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Priya ranganathan.
Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India
In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.
Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.
We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.
Exposure vs outcome.
Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.
In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.
In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.
In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.
Broadly, study designs can be classified as descriptive or analytical (inferential) studies.
Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.
Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2
Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.
In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.
These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3
Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.
Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4
Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.
Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5
In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.
The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.
In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6
Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7
Source of support: Nil
Conflict of interest: None
by Dirk Sliwka and Timo Vogelsang
Why do bonuses sometimes backfire? It’s because each incentive design choice both signals information about your own beliefs and intentions as an employer and shapes the signaling value of employee behavior within the organization. If you don’t think through these signals carefully, you may end up approving a bonus scheme with results that are the opposite of what you intend. This article offers a way to help you align the signals your incentive scheme sends with your performance goals.
More than 30 years ago, author and lecturer Alfie Kohn, in a rather controversial but often cited HBR article , claimed that “rewards typically undermine the very processes they are intended to enhance.” Yet until recently, nearly all scientific studies that have documented such “backfiring” effects have been confined to laboratory experiments or field settings outside of the firm. This may cause some to question whether these effects are really present in commercial contexts. Our new research, which consists of two large field experiments in retail organizations, demonstrates that they do indeed occur.
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Written by Syed Balkhi
3 September, 2024
Do you ever wonder how websites and apps seem to know exactly what you want? The truth is, this is in large part due to AI in UX design.
There’s no question that AI has fundamentally changed how companies across all industries operate. According to Forbes, the AI industry is expected to hit $407 billion by 2027! For more context, research shows that around 73% of online businesses in the U.S. are already using AI in some way.
And it’s no wonder why. AI saves marketers about 2.5 hours a day – that’s 25 whole days a year!
But if you want to get the most from AI, you need to start thinking about how you can use it in UX research. With the right knowledge and resources, you can continuously streamline your design process, which will ultimately lead to more engagement, clicks, and, most importantly, sales.
Today, we are going to show you how AI is improving UX research and design and how you can use it to your advantage.
Let’s get started!
Let’s start by exploring a few key benefits of adding AI to your existing design process.
Now that you know a little more about the benefits, let’s talk about some of the specific, practical applications for AI in both user testing and research.
Automated user behavior analysis makes it easier to see how your audience interacts with your product. AI can track and analyze every click, scroll, and hover, things that would take humans much longer to uncover. Since all of this information is at your fingertips in minutes, you can quickly identify patterns and take action.
It can handle a whole lot of data at once, which makes large-scale user testing more manageable than ever before. Traditional user testing often limits the number of participants due to logistical constraints. But with AI, you can test with thousands or even millions of users, which means your data will be statistically significant and reliable.
By finding subtle trends and anomalies in the big picture, AI helps you understand user behavior and needs better, which means you can make data-driven decisions that improve user satisfaction and overall experience.
A/B testing is a proven way to see which version of a web page or app performs better based on a number of different metrics. This strategy is also called split-testing, and it’s been around since long before AI.
But there’s no question that machine learning makes this process even better by optimizing it in new and exciting ways. For example, AI algorithms are very good at analyzing different versions of a landing page or offer, which will help you find the best ways to connect with your audience.
Predictive analytics is another element of A/B testing that you should know about. By using historical data and learned patterns, AI can predict which version will perform better even before the testing is complete. This means faster decision-making and better version implementation, so you waste less time and resources on testing.
Ultimately, AB testing helps you deliver the best possible experience based on data, not guesswork or intuition. AI can make it even better.
Brainstorming for new designs is important but time-consuming. Instead of spending hours at meetings, you can use AI to speed up this process.
Basically, you can ‘feed’ your program different designs and campaigns that worked well in the past. The AI-powered software can look through this information and pull out key elements that it can then use to build new campaigns.
Doing this can help you generate a library of great-looking templates and concepts that you can discuss with your design team. Much like traditional brainstorming, not every idea will be a winner. But there’s no question that you’ll save a significant chunk of time by using AI to come up with a pool of ideas.
Rapid iteration on an existing campaign is also possible with AI-driven prototyping, which actually leads back to A/B testing. Instead of spending weeks or months on one prototype, AI tools can generate multiple versions in minutes that you can experiment with on your site or social media.
Trying out different concepts is important for creative growth, and AI makes it possible to experiment more. By making it easy to test and discard many ideas, AI creates an environment where prototype testing can flourish.
Making your product accessible to all users is not only the right thing to do, but it also dramatically expands your audience.
AI-driven accessibility checks can automatically scan your design for issues that will hinder users with disabilities. For example, there are tests to determine if a site is color-blind friendly, with different versions for each type of colorblindness. In my experience, this proactive approach helps you identify and fix problems early in the design process.
There’s no doubt that AI can check for accessibility issues faster and more accurately than manual testing. Since AI is working on this part of your site, the design team can focus on other important parts of the user experience while knowing accessibility is being taken care of.
Using AI for periodic checks makes sure no one is left out. It promotes a welcoming design approach so all users, regardless of ability, can have a good experience with your product. This not only increases user satisfaction but also your brand’s reputation for inclusivity .
Did you know that 74% of shoppers say they’ve felt frustrated by a shopping experience because the content didn’t match their interests? This is exactly why personalization is the key to user engagement. People are more likely to take action on your site if you show them things that are relevant to their interests.
AI can supercharge this concept by automatically adapting content and promotions to each user’s preferences. It can do this by analyzing user behavior, determining what content users will find most interesting based on their actions, and tailoring the experience accordingly. This will create a more engaging experience for every single person who lands on your site.
Cool, right?
AI-driven personalization at scale is especially useful for growing businesses. As your user base grows, it admittedly gets hard to add a personal touch to everything you do. AI is able to process huge amounts of data, which means it can hone in and deliver personalized experiences to millions of users without compromising on quality.
The bottom line is people will engage with content that feels tailored to them, which means a big boost in retention and satisfaction. When you add AI to the mix, it creates a win-win situation for you and your customers.
Knowing what users want and need is a powerful way to improve the user experience. With the right data, AI can predict what someone might want or do next by looking at past behavior and trends. This foresight will help you design interfaces and interactions that are more intuitive and meet each customer’s expectations.
For example, a streaming service like Max will show each person shows and movies they might like based on their watch history.
In the old days, you’d have to wait weeks, sometimes months, and read through tons of user feedback before you could make actionable changes to your campaigns. With AI, you can make these changes essentially in real time based on how people are engaging with your content or offers.
By knowing what they need before they even realize they have a problem, you can offer solutions that add value to their lives. This proactive approach builds loyalty as users feel understood and valued by your brand.
Consistency is a massive part of creating a positive user experience. People want to feel comfortable with a brand, whether they’re browsing their blog or buying their products.
AI can help with this by generating style guides across your entire brand, including products, landing pages, and social media profiles. As a result, all the different parts of your product will have an original look and feel that your customers will recognize.
In this instance, AI will save you a lot of time. Instead of manually checking each component, like color hex codes, against the style guide, AI can do this for you. This reduces the chances of inconsistencies and makes sure every part of the product aligns with your brand.
It’s more clear than ever before that AI is having a huge impact on UX research and design. It’s making things faster, smarter, and more personal.
One of the misconceptions I see around this topic is that it’s meant to replace humans. The truth is, that tools like this actually give designers and researchers better tools to do their job.
The best UX still comes from a mix of AI smarts and human creativity. AI can crunch the numbers and spot patterns, but it takes human insight to turn those insights into experiences that truly connect with users.If you’re ready to bring AI design and research tools to your business, there’s no time like right now to start. Artificial intelligence is getting more helpful every day, so why not take advantage of this remarkable and effective tool today?
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JERUSALEM — Three hundred and thirty-two days after Hersh Goldberg-Polin danced in the courtyard next to his Jerusalem synagogue on the holiday of Simchat Torah, more than a thousand people gathered there in grief and prayer to mourn his murder by Hamas terrorists in Gaza.
During the Sunday night vigil, the courtyard railings were lined with oversized yellow ribbons to symbolize advocacy for the hostages, Hapoel Jerusalem soccer flags — the 23-year-old’s favorite team — and posters that read, “We love you, stay strong, survive,” a mantra coined by his mother, Rachel Goldberg-Polin.
Just hours earlier, one of the posters had been hanging over the balcony of the home of Shira Ben-Sasson, a leader of Hakhel, the Goldberg-Polins’ egalitarian congregation in the Baka neighborhood of Jerusalem.
“We were sure we would take it down when he came home,” Ben-Sasson said.
The community wanted to unite while respecting the Goldberg-Polins’ desire for privacy, she said, prompting them to organize the prayer gathering.
“But it’s like a Band-Aid or giving first aid, it’s what you do in an emergency. I don’t know how we go on after this,” she said.
A covered courtyard at the Hakhel congregation was filled with mourners the day after Hersh Goldberg-Polin, whose family are prominent members, was found to have been killed in Gaza. Hundreds of other people crowded outside the gates, Sept. 1, 2024. (Deborah Danan)
She added that the community, which has a large contingent of English-speaking immigrants, was not prepared for the High Holidays, which begin in about a month. She said, “Seeing his empty seat is hard.”
For Ben-Sasson, who wore a T-shirt bearing the Talmudic dictum “There is no greater mitzvah than the redeeming of captives,” the tragedy is especially painful because, she said, it could have been avoided with a ceasefire agreement that freed hostages.
“Hersh was alive 48 hours ago. We think a deal could have saved him. There is no military solution to this,” she said.
That feeling of bereavement, often mixed with betrayal, pervaded gatherings across Israel on Sunday, as the country struggled with the news that six hostages who may have been freed in an agreement were now dead as negotiations continue to stall. Speakers at protests in Tel Aviv blamed Israeli Prime Minister Benjamin Netanyahu, who himself apologized for not getting the hostages out alive but blamed Hamas for obstructing a deal. The country’s labor union, the Histadrut, has called a national strike on Monday to demand a deal.
A rare early September rain lashed parts of Israel on Sunday, leading to a widespread interpretation: God, too, was weeping.
Some at the Jerusalem gathering, including the relative of another former hostage, said Netanyahu had chosen defeating Hamas over freeing the captives.
Josef Avi Yair Engel’s grandson Ofir was released from Hamas captivity in November. He paid tribute to Hersh Goldberg-Polin, murdered in captivity, in Jerusalem, Sept. 1, 2024. (Deborah Danan)
Josef Avi Yair Engel, whose grandson Ofir, 18, was released from Hamas captivity in November during that month’s ceasefire deal, expressed shock over Hersh’s murder but said he was not surprised, given the wartime policies of Netanyahu’s government.
“We knew months ago this was going to happen. Bibi’s formula, to dismantle Hamas and return the hostages, wasn’t logical. It’s an either/or situation,” Engel said, referring to Netanyahu by his nickname. “He’s tearing the country apart. I’m afraid that in the coming months there won’t be a state at all.”
Engel said he felt a close bond with Hersh’s father Jon Polin, not only because of their joint activism in the hostage families’ tent outside the Prime Minister’s Residence, but also because of their shared identity as Jerusalemites.
“There aren’t many of us in the hostage circle,” he said. “We’re like family.”
Sarah Mann, who did not know the family personally, said the weekend’s tragedy reminded her of Oct. 7.
“This day has sparks of the seventh, which created numbness and an inability to talk. Just complete shock,” she said.
Mourners left notes at a gathering at Hersh Goldberg-Polin’s family synagogue in Jerusalem. Many of the messages used the Hebrew word for “sorry.” (Deborah Danan)
Part of the reason for that, Mann said, was Rachel, who she described as a “force of faith.” Goldberg-Polin’s mother emerged as the most prominent advocate for the hostages globally and became a symbol in her own right as she crisscrossed the world calling for her son’s freedom.
“Millions of people around the world held onto her. Once that was cut, people’s ability to hold onto faith was knocked out today. But even though this has shattered us, we need to keep holding onto God,” Mann said.
For Susi Döring Preston, the day called to mind was not Oct. 7 but Yom Kippur, and its communal solemnity.
She said she usually steers clear of similar war-related events because they are too overwhelming for her.
“Before I avoided stuff like this because I guess I still had hope. But now is the time to just give in to needing to be around people because you can’t hold your own self up any more,” she said, tears rolling down her face. “You need to feel the humanity and hang onto that.”
Like so many others, Döring Preston paid tribute to the Goldberg-Polins’ tireless activism. “They needed everyone else’s strength but we drew so much strength from them and their efforts, “she said. “You felt it could change the outcome. But war is more evil than good. I think that’s the crushing thing. You can do everything right, but the outcome is still devastating.”
Guy Gordon, with his daughter Maya, added a broken heart to the piece of tape he has worn daily to mark the number of days since the hostage crisis began, Sept. 1, 2024. (Deborah Danan)
Guy Gordon, a member of Hakhel who moved to Israel from Dublin, Ireland, in the mid-1990s, said the efforts towards ensuring Hersh’s safe return have been an anchor for the community during the war. The community knew him as the family described him in its announcement of his funeral on Tuesday, as “a child of light, love and peace” who enjoyed exploring the world and coming home to his family, including his parents and younger sisters, Leebie and Orly.
“It gave us something to hope for, and pray for and to demonstrate for,” he said. “We had no choice but to be unreasonably optimistic. Tragically it transpired that he survived until the very end.”
Gordon, like many others in the crowd, wore a piece of duct tape marked with the number of days since Oct. 7 — a gesture initiated by Goldberg-Polin’s mother. Unlike on previous days, though, his tape also featured a broken red heart beside the number.
Nadia Levene, a family friend, also reflected on the improbability of Hersh’s survival.
“He did exactly what his parents begged him to do. He was strong. He did survive. And look what happened,” Levene said.
She hailed Rachel Goldberg-Polin’s “unwavering strength and belief in God,” adding, “There were times I lost faith. I suppose I was angry with God. But she just kept inspiring us all to pray, pray, pray.”
Leah Silver of Jerusalem examined stickers showing Rachel Goldberg-Polin’s mantra for her son Hersh, who was murdered in captivity in Gaza, at a gathering after Hersh’s death, Sept. 1, 2024. (Deborah Danan)
Jerusalem resident Leah Silver rejected politicizing the hostages’ deaths.
“Everything turns political so quickly. I came here because I felt that before all the protests, we need to just mourn for a moment and to pray. And show respect for each other,” she said. “We’ve become confused about who the enemy is. It’s very sad.”
But not everyone at the gathering joined in to sing Israel’s national anthem at the closing of the prayer gathering.
“I’m sorry, I can’t sing ‘Hatikvah,'” Reza Green, a Baka resident who did not know the Goldberg-Polins personally, said. “I’m too angry. We shouldn’t be here.”
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Positive energy districts: fundamentals, assessment methodologies, modeling and research gaps.
State of the art on positive energy districts, 2. methodology.
3.2. quality-of-life indicators in positive energy districts, 3.3. technologies in positive energy districts: development, use and barriers, 3.4. positive energy districts modeling: what is further needed to model peds, 3.5. sustainability assessment of positive energy districts, 3.6. stakeholder engagement within the design process, 3.7. tools and guidelines for ped implementation, 4. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.
Click here to enlarge figure
Question #1 | Question #2 | Question #3 | |
---|---|---|---|
| What are the essential PED DNAs? Can generic PED archetypes be created based on them? | What are the categories of quality-of-life indicators relevant for PED development? | How would you use a database tool to learn about PED development process (e.g., using static information for dynamic decision-making)? |
| Which future technologies would you expect to be adopted in PEDs and cities? | What can be the challenges and the barriers in the future (regarding e.g., control, smart solutions, modeling, technologies) to PED development and diffusion? | What is your expectation for urban and district energy modeling? How can models help to shape PEDs and cities? |
| What is the impact of stakeholders in the PED design/decision process, what are their interests and how are stakeholders likely to be involved in the overall process? | What costs do you expect to bear and what revenues do you expect to realize from the PED implementation? Which aspects should be included in the organizational/business models? | What would you prioritize in terms of energy aspects or efficiency and social implications of living in a PED? Which aspects are more relevant for you? |
| Annex 83 together with other PED initiatives is developing a database of PEDs and PED-Labs: what would be your main interest in consulting the database? | Having the outcomes from PED guidelines analysis, what information would be the most interesting for you to see? | Who can benefit from the PED research studies and Annex 83 results? Which stakeholders are interested? |
Categories | Key Characteristics |
---|---|
Facts and Figures | Physical sizes/population size |
Geographical location | |
Climate | |
Density | |
Built form | |
Land use | |
Energy demand | |
Renewable energy potential | |
Technologies | Renewable energy supplies |
Energy-efficiency measures | |
Energy distribution (e.g., co-generation, district network) | |
Energy storage | |
Mobility solutions | |
Quality of Life | User comfort |
Social-economic conditions | |
Health impacts (e.g., air pollution, noise pollution) | |
Accessibility to green space | |
Accessibility to services (e.g., bike lane, public transportation) | |
Local value/sense of community | |
Others | Regulations/Policies |
Stakeholder involvement | |
Local targets and ambitions | |
Local challenges | |
Impacts of PEDs |
Type | Quality Categories | |
---|---|---|
Tangible | Indoor and outdoor environmental quality | Physical quality and comfort of the environment |
Security and safety | ||
Level and accessibility of servicing | Public and active transport facilities including walkability, energy services (access to affordable energy including access to energy efficiency), sustainable waste management | |
Access to daily life amenities including education, culture, sports, coworking and study places, provisions for children, but even common gardens or community kitchens | ||
Aesthetic quality | ||
Functional mix | ||
Future-proofness | ||
Acceptable cost of life (affordability, inclusivity) | ||
Equity and just transition | ||
Functional links to realizing circularity and reducing emissions | ||
Citizen engagement | Involvement in decision-making | |
Social diversity in participation | ||
Access to greenery | The possibility to reconnect with nature | |
Sufficient open space | ||
Information flow | From creating awareness over enhancing knowledge and literacy up to capacity of control | |
Transparency on energy flows and information for the end prosumer | ||
Insight in applicable PED solutions and in healthy lifestyles | ||
Intangible | Sense of well-being | |
Quality of social connections | ||
Sense of personal achievement | ||
Level of self-esteem | ||
Sense of community | ||
Degree of cooperation and engagement for the common interest | ||
Time spent with friends (outdoor) | ||
Budget available at the end of the month to spend freely | ||
Not being aware or realizing of living in a PED |
Technology Groups | Solutions | |
---|---|---|
Energy efficiency | New energy-efficient buildings and building retrofitting. | |
Nature-based solutions (natural sinks) and carbon capture solutions (CCS) | ||
Efficient resource management | ||
Efficient water systems for agriculture (smart agriculture, hydroponics, agrivoltaics, etc.) | ||
Organic photovoltaics and a circular approach (second life materials, like batteries) | ||
Energy flexibility | Hardware | Storage (long-term and short-term) |
Monitoring systems (sensors, smart meters, PLCs *, energy management systems, etc.) | ||
Vehicle to grid | ||
Heat pumps | ||
Electronic devices like IoT * technologies | ||
Buildings fully automated with real time monitoring behind-the-meter and automated actions | ||
Cybersecurity, data rights and data access | ||
Demand management and remote control of devices | ||
Software | Edge computing | |
Machine learning | ||
Blockchain | ||
Digital twins | ||
5G | ||
City management platform and platforms for city planning (space, refurbishment, climate change, etc.) | ||
E-mobility | Promotion of shared vehicles over individual car use, lift sharing, and alternative ways (like micromobility) to collective transports | |
Soft mobility | Promotion of a lifestyle that require less use of cars, i.e., “soft mobility” solutions like low emission zones or banning the entrance of some type of car (e.g., Singapore and Iran have policies in place to allow only certain car groups to drive freely in certain periods) | |
E-vehicle charging stations and vehicle-to-grid solutions | ||
Low-carbon generation | Photovoltaics | |
Energy communities | ||
Electrification of heating and cooling (H&C) using heat pumps, district heating networks utilizing waste heat, or solar thermal technologies | ||
Virtual production | ||
Fusion technology |
Challenges and Barriers | Key Topics |
---|---|
Capacity building and policy issues | Political and legal barriers |
Regulatory frameworks and policy constraints | |
Tailored legislation | |
Bridging the knowledge gap | |
Inadequate data sharing practices | |
Securing sufficient financial resources | |
Lack of clear regulations defining PED classification | |
Active involvement of policymakers | |
Widespread dissemination of knowledge | |
Collaborative data-sharing efforts | |
Securing adequate funding | |
Establishing supportive policies and regulations | |
Social challenges and considerations | Cultural barriers |
Access to affordable and sustainable energy for all | |
Building social agreements and fostering collaboration | |
Energy literacy | |
Addressing personal behavior acceptance | |
Transition strategy for inclusivity | |
Social inclusion and trust-building | |
Data sharing and privacy concerns | |
Overcoming public opposition and promoting knowledge dissemination | |
Financial barriers | Long-term storage investment and space competition |
Insufficient investment | |
High upfront costs | |
Allocation of costs among stakeholders | |
Incentives for participation | |
Addressing investment challenges for different stakeholders | |
Accounting for battery costs | |
Data management | Data standardization |
Data security measures and protocols | |
Sustainability and maintenance of data infrastructure | |
Privacy regulations and data anonymization techniques | |
Sustainable business models and ownership structures | Standardization of control technologies and replication strategies |
Grid management approaches | |
Deep penetration of sustainable technologies | |
Implementation of predictive models Long-term maintenance activities and resident data collection | |
Balancing diverse requirements | |
Addressing grid operation challenges | |
Managing multiple independent energy districts | |
Inclusivity strategies for digital technology reliance | |
Managing production peaks and defining the role of buildings and districts | |
Effective management strategies for grid congestion and stability |
Categories of Innovation | Innovation Types | Possible Revenues/Advantages in PED Business Model/Governance | Possible Costs/Drawbacks in PED Business Model/Governance |
---|---|---|---|
Configuration | Profit Model | Providing thermal comfort instead of a certain amount of thermal energy to inhabitants | Misconducts or rebound effect |
Network | Inclusion of the PED into larger projects and international networks, possibility of co-financing and knowledge sharing | Misalignment or delay of the PED project to the original timeline due to constrains related to international activities and networking | |
Structure | Participation of the real estate companies/investors in the development and management of the energy infrastructure and EV mobility services as well as building management | Lack of knowledge, involvement in activities out of the usual business of investors | |
Free or almost free thermal energy supply from “waste energy” sources | Failure of the network due to unliteral decisions of a member in ceasing the provision of energy | ||
Process | Involvement of future inhabitants in the design phase of the energy community since the early stage, to share the sense of belonging and ownership | Reluctancy of inhabitants to participate in additional expenses or being involved in “entrepreneurial” activities or bored by the participation in boards and governance structures | |
Offering | Product Performance | Investors and companies involved in the PED development take profit from their role of frontrunner placing them before the competitors or entering in new market niches | Hi-tech BA and BEM systems may result costly in O&M, because of digital components, cloud and computing services, rapid aging of technology |
Product System | Including EV available for PED users may generate new incomes and reduce the need of individual cars. The integration of EV in the energy system may offer “flexibility services” | Lack of knowledge, involvement in activities out of the usual business of investors/real estate companies. Low interest of users in participating to the flexibility market, because of discomfort (unexpected empty battery of the EV) | |
Experience | Services | Provision of high tech and high-performance buildings, with outstanding energy performances (lower heating/cooling costs) and sophisticated Building Automation and Energy Management systems | Sophisticated Building Automation and Energy Management systems may result “invasive” to users, asking for continuous interaction with complicate systems, or leaving them not enough freedom to choose (e.g., opening the windows is not possible to achieve some energy performance) |
Channel | The PED is promoted as a rewarding sustainable investment, this allows the city to attract more clean investments (public funds, investment funds, donors), speeding up the energy transition | The communication of the characteristics of the PED is not done in the proper way | |
Brand | Gold class rated buildings may have an increased value on the market, resulting in higher selling and rental costs, occupancy rate. The high architectural quality is appreciated by the market | The Branding/certification of the PED is not recognized by the market as an added value. | |
The development of the PED takes longer as expected. Technology failures during the implementation or operation phase create a bad reputation and discourage future similar activities | |||
Customer Engagement | The PED is available as a digital twin, users are engaged via a dedicated app, allowing interaction, communication, reporting, monitoring of bills, etc. | The PED is perceived by users (e.g., social housing tenants) as a hassle and not responding to their needs, because they have not been involved in the identification of peculiar traits since the beginning |
Category | Beneficiaries |
---|---|
Citizens and communities | Citizens, inhabitants, residents, general public, local communities and neighborhoods, municipalities and provinces, energy communities, and socially disadvantaged groups. |
City decision-makers and planners | City decision-makers, city planners, local authorities, policy-makers, public administrations, politicians, local and national governments. |
Research | Scientists, publishers, and research organizations. |
Private companies and technology developers | Private companies of RES technologies, ICT companies, start-ups and new companies, entrepreneurs, technology developers and other companies involved in local development (tech development and evaluation). |
Energy providers | Energy providers, grid operators. |
Education stakeholders | Students and teachers. |
Non-governmental organizations (NGOs) | NGOs and other civil society groups |
Category | Comments |
---|---|
Strategies | Most comments dealt with the strategies on how to achieve PEDs, that should focus on success factors of PED initiatives, technologies and stakeholders rather than a standardized approach |
References | Useful information, special attention to Liwen Li, planning principles for integrating community empowerment into zero-carbon transformation |
Definitions | Help to reduce uncertainty |
Boundaries | Energy balance calculations, mobility, definition (of buildings) |
Finance | Financial mechanisms, support schemes |
Citizen engagement | From engagement to empowerment |
Management | Process management, organizing involvement, information provision |
Policy | Incentives, regional policies |
Flexibility/Grid interaction | Timesteps, credit system |
Form | Dissemination through video and other forms (not only written information) |
Category | Comments |
---|---|
Lessons learned | Special reference to real life implementation |
Results | Data analysis and potential research on the field |
Metadata as the useful information that can the real goal of consultation | |
Benchmarking to compare PEDs | |
Need to normalize results depending on a number of factors (size, location…) to really compare different initiatives | |
Privacy and data protection | |
Sets of technologies and solutions | - |
Economic parameters | As a way to benchmark the different PED technologies |
Citizen engagement | Energy poverty |
Prosumers | |
From engagement to empowerment | |
Definition and boundaries | Need to standardize and have a reference framework to establish the energy balance |
Contact persons | It is very valuable to have a contact address to ask more about the initiative |
Regulatory framework | Drivers and Enablers |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Kozlowska, A.; Guarino, F.; Volpe, R.; Bisello, A.; Gabaldòn, A.; Rezaei, A.; Albert-Seifried, V.; Alpagut, B.; Vandevyvere, H.; Reda, F.; et al. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies 2024 , 17 , 4425. https://doi.org/10.3390/en17174425
Kozlowska A, Guarino F, Volpe R, Bisello A, Gabaldòn A, Rezaei A, Albert-Seifried V, Alpagut B, Vandevyvere H, Reda F, et al. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies . 2024; 17(17):4425. https://doi.org/10.3390/en17174425
Kozlowska, Anna, Francesco Guarino, Rosaria Volpe, Adriano Bisello, Andrea Gabaldòn, Abolfazl Rezaei, Vicky Albert-Seifried, Beril Alpagut, Han Vandevyvere, Francesco Reda, and et al. 2024. "Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps" Energies 17, no. 17: 4425. https://doi.org/10.3390/en17174425
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To know the answer for what is research design, it is important to know the characteristics. These are-. 1. Reliability. A reliable research design ensures that each study's results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields: 1.
Experimental Research Design. Experimental research design is used to determine if there is a causal relationship between two or more variables.With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions ...
Put another way, in the honeycomb, the six main elements - namely: (1) research philosophy; (2) research approach; (3) research strategy; (4) research design; (5) data collection and (6) data analysis techniques - come together to form research methodology. This structure is characteristic of the main headings you will find in a methodology ...
In short, a good research design helps us to structure our research. Marketers use different types of research design when conducting research. There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let's take a look at each in more detail.
Typically, a good and well-planned research design consists of the following components, or tasks: 1. Selection of appropriate type of design: Exploratory, descriptive and/or causal design. 2. Identification of specific information needed based problem in hand and the selected design. 3.
Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies. Frequently asked questions. Introduction. Step 1. Step 2.
Types of Research Design. • Quantitative Research: Focuses on numerical data and statistical analysis to quantify relationships and patterns. Common methods include surveys, experiments, and observational studies. • Qualitative Research: Emphasizes understanding phenomena through in-depth exploration and interpretation of non-numerical data.
In our ultimate guide to research design for businesses, we breakdown the process, including research methods, examples, and best practice tips to help you get started. If you have a business problem that you're trying to solve — from product usage to customer engagement — doing research is a great way to understand what is going wrong.
Data helps you price your product, find your customer, make a better feature, or solve a problem. Research design is the strategy or plan you use to gather that data and make sense of it in a way that seems understandable, logical, and actionable. Consider your research design the roadmap of data collection and measurement.
A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features. Research design elements Research design elements include the following:
Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. ... Business: Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the ...
Business research: Definition. Business research is a process of acquiring detailed information on all the areas of business and using such information to maximize the sales and profit of the business. Such a study helps companies determine which product/service is most profitable or in demand. In simple words, it can be stated as the acquisition of information or knowledge for professional or ...
Chapter 3 Business Research Design: Exploratory, Descriptive and Causal Designs. Chapter 3. sLearning ObjectivesAfter reading this chapter, the reader should be able to:Understand the meaning of research design, select an. develop appropriate research design to solve the concerned management dilemma.Understand the basic difference between th.
Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success. Creating a research topic explains the type of research (experimental,survey research,correlational ...
The scope of the term business research is quite broad - it acts as an umbrella that covers every aspect of business, from finances to advertising creative. It can include research methods which help a company better understand its target market. It could focus on customer experience and assess customer satisfaction levels.
Other interesting articles. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.
The five main components of a research design are: Research questions. Course suggestions. Units of analysis. Linking data to propositions. Interpretation of the findings of the study. The components of research design apply to all types of standardised, extra-terrestrial research, whether physical or social sciences.
Here are some of the elements of a good research design: Purpose statement. Data collection methods. Techniques of data analysis. Types of research methodologies. Challenges of the research. Prerequisites required for study. Duration of the research study. Measurement of analysis.
for validity and reliability. Design is basically concerned with the aims, uses, purposes, intentions and plans within the. pr actical constraint of location, time, money and the researcher's ...
Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23 (Suppl 4):S305-S307. Keywords: Clinical trials as topic, Observational studies as topic, Research designs. We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized. Go to:
A design is used to structure the research, to show how all of the major parts of the research project—the samples or groups, measures, treatments or programs, and methods of assignment—work together so as to address the central research questions. From sample results, the researcher generalizes or makes claims about the population.
It's because each incentive design choice both signals information about your own beliefs and intentions as an employer and shapes the signaling value of employee behavior within the organization.
Today, we are going to show you how AI is improving UX research and design and how you can use it to your advantage. Let's get started! How AI Enhances UX Research and the Design Process. Let's start by exploring a few key benefits of adding AI to your existing design process. Speed Things Up - AI tools can crunch data faster than any ...
Josef Avi Yair Engel, whose grandson Ofir, 18, was released from Hamas captivity in November during that month's ceasefire deal, expressed shock over Hersh's murder but said he was not ...
The definition, characterization and implementation of Positive Energy Districts is crucial in the path towards urban decarbonization and energy transition. However, several issues still must be addressed: the need for a clear and comprehensive definition, and the settlement of a consistent design approach for Positive Energy Districts. As emerged throughout the workshop held during the fourth ...