chapter 3 research methodology and procedures

Reliability and Quality of Service Evaluation Methods for Rural Highways: A Guide (2024)

Chapter: 1 introduction.

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CHAPTER 1 Introduction Rural highways account for a very significant portion of the National Highway System and serve many vital mobility purposes, such as the following: • Connectivity between major urban areas. • Access to rural recreational areas (e.g., mountains, lakes, oceans). • Access to special events held in rural areas (e.g., concerts, regional festivals). • Evacuation route for extreme events (e.g., natural disasters). • Diversion of traffic from another disrupted route. Despite the importance of rural highways, infrastructure funding to improve operations is often more limited for rural highways than it is for congested urban roadways. Thus, to ensure effective investment of such funding, it is essential for highway agencies to identify locations of poor operations and consider appropriate mitigation measures. For this to be possible, an agency needs traffic analysis methods that allow for examination of short sections of highway (e.g., a passing zone, signalized intersection) individually and also within the context of an extended length (many miles) of highway. Rural highways, which often span distances of 20 to 60 miles between urban areas, may consist of segments with a variety of cross-section elements (two-lane highway, multilane highway, pass- ing lane sections) as well as intersections with different traffic controls (signal control, stop control, roundabouts with yield control). A sample of these component roadway configurations is shown in Figure 1.1. These component roadway types are described in detail in Chapter 2. These highways are usually more varied in horizontal and vertical alignment than urban roadways. The Highway Capacity Manual (HCM7), the standard reference for traffic analysis methodologies, contains analysis methods for all the individual segments or intersections that may constitute a rural highway; how- ever, it does not include a method, or guidance, for connecting the individual roadway segments into a connected, cohesive, facility-level analysis) (TRB 2022). It is important to continue to extend the capabilities of the HCM7 analysis methodologies, particularly at the facility level, so that roadway design and traffic engineers have the analysis tools they need to perform accurate and comprehen- sive facility evaluations. Furthermore, analysis at the facility level is consistent with the fact that drivers typically evaluate the quality of their trip over its entire length, not just in separate segments. 1.1 Purpose of the Guide This Guide is intended to assist transportation agencies charged with monitoring, maintain- ing, and improving rural highways of regional or statewide importance, specifically with the evaluation of rural highways in three areas: • Motorized vehicle traffic operations per HCM7 analysis methods. • Motorized vehicle traffic operations per probe vehicle data analysis methods. 3 

4   Reliability and Quality of Service Evaluation Methods for Rural Highways: A Guide Figure 1.1.   A sample of rural highway component roadway configurations. 

Introduction  5 • Overview of alternative analysis methods, to the HCM7, for bicycles and recommendations for future bicycle operations’ research needs. With the heterogeneity of cross-section (i.e., roadway segment type) composition over such distances and the disparate HCM7 service measures (density, follower density, delay) across these segment types, the process for performing an HCM7 facility analysis across the variety of contiguous segments contained within a rural highway facility is not necessarily straightforward. This Guide proposes an analysis framework for assessing the level of service (LOS) of auto- mobiles on a long rural highway facility (20+ miles). In addition to LOS, several other facility-level performance measures are presented along with discussion about the analysis context in which such measures are useful for evaluating overall traffic operations along the route. While simulation is always an option for analyzing a stretch of rural highways, the level of effort would be high for typical rural highway distances considered for analysis. In some situa- tions, simulation may be warranted, but the methodology described in this Guide would still be a good first step and may even be sufficient. This methodology would also be much more efficient for performing “what-if ” scenario testing, where relative differences in results are the primary concern. Reliability analyses on freeways and arterials are typically based on HCM7 guidance on sce- nario generation and predictive reliability. However, because of the typically limited data availabil- ity on rural highways, the reliability analysis in this report is focused on historical probe vehicle data and meant to be used in conjunction with the HCM7 automobile LOS methodology. Demand for cycling in urban areas and on rural highways is on the increase, yet it is not clear which analysis procedures are best suited to cover large rural highway facilities or statewide analyses. This Guide summarizes existing HCM7 analysis methods used for the bicycle mode as well as two popular alternatives: Level of Stress and Bicycle Compatibility Index. In addi- tion, it proposes recommendations for future bicycle operations research needs based on two qualitative surveys. This Guide is intended to serve as a companion to the HCM. 1.2 Guide Scope and Limitations The facility-level analysis is important in assessing current conditions along important cor- ridors for people and goods movement. The analysis methodology is also useful for evaluating facility performance for situations where significant changes in traffic demand or capacity may occur, such as in the following scenarios: • Evaluation of a rural highway route to handle potential evacuation traffic demand (e.g., forest fire, hurricane). • Evaluation of a rural highway route to handle potential diversion traffic demand due to an alternative route being closed or restricted due to construction, an incident (e.g., truck roll- over), or a natural disaster (e.g., landslide/avalanche, flooding). • Evaluation of a rural highway route to handle short-term spikes in traffic demand due to recre- ational activities (e.g., weekend ski season, Labor Day weekend beach travel, concert/festival). • Evaluation of a rural highway route to handle short-term spikes in traffic demand and/or heavy vehicles due to season-specific activities (e.g., crop harvesting in farming regions). • Evaluation of a rural highway route to handle a large increase in traffic demand as projected to occur as part of construction of a large generator for regional economic development (e.g., tribal casino, Amazon distribution warehouse). The parameters of the scope for this project generally required that the developed LOS evaluation methodology make use of the existing analysis methodologies within the HCM. However, 

6   Reliability and Quality of Service Evaluation Methods for Rural Highways: A Guide to facilitate the development of a facility-level evaluation methodology, it was necessary to develop a few new computational procedures, largely to connect component pieces of highway into a single facility for evaluation purposes. Furthermore, some planning-level simplifications, such as the classification of terrain and the treatment of signal progression along an arterial, were implemented. Such simplifications were included to (1) reduce the segmentation process effort and/or (2) reduce the complexity of the calculation process where the return on such precision is minimized for the relatively long lengths of rural highway. This Guide also introduces a method for evaluating rural highway operations with the use of probe vehicle data. Over the last decade, the spatial and temporal coverage of probe vehicle data available from third-party vendors has improved immensely. Many state agencies now pay for subscriptions to providers of such data and are making use of the data to supplement their traditional data sources (e.g., fixed-point sensors) for assessing and managing traffic operations on their roadways. These data generally consist of average travel times/speeds and correspond to a sample of the vehicles traveling along a given roadway segment; thus, an important limitation of this data source is that it does not include flow rate. To obtain flow rates, agencies may supplement the probe vehicle data with fixed-point sensors or conduct field data collection using portable sensors on a regular basis. Another limitation of probe data is that their accuracy drops in low-traffic conditions, such as late night or early morning when fewer samples are available. The spatial resolution of the probe data might also pose a challenge, particularly when analyzing very short segments. Probe data are typically reported for predefined segments of roadway— often referred to as traffic message channels (TMCs). The TMCs’ length usually ranges from approximately 0.6 to 2 miles, and the TMC boundaries do not necessarily match those used for other traffic analysis purposes, such as for segments as defined by the HCM. The temporal reso- lution of the measurements ranges from approximately 1 to 5 minutes. Currently, the quantity and quality of probe vehicle data are much greater for urban areas than for rural areas. This gap, however, will continue to narrow with time. The automobile LOS methodology presented in this Guide is not intended to handle over- saturated traffic flow conditions. For multilane and two-lane highway segments, the HCM7 analysis methodologies do not include any mechanism to deal with traffic demand exceeding capacity. In some instances, short periods of demand exceeding capacity can be accounted for in the intersection analysis methodologies. The HCM7 should be consulted for further information on this topic. 1.3 Guide Organization This Guide is organized into three parts. Part I focuses on analysis methodology descriptions and consists of the following chapters: • Chapter 1—Introduction. This chapter provides an overview of the Guide’s purpose, scope, and limitations. It also discusses the format of the Guide and how the user community can contribute to the content. Further, the chapter provides a summary of the research behind this Guide. • Chapter 2—Rural Highway LOS for Automobiles. This chapter provides the methodology used to assess traffic operational quality and LOS for the automobile mode on rural highways. • Chapter 3—Automobile Travel Time Reliability. This chapter describes methods for quanti- fying travel time reliability, from a historical perspective, based on probe vehicle travel speed measurements. 

Introduction  7 • Chapter 4—Bicycle Operations Analysis on Rural Highways. This chapter provides an overview of commonly used methods for assessing bicycle operations, recommendations for which methods are most appropriate for certain bicycle analysis situations, and recom- mendations for enhancements to the commonly used analysis methods. Part II consists of Chapters 5 through 12. It provides an overview of the component HCM7 analysis methodologies that are incorporated in the rural highway analysis methodology for automobiles. This part does not replicate the full content of the relevant HCM7 analysis method- ologies but rather summarizes the chapters and sections that are used within the rural highway analysis methodology. This material will be updated as necessary to reflect updates to the HCM. Part III consists of Chapters 13 through 19. It focuses on case studies using real-world routes to demonstrate the analysis methodologies in the Guide. The material is contained in a separate part of the report to facilitate the inclusion of additional case studies in the future. 1.4 Supporting Resources and Tools Several complementary resources are provided with this Guide. LOS Calculation Software and Case Study Input Files. The LOS calculation methodology described in this Guide is available in the software tool HCM-CALC. This program can be downloaded from https://github.com/swash17/HCM-CALC. The Computational Engine chapter also provides an overview of the software tool. Input data files for the case studies for the HCM-CALC software are also available. Scripting Code/Tools for “Reliability” Calculations/Output. Scripts, written in Python programming code (https://www.python.org/about/), to process probe vehicle data and produce a variety of visualizations are provided. More information is provided in Chapter 3. KML Files for Case Studies. For each of the case studies, supporting information for the segmentation process is included in a KML file. [A Keyhole Markup Language (KML) file contains geographic and supporting data for use with geographic software visualization tools. More information can be found at https://en.wikipedia.org/wiki/Keyhole_Markup_Language.] Detailed information about the KML files is provided in the introduction to Part 3: Case Studies. More information about these resources is contained in Chapter 15. This Guide was developed through NCHRP Project 08-135: “Reliability and Quality of Service Evaluation Methods for Rural Highways.” A conduct of research report was also produced for this project, published as NCHRP Web-Only Document 392: Developing a Guide for Rural High- ways: Reliability and Quality of Service Evaluation Methods (Washburn et al. 2024). That report contains additional details about the development of the material contained in this Guide. 

Rural highways account for a significant portion of the National Highway System and serve many vital mobility purposes. The Highway Capacity Manual , the standard reference for traffic analysis methodologies, contains analysis methodologies for all of the individual segments or intersections that may constitute a rural highway; however, it does not include a methodology or guidelines for connecting the individual roadway segments into a connected, cohesive, facility-level analysis.

NCHRP Research Report 1102: Reliability and Quality of Service Evaluation Methods for Rural Highways: A Guide , from TRB's National Cooperative Highway Research Program, presents a guide for traffic analysis of rural highways that connects the individual highway segments into a connected, cohesive, facility-level analysis.

Supplemental to the report is NCHRP Web-Only Document 392: Developing a Guide for Rural Highways: Reliability and Quality of Service Evaluation Methods .

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Part 3: Using quantitative methods

13. Experimental design

Chapter outline.

• What is an experiment and when should you use one? (8 minute read)
• True experimental designs (7 minute read)
• Quasi-experimental designs (8 minute read)
• Non-experimental designs (5 minute read)
• Critical, ethical, and critical considerations  (5 minute read)

Content warning : examples in this chapter contain references to non-consensual research in Western history, including experiments conducted during the Holocaust and on African Americans (section 13.6).

13.1 What is an experiment and when should you use one?

Learning objectives.

Learners will be able to…

• Identify the characteristics of a basic experiment
• Describe causality in experimental design
• Discuss the relationship between dependent and independent variables in experiments
• Explain the links between experiments and generalizability of results
• Describe advantages and disadvantages of experimental designs

The basics of experiments

The first experiment I can remember using was for my fourth grade science fair. I wondered if latex- or oil-based paint would hold up to sunlight better. So, I went to the hardware store and got a few small cans of paint and two sets of wooden paint sticks. I painted one with oil-based paint and the other with latex-based paint of different colors and put them in a sunny spot in the back yard. My hypothesis was that the oil-based paint would fade the most and that more fading would happen the longer I left the paint sticks out. (I know, it’s obvious, but I was only 10.)

I checked in on the paint sticks every few days for a month and wrote down my observations. The first part of my hypothesis ended up being wrong—it was actually the latex-based paint that faded the most. But the second part was right, and the paint faded more and more over time. This is a simple example, of course—experiments get a heck of a lot more complex than this when we’re talking about real research.

Merriam-Webster defines an experiment   as “an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.” Each of these three components of the definition will come in handy as we go through the different types of experimental design in this chapter. Most of us probably think of the physical sciences when we think of experiments, and for good reason—these experiments can be pretty flashy! But social science and psychological research follow the same scientific methods, as we’ve discussed in this book.

As the video discusses, experiments can be used in social sciences just like they can in physical sciences. It makes sense to use an experiment when you want to determine the cause of a phenomenon with as much accuracy as possible. Some types of experimental designs do this more precisely than others, as we’ll see throughout the chapter. If you’ll remember back to Chapter 11  and the discussion of validity, experiments are the best way to ensure internal validity, or the extent to which a change in your independent variable causes a change in your dependent variable.

Experimental designs for research projects are most appropriate when trying to uncover or test a hypothesis about the cause of a phenomenon, so they are best for explanatory research questions. As we’ll learn throughout this chapter, different circumstances are appropriate for different types of experimental designs. Each type of experimental design has advantages and disadvantages, and some are better at controlling the effect of extraneous variables —those variables and characteristics that have an effect on your dependent variable, but aren’t the primary variable whose influence you’re interested in testing. For example, in a study that tries to determine whether aspirin lowers a person’s risk of a fatal heart attack, a person’s race would likely be an extraneous variable because you primarily want to know the effect of aspirin.

In practice, many types of experimental designs can be logistically challenging and resource-intensive. As practitioners, the likelihood that we will be involved in some of the types of experimental designs discussed in this chapter is fairly low. However, it’s important to learn about these methods, even if we might not ever use them, so that we can be thoughtful consumers of research that uses experimental designs.

While we might not use all of these types of experimental designs, many of us will engage in evidence-based practice during our time as social workers. A lot of research developing evidence-based practice, which has a strong emphasis on generalizability, will use experimental designs. You’ve undoubtedly seen one or two in your literature search so far.

The logic of experimental design

How do we know that one phenomenon causes another? The complexity of the social world in which we practice and conduct research means that causes of social problems are rarely cut and dry. Uncovering explanations for social problems is key to helping clients address them, and experimental research designs are one road to finding answers.

As you read about in Chapter 8 (and as we’ll discuss again in Chapter 15 ), just because two phenomena are related in some way doesn’t mean that one causes the other. Ice cream sales increase in the summer, and so does the rate of violent crime; does that mean that eating ice cream is going to make me murder someone? Obviously not, because ice cream is great. The reality of that relationship is far more complex—it could be that hot weather makes people more irritable and, at times, violent, while also making people want ice cream. More likely, though, there are other social factors not accounted for in the way we just described this relationship.

Experimental designs can help clear up at least some of this fog by allowing researchers to isolate the effect of interventions on dependent variables by controlling extraneous variables . In true experimental design (discussed in the next section) and some quasi-experimental designs, researchers accomplish this w ith the control group and the experimental group . (The experimental group is sometimes called the “treatment group,” but we will call it the experimental group in this chapter.) The control group does not receive the intervention you are testing (they may receive no intervention or what is known as “treatment as usual”), while the experimental group does. (You will hopefully remember our earlier discussion of control variables in Chapter 8 —conceptually, the use of the word “control” here is the same.)

In a well-designed experiment, your control group should look almost identical to your experimental group in terms of demographics and other relevant factors. What if we want to know the effect of CBT on social anxiety, but we have learned in prior research that men tend to have a more difficult time overcoming social anxiety? We would want our control and experimental groups to have a similar gender mix because it would limit the effect of gender on our results, since ostensibly, both groups’ results would be affected by gender in the same way. If your control group has 5 women, 6 men, and 4 non-binary people, then your experimental group should be made up of roughly the same gender balance to help control for the influence of gender on the outcome of your intervention. (In reality, the groups should be similar along other dimensions, as well, and your group will likely be much larger.) The researcher will use the same outcome measures for both groups and compare them, and assuming the experiment was designed correctly, get a pretty good answer about whether the intervention had an effect on social anxiety.

You will also hear people talk about comparison groups , which are similar to control groups. The primary difference between the two is that a control group is populated using random assignment, but a comparison group is not. Random assignment entails using a random process to decide which participants are put into the control or experimental group (which participants receive an intervention and which do not). By randomly assigning participants to a group, you can reduce the effect of extraneous variables on your research because there won’t be a systematic difference between the groups.

Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other related fields. Random sampling also helps a great deal with generalizability , whereas random assignment increases internal validity .

We have already learned about internal validity in Chapter 11 . The use of an experimental design will bolster internal validity since it works to isolate causal relationships. As we will see in the coming sections, some types of experimental design do this more effectively than others. It’s also worth considering that true experiments, which most effectively show causality , are often difficult and expensive to implement. Although other experimental designs aren’t perfect, they still produce useful, valid evidence and may be more feasible to carry out.

Key Takeaways

• Experimental designs are useful for establishing causality, but some types of experimental design do this better than others.
• Experiments help researchers isolate the effect of the independent variable on the dependent variable by controlling for the effect of extraneous variables .
• Experiments use a control/comparison group and an experimental group to test the effects of interventions. These groups should be as similar to each other as possible in terms of demographics and other relevant factors.
• True experiments have control groups with randomly assigned participants, while other types of experiments have comparison groups to which participants are not randomly assigned.
• Think about the research project you’ve been designing so far. How might you use a basic experiment to answer your question? If your question isn’t explanatory, try to formulate a new explanatory question and consider the usefulness of an experiment.
• Why is establishing a simple relationship between two variables not indicative of one causing the other?

13.2 True experimental design

• Describe a true experimental design in social work research
• Understand the different types of true experimental designs
• Determine what kinds of research questions true experimental designs are suited for
• Discuss advantages and disadvantages of true experimental designs

True experimental design , often considered to be the “gold standard” in research designs, is thought of as one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity and its ability to establish ( causality ) through treatment manipulation, while controlling for the effects of extraneous variable. Sometimes the treatment level is no treatment, while other times it is simply a different treatment than that which we are trying to evaluate. For example, we might have a control group that is made up of people who will not receive any treatment for a particular condition. Or, a control group could consist of people who consent to treatment with DBT when we are testing the effectiveness of CBT.

As we discussed in the previous section, a true experiment has a control group with participants randomly assigned , and an experimental group . This is the most basic element of a true experiment. The next decision a researcher must make is when they need to gather data during their experiment. Do they take a baseline measurement and then a measurement after treatment, or just a measurement after treatment, or do they handle measurement another way? Below, we’ll discuss the three main types of true experimental designs. There are sub-types of each of these designs, but here, we just want to get you started with some of the basics.

Using a true experiment in social work research is often pretty difficult, since as I mentioned earlier, true experiments can be quite resource intensive. True experiments work best with relatively large sample sizes, and random assignment, a key criterion for a true experimental design, is hard (and unethical) to execute in practice when you have people in dire need of an intervention. Nonetheless, some of the strongest evidence bases are built on true experiments.

For the purposes of this section, let’s bring back the example of CBT for the treatment of social anxiety. We have a group of 500 individuals who have agreed to participate in our study, and we have randomly assigned them to the control and experimental groups. The folks in the experimental group will receive CBT, while the folks in the control group will receive more unstructured, basic talk therapy. These designs, as we talked about above, are best suited for explanatory research questions.

Before we get started, take a look at the table below. When explaining experimental research designs, we often use diagrams with abbreviations to visually represent the experiment. Table 13.1 starts us off by laying out what each of the abbreviations mean.

Pretest and post-test control group design

In pretest and post-test control group design , participants are given a pretest of some kind to measure their baseline state before their participation in an intervention. In our social anxiety experiment, we would have participants in both the experimental and control groups complete some measure of social anxiety—most likely an established scale and/or a structured interview—before they start their treatment. As part of the experiment, we would have a defined time period during which the treatment would take place (let’s say 12 weeks, just for illustration). At the end of 12 weeks, we would give both groups the same measure as a post-test .

In the diagram, RA (random assignment group A) is the experimental group and RB is the control group. O 1 denotes the pre-test, X e denotes the experimental intervention, and O 2 denotes the post-test. Let’s look at this diagram another way, using the example of CBT for social anxiety that we’ve been talking about.

In a situation where the control group received treatment as usual instead of no intervention, the diagram would look this way, with X i denoting treatment as usual (Figure 13.3).

Hopefully, these diagrams provide you a visualization of how this type of experiment establishes time order , a key component of a causal relationship. Did the change occur after the intervention? Assuming there is a change in the scores between the pretest and post-test, we would be able to say that yes, the change did occur after the intervention. Causality can’t exist if the change happened before the intervention—this would mean that something else led to the change, not our intervention.

Post-test only control group design

Post-test only control group design involves only giving participants a post-test, just like it sounds (Figure 13.4).

But why would you use this design instead of using a pretest/post-test design? One reason could be the testing effect that can happen when research participants take a pretest. In research, the testing effect refers to “measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself” (Engel & Schutt, 2017, p. 444) [1] (When we say “measurement error,” all we mean is the accuracy of the way we measure the dependent variable.) Figure 13.4 is a visualization of this type of experiment. The testing effect isn’t always bad in practice—our initial assessments might help clients identify or put into words feelings or experiences they are having when they haven’t been able to do that before. In research, however, we might want to control its effects to isolate a cleaner causal relationship between intervention and outcome.

Going back to our CBT for social anxiety example, we might be concerned that participants would learn about social anxiety symptoms by virtue of taking a pretest. They might then identify that they have those symptoms on the post-test, even though they are not new symptoms for them. That could make our intervention look less effective than it actually is.

However, without a baseline measurement establishing causality can be more difficult. If we don’t know someone’s state of mind before our intervention, how do we know our intervention did anything at all? Establishing time order is thus a little more difficult. You must balance this consideration with the benefits of this type of design.

Solomon four group design

One way we can possibly measure how much the testing effect might change the results of the experiment is with the Solomon four group design. Basically, as part of this experiment, you have two control groups and two experimental groups. The first pair of groups receives both a pretest and a post-test. The other pair of groups receives only a post-test (Figure 13.5). This design helps address the problem of establishing time order in post-test only control group designs.

For our CBT project, we would randomly assign people to four different groups instead of just two. Groups A and B would take our pretest measures and our post-test measures, and groups C and D would take only our post-test measures. We could then compare the results among these groups and see if they’re significantly different between the folks in A and B, and C and D. If they are, we may have identified some kind of testing effect, which enables us to put our results into full context. We don’t want to draw a strong causal conclusion about our intervention when we have major concerns about testing effects without trying to determine the extent of those effects.

Solomon four group designs are less common in social work research, primarily because of the logistics and resource needs involved. Nonetheless, this is an important experimental design to consider when we want to address major concerns about testing effects.

• True experimental design is best suited for explanatory research questions.
• True experiments require random assignment of participants to control and experimental groups.
• Pretest/post-test research design involves two points of measurement—one pre-intervention and one post-intervention.
• Post-test only research design involves only one point of measurement—post-intervention. It is a useful design to minimize the effect of testing effects on our results.
• Solomon four group research design involves both of the above types of designs, using 2 pairs of control and experimental groups. One group receives both a pretest and a post-test, while the other receives only a post-test. This can help uncover the influence of testing effects.
• Think about a true experiment you might conduct for your research project. Which design would be best for your research, and why?
• What challenges or limitations might make it unrealistic (or at least very complicated!) for you to carry your true experimental design in the real-world as a student researcher?
• What hypothesis(es) would you test using this true experiment?

13.4 Quasi-experimental designs

• Describe a quasi-experimental design in social work research
• Understand the different types of quasi-experimental designs
• Determine what kinds of research questions quasi-experimental designs are suited for
• Discuss advantages and disadvantages of quasi-experimental designs

Quasi-experimental designs are a lot more common in social work research than true experimental designs. Although quasi-experiments don’t do as good a job of giving us robust proof of causality , they still allow us to establish time order , which is a key element of causality. The prefix quasi means “resembling,” so quasi-experimental research is research that resembles experimental research, but is not true experimental research. Nonetheless, given proper research design, quasi-experiments can still provide extremely rigorous and useful results.

There are a few key differences between true experimental and quasi-experimental research. The primary difference between quasi-experimental research and true experimental research is that quasi-experimental research does not involve random assignment to control and experimental groups. Instead, we talk about comparison groups in quasi-experimental research instead. As a result, these types of experiments don’t control the effect of extraneous variables as well as a true experiment.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention.  We’re able to eliminate some threats to internal validity, but we can’t do this as effectively as we can with a true experiment.  Realistically, our CBT-social anxiety project is likely to be a quasi experiment, based on the resources and participant pool we’re likely to have available. 

It’s important to note that not all quasi-experimental designs have a comparison group.  There are many different kinds of quasi-experiments, but we will discuss the three main types below: nonequivalent comparison group designs, time series designs, and ex post facto comparison group designs.

Nonequivalent comparison group design

You will notice that this type of design looks extremely similar to the pretest/post-test design that we discussed in section 13.3. But instead of random assignment to control and experimental groups, researchers use other methods to construct their comparison and experimental groups. A diagram of this design will also look very similar to pretest/post-test design, but you’ll notice we’ve removed the “R” from our groups, since they are not randomly assigned (Figure 13.6).

Researchers using this design select a comparison group that’s as close as possible based on relevant factors to their experimental group. Engel and Schutt (2017) [2] identify two different selection methods:

• Individual matching : Researchers take the time to match individual cases in the experimental group to similar cases in the comparison group. It can be difficult, however, to match participants on all the variables you want to control for.
• Aggregate matching : Instead of trying to match individual participants to each other, researchers try to match the population profile of the comparison and experimental groups. For example, researchers would try to match the groups on average age, gender balance, or median income. This is a less resource-intensive matching method, but researchers have to ensure that participants aren’t choosing which group (comparison or experimental) they are a part of.

As we’ve already talked about, this kind of design provides weaker evidence that the intervention itself leads to a change in outcome. Nonetheless, we are still able to establish time order using this method, and can thereby show an association between the intervention and the outcome. Like true experimental designs, this type of quasi-experimental design is useful for explanatory research questions.

What might this look like in a practice setting? Let’s say you’re working at an agency that provides CBT and other types of interventions, and you have identified a group of clients who are seeking help for social anxiety, as in our earlier example. Once you’ve obtained consent from your clients, you can create a comparison group using one of the matching methods we just discussed. If the group is small, you might match using individual matching, but if it’s larger, you’ll probably sort people by demographics to try to get similar population profiles. (You can do aggregate matching more easily when your agency has some kind of electronic records or database, but it’s still possible to do manually.)

Time series design

Another type of quasi-experimental design is a time series design. Unlike other types of experimental design, time series designs do not have a comparison group. A time series is a set of measurements taken at intervals over a period of time (Figure 13.7). Proper time series design should include at least three pre- and post-intervention measurement points. While there are a few types of time series designs, we’re going to focus on the most common: interrupted time series design.

But why use this method? Here’s an example. Let’s think about elementary student behavior throughout the school year. As anyone with children or who is a teacher knows, kids get very excited and animated around holidays, days off, or even just on a Friday afternoon. This fact might mean that around those times of year, there are more reports of disruptive behavior in classrooms. What if we took our one and only measurement in mid-December? It’s possible we’d see a higher-than-average rate of disruptive behavior reports, which could bias our results if our next measurement is around a time of year students are in a different, less excitable frame of mind. When we take multiple measurements throughout the first half of the school year, we can establish a more accurate baseline for the rate of these reports by looking at the trend over time.

We may want to test the effect of extended recess times in elementary school on reports of disruptive behavior in classrooms. When students come back after the winter break, the school extends recess by 10 minutes each day (the intervention), and the researchers start tracking the monthly reports of disruptive behavior again. These reports could be subject to the same fluctuations as the pre-intervention reports, and so we once again take multiple measurements over time to try to control for those fluctuations.

This method improves the extent to which we can establish causality because we are accounting for a major extraneous variable in the equation—the passage of time. On its own, it does not allow us to account for other extraneous variables, but it does establish time order and association between the intervention and the trend in reports of disruptive behavior. Finding a stable condition before the treatment that changes after the treatment is evidence for causality between treatment and outcome.

Ex post facto comparison group design

Ex post facto (Latin for “after the fact”) designs are extremely similar to nonequivalent comparison group designs. There are still comparison and experimental groups, pretest and post-test measurements, and an intervention. But in ex post facto designs, participants are assigned to the comparison and experimental groups once the intervention has already happened. This type of design often occurs when interventions are already up and running at an agency and the agency wants to assess effectiveness based on people who have already completed treatment.

In most clinical agency environments, social workers conduct both initial and exit assessments, so there are usually some kind of pretest and post-test measures available. We also typically collect demographic information about our clients, which could allow us to try to use some kind of matching to construct comparison and experimental groups.

In terms of internal validity and establishing causality, ex post facto designs are a bit of a mixed bag. The ability to establish causality depends partially on the ability to construct comparison and experimental groups that are demographically similar so we can control for these extraneous variables .

Quasi-experimental designs are common in social work intervention research because, when designed correctly, they balance the intense resource needs of true experiments with the realities of research in practice. They still offer researchers tools to gather robust evidence about whether interventions are having positive effects for clients.

• Quasi-experimental designs are similar to true experiments, but do not require random assignment to experimental and control groups.
• In quasi-experimental projects, the group not receiving the treatment is called the comparison group, not the control group.
• Nonequivalent comparison group design is nearly identical to pretest/post-test experimental design, but participants are not randomly assigned to the experimental and control groups. As a result, this design provides slightly less robust evidence for causality.
• Nonequivalent groups can be constructed by individual matching or aggregate matching .
• Time series design does not have a control or experimental group, and instead compares the condition of participants before and after the intervention by measuring relevant factors at multiple points in time. This allows researchers to mitigate the error introduced by the passage of time.
• Ex post facto comparison group designs are also similar to true experiments, but experimental and comparison groups are constructed after the intervention is over. This makes it more difficult to control for the effect of extraneous variables, but still provides useful evidence for causality because it maintains the time order of the experiment.
• Think back to the experiment you considered for your research project in Section 13.3. Now that you know more about quasi-experimental designs, do you still think it’s a true experiment? Why or why not?
• What should you consider when deciding whether an experimental or quasi-experimental design would be more feasible or fit your research question better?

13.5 Non-experimental designs

• Describe non-experimental designs in social work research
• Discuss how non-experimental research differs from true and quasi-experimental research
• Demonstrate an understanding the different types of non-experimental designs
• Determine what kinds of research questions non-experimental designs are suited for
• Discuss advantages and disadvantages of non-experimental designs

The previous sections have laid out the basics of some rigorous approaches to establish that an intervention is responsible for changes we observe in research participants. This type of evidence is extremely important to build an evidence base for social work interventions, but it’s not the only type of evidence to consider. We will discuss qualitative methods, which provide us with rich, contextual information, in Part 4 of this text. The designs we’ll talk about in this section are sometimes used in qualitative research  but in keeping with our discussion of experimental design so far, we’re going to stay in the quantitative research realm for now. Non-experimental is also often a stepping stone for more rigorous experimental design in the future, as it can help test the feasibility of your research.

In general, non-experimental designs do not strongly support causality and don’t address threats to internal validity. However, that’s not really what they’re intended for. Non-experimental designs are useful for a few different types of research, including explanatory questions in program evaluation. Certain types of non-experimental design are also helpful for researchers when they are trying to develop a new assessment or scale. Other times, researchers or agency staff did not get a chance to gather any assessment information before an intervention began, so a pretest/post-test design is not possible.

A significant benefit of these types of designs is that they’re pretty easy to execute in a practice or agency setting. They don’t require a comparison or control group, and as Engel and Schutt (2017) [3] point out, they “flow from a typical practice model of assessment, intervention, and evaluating the impact of the intervention” (p. 177). Thus, these designs are fairly intuitive for social workers, even when they aren’t expert researchers. Below, we will go into some detail about the different types of non-experimental design.

One group pretest/post-test design

Also known as a before-after one-group design, this type of research design does not have a comparison group and everyone who participates in the research receives the intervention (Figure 13.8). This is a common type of design in program evaluation in the practice world. Controlling for extraneous variables is difficult or impossible in this design, but given that it is still possible to establish some measure of time order, it does provide weak support for causality.

Imagine, for example, a researcher who is interested in the effectiveness of an anti-drug education program on elementary school students’ attitudes toward illegal drugs. The researcher could assess students’ attitudes about illegal drugs (O 1 ), implement the anti-drug program (X), and then immediately after the program ends, the researcher could once again measure students’ attitudes toward illegal drugs (O 2 ). You can see how this would be relatively simple to do in practice, and have probably been involved in this type of research design yourself, even if informally. But hopefully, you can also see that this design would not provide us with much evidence for causality because we have no way of controlling for the effect of extraneous variables. A lot of things could have affected any change in students’ attitudes—maybe girls already had different attitudes about illegal drugs than children of other genders, and when we look at the class’s results as a whole, we couldn’t account for that influence using this design.

All of that doesn’t mean these results aren’t useful, however. If we find that children’s attitudes didn’t change at all after the drug education program, then we need to think seriously about how to make it more effective or whether we should be using it at all. (This immediate, practical application of our results highlights a key difference between program evaluation and research, which we will discuss in Chapter 23 .)

After-only design

As the name suggests, this type of non-experimental design involves measurement only after an intervention. There is no comparison or control group, and everyone receives the intervention. I have seen this design repeatedly in my time as a program evaluation consultant for nonprofit organizations, because often these organizations realize too late that they would like to or need to have some sort of measure of what effect their programs are having.

Because there is no pretest and no comparison group, this design is not useful for supporting causality since we can’t establish the time order and we can’t control for extraneous variables. However, that doesn’t mean it’s not useful at all! Sometimes, agencies need to gather information about how their programs are functioning. A classic example of this design is satisfaction surveys—realistically, these can only be administered after a program or intervention. Questions regarding satisfaction, ease of use or engagement, or other questions that don’t involve comparisons are best suited for this type of design.

Static-group design

A final type of non-experimental research is the static-group design. In this type of research, there are both comparison and experimental groups, which are not randomly assigned. There is no pretest, only a post-test, and the comparison group has to be constructed by the researcher. Sometimes, researchers will use matching techniques to construct the groups, but often, the groups are constructed by convenience of who is being served at the agency.

Non-experimental research designs are easy to execute in practice, but we must be cautious about drawing causal conclusions from the results. A positive result may still suggest that we should continue using a particular intervention (and no result or a negative result should make us reconsider whether we should use that intervention at all). You have likely seen non-experimental research in your daily life or at your agency, and knowing the basics of how to structure such a project will help you ensure you are providing clients with the best care possible.

• Non-experimental designs are useful for describing phenomena, but cannot demonstrate causality.
• After-only designs are often used in agency and practice settings because practitioners are often not able to set up pre-test/post-test designs.
• Non-experimental designs are useful for explanatory questions in program evaluation and are helpful for researchers when they are trying to develop a new assessment or scale.
• Non-experimental designs are well-suited to qualitative methods.
• If you were to use a non-experimental design for your research project, which would you choose? Why?
• Have you conducted non-experimental research in your practice or professional life? Which type of non-experimental design was it?

13.6 Critical, ethical, and cultural considerations

• Describe critiques of experimental design
• Identify ethical issues in the design and execution of experiments
• Identify cultural considerations in experimental design

As I said at the outset, experiments, and especially true experiments, have long been seen as the gold standard to gather scientific evidence. When it comes to research in the biomedical field and other physical sciences, true experiments are subject to far less nuance than experiments in the social world. This doesn’t mean they are easier—just subject to different forces. However, as a society, we have placed the most value on quantitative evidence obtained through empirical observation and especially experimentation.

Major critiques of experimental designs tend to focus on true experiments, especially randomized controlled trials (RCTs), but many of these critiques can be applied to quasi-experimental designs, too. Some researchers, even in the biomedical sciences, question the view that RCTs are inherently superior to other types of quantitative research designs. RCTs are far less flexible and have much more stringent requirements than other types of research. One seemingly small issue, like incorrect information about a research participant, can derail an entire RCT. RCTs also cost a great deal of money to implement and don’t reflect “real world” conditions. The cost of true experimental research or RCTs also means that some communities are unlikely to ever have access to these research methods. It is then easy for people to dismiss their research findings because their methods are seen as “not rigorous.”

Obviously, controlling outside influences is important for researchers to draw strong conclusions, but what if those outside influences are actually important for how an intervention works? Are we missing really important information by focusing solely on control in our research? Is a treatment going to work the same for white women as it does for indigenous women? With the myriad effects of our societal structures, you should be very careful ever assuming this will be the case. This doesn’t mean that cultural differences will negate the effect of an intervention; instead, it means that you should remember to practice cultural humility implementing all interventions, even when we “know” they work.

How we build evidence through experimental research reveals a lot about our values and biases, and historically, much experimental research has been conducted on white people, and especially white men. [4] This makes sense when we consider the extent to which the sciences and academia have historically been dominated by white patriarchy. This is especially important for marginalized groups that have long been ignored in research literature, meaning they have also been ignored in the development of interventions and treatments that are accepted as “effective.” There are examples of marginalized groups being experimented on without their consent, like the Tuskegee Experiment or Nazi experiments on Jewish people during World War II. We cannot ignore the collective consciousness situations like this can create about experimental research for marginalized groups.

None of this is to say that experimental research is inherently bad or that you shouldn’t use it. Quite the opposite—use it when you can, because there are a lot of benefits, as we learned throughout this chapter. As a social work researcher, you are uniquely positioned to conduct experimental research while applying social work values and ethics to the process and be a leader for others to conduct research in the same framework. It can conflict with our professional ethics, especially respect for persons and beneficence, if we do not engage in experimental research with our eyes wide open. We also have the benefit of a great deal of practice knowledge that researchers in other fields have not had the opportunity to get. As with all your research, always be sure you are fully exploring the limitations of the research.

• While true experimental research gathers strong evidence, it can also be inflexible, expensive, and overly simplistic in terms of important social forces that affect the resources.
• Marginalized communities’ past experiences with experimental research can affect how they respond to research participation.
• Social work researchers should use both their values and ethics, and their practice experiences, to inform research and push other researchers to do the same.
• Think back to the true experiment you sketched out in the exercises for Section 13.3. Are there cultural or historical considerations you hadn’t thought of with your participant group? What are they? Does this change the type of experiment you would want to do?
• How can you as a social work researcher encourage researchers in other fields to consider social work ethics and values in their experimental research?

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• Engel, R. & Schutt, R. (2016). The practice of research in social work. Thousand Oaks, CA: SAGE Publications, Inc. ↵
• Sullivan, G. M. (2011). Getting off the “gold standard”: Randomized controlled trials and education research. Journal of Graduate Medical Education ,  3 (3), 285-289. ↵

an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.

explains why particular phenomena work in the way that they do; answers “why” questions

variables and characteristics that have an effect on your outcome, but aren't the primary variable whose influence you're interested in testing.

the group of participants in our study who do not receive the intervention we are researching in experiments with random assignment

in experimental design, the group of participants in our study who do receive the intervention we are researching

the group of participants in our study who do not receive the intervention we are researching in experiments without random assignment

using a random process to decide which participants are tested in which conditions

The ability to apply research findings beyond the study sample to some broader population,

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief

An experimental design in which one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed

a type of experimental design in which participants are randomly assigned to control and experimental groups, one group receives an intervention, and both groups receive pre- and post-test assessments

A measure of a participant's condition before they receive an intervention or treatment.

A measure of a participant's condition after an intervention or, if they are part of the control/comparison group, at the end of an experiment.

A demonstration that a change occurred after an intervention. An important criterion for establishing causality.

an experimental design in which participants are randomly assigned to control and treatment groups, one group receives an intervention, and both groups receive only a post-test assessment

The measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself

a subtype of experimental design that is similar to a true experiment, but does not have randomly assigned control and treatment groups

In nonequivalent comparison group designs, the process by which researchers match individual cases in the experimental group to similar cases in the comparison group.

In nonequivalent comparison group designs, the process in which researchers match the population profile of the comparison and experimental groups.

a set of measurements taken at intervals over a period of time

Research that involves the use of data that represents human expression through words, pictures, movies, performance and other artifacts.

Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• First Online: 29 June 2019

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• Vaneet Kaur 3  

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

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The chapter presents methodology employed for examining framework developed, during the literature review, for the purpose of present study. In light of the research objectives, the chapter works upon the ontology, epistemology as well as the methodology adopted for the present study. The research is based on positivist philosophy which postulates that phenomena of interest in the social world, can be studied as concrete cause and effect relationships, following a quantitative research design and a deductive approach. Consequently, the present study has used the existing body of literature to deduce relationships between constructs and develops a strategy to test the proposed theory with the ultimate objective of confirming and building upon the existing knowledge in the field. Further, the chapter presents a roadmap for the study which showcases the journey towards achieving research objectives in a series of well-defined logical steps. The process followed for building survey instrument as well as sampling design has been laid down in a similar manner. While the survey design enumerates various methods adopted along with justifications, the sampling design sets forth target population, sampling frame, sampling units, sampling method and suitable sample size for the study. The chapter also spells out the operational definitions of the key variables before exhibiting the three-stage research process followed in the present study. In the first stage, questionnaire has been developed based upon key constructs from various theories/researchers in the field. Thereafter, the draft questionnaire has been refined with the help of a pilot study and its reliability and validity has been tested. Finally, in light of the results of the pilot study, the questionnaire has been finalized and final data has been collected. In doing so, the step-by-step process of gathering data from various sources has been presented. Towards end, the chapter throws spotlight on various statistical methods employed for analysis of data, along with the presentation of rationale for the selection of specific techniques used for the purpose of presentation of outcomes of the present research.

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• Published: 10 August 2024

Mapping biomimicry research to sustainable development goals

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

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

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• Environmental social sciences

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

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Introduction.

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

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

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

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

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

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

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

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

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

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

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

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

Literature review

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

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

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

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

PRISMA framework

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

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

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

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

SDG framework

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

Topic prominence percentile

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

Results and discussion

Rq1: sdg framework and interdisciplinary research (rq4).

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

Interdisciplinary SDG network of biomimicry research.

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

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

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

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

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

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

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

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

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

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

RQ2: Emerging research topics

Temporal evolution of emerging topics.

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

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

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

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

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

Niche biomimetic applications

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

Myeloperoxidase; colorimetric; chromogenic compounds

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

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

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

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

Water; hydrophobicity; aerogels

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

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

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

Growing interest in bioinspired healthcare

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

Exosomes; extracellular vesicles; MicroRNAs

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

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

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

Nanogenerators; piezoelectric; energy harvesting

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

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

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

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

Leading edge of biomimetic sensing and electronics

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

Strain sensor; flexible electronics; sensor

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

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

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

Cancer; photoacoustics; theranostic nanomedicine

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

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

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

Established biomimetic foundations

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

Metaheuristics; Fireflies; Chiroptera

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

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

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

Bioprinting; three-dimensional printing; tissue engineering

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

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

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

RQ3: Translation and commercialization

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

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

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

RQ4: Interdisciplinary collaboration

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

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

Educational models (SDG 4—Quality education)

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

Gender-inclusive design (SDG 5—Gender inequality)

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

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

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

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

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

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

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

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

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

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

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

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

Data availability

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

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Chapter 3 - Research Methodology and Research Method

2012, Research Methodology and Research Method

This chapter looks at the various research methodologies and research methods that are commonly used by researchers in the field of information systems. The research methodology and research method used in this research is acknowledged and discussed. The chapter starts off by providing a comprehensive introduction to research. Then the research methodologies and research methods particularly used in information systems are discussed. A significant effort has been made to clarify and provide distinctions between research methodology and research method. During the course of this research, when investigating the literature on research methodology and research methods, it was found that many researchers were using the two interchangeably. Therefore the two sections on research methodology and research methods have been treated separately. A section that compares and differentiates between the two is presented first, followed by the section on research methodology. Then the different types of research methodology are described and the two main types of research methodologies namely qualitative research methodology and qualitative research methodology is discussed. The research methodology that has been utilised for this research is discussed and the reason why the particular research method was chosen with proper justification is explained. Then research methods in general are discussed and the types of research methods suitable for information systems research are explained. The differences between the qualitative and quantitative research methods are elaborated upon. Since secondary data sources have been used in this research, a section is included to discuss the differences between the two and to explain the advantages of using secondary data sources for research. Then the research method, that is, the actual data collection and data analysis method is described and justification is provided on why the particular research method was chosen. Case study research method is combined with grounded theory research method for document analysis of archival data that was accessed via the Internet. Descriptive methods have been used to investigate the opportunities and issues of cloud computing with mobile phones for developing countries.

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REVIEW article

Research on digital governance based on web of science—a bibliometric analysis.

• Faculty of Social Sciences and Humanities, Universiti of Kebangsaan Malaysia, Kuala Lumpur, Malaysia

Exploring digital governance is essential for grasping how technology can be employed to enhance public services, boost efficiency, and foster transparency and engagement. This study aims to conduct a systematic review of digital governance research in order to explore its development, emerging research trends, network of leading researchers, countries and institutions that contribute greatly to this field. A bibliometric study was conducted on digital governance works using the following terms: “digital governance,” “E-governance,” “digital government,” and “E-government” as the keywords. VOSviewer and CiteSpace were two tools used for the bibliometric analysis. Results showed that the United States played a dominate role in digital governance studies, followed by China, the United Kingdom, India, and Spain. Brunel University, University at Albany, and University of Johannesburg were the top three research institutes for digital governance. Reddick C.G., Weerakkody V., Dwivedi, Y. K., Mensah, I. K., and Jaeger, P.T. served as the representative researchers in this field. In addition, topics including usability and reliability of digital governance system, quality assurance under the framework of digital governance, the quality of digital service, impact of digital governance on public perception, effects of digital transformation on public value perceptions were the focal points in recent years.

1 Introduction

Just as the term E-Commerce came into existence during the rapid expansion of the Internet, the term E-Government also emerged in response to the technological advancements and increased connectivity facilitated by the Internet boom. The concept of E-Gov (Electronic Government or Electronic Governance) originated in the late 1990's as a platform for practitioners to exchange experiences. This period witnessed the integration of digital technologies into governmental processes, giving rise to the concept of leveraging electronic means for more efficient and accessible government operations.

Various institutions and scholars have provided distinct definitions for E-Government. As articulated by the U.S. Congress in 2002, it refers to the government's strategic use of web- based Internet applications and other information technologies. This, coupled with associated processes, serves the dual purpose of enhancing access to and delivery of government information and services to the public, other agencies, and government entities, while concurrently fostering improvements in government operations—encompassing effectiveness, efficiency, service quality, and overall transformation.

Expanding on this perspective, Grönlund (2010) and Grönlund and Horan (2005) described E-Government as the transformative integration of information technologies by government agencies, which is aimed at revolutionizing interactions with citizens, businesses, and various governmental sectors, thus enhancing the delivery of government services to citizens, improving interactions with businesses, empowering citizens through information access, and building a more efficient process in government management.

Initially, E-Government primarily concentrated on developing citizen-centric services and improving government operations without considering factors outside of the public sector ( Janssen and Estevez, 2013 ). Contemporarily, E-Government research presents a multifaceted exploration of the evolution, implementation, challenges, and future prospects of digital governance. Digital government, originating in the late twentieth century, involves the use of information and communication technologies to enhance the efficiency, accessibility, and quality of public sector operations. The evolution of digital government reflects a shift toward networked, transparent, and user-oriented governance models, influenced by internet-era technological capabilities and changing expectations of citizens and businesses ( Dunleavy and Margetts, 2006 ). Stages of development range from basic information spreading to complex integration and transactional capabilities ( Layne and Lee, 2001 ). Globally, digital government implementation varies due to technological, political, and socio-economic factors. Developed countries lead in rankings, while developing countries make strides with innovative models ( Richard, 2002 ). Comparative studies offer insights and lessons applicable in different contexts ( Andersen et al., 2010 ). In China, a comprehensive digital transformation strategy includes significant investments in digital infrastructure, innovation, and technology-led development. Initiatives like smart cities, digital identity systems, and nationwide e-service platforms demonstrate the government's ambition to enhance state capacity and public service delivery ( Ma, 2015 ; Zheng L., 2017 ). Despite progress, challenges persist, including the digital divide, privacy concerns, and institutional resistance. Disparities in technology access hinder service effectiveness and equity ( Norris, 2001 ), and privacy and security issues are of paramount importance with government transactions ( Bertot et al., 2010 ). Transforming bureaucratic structures faces resistance, requiring investment in technology, infrastructure, human capital, and policy frameworks ( Dawes, 1996 ; Schöll and Scholl, 2014 ). The future of digital government will be shaped by ongoing technological innovations and public expectations. Emerging technologies like AI, blockchain, and IoT offer possibilities for personalized, predictive, and participatory government services ( Bertot et al., 2010 ). The emphasis is growing on using digital government to achieve broader societal goals, including sustainability, social inclusion, and economic development ( Meijer and Bolívar, 2016 ). Over time, E-government policies and research have moved away from a predominantly technology-centered approach. Instead, there has been a shift toward seeing citizens as customers and prioritizing the creation of services driven by customer needs and preferences ( Janssen and Estevez, 2013 ).

E-Government pertains specifically to activities within government organizations, with its use in IS (Information Systems) research often limited to those government entities offering services to citizens or companies. In contrast, E-Governance encompasses the entire societal management system. This broader system involves activities not only by government organizations but also by companies, voluntary organizations, and, notably, citizens—a facet often overlooked ( Grönlund and Horan, 2005 ). E-Governance can be defined as the provision of government services and information to the public through electronic methods. Enhanced informational capabilities could greatly improve the interactions between the government and citizens ( Christine and John, 1998 ). Numerous governments and companies hold the belief that technology can replace traditional governance and human accountability ( Gil et al., 2019 ). The utilization of Information Technology (IT) facilitates an efficient, rapid, and transparent process for sharing information with the public and other agencies, as well as for conducting government administrative activities. With digital transformation, governance now encompasses more than just the decision-making rights and responsibilities of the IT department. Technology has become integral to the entire organization and its operations, rather than being merely an organizational unit ( Jewer and Van Der Meulen, 2022 ).

Dunleavy and Margetts (2006) and Meijer et al. (2012) , used the term digital governance, defining it as a transformative concept, covering mechanisms, processes, and traditions determining power exercise, stakeholder engagement, and decision-making in the digital age. It presents the impact of rapidly evolving digital technologies over traditional governance structure, where policy- making, regulation, and service delivery are all adapted. Technologies have profound influence on digital governance. Technologies such as blockchain, AI, and big data play a transformative role in reshaping governance expectations, presenting opportunities for transparency and citizen engagement. However, challenges related to privacy and security occurred simultaneously, as noted by Bertot et al. (2010) and Wirtz and Müller (2018) . Governments should prioritize improving infrastructure in rural areas and provide free or subsidized access to e-government services, while also implementing digital skills training programs, particularly for older and less educated individuals, to bridge the digital divide ( Sharma and Soliman, 2003 ). Ranging from the digital divide to concerns about data privacy, these obstacles require targeted policies, infrastructure development, and education investments. Ethical considerations emerge as paramount in ensuring public trust and legitimacy in the ongoing efforts toward digital governance, as highlighted by Jaeger and Thompson (2003) and Norris (2001) . In addition, Bellamy and Taylor (1998) and Bovens and Zouridis (2002) believed that the establishment of adaptive and responsive governance structures is of great importance, since it is crucial for effectively harnessing the benefits of technology while navigating and mitigating associated risks.

The diverse political, social, and technological dynamics of each country significantly influence the construction of global digital governance. It is evident as leading nations such as Estonia and Scandinavia adopt citizen-centric approaches and innovative digital services, while larger countries like the United States and China concentrate on expanding digital initiatives. These endeavors encounter challenges associated with access and regulatory intricacies, as discussed by Margetts and Dunleavy (2013) and Zheng Y. (2017) . In China, digital governance initiatives strategically target transparency, public sector efficiency, and citizen engagement. The most typical one is the “Internet Plus” strategy, where internet-based technologies are integrated, reflecting China's distinctive political and regulatory emphasis on state-led governance, as stated by Zhang and Cheng (2019) and Ma (2015) . The future of digital governance is shaped by technological innovations and societal shifts. Networked governance models, citizen-led initiatives, and cross-border collaborations suggest a move toward open, flexible structures ( Luna-Reyes and Gil-García, 2014 ; Meijer and Bolívar, 2016 ).

E-governance offers critical improvements to the efficiency and effectiveness of governance. It enhances government processes by automating and streamlining internal operations, thereby reducing costs and increasing productivity. E-governance also serves to connect citizens with their government more directly by providing online services and information, which improves the quality and convenience of public services and fosters a more transparent and accountable government. Furthermore, it builds external interactions by strengthening the ties between government and other institutions, including businesses and civil society organizations, which can lead to better policy-making and more responsive governance. By leveraging ICTs, E-governance can empower local communities, support decision-making with timely and accurate data, and contribute to broader societal goals such as sustainable development and social inclusion. E-governance extends beyond the realm of E-government. E-government focuses on delivering government services and information to the public through electronic means. In contrast, E-governance facilitates direct citizen involvement in political activities, surpassing traditional government functions. Kar et al. (2017) highlighted that the transformative impact of digital tools and platforms on citizen engagement, emphasizing their role in fostering participatory governance. By shifting from traditional top-down models to inclusive digital platforms, and through initiatives like e-participation tools, innovative engagement models, and open data, governments can enhance transparency, accountability, and citizen involvement in decision-making processes. It encompasses aspects like e-democracy, e-voting, and online political participation. Governance is influencing governments, prompting consideration of social contracts within a digital governance framework ( Idzi and Gomes, 2022 ). Essentially, E-governance includes government functions, citizen engagement, political parties and organizations, as well as parliamentary and judicial roles ( Jayashree and Marthandan, 2010 ).

Although studies on digital governance are increasing, there is a relative scarcity of systematic reviews, including its development, emerging research trends, leading researchers, and countries and institutions that have made significant contributions to this field. Therefore, this study serves as a supplement to systematic reviews of digital governance. Additionally, through a systematic analysis, this study highlights that digital governance is an interdisciplinary field involving multiple disciplines such as computer technology, public administration, political science, and economics. However, current research may not fully explore the intersections and integrations among these different fields. Furthermore, while digital governance brings many opportunities, it also comes with challenges such as privacy, security, and the digital divide. These challenges require more attention and research.

2 Materials and methods

2.1 data collection.

To ensure the scientific credibility and reliability of the data origin, this scholarly literature is derived from the primary collection of the Web of Science (WOS) database. WOS is recognized as the most extensively utilized and authoritative database for academic publications and citations.

The data were obtained by searching the topic terms “digital governance,” “E-governance,” “E-government,” and “digital government” from 2000 to 2023. The document type specified for retrieval was journal articles. Following the retrieval process and the elimination of duplicated and irrelevant articles, this research ultimately gathered 2,876 published papers within the 24-year timeframe.

2.2 Research method

Bibliometric approaches have been employed for a quantitative examination of written publications, offering valuable insights into the intellectual terrain of particular research domains. This method allows for a systematic literature review, aiding in the extraction of information and recognition of patterns within the scholarly field. Up to now, there are many visualization software tools, with notable attention given to CiteSpace developed by Chen (2006) from Drexel University in the United States and VOSviewer developed by CWTS at Leiden University in the Netherlands. Both tools excel in generating comprehensive visualizations, offering excellent visual effects, and providing research perspectives from different angles ( Song and Chi, 2016 ).

This research utilized VOSviewer for analyzing keywords concurrence, cooperation networks, presenting hotspots, collaborations among researchers, nations and institutions in the field of digital governance. Additionally, CiteSpace, known for its trend depiction and customizable parameters, was employed to visualize keywords time zone and burst detection, through which the focal areas, and prospective trends within the realm of digital governance will be explored. Figure 1 is the technical road map of this study, which generally presents the process of the research including data collection, data screening, the use of analytical tools as well as the specific analytical framework.

Figure 1 . Technical road map (source: authors' illustration).

3 Discussion

3.1 literature development trends.

Based on statistical analysis obtained from the Web of Science, the total number of publications in the field of digital governance or digital government is 2,876 articles. Figure 2 illustrates the annual publication volume in this field from 2000 to 2023. Overall, it shows an increasing trend, indicating that over the past two decades with the development of information technology, research on digital governance and digital government has gained more attention. Findings suggest that initial interest in this field was rare before 2001, followed by a rapid increase in attention after 2002, with publication volumes surpassing 100 annually since 2007. The highest publication volume was observed in 2012, and although there was a subsequent decline, there has been a steady increase since 2020. To some extent, this is closely related to the impact of the COVID-19 pandemic on government digital governance. The pandemic accelerated the adoption of digital government, prompting governments worldwide to swiftly implement digital solutions to address the challenges posed by the crisis. This indicates that the transformation of government, digital government, and digital governance have been significantly influenced by the combination of public emergencies and the development of digital technologies.

Figure 2 . Diagram of publication (source: authors' illustration).

3.2 Cooperation network

3.2.1 contribution and cooperation on national level.

VOSviewer enables researchers to visually represent co-authorship networks on a country level, offering valuable insights into collaborative patterns among nations. This facilitates the identification of both robust and less pronounced connections between countries, providing a holistic comprehension of international cooperation across diverse research domains. In this section, “country” was chosen as the node for research to get the cooperation network among countries in the field of digital governance. A total of 125 countries were involved, where 42 countries came to a threshold of 20 or more publications. As can be seen in Figure 3 , the size of the node represents the contribution of each country in this field, of which the biggest is the USA, proving its leading role of the research in this field. Table 1 presents the top ten countries that contribute to digital governance. In addition to USA, the other top countries are China, England, India, Spain, Australia, South Korea, Greece, South Africa and Canada, showing that scholars in these countries also have strong interest in the field of digital governance. The other top countries are China, England, India and Spain, showing that scholars in these four countries also have strong interest in the field of digital governance. As can be seen from the depth and complex connection between countries, the USA has a close relationship with China, South Korea and Mexico, while China as a super power in digital technology, keeps close a cooperation with USA, Pakistan and Malaysia.

Figure 3 . Diagram of cooperation among nations (source: authors' illustration).

Table 1 . Top 10 contributed nations (source: authors' illustration).

For the overlay visualization, there is a bar on the bottom right of Figure 4 , with the colors that illustrate the time when each country engaged in the study of digital governance. It is evident that the USA, England, Greece, Canada were the earliest ones. Regarding the top five contributing countries, China and India were the later ones, demonstrating the rapid development of digital technology and implementation of it on governance of the two big countries.

Figure 4 . Overlay visualization of cooperation among nations (source: authors' illustration).

3.2.2 Contribution and cooperation on institutional level

A total of 2,300 research institutions were involved in the digital governance study. Among them, 60 research institutions reached a threshold of 10 or more publications in this field. Based on Figures 5 , 6 , together with the summary from Table 2 , it can be seen that the most influential research institution is Brunel University with a total number of publications of 45, and a total link strength of 16. The next two are the University at Albany and the University of Johannesburg with a total number of publications of 42 and 33, respectively. The map also illustrates that institutional collaboration in digital research exhibits regional characteristics, with key institutions primarily located in countries that contribute greatly in this field including the United States and the United Kingdom. Notably, based on the top five institutions, the University of Johannesburg, South Africa and the National University of Singapore, Singapore, rank third and fifth, albeit in terms of nation contributors, South Africa ranks ninth and Singapore is not listed in the top ten. In addition, as for the second nation contributor, China, Tsinghua University, Huazhong University of Science and Technology and Shanghai Jiaotong University, rank 12, 16, and 19, respectively. As can be seen from the overlay visualization, the institutions from South Africa and China are colored green and yellow, showing that they recently started their interest with digital governance, which to some extent, matches with the overlay visualization of nation cooperation.

Figure 5 . Diagram of cooperation among institutions (source: authors' illustration).

Figure 6 . Overlay visualization of cooperation among institutions (source: authors' illustration).

Table 2 . Top 10 contributed institutions (source: authors' illustration).

3.2.3 Contribution and cooperation on researchers' level

A total of 5,267 researchers were involved in the digital governance study. Among them, 97 different pieces of research reached a threshold of 5 or more publications in this field. Through the analysis of Figure 7 on researchers' contribution, it can be found from the Table 3 that the top five contributors on publication in this field are Reddick C.G., Weerakkody V., Dwivedi, Y. K., Mensah, I. K. and Jaeger, P.T. According to the index of citations, the top five are Jaeger Paul T., Reddick C.G., Weerakkody V., Bertot, J. C., and Kassen, M. Furthermore, it can be seen from the density visualization that there are 46 clusters of author cooperation, of which 14 of them derive from more than three authors in a group. Since the deeper the color is, the more active the author will be in this field. Therefore, it can be seen that Weerakkody V., Dwivedi, Y. K., Reddick C.G., Gil-García, J. R., Mensah, I. K., Joseph, B. K., and Jaeger, P.T. are the most active researchers in this field and maintain close cooperation with other researchers.

Figure 7 . Diagram of cooperation among researchers (source: authors' illustration).

Table 3 . Top 10 contributed researchers (source: authors' illustration).

3.3 Researching hotspots and emerging trends

3.3.1 keyword: co-occurrence.

Analyzing keywords in VOSviewer is a crucial tool for researchers seeking to comprehend the composition of a research domain, identify emerging patterns, and enhance collaboration with fellow researchers. The VOSviewer software possesses unique advantages in the field of keyword co-occurrence clustering technology. The author utilized VOSviewer software, choosing the fractional counting method, with the total number of keywords at 5,726, a threshold of 20, to create a keyword co-occurrence weighted graph for the study of digital governance ( Figure 8 ). The size of the circle representing a keyword indicates its frequency, with larger circles denoting higher frequencies, signifying more popular topics in the field of digital governance. The placement of keywords toward the center of the graph indicates their greater importance, highlighting them as crucial concepts within the research field. The TOP 30 keywords in digital governance research were organized and ranked, as presented in Table 4 .

Figure 8 . Diagram of keywords co-occurrence (source: authors' illustration).

Table 4 . Top 30 keywords (source: authors' illustration).

Figure 8 and Table 4 reveal that keywords such as “E-government,” “adoption,” “trust,” “service,” and “model” hold a central position in the study of digital governance trends. These keywords have a certain representation and have gained high attention within the academic community around the globe. Xanthopoulou et al. (2023) show how digital governance adoption integrates digital technologies and methods into organizational or government operations. Digital adoption enhances internal efficiency, meets regulatory requirements, enables data- driven decision-making, and improves public services, leads to more efficient service delivery and resource utilization for government agencies and businesses, thus making it an important topic for researchers to explore. Establishing digital trust is a crucial element for organizations to facilitate and safeguard their digital transformation. Institutional trust pertains to the confidence that citizens have in government and political institutions, serving as a significant indicator of the vitality of democracy in contemporary nations ( Chen et al., 2023 ). It can be found that digital governance and trust is closely intertwined, with trust being a pivotal element in the acceptance and prosperity of digital initiatives. According to Milakovich (2012) , the adoption of digital information and communication technologies (ICTs) to reform governmental structures and public services is widely perceived as an enlightened strategy for the twenty-first century. It is seen as a potential solution to reinvigorate democracy, reduce costs, and enhance the quality of public services. The digital governance models are constantly changing and adapting to leverage the capabilities of emerging technologies, and there are no fixed or definitive models for digital governance. In terms of “model,” numerous digital governance models exist, and they are consistently adapting to leverage the capabilities of emerging technologies. These models are not fixed or limited, and their examination should be contextualized within discussions surrounding the concept of digital governance. Additionally, topics including information technology, user acceptance for digital platforms, management of digital governance, and framework for digital governance, have also become research hotspots in this field.

3.3.2 Keyword: clustering

Figure 9 illustrates a clustering view of keywords in digital governance research, where the same color represents a cluster, indicating a specific theme within digital governance studies. As depicted in Figure 9 , digital governance research primarily encompasses seven clustered themes.

Figure 9 . Diagram of keywords clustering (source: authors' illustration).

3.3.2.1 Cluster 1: E-government adoption and trust

The keywords in this cluster include adoption, trustworthy, service, etc. E-government relies significantly on adoption and trust, as they play a pivotal role in motivating citizens to embrace E-government services. They contribute to diminishing perceived risks, fostering civic engagement, surmounting initial obstacles, and establishing enduring relationships between citizens and the government. According to Papadopoulou et al. (2010) , trust is a fundamental component of E-government, and therefore, it should not be considered as an afterthought. Instead, it should be carefully considered early in the process, integrated with the design and deployment of an E-government system. Warkentin et al. (2002) believed that to achieve cost reduction, enhance services, and enhance responsiveness to citizens, it is crucial for both national and local governments to instill trust in the online services they currently offer or plan to offer in the future. Dashti et al. (2010) suggested that feeling trust, distinct from conventional adoption models, centers not only on users' beliefs about the e-service provider but specifically on how the provider perceives them. This will enhance our understanding of user evaluation and the use of E-government while elucidating the reciprocal nature of user interactions with E-government and other e-service providers.

3.3.2.2 Cluster 2: digital governance and citizen participation

The high frequency keywords in this cluster cover digital governance, transparency, citizen participation, etc. In digital governance, citizen participation plays a crucial role by promoting a feeling of shared ownership, collective responsibility, and accountability. It entails involving citizens in public affairs through the utilization of digital tools and platforms like social media, mobile applications, and online digital platforms. Muhlberger (2006) explored citizen involvement in local governance to gauge the adequacy of current information and communication technology (ICT) in meeting community communication and information requirements. Luciano et al. (2018) first identified strategies and barriers for the adoption of digital governance (DGO) in the Brazilian public administration, specifically focusing on strategic objectives related to social participation outlined in the Brazilian Digital Governance Policy (DGP), then proposed three strategic objectives related to social participation, including promoting collaboration in the public policy cycle, expanding and encouraging social participation in enhancing public services, and enhancing direct interaction between the government and society. Rodriguez-Hevía et al. (2020) , by reviewing citizen's involvement in E-government of the European Union, proposed that while many citizens have access to the Internet, it does not automatically imply that they will extensively use e- government services. Digital literacy appears to be the most critical factor influencing overall E-government usage. In addition, Matheus et al. (2023) pointed out that transparency is only created when open data is useful to the public. Functionality can enhance transparency, thereby increasing perceived efficiency and perceived usefulness. Special attention should be paid to the diverse needs of citizens and ensuring efficiency, as the time of citizens and other users is limited. Transparency can enhance the credibility of open government, but usefulness may not always necessitate transparency, therefore it is suggested to create classifications for transparency initiatives.

3.3.2.3 Cluster 3: digital governance framework and digital transformation

The high frequency keywords in this cluster include digital transformation, framework, management, innovation, etc. Digital governance provides a strategic framework for developing and enacting innovative approaches to transition from bureaucracy-oriented to citizen-focused public services ( Milakovich, 2014 ). Al-Badi et al. (2018) proposed that a comprehensive framework for Big Data governance is essential for establishing guidelines, procedures, and criteria to efficiently manage and safeguard the availability, usability, integrity, consistency, auditability, and security of Big Data. Jia and Chen (2022) expanded the “issue-actor-mechanism (IAM)” model into an analytical framework for delineating global digital governance. Tannou et al. (2012) suggested that governance plays a pivotal role in the success of digital transformation, ensuring that digital initiatives are coordinated and shared appropriately within the company, aligning with its structure, culture, and strategic objectives. Gil-Garcia et al. (2018) addressed that the scholarly communities of Digital Governance (DG) and Public Management (PM) exhibit variations across multiple dimensions while also sharing significant similarities. Importantly, there is evidence of potential for collaborative efforts that could benefit both fields of study.

3.3.2.4 Cluster 4: digital divide

The high frequency keywords in this cluster include digital divide, e- government implementation, developing countries, etc. The digital divide significantly affects digital governance, leading to adverse outcomes for individuals in underdeveloped regions and those with lower socio-economic status. Limited access to digital technologies and the Internet can create obstacles in utilizing E-government services, resulting in disparities in resource access and public service utilization. Yigitcanlar and Baum (2008) indicate that the term “global divide” signifies the gap in Internet accessibility between advanced and developing nations, while the “social divide” pertains to the disparity in information access within individual countries, separating the affluent from the disadvantaged. Mariscal (2005) , Ogunsola and Okusaga (2006) , and Acilar (2011) , analyzed the situation of digital divide in developing countries by reviewing the facts in Mexico, African countries, and Turkey.

3.3.2.5 Cluster 5: digital governance and sustainability

The high frequency keywords in this cluster cover sustainability, corruption, sustainable development, public policy, etc. Barbosa (2017) pointed out that digital transformation serves as a tool to promote sustainable development and more inclusive societies. From a practical standpoint, digitization presents the opportunity to enhance governmental operations and, if properly orchestrated, can play a role in advancing the imperative sustainable development agenda in the foreseeable future. Xu et al. (2022) believed that the bolstering of digitization contributes to sustainable development, as evidenced by the broad array of literature and theoretical frameworks. The belief that notable advancements in digital governance inevitably result in improved sustainable governance is erroneous. The dynamics between digital governance and sustainable governance are complex, involving various potential moderating and mediating factors that warrant additional investigation ( Durkiewicz and Janowski, 2021 ). In terms of corruption and digital governance, Martins et al. (2018) highlighted a significant finding: the correlation between digital government and corruption differs among income groups, suggesting that policymakers in the least developed nations should not view E-government as a definitive remedy for combating corruption.

3.3.2.6 Cluster 6: smart city and information security

The high frequency keywords in this cluster cover smart city, information security, privacy, etc. According to Sucupira Furtado et al. (2023) , the connection between smart cities and digital governance is substantial, as digital governance serves as a fundamental element of the smart city initiative and a crucial avenue for realizing sustainable development objectives through digital transformation. Smart city governance has evolved from the wider realm of E-governance and seeks to improve the effectiveness of public institutions by leveraging digital technologies and institutional innovation ( Myeong and Bokhari, 2023 ). By reviewing the situation of smart city in Belgium, Esposito et al. (2023) provided fresh insights into the multifaceted nature of the smart city concept, revealing that local authorities can flexibly employ smart city initiatives as tools to address various location-specific environmental, social, and economic challenges. Singh and Karaulia (2011) suggested that information security is crucial in E-governance initiatives, ensuring the confidentiality of transactions and data, protecting government documents from unauthorized access, and integrating robust security systems to ensure successful project implementation. In digital governance, safeguarding information security and integrity is paramount, as any security loopholes may result in severe repercussions, undermining public trust and government credibility, necessitating comprehensive measures spanning policy, processes, and training for successful implementation.

3.3.2.7 Cluster 7: public administration under digital governance

The high frequency keywords in this cluster cover public administration, infrastructure, efficiency, etc. Digital governance provides a framework and approach to leveraging digital technologies to enhance the efficiency and transparency of public administration while promoting the development and maintenance of public infrastructure. Margetts (2008) explored the relationship between E-government and public management reform, suggesting that “digital-era governance” (DEG), which challenges or transcends the managerial styles of New Public Management (NPM), is a valuable approach to understanding contemporary administrative reform, outlining three main themes of DEG: reintegration, needs-based holism, and digitization, illustrating how the widespread use of digital technologies by governments, businesses, and society at large triggers organizational responses in government entities. As public administrations are still in the process of transitioning to E-government and/or E-governance perspectives and have not fully undergone digital transformation, the empirical data were collected from administrations at various stages of this process ( Mergel et al., 2019 ). According to Chantillon (2021) , as suggested by the concept of “good enough governance,” public administrations are indeed continuously balancing the public values they strive to achieve, the use of coordination tools, and the interrelated objectives set by different public management departments. To facilitate digital transformation, changes in how public administrations function and the public values they strive for will be necessary. However, these changes will only occur gradually and sometimes may not lead to the desired success, thus necessitating monitoring and evaluation.

3.3.3 Evolution of research hotspots

The time zone analysis of CiteSpace represents the chronological evolution of scholarly literature and pinpoints emerging trends and patterns over time. By organizing articles and terms based on their publication dates, CiteSpace's time zone view enables researchers to examine the fluctuation of research topics, notable terms, and significant advancements within a specific field. This functionality is particularly beneficial for monitoring the advancement of scientific knowledge, recognizing influential publications, and comprehending the temporal context of research trends. The time zone view offers researchers a distinctive viewpoint to glean insights into the historical progression of a discipline and choose informed assessments regarding the significance and impact of scientific endeavors throughout time.

From Figure 10 , it can be observed that research in the field of digital governance in the core collection of the foreign Web of Science database can be divided into three phases, with the earliest research traced back to 2001. Specifically, from 2001 to 2008, scholars primarily focused on information technology, which is the prerequisite of digital governance or digital government, then shifted their attention on the adoption, acceptance, and management of digital governance. From 2009 to 2018, there was a notable emphasis on quality, transparency, trust and satisfaction with digital governance. It is found that the less attention has been put on the technical level but more on the effects and feedback of digital governance. Finally, from 2019 to 2023, scholars predominantly investigated corruption, digital divide, artificial intelligence, and sustainable development in digital governance. According to these keywords, it can be summarized that during this phase more specific points and long-term goals in a wider scale have become the interest of researchers. As mentioned in previous sections in this article, digital divide's impact over developing countries, whether digital governance advances sustainable development, artificial intelligence's impact over digital governance, plays a role.

Figure 10 . Diagram of keywords time zone (source: authors' illustration).

3.3.4 Research frontiers in the field of digital governance

Burst detection in CiteSpace is a functionality designed to pinpoint spikes in activity, such as sudden surges in citations to a specific publication or rapid escalations in the output of publications by a particular author. Utilizing Kleinberg's algorithm, this feature can detect bursts that span multiple years or occur within a single year. The visual representation of burst detection can be applied across different types of nodes, including articles, authors, or keywords, aiding in the identification of highly active research areas or emerging trends ( Chen, 2014 ).

From the Figure 11 , it can be observed that the word “technology” has the longest burst period from 2001 to 2008, while communication technologies owns the strongest burst with the strength of 19.65, showing that at the beginning stages of digital governance most of the attention was given on technical level, which complies with the result of time zone analysis in the previous part. As for the latest burst, it can be found that there are four words, “system”, “quality”, “perceptions” and “public value”. According to these words, it can be uncovered that the research frontiers for digital governance include the usability and reliability of digital governance system, quality assurance under the framework of digital governance, the quality of digital service, impact of digital governance on public perception, effects of digital transformation on public value perceptions, etc.

Figure 11 . Diagram of keywords citation burst (source: authors' illustration).

4 Conclusion

(1) The bibliometric analysis of academic literature demonstrates that since 2000, there has been a growing focus in research areas such as digital government, digital governance, digital transformation, and sustainable governance. Examination of the distribution and growth of these topics suggests that research in digital governance is characterized by its interdisciplinary nature, intersecting with disciplines such as computer technology, public administration, political science, and economics. Otherwise, the widespread adoption of digital government has been hastened by global public crises, prompting governments worldwide to swiftly implement digital solutions to tackle crisis-related challenges. This underscores the significant impact of public emergencies alongside the evolution of digital technologies on government transformation, digital governance, and digital government.

(2) At the national level, the United States holds a leading position in the field of digital governance research. The other four countries of China, the United Kingdom, India, and Spain, have also made great contribution in digital governance research. The density of collaboration between countries indicates close relationships between the United States and China, South Korea, and Mexico, while China, as a superpower in digital technology, maintains close cooperation with the United States, Pakistan, and Malaysia. Additionally, the United States, the United Kingdom, Greece, and Canada were among the earliest countries to initiate research related to digital governance. Among the top five contributing countries, although China and India started later, the rapid development of digital technology in these two major countries has swiftly propelled them to the forefront of research in this field.

(3) In terms of institution contributions, the most influential research institutions are Brunel University, the University at Albany, and the University of Johannesburg. Institutional collaboration exhibits regional characteristics, with key institutions primarily located in countries that have made significant contributions in this field, including the United States and the United Kingdom. Among the top five institutions, the University of Johannesburg, South Africa, and the National University of Singapore, Singapore, rank third and fifth respectively, despite South Africa ranking ninth and Singapore not listed in the top 10 in terms of national contributions. This indicates that institutional contributions do not completely overlap with national contributions. From the overlay visualization, it can be seen that the institutions from South Africa and China have made significant contributions to digital governance research in recent years.

(4) In terms of author contributions, the top five contributors in this field are Reddick C.G., Weerakkody V., Dwivedi, Y. K., Mensah, I. K., and Jaeger, P.T. Additionally, Weerakkody V., Dwivedi, Y. K., Reddick C.G., Gil-García, J. R., Mensah, I. K., Joseph, B. K., and Jaeger, P.T., are the most active researchers in this field, maintaining close collaboration with other researchers.

(5) According to the analysis of keywords co-occurrence, clustering, burst and time zone distribution, shows that the current research hotspots and trends in digital governance encompass various aspects. Initially, in the early stages, research primarily focused on the technological level, particularly the application of information technology, as a prerequisite for digital governance or digital government. Subsequently, the focus gradually shifted toward the adoption, acceptance, and management of digital governance, emphasizing the importance of quality, transparency, trustworthiness, and satisfaction. The latest research now increasingly addresses the usability and reliability of digital governance systems, the quality of digital services, the impact of digital governance on public perceptions, and the effects of digital transformation on public values. Additionally, researchers have started to pay attention to some challenges and issues in digital governance, such as corruption, the digital divide, artificial intelligence, and sustainable development. This indicates that digital governance research is gradually expanding from the technological level to broader social and political domains, with a greater focus on the impacts and practices of digital governance, as well as how to address the challenges and opportunities of the digital age. However, this study also has some limitations. The research data solely comes from the Web of Science database, overlooking literature from other databases such as Elsevier Science and Google Scholar, which may introduce some information bias. The overall coverage of the WoS database is less extensive compared to some other databases such as Scopus, especially in the fields of social sciences and humanities. Other than that, in the humanities, Scopus covers a significant portion of the data available in WoS, whereas WoS only covers a smaller portion of the data available in Scopus. Additionally, due to subscription terms, the accessible timeframe for citations in WoS is relatively short ( Pranckute, 2021 ). Therefore, the study may be limited by the availability of data sources. This implies that it may not comprehensively capture all relevant academic discussions and research findings within the field of digital governance, particularly those published in related disciplinary domains. In the process of literature screening, the diversity in keyword selection may lead to the appearance of semantically similar terms, affecting the accuracy of data analysis. Additionally, only English articles were selected, resulting in some data gaps. Furthermore, the study covers relatively a large time span, constrained by sample selection and the knowledge level of the authors, which may lead to insufficient depth and comprehensiveness in certain parts of the analysis.

Finally, according to previous analysis on research hotspots as well as the evolution of the research of digital governance, it can be assumed that future research on digital governance will focus on the usability and reliability of systems, exploring user acceptance, system performance, and service quality. Additionally, studies will examine how digital governance affects public perception and trust in government services, as well as the impact of digital transformation on public expectations and values. Other than that, challenges and issues in digital governance, such as corruption, the digital divide, artificial intelligence, and sustainable development will continue to be the focus of researchers. What's more, key areas of future research will also include how emerging technologies enhance the personalization and participatory nature of government services, and how to construct a global digital governance framework.

Author contributions

ZL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. MY: Supervision, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

Thanks to UKM for the support of this research. We also would like to extend special thanks to the editor and the reviewers for their insightful comments and constructive suggestions.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Keywords: digital governance, bibliometric, developmental trends, research hotspots, VOSviewer, CiteSpace

Citation: Lin Z and Yaakop MR (2024) Research on digital governance based on Web of Science—a bibliometric analysis. Front. Polit. Sci. 6:1403404. doi: 10.3389/fpos.2024.1403404

Received: 25 April 2024; Accepted: 24 July 2024; Published: 16 August 2024.

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*Correspondence: Zhao Lin, P118333@siswa.ukm.edu.my

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