshop on Models and Theories of Design that gave rise to this book. Taken together, the contributions and the workshop outcomes showcase the rich but varied tapestry of thoughts, concepts and results. At the same time, they highlight the effort still required to establish a sound, generally accepted theoretical and empirical basis for further research into design.

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Appendix A: Summary of Discussions from the International Workshop on Models and Theories of Design

Discussions in the workshop, carried out primarily in three, parallel breakout sessions that continued through the days of the workshop, and culminated in a subsequent, common, final discussion session on the last day, focused on the four questions discussed below. This appendix provides a summary of the outcomes from these discussion sessions, which, we hope, will add to the richness of the knowledge already encapsulated in the individual chapters. As will be seen, while it is far from being conclusive, some major similarities in (lack of) understanding about theories and models, their purposes and criteria, and as to how they should be validated, have already began to emerge, and a number of common directions for further activity in this area have been proposed.

1. What is the distinction between a theory and a model?

Team 1: Rapporteur: John Gero; Scribe: Sonal Keshwani . The team took a broad approach of decomposition, and looked at the elements that constituted a model. A model was taken as a representation (i.e. away in which a language is used to describe something) of some observable phenomena. It had been noted that some phenomena may not be observable, and observation of phenomena may sometimes change the phenomena themselves. It was noted that the point of view of the observer plays an important role in what will be observed and how it will be interpreted: ‘what you come up with is always limited by how you see the world and your output is evaluated by how the world looks at it’. All representations, it was felt, are limited, ideally by the purpose of the representation; hence, all models are also purposively limited. Models have generality and causality. Models project or predict, and can be used to explain. The team defined a theory to be an abstract representation of a generalisation of phenomena; a theory may have axioms that explain how a world behaves. Three views on the distinction between a model and a theory emerged: (i) a theory may be composed of multiple models; (ii) a model may be more concrete and specialised in its context than a theory, which is more abstract and general; (iii) a model may embed explanation of phenomena, while a theory may allow for such explanation. A theory may be represented by different models. There may be theory-driven and phenomena-driven models.

Overall, the team summarised its findings as follows. A model is a representation of some phenomena and relationships among these phenomena. With features that are operationalisable, a model provides some generality with respect to the phenomena, which can be causal, speculative and dynamic, and independent from theory. A theory is an abstract generalisation of phenomena, which can be modelled in multiple ways. Models, but not theories, can change with time. Phenomena are things that have regularity and are directly or indirectly observable, and are interpretable. A representation is an externalisation of a description of phenomena. Any representation leads to a reduction in some aspects of the phenomena and its granularity. What is represented is limited by the purpose or intention of the representation.

Team 2: Rapporteur: Udo Lindemann; Scribe: S Harivardhini, Praveen Uchil . The team distinguished between two types of models: research-based (driven by truth) and practice-based (driven by utility). The team raised the question: should models and theories in design be able to explain only (as in natural sciences) or should they also be useful, since the purpose of design research is to improve knowledge to improve design practice? The team also discussed what constituted goodness of a model, and argued that the goodness of a model depends on understanding of its system boundary, i.e. the context and purpose of the model. The team felt that there is an overlap in meaning between models and theories. A model may simulate a part of the world, but does not necessarily explain it. A model could be a subset of a theory, in that a theory provides explanation at a higher level than a model does.

Team 3: Rapporteur: Lauri Koskela; Scribe: Boris Eisenbart . The team distinguished between two types of models: models of design (i.e. of outcomes of design activity), and models of designing (of design activity). The latter is often used synonymously to theories of design. The team distinguished between a model and theory in the following. A model is an abstraction of reality created for a specific purpose, and the purpose includes representation of a theory; a model is helpful: it may serve multiple purposes and may be applied in multiple ways. A theory, on the other hand, may involve a number of hypotheses, each of which should be possible to be falsified. They recognised that describing something as a theory is sometimes a cultural issue; for instance, in some fields of research, less comprehensive approaches, frameworks etc. are called theories for the only reason that the term ‘theory’ added some kind of value to the proposition. The team recognised that while taxonomies are typically not considered theories in natural sciences, design research should consider theories as a spectrum with various levels of maturity in its context and purpose of use.

Overall, the team felt that a model and a theory have several aspects in common: both models and theories serve a (set of) specific purpose(s) that are useful for researchers and/or practitioners; both are explanatory in character which facilitates prediction and prescription. A goal of theories that is distinct from those of models is to provide an explanation of what design and designing mean within the context of use of the theory.

2. What is a model or a theory expected to describe, explain or predict? What criteria must it satisfy?

Team 1: Rapporteur: John Gero; Scribe: Sonal Keshwani . The purpose of a model is to transform something (e.g. produce an output given an input, which can form a prediction), to explain something. Explanatory power of the model comes from the result produced when using the model. A theory is a set of beliefs that are proposed as a generalisation of some phenomena, which are intended to give an explanation for the phenomena. Models have to be useful; theories have to be falsifiable. A model may help in prediction or exploration. A theory has to be testable/refutable. A model has to be usable in design, if this is a model for design. A theory cannot be evaluated directly, but can be evaluated only after its implementation. Theories contain rules and principles which together form their explanatory framework; this characteristic (i.e. of being constituted of rules and principles) is one of the criteria that a theory should satisfy.

Team 2: Rapporteur: Udo Lindemann; Scribe: S Harivardhini, Praveen Uchil . The team argued that a major distinction in the nature of phenomena dealt with between natural sciences and design research is that, design research focuses on design processes that are unique and operate within incomplete information and uncertainty. It is important to distinguish between different models in terms of their system boundary (i.e. scope of application) and their purpose. The purpose can be truth (in research) or utility (in practice). For a model to be good for truth, it should be true at least with the scope of its application. Goodness criteria for models for utility include: usability, ease of use, how quickly it can be used, system boundary, and limits of the model. Many theories and models are not used well in practice because it is hard for practitioners to understand the terms used in these theories and models. A theory or a model should be able to provide insight. A theory must be falsifiable.

Team 3: Rapporteur: Lauri Koskela; Scribe: Boris Eisenbart . The team felt that theories need to be useful: they can be curiosity-driven where the goal is to understand the nature and characteristics of objects, entities and their relationships, or problem-driven where the goal is to support practitioners and provide utility, or to support education. Understanding is necessary for predicting an outcome, and eventually prescribing how to perform design to achieve an expected outcome. Theories in design may be more probability-driven rather than being strictly causal, given the large number of influences, and may take the form of narratives rather than strict propositions. The team asked for whom theories are to be developed, and felt that these would be primarily for researchers or managers. The team discussed what phenomena a theory should address. While it noticed there may not be a single phenomenon of designing, there might be something fundamental to designing that every designer or design team does or shares, e.g. similar activities, aspects etc. appear across different design projects and disciplines. Overall, it was agreed that there are similarities and differences across designing in different contexts, and a theory of design should explain both similarities and differences across the contexts. It was strongly felt that ‘We do not have a thorough understanding of all the assertions we make about designing. We ought to have theories about how to differentiate between different types of design’.

The team felt that phenomena of designing essentially refer to ‘how design works’; various aspects (e.g. people, process, product, knowledge etc.) play a role in this, and therefore, designing may look very different as these aspects change. There are also many partial activities within designing (e.g. the work of an FEM engineer), i.e. there is ‘designing within designing’, which theories currently do not capture. Design processes are seen as a major aspect, and therefore, need to be comprehensively understood. Since human reasoning is an essential part of the phenomena of design, and since there is a variety of different kinds of reasoning that exist in design (e.g. logical, informal etc.), a theory should account for these differences and their influences.

Overall, the team argued that the criteria which a theory should satisfy is its amenability to validation and testing, where correspondence between what can be concluded from the theory and the phenomena it tries to explain are assessed. Another criterion is that a theory helps prediction which is useful; this can also be in the form of justification in a historical context. Theories are evolutionary rather than stationary. All assumptions underlying a theory should be made explicit, and one should be aware, as a researcher, about the process by which is a theory is developed.

3. How should a theory or model be evaluated or validated?

Team 1: Rapporteur: John Gero; Scribe: Sonal Keshwani . The team felt that all theories have to be falsifiable. The team defined evaluation as assessment of usefulness, and validation as assessment of consistency. It noted that a model that has so far always given correct results can still give incorrect results: theories are never tested to be true, but with more evidence, confidence in the theory grows. A model has to be validated (checked for internal consistencies) followed by evaluation (checked for usefulness). A difference between models and theories is that, ‘hypotheses are derived from theories, while hypotheses are derived from application of models’. A causal model is a network of hypotheses. In evaluating, one has to test each of these hypotheses. To evaluate a theory, one has to operationalise its hypotheses and test these.

Two aspects are critical to pay attention to, when discussing validation: the first is, what should be taken as true and false, and what the process of refutation is whereby truth and falsity should be adjudged. According to this team, validation involves application of the theory or model in design, checking for their internal and external consistencies, and checking them against other, already validated theories or models.

Team 2: Rapporteur: Udo Lindemann; Scribe: S Harivardhini, Praveen Uchil . Validation, the team argues, is about finding the limits of a theory. A major difficulty in validating theories and models of design is that, unlike much of natural sciences, being able to carry out repeatable experiments is hard to impossible. The team proposes that one way of validating a model or theory would be in terms of the level of reliability of the model or theory to achieve its purpose. The team proposed several ways of validation e.g. by comparative studies, by comparing and reducing gaps between research and practice models, by comparing multiple practice based models, or by referring to an existing theory which is already validated.

Team 3: Rapporteur: Lauri Koskela; Scribe: Boris Eisenbart . No design is ever repeatable; however for many areas of natural sciences too. There are various levels of variation across so called repeatable phenomena (e.g. the breaking stress of no two samples of the same material is exactly the same, the effect of the same medicine on no two people is exactly the same, etc.). If the discipline looks into a vast number of design projects in various fields, it might find the phenomena at some level of repeatability (as both material science and medical science already do by taking a statistically large set of samples or subjects). However, two distinct challenges for our discipline are: (i) comparable data in our discipline is currently missing, and (ii) such data is hard to generate. For instance, designers may not be aware of what they do during designing, or may distort certain aspects of their work (e.g. to hide failure, due to miscommunication, post facto rationalisation, forgetting, etc.).

A major issue in validation is that, while some researchers develop theories and others develop empirical results, the two rarely discuss their results with one another to bootstrap their work. A platform to support such discussion is necessary. Another issue is that, many empirical studies are carried out with students only; as a consequence, what can be learnt from these about design in practice is relatively limited. In these studies, and even more so for studies of practice, sample sizes are small due to lack of availability of subjects and constraints on time for detailed analyses. There is a strong need for developing appropriate design research methods to tackle these issues. Another issue is the lack of information of the contexts in which a theory of design is applicable. Given the complexity and variety of designing, it may be too ambitious to develop one theory of design; the community needs to develop many theories, each of which applies in a particular context for a particular purpose. These may then form the basis for developing more comprehensive theories. Another challenge is the difficulty of validating prescriptive theories in practice, e.g. asking practicing designers to change their thinking or process of designing may be hard. Validation need not be done only via practice, but also via teaching, training budding designers into preferred ways of thinking and processes of designing. A possible, new direction for validating theories is theory-driven prediction of new, hitherto non-existing, types of design or design fields.

Overall conclusions about these three questions

Regarding the definition of models and theories, two main points emerged. One is that the term ‘model’ has multiple meanings. In one meaning, models are used as a means to carry out design, e.g. a digital model of the product; we may call these models for design. In the other meaning, models describe, explain or predict how designs and designing are, and how aspects of these are related to various criteria that are of importance to practice, e.g. how designing relate to costs of designs. We may call these models of design.

The second point is that there is considerable overlap between the meanings of models of design and theories of design. A spectrum of meanings emerged, starting from having ‘no distinction in how these terms are currently used in our area’, to one where ‘Theory defines a framework from which multiple models could be derived’. A consensus emerged that there is need to understand ‘theory as a spectrum’, with terms such as taxonomies, models and theories having varying degrees of maturity in context, purpose and explanatory capacity.

The purpose of the need for understanding these terms was also discussed. It was felt that for a practitioner, it made no difference as to what these terms meant. However, for a discipline of research such as design, understanding of these terms is crucial, since this forms the basis for research. Overall, it was agreed that a clear understanding of the terms model and theory in the context of design research is necessary. It is also felt that any proposal for a model or theory should be accompanied with its purpose and context.

A strong consensus arrived at across the teams is in the criteria to be considered a theory: theories should be testable and refutable (i.e. falsifiable), and this should be possible to be carried out within the context and purpose of the theories, i.e. where it applies, and how well.

Validation was seen to be testing the limits of a theory or a model. Validation, too, emerged to have a spectrum of meanings, from testing for internal consistency, to truth and usefulness, in terms of providing explanation or insight in the form of predictions or post-dictions.

Several challenges to validation were identified: difficulty or lack of repeatability of phenomena, the large number of factors blurring clear and identifiably strong influences, difficulty of finding statistically large number of appropriate subjects or cases, and difficulty of generating reliable data about the phenomena under investigation.

4. What are Gaps in our Current Understanding and What are the Directions for Further Research?

Several directions emerged.

One major issue identified in the discussions is the general lack of a common understanding that can act as the underlying basis for the discipline. One symptom or a possible cause of this lack is the poor citing of each other’s work in the discipline. A need for an overview, or even consolidation, of research carried out so far was strongly emphasised. As a discipline, we need good ‘demarcating theories’ that provide a clearer understanding of what constitutes (and what does not constitute) part of the phenomena of designing (e.g. designing is demarcated by intentionality), the different types of designs and designing that form our discipline; and position the models and theories with respect to these.

This base, it was suggested, might be initiated by including these:

The philosophies of the discipline, including what design means, and what the ‘phenomena of designing constitute’. ‘We need a philosophy of design, like a philosophy of science’.

A list of ‘demarcating theories’ that provide an understanding of the different types of designs and designing that form our discipline.

A list of terms that are used within the discipline, including theory and model, along with their contexts and purpose.

A list of research methodologies and methods within the discipline, along with their contexts and purpose.

A list of empirical results, along with their context and purpose.

A list of models and theories of design, along with their context and purpose.

A list of influences of results of design research on practice.

Another major point was the need to clarify the common purpose of design research, and identify what the pressing, concrete questions are that the discipline needs to address. Also emphasised was the need for investigating the specific characteristics, benefits and complementarities across the various theories and models, rather than discussing only about which one among these.

A further major point was the challenge of validating theories of models of phenomena of design, which pointed to the need to develop research methods that are appropriate for scientific studies within the constraints and expectations of design research: how to develop and validate testable, refutable theories and models of adequate accuracy within the constraints of complexity of the phenomena observed and within the low availability of appropriate cases and subjects?

Towards addressing the above directions, several suggestions were made:

Have more discussion events at various levels, e.g. students, researchers, educators, etc., to discuss these issues. Getting together is the first step to ‘form the discipline’. Developers of theories and empirical results should interact more with one another.

Like in other disciplines, teach the common understanding to those (intending to be) in this discipline. This knowledge should be taught in a context-specific manner, i.e. ‘make explicit what is applicable in which specific situation’.

Interact with other disciplines with similar goals, such as management, and learn from their perspectives.

Carry out more empirical studies that are unbiased, of high value, high-quality, and are clearly explained, as we still do not understand in sufficient depth why design processes happen the way they do.

Have ‘grand debates’ where specific models are discussed and contrasted together.

Work more on developing research methods that are appropriate for serving the specific needs of design research. A starting point can be to propose Special Interest Groups (SIG) to work on these, e.g. on research methodology.

Appendix B: Major Theories and Models not Contained in this Book

This appendix provides a summary of some of the major theories not contained in this book, but are necessary to point to for the sake of completeness. The summaries are not meant to be comprehensive, but only as a pointer to more detailed sources.

General Design Theory (GDT) was proposed by Yoshikawa [ 86 ] and later expanded by Tomiyama and Yoshikawa [ 79 ]. It is one of the first design theories at the knowledge level—a concept originally proposed by Newell [ 56 ] in the context of computational theories. GDT describes design as a transformation between two spaces—function and attribute, and discusses the nature of this transformation in relation to availability of complete and incomplete knowledge.

Axiomatic Design Theory was proposed by Suh and colleagues [ 74 , 75 ]. It describes design as a transformation between functions and parameters, and argues that good designs can be described by two axioms: axiom of independence and axiom of information content. According to Axiomatic Design Theory, the less coupled the functions are in a design and the less information content the design has, the better it is.

Another Knowledge Level theory— \( {\text{K}}^{\text{L}} {\text{D}}^{\text{E}}_{0} \) —was proposed by Smithers [ 69 , 70 ]. This theory was tested by the author on design of a new font that the author himself designed. \( {\text{K}}^{\text{L}} {\text{D}}^{\text{E}}_{0} \) distinguishes six types of knowledge needed in design: 1. knowledge needed to form requirements , knowledge of the requirements descriptions actually developed, and their associated justifications; 2. knowledge of how to develop well - formed problem descriptions and knowledge of the well-formed problem descriptions developed and their justifications; knowledge needed to solve well - formed problems , and the knowledge of the solutions and justifications actually formed; 4. knowledge needed to analyse and evaluate problem solutions, knowledge of the analyses and evaluations actually performed together with their justifications; 5. knowledge needed to form design descriptions , and the knowledge of the actual design descriptions and justifications; 6. knowledge needed to construct design presentations , and the knowledge of the presentations actually formed and their justifications.

A quest for a Universal Design Theory (UDT) was made by Grabowski et al. [ 37 , 53 ]. UDT is attempted to be a design theory containing findings and knowledge about design from different engineering disciplines in a consistent, coherent and compact form [ 52 ]. It is aimed at serving as a scientific basis for rationalizing interdisciplinary product development. The aim of UDT is to provide models of explanation and prediction of artefacts and away of designing them. The theory takes the ‘process of design as the mapping of a set of requirements onto a set of design parameters’ that constitute a design solution. The process is proposed to be carried out in by transition through four linked, abstraction levels: modelling requirements, modelling functions, modelling effective geometry, and embodiment design. A design solution is a specification of information sets associated with levels of functions, effective geometry, and embodiment. UDT proposes three axioms: the first states that there is a finite number of levels of abstraction; the second axiom states that the ‘the set of well-known basic elements on each level of abstraction is finite at a certain point of time’; the third axiom states that ‘the number of transitions between the different levels of abstraction is also finite’. Based on these axioms, the authors considered that ‘Elements of a design theory…can only include the components currently known to us whereas the invention of new effects etc. has to be the concern of research work’. In line with this, they hypothesised the following: ‘The invention of a product is always a new combination of known basic elements’, and that ‘Discovery, achieved through research, is defined as the finding of new basic elements’. In this sense, the scope the universal design theory is limited to those types of design where new designs can be seen only as a combination of old basic elements.

Based on the methodological framework used for the development of Grabowski’s universal design theory [ 52 ], Lossack [ 50 , 51 ] proposes the foundations of a Domain Independent Design Theory. The theory describes design knowledge, design process knowledge and system theoretical approaches for processing this knowledge system. The underlying concept consists of three elements: object patterns, process patterns and design working-spaces. Lossack emphasises that ‘design is not a workflow […] workflows represent processes in a deterministic manner, whereas design is intrinsically indeterministic’. He therefore proposes an approach based on solution patterns to support indeterministic design processes, which include solution finding processes and creativity. A solution pattern is an aggregation of an object and a process pattern, although an object pattern can be used without process patterns. Object and process patterns describe design knowledge with which a mapping between properties of the design stages is defined. To define the design context, design working-spaces are introduced [ 36 ]. A design working space is a system (with elements, relationships and boundaries) which builds a framework to support the solution finding processes with object and process patterns. The approach is regarded to be general enough to support designing in mechanical, electrical and software engineering.

The theory of synthesis by Takeda et al. [ 76 ] focuses on the properties that the synthesis process should have as a thought process and propose a theory for synthesis. Knowledge for synthesis in design, they argue, ‘needs physicality, unlikeness, and desirability’. Physicality ensures possibility of existence, while unlikeness and desirability ensure newness and value. The theory is based on the assumptions that a design process is an iterative logical process of abduction and deduction on design solutions, their properties and behaviours, and knowledge of objects. The synthesis theory for design is defined as a process of reconstruction of design experiences, where each experience contains a logical design process having three steps: ‘collecting design experiences, building a model that includes the collected design experiences, and minimizing an element that designers want to find newness’.

Infused design [ 66 ] is an approach for ‘establishing effective collaboration between designers from different engineering fields’. Infused design provides representation of the design problem at a mathematical meta-level that is common to all engineering disciplines. The problem solving is carried out by using mathematical terminology and tools that, due to generality, are common across design disciplines. The meta-level proposed consists of general discrete mathematical models termed combinatorial representations (CR). In particular, Infused design demonstrates ‘how methods and solutions could be generated systematically from corresponding methods and solutions in other disciplines’, and ‘guarantees the correctness of results by relying on general ontology of systems that is embedded in the different representations’. Taura and Nagai [ 78 ], in their systematised theory of creative concept generation in design, proposed a theory on the thinking process at the ‘very early stage of design’, they define as the phase that ‘includes the time just prior to or the precise beginning of the so-called conceptual design’. They segregate concept generation into two phases—the problem-driven phase and the inner sense-driven phase. They found that the concept generation process could be categorised into two types: first-order concept generation, which is related to the problem-driven phase, and high-order concept generation, which is related to the inner sense-driven phase.

Appendix C: Overview of Theories, Models and Key Concepts Proposed by the Authors

As discussed in Sect. 1.3.5 some authors have proposed ontologies for the development of their theories and models, others have defined their main concepts but not yet put these together into an ontology. In this section, we summarise the proposed theories or models and the related key concepts. What is immediately visible is the differences in concepts used, as well as the difference in their number. Some overlap in key concepts exists. As expected, this is the case where a theory or model has been built on other theories and models. The differences suggest that the phenomenon of design is (as yet) too large, or maybe its boundaries not fixed enough, to be treated as a whole, as also suggested by Eckert and Stacey [ 28 ], Chap. 19 .

Agogué and Kazakçi [ 1 ], Chap. 11 : Concept-Knowledge-theory of C–K theory, a theory of creative design reasoning.

Key concepts: K-space, C-space, logical status, properties, restrictive and expensive partitions, co-evolution of C- and K-spaces through operators (conjunction, disjunction, expansion by partition/inclusion, expansion by deduction/experiments), d-ontologies, generic expansion, object revision, preservation of meaning, K-reordering.

Albers and Wintergerst [ 2 ], Chap. 8 : Contact and Channel (C&C) Model and Approach to integrate functions and physical structure of a product in a shared representation using product models that are widely spread in practice.

Key concepts: Channel and support structures, working surface pairs, connectors, Wirk-Net, Wirk-structure, operation mode, input parameter characteristic, environmental conditions system state property.

Andreasen et al. [ 6 ], Chap. 9 : Domain Theory as a systems approach for the analysis and synthesis of products.

Key concepts: Activity, organ, part, structure, elements, behaviour and function, state, property, characteristic, technical activity, need, operands, effects, surroundings, use function, wirk function transformation.

Badke-Schaub and Eris [ 7 ], Chap. 17 : Understanding the role intuitive processes play in the thinking and acting of designers, to inform their Human Behaviour in Design (HBiD) framework which aims to understand the complex interplay between the designer, the design process, design output, and the related patterns and networks of influencing variable.

Key concepts: Intuition (physical, emotional, mental and spiritual), un/sub-consciousness, reasoning.

Cavallucci [ 21 ], Chap. 12 : Inventive Design Method based on and an extension of TRIZ theory, to rapidly arrive at a reasonable number of inventive solution concepts to evolve a complex initial situation that is currently unsatisfactory.

Key concepts: Contradiction (administrative, technical, physical), problem, partial solution, action parameter, evaluation parameter.

Culley [ 24 ], Chap. 18 : An information-driven, rather than task-driven, design process to manage and control design activity.

Key concepts: ‘Information as thing’, knowledge (embedded, encoded, encultured, embrained, embodied).

Eckert and Stacey [ 28 ], Chap. 19 : Identifying the causal drivers of design behaviour as a first step to generate partial theories of design.

Key concepts: Constraints (problem, process, solutions and meeting constraints), causal drivers (characteristics of classes of products or processes, conditions in which they are created), and requirements.

Eder [ 29 ], Chap. 10 : Theory of Technical Systems and an engineering design methodology based on this theory.

Key concepts: Transformation process (operands and related states, effects, operators, technology, assisting inputs, secondary inputs and secondary outputs, active and reactive environment) and Technical System (function, organ, organ connector, constructional parts and their relationships: functional structure, constructional structure), life cycle of a technical system (a sequence of transformation systems), properties of transformation processes and technical systems (observable, mediating, elemental) and their related states.

Gero and Kannengiesser [ 33 ], Chap. 13 : The Function-behaviour-structure (FBS) ontology to describe all designed things, irrespective of design domain, the FBS and the situated FBS (sFBS) frameworks to represent the process of designing, and its situatedness, respectively, irrespectively of the specific domain or methods used.

Key concepts: Function, behaviour (expected, derived from structure), situatedness (interactions between external, expected and interpreted world), interaction (interpretation, focussing, action), function, requirements, structure, design description, transformation (formulation, synthesis, analysis, evaluation, documentation, reformulation types 1–3), comparison.

Goel and Helms [ 34 ], Chap. 20 : A knowledge model of design problems called SR.BID, derived from the Structure-Behaviour-Function knowledge model, and grounded in empirical data about biologically inspired design practice to capture problem descriptions more deeply than with the SBF knowledge model.

Key concepts: Function, performance criteria, solution, deficiencies/benefits, constraints/specification, and operating environment, structure, behaviour and function.

Goldschmidt [ 35 ], Chap. 21 : A model of the role of sketching in the early, search phase of design.

Key concepts: Problem, search space, internal and external representations, rapid sketch, cognitive benefits and affordances (time effective/fluent, minimal cognitive resources, minimally rule-bound, transformable/reversible, tolerant to incompletion, tolerant to inaccuracy/lack of scale, provides unexpected cues).

Koskelaet al. [ 46 ], Chap. 14 : The first theory—proto-theory—of design proposed by Aristotle based on the claim that design is similar or analogous to geometric analysis.

Key concepts: Analysis (theoretical and problematical), synthesis, deliberation, science of production, causes (efficient, formal, material and final), types of reasoning (regressive, transformational, decompositional or configurational).

Lindemann [ 49 ], Chap. 6 : Definition and nature of the variety of models used for design, discussion on quality and requirements for modelling based on important characteristics like transformation and reduction, purpose and subject, and nature of the process of modelling.

Key concepts: Transformation, reduction, pragmatism (purpose, users, time frame), modelling conventions (accuracy, clearness, profitability, relevance, comparability, systematic settings), process of modelling (intention, modelling, validation, usage).

Maier et al. [ 54 ], Chap. 7 : A cybernetic systems perspective to understand designing as a self-regulated modelling system, i.e. to consider the synthetic role of models in designing.

Key concepts: Sensoring, actuating.

Ranjan et al. [ 59 ], Chap. 15 : Integrated Model of Designing’ (IMoD) for describing task clarification and conceptual design, and for explaining how various characteristics of these stages relate to one another, by combining different views (or models).

Key concepts: Activity view (generate, evaluate, modify, select), outcome view (phenomenon, state change, effect, input, action, organ, part, other), requirement-solution view (requirement, solution, associated-information), and system-environment view (relationships, elements, subsystem, system and environment).

Sonalkar et al. ([ 72 ], Chap. 3 ): Two-dimensional structure for design theory: describing the theoretical constructs and relationships between them, and providing the perceptual field and action repertoire that makes a theory relevant in situations of professional practice.

Key concepts: Perceptual field, action repertoire, event, relationship,

Taura [ 77 ], Chap. 4 : A framework composed of the Pre-Design, Design, and Post-Design stages is introduced to allow the explicit capture of the motive of design , as an underlying reason for the design of highly advanced products, that links the Post-Design and Pre-Design stages.

Key concepts: Pre-Design, Design, Post-Design, deductive, inductive and abductive processes, personal/social motive, inner/outer motive, need, problem, personal inner sense, inner criteria, function (visible/latent), force of a product, standard, field (physical/scenic/semantic; visible/latent).

Weber [ 85 ], Chap. 16 : The CPM/PDD approach to modelling products and product development based on characteristics and properties (CPM: Characteristics-Properties Modelling, PDD: Property-Driven Development).

Key concepts: Characteristics, properties (current, desired), relations, external conditions, analysis, synthesis, solution elements/patterns.

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Chakrabarti, A., Blessing, L.T.M. (2014). Theories and Models of Design: A Summary of Findings. In: Chakrabarti, A., Blessing, L. (eds) An Anthology of Theories and Models of Design. Springer, London. https://doi.org/10.1007/978-1-4471-6338-1_1

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Self-determination theory has shaped our understanding of what optimizes worker motivation by providing insights into how work context influences basic psychological needs for competence, autonomy and relatedness. As technological innovations change the nature of work, self-determination theory can provide insight into how the resulting uncertainty and interdependence might influence worker motivation, performance and well-being. In this Review, we summarize what self-determination theory has brought to the domain of work and how it is helping researchers and practitioners to shape the future of work. We consider how the experiences of job candidates are influenced by the new technologies used to assess and select them, and how self-determination theory can help to improve candidate attitudes and performance during selection assessments. We also discuss how technology transforms the design of work and its impact on worker motivation. We then describe three cases where technology is affecting work design and examine how this might influence needs satisfaction and motivation: remote work, virtual teamwork and algorithmic management. An understanding of how future work is likely to influence the satisfaction of the psychological needs of workers and how future work can be designed to satisfy such needs is of the utmost importance to worker performance and well-being.

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Eva Ariño-Mateo, Matías Arriagada Venegas, … David Pérez-Jorge

Introduction

The nature of work is changing as technology enables new forms of automation and communication across many industries. Although the image of human-like robots replacing human jobs is vivid, it does not reflect the typical ways people will engage with automation and how technology will change job requirements in the future. A more relevant picture is one in which people interact over dispersed networks using continuously improving communication platforms mediated by artificial intelligence (AI). Examples include the acceleration of remote working arrangements caused by the COVID-19 pandemic and the increased use of remote control operations across many industries including mining, manufacturing, transport, education and health.

Historically, automation has replaced more routine physically demanding, dangerous or repetitive work in industries such as manufacturing, with little impact on professional and managerial occupations 1 . However, since the mid-2010s, automation has replaced many repetitive error-prone administrative tasks such as processing legal documents, directing service queries and employee selection screening 2 , 3 . Thus, work requirements for employees are increasingly encompassing tasks that cannot be readily automated, such as interpersonal negotiations and service innovations 4 : in other words, work that cannot be easily achieved through algorithms.

The role of motivation is often overlooked when designing and implementing technology in the workplace, even though technological changes can have a major impact on people’s motivation. Self-determination theory offers a useful multidimensional conceptualization of motivation that can help predict these impacts. According to self-determination theory 5 , 6 , three psychological needs must be fulfilled to adequately motivate workers and ensure that they perform optimally and experience well-being. Specifically, people need to feel that they are effective and masters of their environment (need for competence), that they are agents of their own behaviour as opposed to a ‘pawn’ of external pressures (need for autonomy), and that they experience meaningful connections with other people (need for relatedness) 5 , 7 . Meta-analytic evidence shows that satisfying these three needs is associated with better performance, reduced burnout, more organizational commitment and reduced turnover intentions 8 .

Self-determination theory also distinguishes between different types of motivation that workers might experience: intrinsic motivation (doing something for its own sake, out of interest and enjoyment), extrinsic motivation (doing something for an instrumental reason) and amotivation (lacking any reason to engage in an activity). Extrinsic motivation is subdivided according to the degree to which external influences are internalized (absorbed and transformed into internal tools to regulate activity engagement) 5 , 9 . According to meta-analytic evidence, more self-determined (that is, intrinsic or more internalized) motivation is more positively associated with key attitudinal and performance outcomes, such as job satisfaction, organizational commitment, job performance and proactivity than more controlled motivation (that is, extrinsic or less internalized) 10 . Consequently, researchers advocate the development and promotion of self-determined motivation across various life domains, including work 11 . Satisfaction of the three psychological needs described above is significantly related to more self-determined motivation 8 .

Given the impact of the needs proposed in self-determination theory on work motivation and consequently work outcomes (Fig.  1 ), it is important to find ways to satisfy these needs and avoid undermining them in the workplace. Organizational research has consequently focused on managerial and leadership behaviours that support or thwart these needs and promote different types of work motivation 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 (Fig.  2 ). There is also substantial research on the effects of work design (the nature and organization of people’s work tasks within a job or role, such as who makes what decisions, the extent to which people’s tasks are varied, or whether people work alone or in a team structure) and compensation systems on need satisfaction and work motivation 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , and how individuals can seek to meet their needs and enhance their motivation through proactive efforts to craft their jobs 38 , 39 , 40 .

According to self-determination theory, satisfaction of three psychological needs (competence, autonomy and relatedness) influences work motivation, which influences outcomes. More intrinsic and internalized motivations are associated with more positive outcomes than extrinsic and less internalized motivations. These needs and motivations might be influenced by the increased uncertainty and interdependence that characterize the future of work.

Summary of research findings 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 and available meta-analyses 8 , 10 . In cases where the evidence is mixed, a negative sign indicates a negative correlation, a positive sign indicates a positive correlation, and a zero indicates no statistically significant correlation.

Importantly, the work tasks that people are more likely to do in future work will require high-level cognitive and emotional skills that are more likely to be developed, used, and sustained when underpinned by self-determined motivation 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 . Therefore, if individuals are to be effective in future work, it is important to understand how future work might meet — or fail to meet — the psychological needs proposed by self-determination theory.

In this Review, we outline how work is changing and explain the consequences of these changes for satisfying workers’ psychological needs. We then focus on two areas where technology is already changing the worker experience: when workers apply for jobs and go through selection processes; and when the design of their work — what work they do, as well as how, when and where they do it — is transformed by technology. In particular, we focus on three domains where technology is already changing work design: remote work, virtual teams and algorithmic management. We conclude by discussing the importance of satisfying the psychological needs of workers when designing and implementing technologies in the workplace.

Future work requirements

The future workplace might evolve into one where psychological needs are better fulfilled, or one where they are neglected. In addition, there is growing concern that future work will meet the needs of people with adequate access to technology and the skills to use it, but will further diminish fulfillment for neglected and disadvantaged groups 51 (Box  1 ). To understand how future work might align with human needs, it is necessary to map key work features to core constructs of self-determination theory. Future work might be characterized by environmental uncertainty interdependence, complexity, volatility and ambiguity 52 . Here we focus on uncertainty and interdependence because these features capture core concerns about the future and its implication for connections among people in the changing context of work 53 . Higher levels of uncertainty require more adaptive behaviours, whereas higher levels of interdependence require more social, team-oriented and network-oriented behaviours 54 .

We first consider the increasing role of uncertainty in the workplace. Rapid changes in technology and global supply chains mean that the environment is more unpredictable and that there is increasing uncertainty about what activities are needed to be successful. Reducing uncertainty is central to most theories of human adaptation 55 and is a strong motivational basis for goals and behaviour 56 . If uncertainty becomes a defining and pervasive feature of organizational life, organizational leaders should think beyond reducing uncertainty and instead leverage and even create it 55 . In other words, in a highly dynamic context, it might be more functional and adaptive for employees and organizational leaders to consider more explorative approaches to coping with uncertainty, such as experimentation and improvization. All of these considerations imply that future effective work will require adaptive behaviours such as modifying the way work is done, and proactive behaviours such as innovating and creating new ways of working 54 .

Under higher levels of uncertainty, specific actions are difficult to define in advance. In contrast to action sequences that can be codified (for example, with algorithms) and repeated in predictable environments, the best action sequence is likely to involve flexibility and experimentation when the workplace is more uncertain. In this context, individuals must be motivated to explore new ideas, adjust their behaviour and engage with ongoing change. In stable and predictable environments, less self-determined forms of motivation might be sufficient to maintain the enactment of repetitive tasks and automation is more feasible as a replacement or support. However, under conditions of uncertainty, individuals will benefit from showing cognitive flexibility, creativity and proactivity, all behaviours that are more likely to emerge when people have self-determined motivation 40 , 41 , 44 , 46 , 47 , 48 , 49 , 57 .

Adaptive (coping with and responding to change) and proactive (initiating change) performance can be promoted by satisfying the needs for competence, autonomy and relatedness, and self-determined motivation 4 , 58 . For example, when individuals experience internalized motivation, they have a ‘reason to’ engage in the sometimes psychologically risky behaviour of proactivity 40 . Both adaptivity and proactivity depend on individuals having sufficient autonomy to work differently, try new ideas and negotiate multiple pathways to success. Hence, successful organizational functioning depends on people who can act autonomously to regulate their behaviour in response to a more unpredictable and changing environment 31 , 54 , 59 .

The second feature of the evolving workplace is an increasing level of interdependence among people, systems and technology. People will connect with each other in more numerous and complex ways as communication technologies become more reliable, deeply networked and faster. For example, medical teams from disparate locations might collaborate more easily in real time to support remote surgical procedures. They will also connect with automated entities such as cobots (robots that interact with humans) and decision-making aids supported by constantly updating algorithms. For example, algorithms might provide medical teams with predictive information about patient progress based on streaming data such as heart rate. As algorithms evolve in complexity and predictive accuracy, they will modify the work context and humans will need to adapt to work with the new information created 60 .

This interconnected and evolving future workplace requires individuals who can interact effectively across complex networks. The nature of different communication technologies can both increase and decrease feelings of relatedness depending on the extent to which they promote meaningful interactions. Typically, work technologies are developed to facilitate productivity and efficiency. However, given that human performance is also influenced by feelings of relatedness 8 , it is important to ensure that communication technologies and the way networks of people are managed by these technologies can fulfill this need.

The rapid growth of networks enabled by communication technologies (for example, Microsoft Teams, Slack and Webex) has produced positive and negative effects on performance and well-being. For example, these technologies can be a buffer against loneliness for remote workers or homeworkers 61 and enable stronger connections among distributed workers 62 . However, networking platforms lead some individuals to experience more isolation rather than more connectedness 63 . Workplace networks might also engender these contrasting effects by, for example, building a stronger understanding between individuals in a work group who do not usually get to interact or by limiting contact to more superficial communication that prevents individuals from building stronger relationships.

Both uncertainty and interdependence will challenge people’s feelings of competence. Uncertainty can lead to reduced access to predictable resources and less certainty about the success of work effort; the proliferation of networks and media can lead to feeling overwhelmed and to difficulties in managing communication and relationships. Moreover, technologies and automation can lead to the loss of human competencies as people stop using these skills 64 , 65 , 66 , 67 . For example, automating tasks that require humans to have basic financial skills diminishes opportunities for humans to develop expertise in financial skills.

Uncertainty and interdependence are likely to persist and increase in the future. This has implications for whether and how psychological needs will be satisfied or frustrated. In addition, because uncertainty and interdependence require people to behave in more adaptive and proactive ways, it is important to create future work that satisfies psychological needs.

Box 1 Inequalities caused by future work

Future work is likely to exacerbate inequalities. First, the digital divide (unequal access to, and ability to use, information communication technologies) 51 is likely to be exacerbated by technological advances that might become more costly and require more specialized skills. Moreover, the COVID-19 pandemic exacerbated work inequalities by providing better opportunities to those with digital access and skills 210 , 211 . The digital divide now also includes ‘algorithm awareness’ (knowing what algorithms do) which influences whether and how people are influenced by technology. Indeed, the degree to which algorithms influence attitudes and behaviours is negatively associated with the degree to which people are aware of algorithms and understand how they work 212 .

Second, future work is likely to require new technical and communication skills, as well as adaptive and proactive skills. Thus, people with such skills are more likely to find work than those who do not or who have fewer opportunities (for example, education access) to develop them. Even gig work requires that workers have access to relevant platforms and adequate skills for using them. These future work issues are therefore likely to increase gaps between skilled and non-skilled segments of the population, and consequently to increase societal pay disparities and poverty.

For example, workforce inequalities between mature and younger workers are likely to increase owing to real or perceived differences in technology-related skills, with increased disparities in the type of jobs these workers engage in 210 , 213 . Older workers might miss out on opportunities to upskill or might choose to leave the workforce early rather than face reskilling. This could decrease workforce diversity and strengthen negative stereotypes about mature workers (such as that they are not flexible, adaptable or motivated to keep up with changing times) 214 . Furthermore, inequalities in terms of pay have already been observed between men and women 215 . Increased robotization increases the gender pay gap 216 , and this gap is likely to be exacerbated as remote working becomes more common (as was shown during the pandemic) 217 . For example, one study found that salaries did not increase as much for women working flexibly compared to men 218 ; another study found that home workers tended to be employees with young children and these workers were 50% less likely to be promoted than those based in the office 140 .

To promote equality in future work and ensure that psychological needs are met, managers will need to adopt ‘meta-strategies’ to promote inclusivity (ensuring that all employees feel included in the workplace and are treated fairly, regardless of whether they are working remotely or not), individualization of work (ensuring that work is tailored to individual needs and desires) and employee integration (promoting interaction between employees of all ages, nationalities and backgrounds) 213 .

The future of employee selection

Changing economies are increasing demand for highly skilled labour, meaning that employers are forced to compete heavily for talent 68 . Meanwhile, technological developments, largely delivered online, have radically increased the reach, scalability and variety of selection methods available to employers 69 . Technology-based assessments also afford candidates the autonomy to interact with prospective employers at times and locations of their choosing 70 , 71 . Furthermore, video-based, virtual, gamified and AI-based assessment technologies 3 , 72 , 73 , 74 have improved the fidelity and immersion of the selection process. The fidelity of a selection assessment represents the extent to which it can reproduce the physical and psychological aspects of the work situation that the assessment is intended to simulate 75 . Virtual environments and video-based assessments can better reproduce working environments than traditional ‘paper and pencil’ assessments, and AI is being used to simulate social interactions in work or similar contexts 74 . Immersion represents how engrossing or absorbing an assessment experience is. Immersion is enhanced by richer media and gamified assessment elements 75 , 76 . These benefits have driven the widespread adoption of technology in recruitment practices 77 , but they have also attracted criticism. For example, the use of AI to analyse candidate data (such as CVs, social media profiles, text-based responses to interview questions, and videos) 78 raises concerns about the relevance of data being collected for selecting employees, transparency in how the data are used, and biases in selection based on these data 79 .

Candidates with a poor understanding of what data are being collected and how they are being used might experience a technology-based selection process as autonomy-thwarting. For example, the perceived job-relatedness of an assessment is associated with whether or not candidates view the assessment positively 69 , 80 . However, with today’s technology, assessments that appear typical or basic (such as a test or short recorded interview response) might also involve the collection of additional ‘trace’ data such as mouse movements and clicks (in the case of tests), or ancillary information such as ‘micro-expressions’ or candidates’ video backdrops 81 . We expect that it would be difficult for candidates to evaluate the job-relatedness of this information, unless provided with a rationale. Candidates may also feel increasing pressure to submit to employers’ requests to share personal information, such as social media profiles, which may further frustrate autonomy to the extent that candidates are reluctant to share this information 82 .

Furthermore, if candidates do not understand how technology-driven assessments work and are not able to receive feedback from assessment systems, their need for competence may be thwarted 83 . For example, initial research shows that people perceive fewer opportunities to demonstrate their strengths and capabilities in interviews they know will be evaluated by AI, compared to those evaluated by humans 83 .

Finally, because candidates are increasingly interacting with systems, rather than people, their opportunities to build relatedness with employers might be stifled. A notable exemplar is the use of asynchronous video interviews 70 , 71 , a type of video-based assessment where candidates log into an online system, are presented with a series of questions, and are asked to video-record their responses. Unlike a traditional or videoconference interview, candidates completing an asynchronous video interview do not interact directly with anyone from the employer organization, and they consequently often describe the experience as impersonal 84 . Absent any interventions, the use of asynchronous video interviews removes the opportunity for candidates to meet the employer and get a feel for what it might be like to work for the employer, or to ask questions of their own 84 .

Because technologies have changed rapidly, research on candidates’ reactions to these new selection methods has not kept up 69 . Nonetheless, to the extent that test-related and technology-related anxiety influences motivation and performance when completing an online assessment or a video interview, the performance of applicants might be adversely affected 85 . Furthermore, candidate experience can influence decisions to accept a job offer and how positively the candidate will talk about the organization to other potential candidates and even clients, thereby influencing brand reputation 86 . Thus, technology developments offer clear opportunities to improve the satisfaction of candidates’ needs and to assess them in richer environments that more closely resemble work settings. However, there are risks that technology that is needs-thwarting or is implemented in a needs-thwarting manner, will add to the uncertainty already inherent in competitive job applications. In the context of a globally competitive skills market, employers risk losing high-quality candidates.

The future of work design

Discussion in the popular press about the impact of AI and other forms of digitalization focuses on eradicating large numbers of jobs and mass unemployment. However, the reality is that tasks within jobs are being influenced by digitalization rather than whole jobs being replaced 87 . Most occupations in most industries have at least some tasks that could be replaced by AI, yet currently there is no occupation in which all tasks could be replaced 88 . The consequence of this observation is that people will need to increasingly interact with machines as part of their jobs. This raises work design questions, such as how people and machines should share tasks, and the consequences of different choices in this respect.

Work design theory is intimately connected to self-determination theory, with early scholars arguing that work arrangements should create jobs in which employees can satisfy their core psychological needs 89 . Core aspects of work design, including decision-making power, the opportunity to use skills and do a variety of tasks, the ability to ascertain the impact of one’s work, performance feedback 90 , social contact, time pressure, emotional demands and role conflict 91 are important predictors of job satisfaction, job performance 92 and work motivation 93 . Some evidence suggests that these motivating characteristics (considered ‘job resources’ according to the jobs demands–resources model) 94 are especially important for fostering motivation or reducing strain when job demands (aspects of a job that require sustained physical, emotional or mental effort) are high 93 , 95 . For example, autonomy and social support can reduce the effect of workload on negative outcomes such as exhaustion 96 .

Technology can potentially influence work design and therefore employee motivation in positive ways 1 . Increasing workers’ task variety and opportunities for more complex problem-solving should occur whenever technology takes over tasks (such as assembly line or mining work). Leaving the less routine and more interesting tasks for people to do 97 increases the opportunity for workers to fulfill their need for competence. For example, within manufacturing, complex production systems in which cyber-machines are connected in a factory-wide information network require strategic human decision-makers operating in complex, varied and high-level autonomy jobs 98 . Technology (such as social media) can also enhance social contact and support in some jobs and under some circumstances 86 , 87 (but see ref. 63 ), increasing opportunities for meeting relatedness needs.

However, new technologies can also undermine the design of motivating work, and thus reduce workers’ need satisfaction 1 . For example, in the aviation industry, manual flying skills can become degraded due to a lack of opportunity to practice when aircraft are highly automated 99 , decreasing the opportunity for pilots to meet their need for competence. As another example, technology has enabled the introduction of ‘microwork’ in which jobs are broken down into small tasks that are then carried out via information communication technologies 100 . Such jobs often lack variety, skill use and meaning 101 , again reducing the opportunity for the work to meet competence needs. In an analysis of robots in surgery, technology designed purely for ‘efficiency’ reduced the opportunities for trainee surgeons to engage in challenging tasks and resulted in impaired skill development 102 , and therefore probably reduced competence need satisfaction. Thus, poor work design might negatively influence work motivation through poor need satisfaction, especially the need for competence, owing to the lack of opportunity to maintain one’s skills or gain new ones 2 .

As the above examples show, the impact of new technologies on work design, and hence on need satisfaction, is powerful — but also mixed. That is, digital technologies can increase or decrease motivational work characteristics and can thereby influence need satisfaction (Fig.  3 ). The research shows that there is no deterministic relationship between technology and work design; instead, the effect of new technology on work design, and hence on motivation, depends on various moderating factors 1 . These moderating factors include individual aspects, such as the level of skill an individual has or the individual’s personality. Highly skilled individuals or those with proactive personalities might actively shape the technology and/or craft their work design to better meet their needs and increase their motivation 1 . For example, tech-savvy Uber drivers subject to algorithmic management sometimes resist or game the system, such as by cancelling rides to avoid negative ratings from passengers 103 .

The causal relationships among the possible (but not exhaustive) variables implicated in the influence of technology on work design and work motivation discussed in this Review.

More generally, individuals proactively seek a better fit with their job through behaviours such as idiosyncratic deals (non-standard work arrangements negotiated between an employee and an employer) and job crafting (changing one’s work design to align one’s job with personal needs, goals and skills) 39 , 40 (Box  2 ). Consequently, although there is relatively little research on proactivity in work redesign through technology, it is important to recognize that individuals will not necessarily be passive in the face of negative technologies. Just as time pressure can stimulate proactivity 104 , we should expect that technology that creates poor work design will motivate job crafting and other proactive behaviours from workers seeking to meet their psychological needs better 105 . This perspective fits with a broader approach to technology that emphasizes human agency 106 .

Importantly, mitigating and managing the impact of technology on work is not the sole responsibility of individuals. Organizational implementation factors (for example, whether technology is selected, designed and implemented in a participatory way or how much training is given to support the introduction of technology) and technological design factors (for example, how much worker control is built into automated systems) are also fundamental in shaping the effect of technology on work design. Understanding these moderating factors is important because they provide potential ‘levers’ for creating more motivating work while still capitalizing on the advantages of technologies. For example, in one case study 107 , several new digital technologies such as cobots and digital paper flow (systems that integrate and automate different organizational functions, such as sales and purchasing with accounting, inventory control and dispatch) were implemented following a strong technocentric approach (that is, highly focused on engineering solutions) with little worker participation, and with limited attention to creating motivating work design. A more human-centred approach could have prevented the considerable negative outcomes that followed (including friction, reduced morale, loss of motivation, errors and impaired performance) 107 . Ultimately, how technology is designed and implemented should be proactively adapted to better meet human competencies, needs and values.

Box 2 The future of careers

Employment stability started to decline during the 1980s with the rise of public ownership and international trade, the increased use of performance-based incentives and contracts, and the introduction of new technologies. Employment stability is expected to continue to decline with the growth of gig work and continued technological developments 219 , 220 . Indeed, people will more frequently be asked to change career paths as work is transformed by technology, to use and ‘sell’ their transferrable skills in creative ways, and to reskill. The rise of more precarious work and new employment relationships (for example, in gig work) adds to these career challenges 221 . The current generation of workers is likely to experience career shocks (disruptive events that trigger a sensemaking process regarding one’s career) caused by rapid technological changes, and indeed many workers have already experienced career shocks from the pandemic 222 . Moreover, rapid technological change and increasing uncertainty pushes organizations to hire for skill sets rather than fitting people into set jobs, requiring people to be aware of their skills and to know how to market them.

In short, the careers of the current and future workforce will be non-linear and will require people to be more adaptive and proactive in crafting their career. For this reason, the concept of a protean career, whereby people have an adaptive and self-directed career, is likely to be increasingly important 223 . A protean career is a career that is guided by a search for self-fulfillment and is characterized by frequent learning cycles that push an individual into constant transformation; a successful protean career therefore requires a combination of adaptivity skills and identity awareness 224 , 225 . Adaptivity allows people to forge their career by using, or even creating, emerging opportunities. Having a solid sense of self helps individuals to make choices according to personal strengths and values. However, a protean career orientation might fit only a small segment of the labour market. Change-averse individuals might regard protean careers as career-destructive and the identity changes associated with a protean career might be regarded as stressful. In addition, overly frequent transitions might limit deep learning opportunities and achievements, and disrupt important support networks 221 .

Nonetheless, career-related adaptive and proactive behaviours can be encouraged by satisfying psychological needs. In fact, protean careers tend to flourish in environments that provide autonomy and allow for proactivity, with support for competence and learning 223 , 226 . Moreover, people have greater self-awareness when they feel autonomous. Indeed, self-awareness is a component of authenticity and mindfulness, both of which are linked to the satisfaction of the need for autonomy 227 , 228 . Thus, supporting psychological needs during training, development and career transitions is likely to assist people in crafting successful careers.

Applications

In what follows, we describe three specific cases where technology is already influencing work design (virtual and remote work, virtual teamwork, and algorithmic management), and consider the potential consequences for worker need satisfaction and motivation.

Virtual and remote work

Technologies have significantly altered when and where people can work, with the Covid-19 pandemic vastly accelerating the extent of working from home (Box  3 ). Remote work has persisted beyond the early stages of the Covid-19 pandemic with hybrid working — where people work from home some days a week and at the workplace on other days — becoming commonplace 108 . The development of information communication technologies (such as Microsoft Teams) has enabled workers to easily connect with colleagues, clients and patients remotely 105 , for example, via online patient ‘telehealth’ consultations, webinars and discussion forums. Technology has even enabled the remote control of other technologies, such as manufacturing machinery, vehicles and remote systems that monitor hospital ward patient vital signs through AI 1 . However, even when people are working on work premises (that is, not working remotely), an increasing amount of work in many jobs is done virtually (for example, online training or communicating with a colleague next door via email).

Working virtually is inherently tied to changes in uncertainty and interdependence. Virtual work engenders uncertainty because workplace and interpersonal cues are less available or reliable in providing virtual employees with role clarity and ensuring smooth interactions. Indeed, ‘screen’ interactions are more stressful and effortful than face-to-face interactions. It is more difficult to decipher and synchronize non-verbal behaviour on a screen than face-to-face, particularly given the lack of body language cues due to camera frame limitations, increasing the cognitive load for meeting attendees 109 , 110 , 111 , 112 . Non-verbal synchrony can be affected by the video streaming speed, which also increases cognitive load 109 , 110 , 111 , 112 . Virtual interactions involve ‘hyper gaze’ from seeing grids of staring faces, which the brain interprets as a threat 109 , 110 , 111 , 112 . Seeing oneself on screen increases self-consciousness during social interactions, which can cause anxiety, especially in women and those from minoritized groups 109 , 110 , 111 , 112 . Finally, reduced mobility from having to stay in the camera frame has been shown to reduce individual performance relative to face-to-face meetings 109 , 110 , 111 , 112 . Research on virtual interactions is still in its infancy. In one study, workers were randomly assigned to have their camera either on or off during their daily virtual meetings for a week. Those with the camera on during meetings experienced more daily fatigue and less daily work engagement than those with the camera off 113 .

Lower-quality virtual communication between managers and colleagues can leave individuals unclear about their goals and priorities, and how they should achieve them 114 . This calls for more self-regulation 115 because employees must structure their daily work activities and remind themselves of their work priorities and goals, without relying on the physical presence of colleagues or managers. If virtual workers must coordinate some of their work tasks with colleagues, it can be difficult to synchronize and coordinate actions, working schedules and breaks, motivate each other, and assist each other with timely information exchange 115 . This can make it harder for employees to acquire and share information 53 .

Virtual work also affects work design and changes how psychological needs can be satisfied and frustrated (Table  1 ), which has implications for both managers and employees. Physical workplace cues that usually guide work behaviours and routines in the office do not exist in virtual work, consequently demanding more autonomous regulation of work behaviours 116 , 117 . Some remote workers experience an increased sense of control and autonomy over their work environment 118 , 119 , 120 under these circumstances, resulting in lower family–work conflict, depression and turnover 121 , 122 . However, managers and organizations might rob workers of this autonomy by closely monitoring them, for example by checking their computer or phone usage 123 . This type of close monitoring reflects a lack of manager trust in individuals’ abilities or intentions to work effectively remotely. This lack of trust leads to decreased feelings of autonomy 124 , increased employee home–work conflict 105 and distress 125 , 126 . Surveillance has been shown to decrease self-determined motivation 127 . It is therefore important to train managers in managing remote workers in an autonomy-supportive way to avoid these negative consequences 128 . The negative effects of monitoring can also be reduced if monitoring is used constructively to help employees develop through feedback 129 , 130 , 131 , 132 , 133 , and when employees participate in the design and control of the monitoring systems 134 , 135 .

Information communication technology might satisfy competence needs by increasing access to global information and communication and the ability to analyse data 136 . For example, online courses, training and webinars can improve workers’ knowledge, skills and abilities, and can therefore help workers to carry out their work tasks more proficiently, which increases self-efficacy and a sense of competence. Furthermore, the internet allows people to connect rapidly and asynchronously with experts around the world, who may be able to provide information needed to solve a work problem that local colleagues cannot help with 136 . This type of remote work is increasingly occurring whether or not individuals themselves are based remotely, and can potentially enhance performance.

At the same time, technology might thwart competence needs, and increase fatigue and stress. For example, constant electronic messages (such as email or keeping track of online messaging platforms such as Slack or Microsoft Teams) are likely to increase in volume when working remotely, but can be distracting and prevent individuals from completing core tasks while they respond to incoming messages 136 . The frustration of the need for competence can increase if individuals are constantly switching tasks to deal with overwhelming correspondence and failing to finish tasks in a timely manner. In addition, information communication technology enables access to what some individuals might perceive as an overwhelming amount of information (for example, through the internet, email and messages) which can lead to a lot of time spent sifting and processing information. This can be interpreted as a job demand that might make individuals feel incompetent if it is not clear what information is most important. Individuals might also require training in the use of information communication technology, and even then, technology can malfunction, preventing workers from completing tasks, and causing frustration and distress 136 , 137 .

Finally, remote workers can suffer from professional isolation because there are fewer opportunities to meet or be introduced to connections that enable career development and progression 138 , which could influence their feelings of competence in the long run. Although some research suggests that those who work flexibly are viewed as less committed to their career 139 and might be overlooked for career progression 140 , other research has found no relationship between remote working and career prospects 119 .

Virtual work can also present challenges for meeting workers’ need for relatedness 141 . Remote workers can feel isolated from, and excluded by, colleagues and fail to gain the social support they might receive if co-located 142 , 143 , weakening their sense of belonging to a team or organization 144 and their job performance 145 . This effect will probably be accentuated in the future: if the current trend for working from home continues, more people will be dissociated from office social environments more often and indefinitely. Office social environments could be degraded permanently if fewer people frequent the office on a daily basis, such that workers may not be in the office at the same time as collaborators, and there might be fewer people to ask for help or talk with informally. We do not yet know the long-term implications of a degraded social environment, but some suggest that extended virtual working could create a society where people have poor communication skills and in which social isolation and anxiety are exacerbated 146 . Self-determination theory suggests that it will be critical to actively design hybrid and remote work that meets relatedness needs to prevent these long-term issues. When working remotely, simple actions could be effective, such as actively providing opportunities for connecting with others, for example, through ‘virtual coffee breaks’ 147 . Individuals could also be ‘buddied’ up into pairs who regularly check in with each other via virtual platforms.

Hybrid work seems to offer the best of both worlds, providing opportunities for connection and collaboration while in the workplace, and affording autonomy in terms of flexible working. Some research suggests that two remote workdays a week provides the optimum balance 148 . However, it is likely that this balance will be affected by individual characteristics and desires, as well as by differences in work roles and goals. For example, Israeli employees with autism who had to work from home during the COVID-19 pandemic experienced significantly lower competence and autonomy satisfaction than before the pandemic 149 . Yet remote workers high in emotional stability and job autonomy reported higher autonomy and relatedness satisfaction compared to those with low emotional stability 120 . These findings suggest that managers and individuals should consider the interplay between individual characteristics, work design and psychological need satisfaction when considering virtual and remote work.

Box 3 The ‘great resignation’

‘The great resignation’ refers to the massive wave of employee departures during the COVID-19 pandemic in several parts of the world, including North America, Europe and China 229 , 230 , that can be attributed in part to career shocks caused by the pandemic 222 . In the healthcare profession, the shock consisted of an exponential increase in workload and the resulting exhaustion, coupled with the disorganization caused by lack of resources and compounded by health fears 231 . In other industries, the pandemic caused work disruptions by forcing or allowing people to work from home, furloughing employees for varying periods of time, or lay-offs caused by an abrupt loss of business (such as in the tourism and hospitality industries).

Scholars have speculated that these shocks have resulted in a staggering number of people not wanting to go back to work or quitting their current jobs 232 . For example, the hospitality and tourism industries failed to attract employees back following lay-offs 233 . Career shocks can trigger a sensemaking process that can lead one to question how time is spent at work and the benefits one draws from it. For example, the transition to working from home made employees question how and why they work 234 . Frequent health and financial concerns, juggling school closures and complications in caring for dependents have compounded exhaustion and disorganization issues. Some have even renamed ‘the great resignation’ as ‘the great discontent’ to highlight that many people reported wanting to quit because of dissatisfaction with their work conditions 235 .

It might be helpful to understand ‘the great resignation’ through the lens of basic psychological need satisfaction. Being stretched to the limit might influence the need for competence and relatedness when workers feel they have suboptimal ways to connect with colleagues and insufficient time to balance work with other life activities that connect them to family and friends 128 , 236 . The sensemaking process that accompanies career shocks might highlight a lack of meaningful work that decreases the satisfaction of the need for autonomy. This lack of need satisfaction might lead people to take advantage of the disruption to ‘cut their losses’ by reorienting their life priorities and career goals, leading to resignation from their current jobs 237 , 238 .

Alternatively, the experiences gained from working differently during the COVID-19 pandemic might have made many workers aware of how work could be (for example, one does not have to commute), emboldening them to demand better work design and work conditions for themselves. Not surprisingly, barely a year after ‘the great resignation’ many are now talking about ‘the great reshuffle’, suggesting that many people who quit their jobs used this time to rethink their careers and find more satisfying work 239 . Generally, this has meant getting better pay and seeking work that aligns better with individual values and that provides a better work–life balance: in other words, work that better meets psychological needs for competence, autonomy and relatedness.

Virtual teamwork

Uncertainty and interconnectedness make work more complex, increasing the need for teamwork across many industries 150 . Work teams are groups of individuals that must both collaborate and work interdependently to achieve shared objectives 151 . Technology has created opportunities to develop work teams that operate virtually. Virtual teams are individuals working interdependently towards a common goal but who are geographically dispersed and who rely on electronic technologies to perform their work 152 , 153 . Thus, virtual teamwork is a special category of virtual work that also involves collective psychological experiences (that are shaped by and interact with virtual work) 154 . This adds another layer of complexity and therefore requires a separate discussion.

Most research conceptualizes team virtuality as a construct with two dimensions: geographical dispersion and reliance on technology 153 , 155 . Notably, these dimensions are not completely independent because team members require technology to communicate and coordinate tasks when working in different locations 156 , 157 . Virtuality differs between and within teams. Team members might be in different locations on some days and the same location on other days, which changes the level of team virtuality over time. Thus, teams are not strictly virtual or non-virtual. Team virtuality influences how team members coordinate tasks and share information 130 , which is critical for team effectiveness (usually assessed by a team’s tangible outputs, such as their productivity, and team member reactions, such as satisfaction with, or commitment to, the team) 158 .

Although individual team members might react differently to working in a virtual team, multi-level theory suggests that team members collectively develop shared experiences, called team emergent states 159 , 160 . Team emergent states include team cohesion (the bond among group members) 161 , team trust 162 , and team motivation and engagement 159 , 163 . These emergent states arise out of individual psychological behaviours and states 164 and are influenced by factors that are internal (for example, interactions between team members) and external (for example, organizational team rewards, organizational leadership and project deadlines) to the team, as well as team structure (for example, team size and composition). Team emergent states, particularly team trust, are critical for virtual team effectiveness because reliance on technology often brings uncertainties and fewer opportunities for social control 165 .

Team virtuality is likely to affect team functioning via its impact on psychological need satisfaction, in a fashion similar to remote work. However, the need for coordination and information sharing to achieve team goals is likely to be enhanced by how team members support and satisfy each other’s psychological needs 166 , which might be more difficult under virtual work conditions. In addition to affecting individual performance, need satisfaction within virtual teams can also influence collective-level team processes, such as coordination and trust, which ultimately affect team performance. For example, working in a virtual team might make it more difficult to feel meaningful connections because team members in different locations often have less contact than co-located team members. Virtual team members predominantly interact via technology, which — as described in the previous section — might influence the quality of relationships they can develop with their team members 141 , 167 , 168 and consequently the satisfaction of relatedness needs 169 .

Furthermore, virtual team members must master electronic communication technology (including virtual meeting and breakout rooms, internet connectivity issues, meeting across different time zones, and email overload), which can lead to frustrations and ‘technostress’ 170 . Frustrations with electronic communication might diminish the psychological need for competence because team members might feel ineffective in mastering their environment.

In sum, virtual team members might experience lower relatedness and competence need satisfaction. However, these needs are critical determinants of work motivation. Furthermore, virtual team members can also develop shared collective experiences around their need satisfaction. Thus, self-determination theory offers explanatory mechanisms (that is, team members’ need satisfaction, which influences work motivation) that are at play in virtual teams and that organizations should consider when implementing virtual teams.

Algorithmic management

Algorithmic management refers to the use of software algorithms to partially or completely execute workforce management functions (for example, hiring and firing, coordinating work, and monitoring performance) 2 , 123 , 171 , 172 . This phenomenon first appeared on gig economy platforms such as Uber, Instacart and Upwork, where all management is automated 173 . However, it is rapidly spreading to traditional work settings. Examples include monitoring the productivity, activity and emotions of remote workers 174 , the algorithmic determination of truck drivers’ routes and time targets 175 , and automated schedule creation in retail settings 176 . The constant updating of the algorithms as more data is collected and the opacity of this process makes algorithmic management unpredictable, which produces more uncertainty for workers 177 .

Algorithmic management has repercussions for work design. Specifically, whether algorithmic management systems consider human motivational factors in their design influences whether workers are given enough autonomy, skills usage, task variety, social contact, role clarity (including knowing the impact of one’s work) and a manageable workload 123 . So far, empirical evidence show that algorithmic management features predominantly reduce employees’ basic needs for autonomy, competence and relatedness because of how they influence work design (Fig.  4 ).

Summary of the features and consequences of algorithmic management on autonomy needs, relatedness needs and competence needs.

Algorithmic management tends to foster the ‘working-for-data’ phenomenon (or datafication of work) 172 , 178 , 179 , leading workers to focus their efforts on aspects of work that are being monitored and quantified at the expense of other tasks that might be more personally valued or meaningful. This tendency is reinforced by the fact that algorithms are updated with new incoming data, increasing the need for workers to pay close attention to what ‘pays off’ at any given moment. Monitoring and quantifying worker behaviours might reduce autonomy because it is experienced as controlling and narrows goal focus to only quantifiable results 127 , 180 ; there is some evidence that this is the case when algorithmic management systems are used to this end 172 , 178 , 181 . Rigid rules about how to carry out work often determine performance ratings (for example, imposing a route to deliver goods or prescribing how equipment and materials must be used) and even future task assignments and firing decisions, with little to no opportunity for employee input 182 , 183 , 184 . Thus, the combination of telling workers what to do to reach performance targets and how to get it done significantly limits their autonomy to make decisions based on their knowledge and skills.

Some algorithmic management platforms do not reveal all aspects of a given task (for example, not revealing the client destination before work is accepted) or penalize workers who decline jobs 185 , thereby severely restricting their choices. This encourages workers to either overwork to the point of exhaustion, find ways to game the system 184 , or misbehave 186 . Moreover, the technical complexity and opacity of algorithmic systems 187 , 188 , 189 deprives workers of the ability to understand and master the system that governs their work, which limits their voice and enpowerment 172 , 185 , 190 . Workers’ typical response to the lack of transparency is to organize themselves on social media to share any insights they have on what the algorithm ‘wants’ as a way to gain back some control over their work 183 , 191 .

Finally, algorithmic management usually provides comparative feedback (comparing one’s results to other workers’) and is linked to incentive pay structures, both of which reduce self-determined motivation as they are experienced as more controlling 26 , 192 . For instance, after algorithms estimated normal time standards for each ‘act’, algorithmic tracking and case allocation systems forced homecare nurses to reduce the ‘social’ time spent with patients because they were assigned more patients per day, thereby limiting nurses’ autonomy to decide how to perform their work 181 . Because these types of quantified metric are often directly linked to performance scores, pay incentives and future allocation of tasks or schedules (that is, getting future work), algorithmic management reduces workers’ freedom in decision-making related to their work, which can significantly reduce their self-determined motivation 123 .

Algorithmic management also tends to individualize work, which affects the need for relatedness. For example, algorithmic management inevitably transforms or reduces (sometimes even eliminates) contact with a supervisor 2 , 182 , 193 , leading to the feeling that the organization does not care about the worker and provides little social support 194 , 195 . ‘App-workers’, who obtain work through gig-work platforms such as Uber, reportedly crave more social interactions and networking opportunities 179 , 185 , 194 and often attempt to compensate for a lack of relatedness by creating support groups that connect virtually and physically 183 , 191 , 195 . Increased competitive climates due to comparative feedback or displaying team members’ individual rankings 175 , 196 can also hamper relatedness. Indeed, when workers have to compete against each other to rank highly (which influences their chances of getting future work and the financial incentives they receive), they are less likely to develop trusting and supportive relationships.

Researchers have formulated contradictory predictions about the potential implications of algorithmic management on competence satisfaction. On the one hand, using quantified metrics, algorithmic management systems can provide more frequent, unambiguous and performance-related feedback, often in the form of ratings and rankings 177 , and simultaneously link this feedback to financial rewards. Informational feedback can enhance intrinsic motivation because it provides information about one’s competence. At the same time, linking rewards to this feedback could decrease intrinsic motivation, because the contingency between work behaviour and pay limits worker discretion and therefore reduces their autonomy 26 . The evidence so far suggests that the mostly comparative feedback provided by algorithmic management is insufficiently informative because the value of the feedback is short-lived — continuously updating algorithms change what is required to perform well 177 , 183 , 185 . This short-lived feedback can undermine feelings of mastery or competence. In addition, algorithmic management is often associated with simplified tasks, and with lower problem-solving opportunities and job variety 123 . However, gamification features on some platforms might increase intrinsic motivation 179 , 183 .

The nascent research on the effects of algorithmic management on workers’ motivation indicates mostly negative effects on self-determined forms of motivation, because the way it is designed decreases the satisfaction of competence, autonomy and relatedness needs. Algorithmic management is being rapidly adopted across an increasing number of industries. Thus, technology developers and those who implement the technology in organizations will need to pay closer attention to how it changes work design to avoid negative effects on work motivation.

Summary and future directions

Self-determination theory can help predict the motivational consequences of future work and these motivational considerations should be taken into account when designing and implementing technology. More self-determined motivation will be needed to deal with the uncertainty and interdependence that will characterize future work. Thus, research examining how need satisfaction and work motivation influence people’s ability to adapt to uncertainty, or even leverage it, is needed. For example, future research could examine how different managerial styles influence adaptivity and proactivity in highly uncertain work environments 197 . Need-satisfying leadership, such as transformational leadership (charismatic or inspirational) 15 , can encourage job crafting and other proactive work behaviours 198 , 199 . Transactional leadership (focused on monitoring, rewarding and sanctioning) might promote self-determined motivation during organizational crises 23 . In addition, research on the quality of interconnectedness (the breadth and depth of interactions and networks) could provide insight on how to manage the increased interconnectedness workers are experiencing.

Technology can greatly assist in recruiting and selecting workers; self-determination theory can inform guidelines on how to design and use such technologies. It is important that the technology is easy to use and perceived as useful to the candidates for best representing themselves 200 , 201 . This can be done by ensuring that candidates have complete instructions before an assessment starts, even possibly getting a ‘practice run’, to improve their feelings of competence. It is also important for candidates to feel some amount of control and less pressure associated with online asynchronous assessments. Giving candidates some choice over testing platforms and the order of questions or settings, explaining how the results will be used, or allowing candidates to ask questions, could improve feelings of autonomy 70 . Finally, it is crucial to enhance perceptions that the organization cares about getting to know candidates and forging connections with them despite using these tools. For example, enhancing these tools with personalized videos of organizational members and providing candidates with feedback following selection decisions might increase feelings of relatedness. These suggestions need to be empirically tested 202 .

More research is also needed on how technology is transforming work design, and consequently influencing worker need satisfaction and motivation. Research in behavioural health has examined how digital applications that encourage healthy behaviours can be designed to fulfill the needs for competence, autonomy and relatedness 203 . Whether and how technology designed for other purposes (such as industrial robots, information communication technology, or automated decision-making systems) can be deliberately designed to meet these core human needs remains an open question. To date, little research has examined how work technologies are created, and what can be done to influence the process to create more human-centred designs. Collaborative research across social science and technical disciplines (such as engineering and computing) is needed.

In terms of implementation, although there is a long history of studies investigating the impact of technology on work design, current digital technologies are increasingly autonomous. This situation presents new challenges: a human-centred approach to automation in which the worker has transparent influence over the technical system has frequently been recommended as the optimal way to achieve high performance and to avoid automation failures 1 , 204 . But it is not clear that this work design strategy will be equally effective in terms of safety, productivity and meeting human needs when workers can no longer understand or control highly autonomous technology.

Given the likely persistence of virtual and remote work into the future, there is a critical need to understand how psychological needs can be satisfied when working remotely. Multi-wave studies that explore the boundary conditions of need satisfaction would advance knowledge around who is most likely to experience need satisfaction, when and why. Such knowledge can be leveraged to inform the design of interventions, such as supervisor training, to improve well-being and performance outcomes for virtual and remote workers. Similarly, no research to date has used self-determination theory to better understand how team virtuality affects how well team members support each other’s psychological needs. Within non-virtual teams, need satisfaction is influenced by the extent to which team members exhibit need-supportive behaviours towards each other 205 . For example, giving autonomy and empowering virtual teams is crucial for good team performance 206 . Studies that track team activities and interaction patterns, including virtual communication records, over time could be used to examine the effects of need support and thwarting between virtual team members 207 , 208 .

Finally, although most studies have shown negative effects of algorithmic management on workers’ motivation and work design characteristics, researchers should not view the effects of algorithmic management as predetermined and unchangeable. Sociotechnical aspects of the system 2 , 209 (such as transparency, privacy, accuracy, invasiveness and human control) and organizational policies surrounding their use could mitigate the motivational effects of algorithmic management. In sum, it is not algorithms that shape workers’ motivation, but how organizations design and use them 3 . Given that applications that use algorithmic management are developed mostly by computer and data scientists, sometimes with input from marketing specialists 185 , organizations would benefit from employing psychologists and human resources specialists to enhance the motivational potential of these applications.

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Barriers and facilitators to clinical behaviour change by primary care practitioners: a theory-informed systematic review of reviews using the Theoretical Domains Framework and Behaviour Change Wheel

• Melissa Mather   ORCID: orcid.org/0000-0001-9746-0131 1 ,
• Luisa M. Pettigrew 2 , 3 &
• Stefan Navaratnam 4  

Systematic Reviews volume  11 , Article number:  180 ( 2022 ) Cite this article

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Understanding the barriers and facilitators to behaviour change by primary care practitioners (PCPs) is vital to inform the design and implementation of successful Behaviour Change Interventions (BCIs), embed evidence-based medicine into routine clinical practice, and improve quality of care and population health outcomes.

A theory-led systematic review of reviews examining barriers and facilitators to clinical behaviour change by PCPs in high-income primary care contexts using PRISMA. Embase, MEDLINE, PsychInfo, HMIC and Cochrane Library were searched. Content and framework analysis was used to map reported barriers and facilitators to the Theoretical Domains Framework (TDF) and describe emergent themes. Intervention functions and policy categories to change behaviour associated with these domains were identified using the COM-B Model and Behaviour Change Wheel (BCW).

Four thousand three hundred eighty-eight reviews were identified. Nineteen were included. The average quality score was 7.5/11. Reviews infrequently used theory to structure their methods or interpret their findings. Barriers and facilitators most frequently identified as important were principally related to ‘ Knowledge ’, ‘ Environmental context and resources ’ and ‘ Social influences ’ TDF domains. These fall under the ‘Capability’ and ‘Opportunity’ domains of COM-B, and are linked with interventions related to education, training, restriction, environmental restructuring and enablement. From this, three key areas for policy change include guidelines, regulation and legislation. Factors least frequently identified as important were related to ‘Motivation’ and other psychological aspects of ‘Capability’ of COM-B. Based on this, BCW intervention functions of persuasion, incentivisation, coercion and modelling may be perceived as less relevant by PCPs to change behaviour.

Conclusions

PCPs commonly perceive barriers and facilitators to behaviour change related to the ‘Capability’ and ‘Opportunity’ domains of COM-B. PCPs may lack insight into the role that ‘Motivation’ and aspects of psychological ‘Capability’ have in behaviour change and/or that research methods have been inadequate to capture their function. Future research should apply theory-based frameworks and appropriate design methods to explore these factors. With no ‘one size fits all’ intervention, these findings provide general, transferable insights into how to approach changing clinical behaviour by PCPs, based on their own views on the barriers and facilitators to behaviour change.

Systematic review registration

A protocol was submitted to the London School of Hygiene and Tropical Medicine via the Ethics and CARE form submission on 16.4.2020, ref number 21478 (available on request). The project was not registered on PROSPERO.

Peer Review reports

Known as the “second translational gap” [ 1 ], a gap in translation between evidence-based interventions and everyday clinical practice has been shown across different clinical areas and international settings [ 2 , 3 , 4 ], with numerous organisational and individual factors influencing clinical behaviour. Existing literature has shown that there is particularly wide variation in clinical behaviour in the primary care setting, which cannot be explained by case mix and clinical factors alone [ 5 , 6 ]. This variation is of particular concern, as it is widely accepted that primary care is the cornerstone of a strong healthcare system [ 7 ], and stronger primary care systems are generally associated with better and more equitable population health outcomes [ 8 , 9 , 10 , 11 ]. With an ageing population and unique evolving challenges faced in primary care, understanding the contextual barriers and facilitators to successful behaviour change by primary care practitioners (PCPs) is vital to inform the design and implementation of successful behaviour change interventions (BCIs), and is likely to offer the greatest potential improvement in quality of care and population health outcomes.

Behaviour change interventions

Changing behaviour of healthcare professionals is not easy, but has been shown to be easier when evidence-based theory informs intervention development [ 12 ]. BCIs aimed at healthcare professionals have traditionally been related to incentivisation schemes, guidelines, educational outreach, audit and feedback, printed materials and reminders [ 13 , 14 ]. These have often emerged from approaches to understanding behaviour change, focused on individual attitude-intention processes [ 15 ] and theories emphasising self-interest [ 16 , 17 ]. However, the impact of these interventions on changing clinicians’ behaviour has been found to variable [ 18 ]. Within the context of primary care, attitude-intention processes may not fully explain (lack of) behaviour change, where PCPs face competing pressures, such as caring for multiple patients with limited time, identifying pathology among undifferentiated symptoms, coping with emotional situations, managing uncertainty and keeping up-to-date with substantial volumes of new evidence. Similarly, theories of self-interest may not fully translate to PCPs. BCIs are often implemented through collective action across teams or based on financial levers [ 19 , 20 , 21 ]; however, the organisational context where PCPs work can vary from a single or group community-based practices with variable payment systems [ 22 ]. Therefore, while other healthcare professionals, patients and carers are likely to offer valuable insights, understanding PCPs’ own perspectives on the barriers and facilitators to behaviour change by PCPs is a vital starting point.

Theoretical Domains Framework and Behaviour Change Wheel

The Theoretical Domains Framework (TDF) of behaviour change [ 23 ] simplifies and integrates 33 theories and 128 key theoretical constructs related to behaviour change into a single framework for use across multiple disciplines. Theoretical constructs are grouped into 14 domains in the final paper by Michie et al. [ 24 ], encompassing individual, social and environmental factors, with the majority relating to individual motivation and capability factors [ 25 ] (Fig. 1 ). Skills can be subcategorised into cognitive and interpersonal, and physical, although cognitive and inter-personal skills are more relevant to primary care (Table 1 ).

The Behaviour Change Wheel (BCW) [ 26 ] (above) and the relationship with the Theoretical Domains Framework (TDF) [ 25 ] (below)

The TDF has been widely used to examine clinical behaviour change in healthcare settings [ 25 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Key advantages of the TDF include a comprehensive range of domains useful for synthesising large amounts of data [ 24 ] and the domains can be used to identify the types of interventions and policy strategies necessary to change those mechanisms of behaviour, using the Behaviour Change Wheel (BCW) [ 26 ]. Developed by Michie et al., the BCW can be used to characterise interventions by their “functions” and link these to behavioural targets, categorised in terms of capability (individual capacity to engage in the activity concerned), opportunity (all the factors that lie outside the individual that make the behaviour possible or prompt it) and motivation (brain processes that energize and direct behaviour), known as the COM-B System. Capability encompasses not only individual physical capability, but also psychological capability, defined as the capacity to engage in the necessary thought processes using comprehension, reasoning etc. Strategies to modify behaviour can be identified based on salient TDF and COM-B domains [ 35 ].

The evidence gap

Never having been done before, the aims of this systematic review of reviews were to:

Identify barriers and facilitators to clinical behaviour change by PCPs through the theoretical lenses of the TDF and BCW, from the perspective of PCPs.

Help inform the future development and implementation of theory-led BCIs, to embed EBM into routine clinical practice, improve quality of care and population health outcomes.

A systematic review of reviews was deemed an appropriate method to address these aims, as the literature is substantial and heterogeneous. Existing reviews of reviews have looked at different types of effective BCIs, both in primary care [ 36 , 37 ] and in healthcare in general [ 18 ], however none have looked at barriers and facilitators to PCPs’ behaviour change, using both the TDF and BCW models as a theoretical basis.

We aimed to answer the following questions:

Which TDF domains are most frequently identified as important by PCPs when barriers and facilitators to clinical behaviour change by PCPs are mapped to the TDF framework?

What important themes emerge within these TDF domains?

What intervention functions and policy strategies from the COM-B Model and BCW link to these TDF domains, and what are the implication of this?

Guidance presented in the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis [ 38 ] was used as methodological guidance to conduct the review, which provides guidance for umbrella reviews synthesising qualitative and quantitative data on topics other than intervention effectiveness. This guidance, alongside a modified version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 39 ], were used for reporting (Additional file 1 ).

A comprehensive database search strategy was devised by MM with assistance from a librarian from LSHTM. The search was conducted by MM on April 16th 2020 without date restriction, using the following databases: Embase (1947 to 2020 April 14), MEDLINE (1946 to April week 1 2020), PsychInfo (1806 to April week 1 2020), Health Management Information Consortium (HMIC) (1979 to March 2020) and Cochrane Library (inception to April 2020). The full search strategies are shown in Additional file 2 .

In addition, grey literature was hand-searched by MM on the following websites: Public Health England [ 40 ], the University College London (UCL) Centre for Behaviour Change [ 41 ] and the National Institute for Health and Care Excellence (NICE) Evidence Search [ 42 ]. After screening and selection, reference lists of the included reviews were screened for additional relevant reviews.

Inclusion and exclusion criteria

To be included, articles had to be reviews of qualitative, quantitative or mixed methods empirical studies examining barriers and facilitators to clinical behaviour change by PCPs. Inclusion and exclusion criteria were defined using the PICo framework (Population, phenomena of Interest, Context) [ 43 ], to enable transparency and reproducibility. The element of ‘types of studies’ was added to specify types of evidence included (Table 2 ).

In most HIC settings, general practitioners/family doctors are the main providers of primary care, however often included a mix of PCPs (healthcare professionals working in primary care).

PCPs usually provide the mainstay of care in high-income settings. Common barriers and facilitators across a wide range of high-income settings provides stronger evidence for context-specific recommendations.

Types of studies:

The inclusion of all types of reviews (including but not limited to narrative and realist reviews, meta-ethnography and meta-aggregation) allows for a broader review of available literature and they are not bound by the specificity of systematic reviews [ 44 , 45 ].

Only reviews published in English were included.

Screening and selection

Results from database searches were exported to EndNote X9 software and deduplicated. Titles and abstracts were screened independently by two reviewers (MM and SN). If the abstract contained insufficient information to determine eligibility, a copy of the full text was obtained. The full texts of articles meeting the inclusion criteria were obtained and reviewed. A standardised form including elements of the PICo framework was used at the full text review stage to identify relevant articles in a consistent way. Articles which could not be accessed online were obtained by contacting authors. Authors were also contacted to obtain clarification where eligibility was unclear. Reference lists of included articles were hand searched by MM and SN to identify additional relevant articles, subject to the same screening and selection processes described.

Quality appraisal

The Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses [ 38 ] was used for quality appraisal, conducted independently by MM and SN. This tool was applicable to reviews of observational studies, which constituted the majority of the included articles; therefore, all reviews, regardless of their type, were subject to quality appraisal using the JBI checklist. This also allowed for consistency in scoring and easier comparison between the reviews. A scoring system was pre-defined using a small sample of five articles and guidance in the JBI Manual for Evidence Synthesis [ 38 ]. Some articles fulfilled some but not all of the criteria for each question, which was believed to be reasonable, therefore an additional ‘partial yes’ response was added to reflect this (Additional file 3 ). With a maximum score of 11, scores were used to indicate low (≤ 4 points), moderate (> 4 and < 8 points) and high (≥ 8 points) quality. As outlined by Pope and Mays [ 46 ], the value of specific pieces of qualitative research may only emerge during the synthesis process and may still offer valuable insights despite low quality. No articles were therefore excluded on the basis of low quality scores.

Data extraction

The JBI Data Extraction Form for Reviews of Systematic Reviews and Research Syntheses [ 38 ] was adapted to extract relevant data from included reviews. A citation matrix was created to map the included empirical studies of each review and identify duplicate references.

Data analysis and synthesis

Data analysis was conducted independently by MM and SN. Previously reported analysis methods [ 25 , 47 ] were used to guide data analysis and synthesis methods. A combination of content and framework analysis was used, described in five steps:

Data extraction: full-text versions of the included articles were imported into NVivo software and data were extracted from results and discussion sections and supplementary files. Data included barriers, facilitators and factors which could be both barriers and facilitators.

Deductive analysis: extracted barriers, facilitators and factors were mapped to relevant TDF domains using component constructs of each domain, outlined by Cane et al. [ 24 ]. Almost all reported barriers and facilitators related to skills were cognitive and interpersonal, therefore the TDF domain ‘skills: physical’ was removed.

Counts were used to identify the most frequently-reported TDF domains. Owing to the vast amount of information across the included reviews, counts were also used to identify the TDF domains most frequently reported as important by authors. This was done in three ways: where authors explicitly stated they were important or salient, where they were most frequently reported where authors used frequency counts, and where authors highlighted or focused on them in the discussion section to draw main conclusions.

Inductive analysis: thematic analysis was conducted to identify emergent themes within the TDF domains most frequently identified as important to provide context to the role each barrier, facilitator and factor plays in hindering or facilitating clinical behaviour change. Owing to the vast amount of information across the included reviews, themes reported as important or salient by five or more reviews were labelled as important overall.

TDF domains most frequently identified as important were mapped to the COM-B model of the BCW to identify the associated intervention functions and policy categories.

Discrepancies between reviewers at the screening, selection, quality appraisal and analysis stages were discussed until a consensus was reached.

Search results and selection

Database searches identified 6308 records. After duplicates were removed, there were 4374 records remaining. An additional 14 articles were identified from grey literature and reference list searches. The vast majority of these articles were either not a review of empirical studies, or they did not focus on behaviour change. Where they did focus on behaviour change, they focused on patient behaviour change, rather than that of PCPs. One hundred and nine full-text articles were assessed for eligibility. Clarification was sought from 19 authors on participant roles, search strategies and synthesis methods, and was obtained from 11 authors. Nineteen reviews [ 33 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ] were included in the data synthesis (Fig. 2 ).

Flow chart [ 66 ] of review process

Characteristics of included reviews

Of the 19 included reviews, 17 [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 ] were systematic reviews and 2 [ 33 , 63 ] were narrative reviews. The reviews were all published between 2005 and 2020. Four hundred and one empirical studies were included in total across a wide range of settings and healthcare systems. Almost all studies were conducted in high-income countries, the majority of which were conducted in Europe, USA, Canada, Australia and New Zealand. Five studies (1%) were conducted in upper-middle-income countries, including Jordan, Turkey, South Africa and Bosnia and Herzegovina. Seven reviews [ 48 , 49 , 50 , 51 , 52 , 53 , 65 ] only included qualitative studies, two [ 54 , 55 ] only included quantitative studies, and 10 [ 33 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] included qualitative, quantitative and/or mixed methods studies. Most studies were observational, utilising qualitative interviews and/or focus groups, or cross-sectional surveys. A minority of observational studies from six [ 55 , 58 , 59 , 62 , 63 , 64 ] reviews were part of larger intervention studies.

More than 72,000 PCPs were included in total. Seven [ 33 , 48 , 49 , 52 , 55 , 63 , 64 ] reviews only reported general practitioner (GP) or family physician (FP) data and 12 [ 50 , 51 , 53 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 65 ] reported a mix of PCP data, the majority of which were GPs, FPs, and community paediatrics and obstetrics and gynaecology physicians. Of these, five reviews [ 51 , 56 , 60 , 62 , 65 ] included primary care non-physicians, including nurse practitioners (NPs) and physician assistants. Seven reviews [ 50 , 52 , 53 , 56 , 58 , 61 , 65 ] examined behaviour related to clinical management of a range of topics, of which three specifically examined prescribing behaviour; four reviews [ 33 , 51 , 59 , 63 ] examined diagnostic processes; two [ 49 , 54 ] examined prevention; two [ 55 , 57 ] examined communication and engagement with patients; two [ 48 , 64 ] examined the practice of EBM in general; one [ 62 ] examined collaborative practice; and one [ 60 ] examined service provision. Eighteen studies were referenced by two reviews each due to overlapping phenomena of interest. The most common data synthesis methods were thematic/narrative synthesis and meta-ethnography, used by 13 reviews [ 48 , 49 , 50 , 51 , 52 , 53 , 55 , 56 , 58 , 60 , 61 , 62 , 65 ]. Six reviews [ 33 , 54 , 57 , 59 , 63 , 64 ] used framework synthesis; only three reviews [ 33 , 59 , 64 ] used existing theoretical frameworks or models, such as the TDF and COM-B. A summary of review characteristics is shown in Table 3 . Additional information is shown in Additional file 4 .

Quality of empirical studies

Three reviews [ 33 , 55 , 63 ] did not conduct quality appraisal, two of which [ 33 , 63 ] were narrative reviews. The remaining reviews used a wide range of appraisal tools to suit the type of data they included, which were primarily existing tools in their original or adapted forms. Five reviews [ 56 , 57 , 60 , 62 , 64 ] used more than one tool. The most common appraisal tools used were CASP (Critical Appraisal Skills Programme) Checklists [ 67 ] for qualitative and quantitative research, used by seven reviews [ 48 , 52 , 53 , 56 , 57 , 59 , 65 ]. There was large variation in how authors used the appraisal tools, therefore quality of studies could not be reliably compared between reviews. Detailed information is shown in Additional file 3 .

Quality of reviews

Ten reviews were of high quality, seven were of moderate-quality and two were of low quality using the JBI checklist. The highest score was 10.5/11 and the lowest was 3/11. The average score was 7.5/11, which is considered moderate quality. Reviews generally included well-evidenced recommendations for policy and practice and appropriate directives for future research. On validity, reviews scored highest on appropriate inclusion criteria for the review question, and appropriate methods used to combine studies. Reviews scored poorly on using an appropriate search strategy and assessing for publication bias. Most reviews did not justify search limits and/or did not provide evidence of a search strategy. Scores for each of the criteria are shown in Additional file 3 .

Main findings

A large number of barriers, facilitators and factors were identified by authors, often interacting with each other in a complex way (Table 4 ). As a result, some barriers and facilitators were mapped to more than one TDF domain. All TDF domains were identified. All reviews identified ‘environmental context and resources’ as important, and all but two reviews identified ‘knowledge’ and ‘social influences’ as important. TDF domains identified least frequently as important were ‘goals’, ‘intentions’ and ‘optimism’. Although ‘social/professional role and identity’, ‘skills’ and ‘emotion’ TDF domains were frequently identified, they were less frequently highlighted as important by authors. Table 4 shows how each TDF domain and COM-B domains were mapped to each of the included reviews, as well as which domains were identified as important.

Forty-two themes were identified in total across all TDF domains, 12 of which were labelled as important overall. A theme was labelled as important overall if five or more reviews identified it as important or salient. Within the ‘Knowledge’, ‘Environmental context and resources’, and ‘Social influences’ TDF domains, nine important themes emerged, of which the most frequently cited as important were ‘knowledge, awareness and uncertainty’ and ‘time, workload and general resources’. Across the remaining TDF domains, other important themes included ‘skills and competence’, ‘roles and responsibilities’, and ‘confidence in own ability’. Additional file 5 shows how themes were mapped to each TDF domain and each review, with corresponding quotes.

Capability: psychological (COM-B domain)

Knowledge (tdf domain).

Knowledge, awareness and uncertainty (theme)

Identified as important by 13 reviews [ 33 , 50 , 51 , 52 , 54 , 55 , 58 , 59 , 60 , 61 , 62 , 63 , 65 ] (average quality score 7.2/11).

Inadequate knowledge and awareness and uncertainty were identified as important barriers to depression diagnosis and management [ 51 , 56 ], recognition of insomnia [ 33 ], antibiotic prescribing in childhood infections [ 50 ] and acute respiratory tract infections (ARTIs) [ 65 ], engagement in cancer care [ 58 ], integration of genetics services [ 60 ], discussing smoking cessation [ 55 ], collaborative practice [ 62 ], management of multimorbidity [ 52 ], breast and colorectal screening in older adults [ 54 ] and chlamydia testing [ 59 , 63 ]. This varied from a lack of knowledge of the topic as a whole, to more specific skills or outcomes. For example, PCPs reported a lack of knowledge around the epidemiology and presentation of chlamydia, benefits of testing, how to take specimens, and treatment options [ 59 ]. When prescribing antibiotics for childhood infections and ARTIs, PCPs reported they tend to prescribe “just in case” when they are uncertain of the consequences of not prescribing, such as when the diagnosis is unclear, or where there is no established doctor-patient relationship [ 50 , 65 ]. There was widespread lack of knowledge within the field of genetics, including uncertainty around cancer genetics, genetic testing, genetic discrimination legislation, and local genetics service provision [ 60 ]. As well as a lack of knowledge of national guidelines and strategy [ 60 , 63 ], inadequate guidelines were reported to exacerbate a lack of knowledge. For example, a lack of attention in guidelines on how social problems affect response to depression management was reported to exacerbate PCPs’ uncertainty around their role in managing depression [ 56 ]. Lack of knowledge and uncertainty were frequently reported to cause discomfort, low confidence, and reluctance to fill certain roles.

Opportunity: physical (COM-B domain)

Environmental context and resources (tdf domain).

Time, workload and general resources (theme)

Identified as important by 13 reviews [ 33 , 48 , 50 , 51 , 53 , 54 , 55 , 58 , 59 , 60 , 61 , 63 , 64 ] (average quality score 7.3/11).

A lack of time to implement a variety of different tasks and clinical behaviours was reported, compounded by a large and complex workload and lack of general resources. A prominent barrier was time-pressured consultations, where PCPs reported difficulty in ensuring the clinician and parents are satisfied with the outcome when treating childhood infections [ 50 ], offering alternative interventions [ 53 ], listening to patients with depression [ 56 ], discussing emotions in cancer care [ 58 ] or smoking cessation with patients [ 55 ], introducing chlamydia testing and addressing sexual health-related concerns [ 63 ], recognising, diagnosing and managing child and adolescent mental health problems [ 61 ], and negotiating with patients [ 48 ]. PCPs also reported a lack of time to read and assess evidence and guidelines and reflect on their own practice [ 48 , 64 ].

Guidelines, evidence and decision-making tools (theme)

Identified as important by five reviews [ 48 , 52 , 58 , 64 , 65 ] (average quality score 7.8/11).

Guidelines were a common factor reported to affect clinical behaviour, including a lack of guidelines/guidance, questionable evidence-base, and a disjunction between guidelines and personal experience. For example, PCPs reported difficulty in adapting recommendations to individual patient circumstances and practical constraints of the consultation [ 48 , 51 , 52 ]. PCPs felt that some guidelines lack the necessary flexibility when taking patient preferences and multi-morbidity into account, which can add to complexity and even cause harm in some cases [ 52 , 64 ]. PCPs questioned the evidence-base of the guidelines due to low generalisability and narrow inclusion criteria of trials [ 48 , 64 ] and potential biased sources of research, such as pharmaceutical companies [ 64 ]. The validity of criteria used for depression diagnosis was also questioned, with national guideline criteria defining depressive disorders using symptom counts, as opposed to viewing it as a syndrome requiring aetiological and conceptual thinking [ 51 ]. For non-English PCPs, access to evidence and guidelines in their native language was reported as a major barrier to implementing EBM [ 64 ].

Financial resources and insurance coverage (theme)

Identified as important by 6 reviews [ 49 , 54 , 58 , 61 , 62 , 64 ] (average quality score 8/11).

Poor remuneration and increasing costs were common barriers reported in areas such as PCP involvement in cancer care [ 58 ], child and adolescent mental health [ 61 ] and use of EBM [ 64 ]. A major barrier to recognition and management of child and adolescent mental health problems and cancer in older adults was inadequate insurance coverage, including inadequate coverage of screening tests [ 54 ], restrictions on the number of funded therapy visits, and lack of psychiatrists [ 61 ]. As a result, increased reimbursement was identified as a potential facilitator that could increase child and adolescent mental health diagnoses [ 61 ].

Education and training (theme)

Identified as important by five reviews [ 33 , 59 , 63 , 64 , 65 ] (average quality score 5.7/11).

A lack of education and training was highlighted as an important barrier to chlamydia testing [ 59 , 63 ], recognition of insomnia [ 33 ], antibiotic prescribing [ 65 ], and use of EBM [ 64 ]. PCPs reported that undergraduate sexual health teaching is inadequate [ 63 ] and that they have a lack of appropriate training and skills to discuss sexual health, take a sexual history, offer a test, manage treatment and notify partners. This has led to a reduction in knowledge and confidence to offer testing and discuss sexual health [ 59 ]. More education and training for PCPs and undergraduate students was frequently cited as a facilitator, as PCPs felt this would increase knowledge and confidence to change behaviour. Older male PCPs were identified as potentially in need of specific education on sexual health due to cultural differences with some patients receiving chlamydia testing [ 59 ]. Some PCPs reported that trustworthy and knowledgeable educational sources are important for PCPs to feel added value, with peer-led educational meetings given as an example [ 65 ].

Opportunity: social (COM-B domain)

Social influences (tdf domain).

PCP-patient relationship and patient-centred care (theme)

Identified as important by nine reviews [ 48 , 49 , 51 , 52 , 53 , 57 , 58 , 59 , 65 ] (average quality score 8.2/11).

Some PCPs reported that preservation of the PCP-patient relationship is prioritised over adherence to guidelines, particularly if guidelines recommend rationing services, or if PCPs feel empathetic towards anxious patients [ 48 ]. This dilemma was described as unpleasant and against the principles of patient-centred medicine, but sometimes necessary to avoid the potential litigation that rationing might bring [ 48 ] and loss of patients to other practices [ 53 ]. Similarly, the desire to maintain a good relationship is sometimes in competition with the PCP’s rationing role, leading some PCPs to give patients a “quick fix” when prescribing benzodiazepines [ 53 ]. Although not always reported as important, sensitive and emotive areas of medicine appear to be particularly affected, with PCPs reporting a concern for depriving patients of hope and/or damaging the relationship if they engage in the process of ACP [ 57 ], cancer care [ 58 ], or offer chlamydia testing [ 63 ]. Specifically, PCPs worried about appearing discriminatory and judgemental towards patients by offering chlamydia testing [ 63 ], and being too intrusive and paternalistic in recommending behaviour change to patients to prevent CVD [ 49 ]. This appears to be compounded by different religious and cultural norms between the PCP and patient, particularly if patients are of non-heteronormative orientation [ 63 ].

Establishing a rapport with patients and developing a long-standing, trusting doctor-patient relationship was identified as a facilitator for information-sharing, depression diagnosis [ 51 ], multimorbidity management [ 52 ], changing prescribing behaviour of benzodiazepines [ 53 ] and PCP engagement in ACP [ 57 ].

Patient/carer characteristics (theme)

Identified as important by eight reviews [ 49 , 50 , 53 , 54 , 56 , 59 , 64 , 65 ] (average quality score 8/11).

The majority of reviews identified perceptions of patient/carer perceived ideas, concerns, expectations and motivations as important barriers to preventing CVD [ 49 ], prescribing antibiotics [ 50 , 65 ] and benzodiazepines [ 53 ], chlamydia testing [ 59 ], cancer screening in older adults [ 54 ], and implementing EBM [ 64 ]. Attitudes were often born from stigma towards patients with mental health problems, and cultural diversity between the PCP and patient. For example, PCPs were found to have ambivalent attitudes towards working with depressed people, with some PCPs describing them as “burdens” and “people who bore you” [ 56 ]. Ethnic minorities were also felt to somatise their depression, and patients with social problems were seen to be avoiding work or seeking to medicalise their problems. This was compounded by a perception that management of patients presenting with social problems is complex. These beliefs were considered alongside other complex external factors, such as perceived pressure from parents to prescribe antibiotics [ 50 ], patient expectations different from the evidence [ 64 ], and a reluctance to medicalise unhealthy lifestyles [ 49 ].

Collaboration and communication with other health professionals (theme)

Identified as important by seven reviews [ 49 , 52 , 58 , 59 , 61 , 62 , 65 ] (average quality score 7.8/11).

Poor communication and uncoordinated care between PCPs and specialists were reported to hinder medication overviews, creating a feeling of uncertainty around the role of the PCP [ 52 ]. This was compounded by the perception of hierarchy between doctors and nurses [ 62 ] and negative attitudes towards handing over power [ 59 ]. Co-management with specialists was identified as an important facilitator in CVD prevention, to reinforce specialist advice and strengthen cohesive care [ 49 ]. Specialist input was desired by some PCPs to improve the awareness of the complexity of multimorbidity among specialists and ensure all doctors ‘speak with one voice’ to avoid provoking distrust [ 52 ]. Discussion with peers and personal or local prescribing feedback were identified as important facilitators to changing antibiotic prescribing [ 65 ]. Multiple facilitators to collaboration between nurse and medical practitioners in primary care were also identified [ 62 ]. These ranged from knowing the practitioner and having a good working relationship, reciprocity without hierarchy and control, effective communication including the use of technology, mutual trust and respect, shared responsibility and support from medical practitioners.

Norms, stigma and attitudes (theme)

Identified as important by five reviews [ 56 , 59 , 60 , 63 , 65 ] (average quality score 6.6/11).

The belief that patients would feel stigmatised or embarrassed was identified as an important barrier to depression diagnosis [ 56 ], chlamydia testing [ 59 , 63 ], discussing family history and genetics [ 60 ] and antibiotic prescribing for ARTIs [ 65 ]. Stigma towards depression was seen as an important barrier to addressing psychosocial aspects of depression and commencing treatment amongst patients from the Caribbean and South Asia [ 56 ]. Stigmatising attitudes towards depressed, obese and elderly people was also reported to impact clinical decision-making [ 49 , 56 ] (see ‘Patient and carer characteristics’ section). A major facilitator to reduce stigma and raise awareness was the normalisation of chlamydia testing [ 63 ]. This may include formal policy, guidelines or government programmes, feedback on testing rates, different methods of testing such as urine samples, and the use of non-heteronormative terminology.

BCW intervention functions and policy categories

COM-B components and intervention functions linked to the three TDF domains most frequently identified as important are shown in Table 5 . Based on this, five intervention functions from the BCW were identified as most likely to be successful in changing clinical behaviour by PCPs. Associated with improving ‘capability’ are education (increasing knowledge or understanding), training (imparting skills) and enablement (promoting collective action across networks to overcome barriers, such as behavioural support for smoking cessation) interventions. Associated with improving social and physical ‘Opportunity’ are restriction (using rules to engage in the target behaviour), environmental restructuring (changing the physical or social context) and enablement interventions. The TDF domains ‘intentions’, ‘goals’ and ‘optimism’, which all map to the ‘motivation’ domain of the COM-B, were perceived as the least influential on clinical behaviour change by PCPs. As a result, BCW intervention functions including persuasion, incentivisation, coercion and modelling may be perceived as less relevant by PCPs to change behaviour.

Using the BCW, the three policy categories most commonly associated with supporting the delivery of the five intervention functions identified include guidelines (creating documents that recommend or mandate practice, including all changes to service provision), regulation (establishing rules or principles of behaviour or practice, such as establishing voluntary agreements on advertising), and legislation (making or changing laws, such as prohibiting sale or use) (Table 5 ).

Summary of main results

Evidence across all reviews was heterogeneous, examining 16 different clinical behaviours across a range of primary care settings and healthcare systems. Most reviews were of moderate-to-high quality. All themes identified from the included reviews could be mapped to at least one domain from the TDF. Barriers, facilitators and factors most commonly reported by PCPs were related to ‘knowledge’, ‘environmental context and resources’ and ‘social influences’. Within these domains, ‘knowledge, awareness and uncertainty’ and ‘time, workload and general resources’ were by far the most important themes. Not only did factors affect various clinical behaviours such as diagnosis, management, and communication and collaboration with patients and other healthcare professionals, factors were also linked to each other in a complex way, often exacerbating each other in specific contexts and circumstances. For example, a lack of knowledge and uncertainty amongst PCPs is exacerbated by a poor or unestablished PCP-patient relationship, lack of time and resources, as well as patient characteristics, such as comorbidities and social problems.

Five out of nine intervention functions from the BCW (education, training, restriction, environmental restructuring and enablement) can be linked to the three TDF domains reported as most important by PCPs to help change clinical behaviour. These can be delivered through all seven policy categories of the BCW, although those most frequently associated policy categories with all five intervention categories are guidelines, regulation and legislation.

The TDF domains ‘intentions’, ‘goals’ and ‘optimism’, which all map to the ‘motivation’ domain of the COM-B, were perceived as the least influential on clinical behaviour change by PCPs. The TDF domains ‘behavioural regulation’, ‘memory, attention and decision processes’, ‘emotion’, ‘beliefs about consequences’, ‘reinforcement’, and ‘beliefs about capabilities’ were also perceived by PCPs as less important barriers or facilitators to behaviour change. ‘Behavioural regulation’, and ‘memory, attention and decision processes’ relate to the psychological aspect of ‘capability’ and the others, again, relate to the ‘Motivation’ domain of COM-B. This is a surprising finding, as the central premise of the TDF model is that domains linked to all three areas of the COM-B (capability, opportunity and motivation) model should interact to produce behaviour [ 25 ].

Linked to the automatic and reflective ‘motivation’ domain of COM-B are BCW interventions related to incentivisation, persuasion, coercion and modelling. It is therefore also surprising to find that PCPs did not identify these as important barriers and/or facilitators as substantial evidence exists regarding the widespread use of interventions associated with incentives (e.g. financial pay for performance or reputational league tables—albeit with mixed effects, and those which may utilise persuasion, modelling and even coercion (e.g. peer-to-peer outreach or public reporting) to change aspects of PCP behaviour [ 14 , 68 , 69 , 70 ].

The limited frequency and importance given to aspects of psychological ‘Capability’ and ‘Motivation’ raises questions as to whether PCPs may have less insight into these areas or less desire to identify them as barriers or facilitator. It is possible they may be neglecting the role of brain processes involved in developing psychological capabilities, i.e. the capacity to engage in the necessary thought processes using comprehension and reasoning, and those that energize or direct behaviour, such as habitual processes, emotional responding and automatic decision-making. With most studies using qualitative interviews or cross-sectional surveys, questions may also have focused on domains researchers and BCI designers believed to be relevant, such as external factors including time, guidelines and patients.

Key policy implications

Based on our findings, three TDF domains were most commonly reported across the majority of reviews, regardless of the type of behaviour change and context. This suggests that addressing these common factors through associated BCW intervention functions of education, training, restriction, environmental restructuring and enablement, and applying associated policy categories—namely guidelines, regulation and legislation, if not already addressed, could be prioritised to encourage PCPs to change clinical practice where needed across most clinical behaviours and settings.

Strengths and limitations

The robustness of our findings is supported by several features. A broad, sensitive search strategy maximised the number of eligible reviews identified. Although the extent to which findings are applicable to a specific healthcare system or clinical context is unclear, reviews meeting the inclusion criteria focused on 16 types of clinical behaviours across a breadth of healthcare systems and included over 72,000 PCPs, providing a good starting point to identify commonalities across PCPs from a variety of different primary care settings.

Large amounts of heterogeneous data was summarised in a clear way using two evidence-based frameworks, however precise mapping of barriers, facilitators and factors to the TDF proved challenging, owing to the complex interplay between factors and interpretation of the authors of where they fitted. The integration of the TDF and BCW means important barriers and facilitators can be linked to practical strategies to address them, which does, however, rely on the validity of the frameworks themselves.

Future research

Only a minority of reviews utilised a theory-based framework to synthesise evidence. To maximise the likelihood of intervention success and encourage the use of common terminology and understanding, future research should synthesise evidence using theory-informed frameworks, such as the TDF, paying particular attention to barriers and facilitators to behaviour change associated with PCPs’ own automatic and reflective motivation, and other aspects of psychological capability related to behaviour change. Methods exploring PCP motivation and aspects of psychological capability, as well as methods less reliant on PCPs’ insight, such as direct observation, may provide more valid conclusions.

To the best of our knowledge, this is the first theory-led systematic review of reviews examining barriers and facilitators to clinical behaviour change by PCPs across a variety of primary care settings using the TDF and BCW. From the evidence available, PCPs perceive that factors related to knowledge, environmental context and resources and social influences are influential across a variety of primary care contexts, often interacting with each other in a complex way. It is vital that future research utilises theory-based frameworks and appropriate design methods to explore factors relating to automatic and reflective motivation, such as habitual processes, emotional responding and automatic decision-making that energize or direct behaviour, as well as psychological capability of PCPs, including the capacity to engage in the necessary thought processes using comprehension, reasoning etc. With no ‘one size fits all’ intervention, these findings go some way to offering general, transferable lessons in how to approach changing clinical behaviour by PCPs and improve quality of care and population health outcomes.

Availability of data and materials

All data analysed during this study are included in this published article and its additional information files.

Abbreviations

Acute respiratory tract infection

Behaviour Change Intervention

• Behaviour Change Wheel

Critical Appraisal Skills Programme

Capability Opportunity Motivation Behaviour

Cardiovascular disease

Evidence-based medicine

• Family physician
• General practitioner

Health Management Information Consortium

Joanna Briggs Institute

National Institute for Health and Care Excellence

Nurse practitioner

Primary care practitioner

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Theoretical Domains Framework

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Acknowledgements

LP is funded by a National Institute for Health Research (NIHR) Doctoral Research Fellowship. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

This review was led by MM as her Master’s in Public Health thesis at the London School of Hygiene and Tropical Medicine (LSHTM), no funding was received for this. LP is funded by a NIHR Doctoral Research Fellowship. No funding was received by SN.

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MM co-designed the project title and methods, conducted the searches, screening, selection, quality appraisal, data extraction, data analysis and synthesis, and drafted the report. LP co-designed the project title and methods and revised the draft. SN conducted screening, selection, quality appraisal, data extraction and data analysis. All authors read and approved the final manuscript.

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Additional file 1..

PRISMA checklist.

Additional file 2.

Search strategy. Search concepts, keywords and MeSH terms used to derive search strategies. Search strategy.

Additional file 3.

Quality appraisal. Adapted scoring system for the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. Quality of empirical studies: appraisal instruments and quality scores. Quality appraisal criteria.

Additional file 4.

Additional data. Characteristics of included reviews.

Additional file 5.

Evidence mapping. Mapping of emergent themes to the Theoretical Domains Framework (TDF). Evidence table.

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Mather, M., Pettigrew, L.M. & Navaratnam, S. Barriers and facilitators to clinical behaviour change by primary care practitioners: a theory-informed systematic review of reviews using the Theoretical Domains Framework and Behaviour Change Wheel. Syst Rev 11 , 180 (2022). https://doi.org/10.1186/s13643-022-02030-2

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Allen School News

From models to manufacturing, 2024 sloan research fellows simon shaolei du and adriana schulz push new paradigms in computing.

Fascinated by the inner workings of machine learning models for data-driven decision-making, Allen School professor Simon Shaolei Du constructs their theoretical foundations to better understand what makes them tick and then designs algorithms that translate theory into practice. Du’s faculty colleague Adriana Schulz , meanwhile, has clocked how to make the act of making more accessible and sustainable through novel techniques in computer-aided design and manufacturing, drawing upon advances in machine learning, fabrication, programming languages and more. 

Those efforts received a boost from the Alfred P. Sloan Foundation earlier this year, when Schulz and Du were recognized among the 2024 class of Sloan Research Fellows representing the next generation of scientific leaders.

Simon Shaolei Du: Unlocking the mysteries of machine learning

Deep learning. Reinforcement learning. Representation learning. Recent breakthroughs in the training of large-scale machine learning models are transforming data-driven decision-making across a variety of domains and fueling developments ranging from self-driving vehicles to ChatGPT. But while we know that such models work, we don’t really know why .

“We still don’t have a good understanding of why these paradigms are so powerful,” Du explained in a UW News release . “My research aims to open the black box.”

Already, Du has been able to poke several holes in said box by demystifying several principles underlying the success of such models. For example, Du offered the first proof for how gradient descent optimizes the training of over-parameterized deep neural networks — so-called because the number of parameters significantly exceeds the minimum required relative to the size of the training dataset. Du and his co-authors showed that, with sufficient over-parameterization, gradient descent could find the global minima to achieve zero training loss even though the objective function is non-convex and non-smooth. Du was also able to explain how these models generalize so well despite their enormous size by proving a fundamental connection between deep neural network learning and kernel learning. 

Another connection Du has investigated is that between representation learning and recent advances in computer vision and natural language processing. Representation learning bypasses the need to train on each new task from scratch by drawing upon the commonalities underlying different but related tasks. Du was keen to understand how using large-scale but low-quality data to pre-train foundation models in the aforementioned domains effectively improves their performance on downstream tasks for which data is scarce — a condition known as few-shot learning. He and his collaborators developed a novel theoretical explanation for this phenomenon by proving that a good representation combined with a diversity of source training data are both necessary and sufficient for few-shot learning on a target task. Following this discovery, Du contributed to the first active learning algorithm for selecting pre-training data from the source task based on their relevance to the target task to make representation learning more efficient.

From representation to reinforcement: When it comes to modeling problems in data-driven decision-making, the latter is the gold standard. And the standard wisdom is that long planning horizons and large state spaces are why it is so difficult — or at least it was. Du and his collaborators turned the first assumption on its head by showing that sample complexity in reinforcement learning is not dependent upon whether the planning horizon is long or short. Du further challenged prevailing wisdom by demonstrating that a good representation of the optimal value function — which was presumed to address the state space problem — is not sufficient to ensure sample-efficient reinforcement learning across states. 

“My goal is to design machine learning tools that are theoretically principled, resource-efficient and broadly accessible to practitioners across a variety of domains,” said Du. “This will also help us to ensure they are aligned with human values, because it is apparent that these models are going to play an increasingly important role in our society.”

Adriana Schulz: Making a mark by remaking manufacturing-oriented design

AI’s influence on design is already being felt in a variety of sectors. But despite its promise to enhance quality and productivity, its application to design for manufacturing has lagged. So, too, has the software side of the personalized manufacturing revolution, which has failed to keep pace with hardware advances in 3D-printing, machine knitting, robotics and more. This is where Schulz aims to make her mark.

“Design for manufacturing is where ideas are transformed into products that influence our daily lives,” Schulz said. “We have the potential to redefine how we ideate, prototype and produce almost everything.”

To realize this potential, Schulz develops computer-aided design tools for manufacturing that are grounded in the fundamentals of geometric data processing and physics-based modeling and also draw from domains such as machine learning and programming languages. The goal is to empower users of varying skill levels and backgrounds to flex their creativity while optimizing their designs for functionality and production. 

One strategy is to treat design and fabrication as programs — that is, a set of physical instructions — and leverage formal reasoning and domain-specific languages to enable users to adjust plans on the fly based on their specific goals and constraints. Schulz and her collaborators took this approach with Carpentry Compiler , a tool for exploring tradeoffs between production time, cost of materials and other factors of their design before generating fabrication plans. She subsequently parlayed advances in program synthesis into a new tool for efficiently optimizing plans for both design and fabrication at the same time. Leveraging a technique called equivalence graphs, or e-graphs, Schulz and her team took advantage of inherent redundancies across design variations and fabrication alternatives to eliminate the need to recompute the fabrication cost from scratch with every design change. In a series of experiments, the new framework was shown to reduce project costs by as much as 60%.

Rising capabilities in AI have also given rise to a new field in computer science known as neurosymbolic reasoning , a hybrid approach to representing visual and other types of data that combines techniques from machine learning and symbolic program analysis. Schulz leveraged this emerging paradigm to make it easier for users of parametric CAD models for manufacturing to explore and manipulate variations of their designs while automatically retaining essential structural constraints. Typically, CAD users who wish to engage in such exploration have to go to the time and trouble of modifying multiple parameters simultaneously and then sifting through a slew of irrelevant outcomes to identify the meaningful ones. Schulz and her team streamlined the process by employing large language and image models to infer the space of meaningful variations of a shape, and then applying symbolic program analysis to identify common constraints across designs. Their system, ReparamCAD , offers a more intuitive, efficient and interactive approach to conventional CAD programs.

In addition to introducing more flexible design processes, Schulz has also contributed to more flexibility on the factory floor. Many assembly lines rely on robots that are task-specific, making it complex and costly to pivot the line to new tasks. Schulz and her colleagues sidestepped this problem by enabling the creation of 3D-printable passive grippers that can be swapped out at the end of a robotic arm to handle a variety of objects — including irregular shapes that would be a challenge for conventional grippers to manipulate. She and her team developed an algorithm that, when fed a 3D model of an object and its orientation, co-optimizes a gripper design and lift trajectory that will enable the robot to successfully pick up the item.

Whether it’s repurposed robots or software that minimizes material waste, Schulz’s past work offers a glimpse into manufacturing’s future — one that she hopes will be friendlier not just to people, but also to the planet.

“Moving forward, I plan to expand my efforts on sustainable design, exploring innovative design solutions that prioritize reusability and recyclability to foster circular ecosystems,” she told UW News .

Two other researchers with Allen School connections were among 22 computer scientists across North America to have been recognized among the 2024 class of Sloan Research Fellows. Justine Sherry (B.S., ‘10) is a professor at Carnegie Mellon University, where she leads research to modernize hardware and software for implementing middleboxes to make the internet more reliable, efficient, secure and equitable for users. Former visiting student Arvind Satyanarayan , who earned his Ph.D. from Stanford University while working with Allen School professor Jeffrey Heer in the UW Interactive Data Lab , is a professor at MIT, where he leads the MIT Visualization Group using interactive data visualization to explore intelligence augmentation that will amplify creativity and cognition while respecting human agency.

In addition, a third UW faculty member, chemistry professor Alexandra Velian , earned a Sloan Research Fellowship for her work on new materials to advance decarbonization, clean energy and quantum information technologies. 

For more on the 2024 Sloan Research Fellows, see the Sloan Foundation’s announcement and a related story by UW News .

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