what is the tasks of research

• Research Guides

6 Stages of Research

• 1: Task Definition
• 2: Information Seeking
• 3: Location & Access
• 4: Use of Information
• 5: Synthesis
• 6: Evaluation

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The scope of an investigation determines how large or small your investigation will be. Determining the scope of an investigation is the critical first step in the research process because you will know how far and how deep to look for answers. This lesson will teach you how to develop a research question as a way to determine the scope of an investigation.

Click the image to open the tutorial in a new window.

Keyword(s):  5W Criteria, Ask the Right Questions, Guided Inquiry, Information Literacy, Library, New Literacies Alliance, Research as Inquiry, Research Question

Purpose of this guide

The purpose of this guide is to walk you through the 6 stages of writing an effective research paper. By breaking the process down into these 6 stages, your paper will be better and you will get more out of the research experience. 

The 6 stages are:

• Task Definition (developing a topic)
• Information Seeking (coming up with a research plan)
• Location & Access (finding good sources)
• Use of Information (Reading, taking notes, and generally making the writing process easier)
• Synthesis (coming up with your own ideas and presenting them well)
• Evaluation (reflection)

This research guide is based on the Big6 Information Literacy model from  https://thebig6.org/

Task Definition

The purpose of task definition is to help you develop an effective topic for your paper. .

Developing a topic is often one of the hardest and most important steps in writing a paper or doing a research project. But here are some tips:

• A research topic is a question, not a statement. You shouldn't already know the answer when you start researching.
• Research something you actually care about or find interesting. It turns the research process from a chore into something enjoyable and whoever reads your work can tell the difference. 
• Read the assignment before and after you think you have come up with your topic to make sure you are answering the prompt. 

Steps to Developing a Topic

• Assignment Requirements
• General Idea
• Background Research
• Ask Questions
• Topic Question

Read your assignment and note any requirements.

• Is there a required page length?
• How many sources do you need?
• Does the paper have to be in a specific format like APA?
• Are there any listed goals for the topic, such as synthesizing different opinions, or applying a theory to a real-life example?

Formulate a general idea.

• Look at your syllabus or course schedule for broad topic ideas.
• Think about reading assignments or class lectures that you found interesting.
• Talk with your professor or a librarian. 
• Check out social media and see what has been trending that is related to your course. 
• Think about ideas from popular videos, TV shows, and movies.
• Read The New York Times  (FHSU students have free access through the Library)
• Watch NBC Learn (FHSU students have free access through the Library)
• Search your library for relevant journals and publications related to your course and browse them for ideas
• Browse online discussion forums, news, and blogs for professional organizations for hot topics

Do some background research on your general idea.

• You have access to reference materials through the Library for background research.
• See what your course notes and textbook say about the subject.
• Google it. 

Reference e-books on a wide range of topics. Sources include dictionaries, encyclopedias, key concepts, key thinkers, handbooks, atlases, and more. Search by keyword or browse titles by topic.

Over 1200 cross-searchable reference e-books on a wide variety of subjects.

Mind map it.

A mind map is an effective way of organizing your thoughts and generating new questions as you learn about your topic. 

• Video  on how to do a mind map. 
• Coggle Free mind mapping software that is great for beginners and easy to use.
• MindMup Mindmup is a free, easy to use online software that allows you to publish and share your mind maps with others.

Ask Questions to focus on what interests you.

Who?   What?   When?   Where?   Why?

We can focus our ideas by brainstorming what interests us when asking who, what, when where, and why:

Research Question:  Does flexible seating in an elementary classroom improve student focus?

Write out your topic question & reread the assignment criteria.

• Can you answer your question well in the number of pages required? 
• Does your topic still meet the requirements of the paper? Ex: is the question still about the sociology of gender studies and women?
• Is the topic too narrow to find research? 

Developing a Topic Tutorial

The following tutorial from Forsyth Library will walk you through the process of defining your topic. 

• Next: 2: Information Seeking >>
• Last Updated: Mar 29, 2024 11:34 AM
• URL: https://fhsuguides.fhsu.edu/6stages

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Academic Research

Raysonho/ Wikimedia Commons. Academic research can be intense, stimulating, and rewarding. But it is important to know that a research career involves many activities besides research. Scientists spend their time writing applications for funding to do research, as well as writing scientific papers to report the findings of their research. In addition, they spend time presenting their research in oral or poster form to other scientists at group meetings, institutional meetings, and scientific conferences; they also spend time teaching students about their field of study. A scientist's life is often full of tasks that need to be done and most scientists work very hard, but they also love what they do.

Fields of Study

• Clinical Scientist: David Fredricks
• Epidemiologist: Gloria Coronado
• Geneticist: Katie Peichel
• Clinical Research: Dana Panteleef
• Research Technician: Nanna Hansen

If you're interested in a general sense in academic research, the first thing to figure out is which field of research is best for you.

The fundamental task of research is asking questions. There are many areas of research in the life sciences, and they generally fall into three categories based on the types of questions that are asked and the tools that are used to answer the questions:

Basic Research

Clinical research, population-based research.

Basic researchers ask questions about how fundamental life processes work. Examples of questions include the following:

• What are the mechanisms that determine how and when cells divide?
• How do DNA mutations associated with a disease occur?
• How and why do cells age?
• How and why does one type of cell work differently from another type of cell?

Basic researchers usually work in laboratories with other scientists, usually with one faculty member leading a group of postdoctoral fellows, graduate students, and lab technicians who do most of the lab work. The hours can be very long and the work can be challenging, especially for graduate students and postdoctoral fellows. Basic researchers often ask their questions using model organisms, including yeast, worms, flies, fish, and mice.

• Scientific Recruiter: Scott Canavera
• Staff Scientist: Tom Paulson
• Shared Resources: Julie Randolph-Habecker
• Faculty Member: Wendy Leisenring

Clinical researchers ask questions about how disease occurs and how it can be cured in humans. Examples of questions include the following:

• How can we manipulate the body's immune system to improve treatment of a disease?
• How can we create a drug to improve disease survival?
• What are the long-term impacts of treatment on quality of life?

Clinical researchers work in laboratories that are very similar to basic researchers, but they often work with human tissue samples to ask their questions. Many clinical researchers find it rewarding to work on a question that may have an impact that they will eventually see come to fruition. At the same time, when you're working with human tissue, you usually have a limited amount of it so the risks of making a mistake that will lose your sample could be high. Clinical researchers will often collaborate with biostatisticians to best design and analyze their studies in order to yield the maximum amount of relevant information.

Population-based research is done by epidemiologists who ask questions to determine how diet, genetics, and lifestyle may influence the risk of disease. They ask these questions in one of two ways:

• by following a group of people over time and correlating exposure to who gets a disease;
• by asking a group of people with a disease about their lifestyle and diet choices and comparing the data to a randomly chosen group without the disease in order to look for differences between the two groups.

The types of questions they ask include the following:

• How can we best prevent teenagers from starting to smoke?
• Do some genetic variants place a person at greater risk for cancer?
• Do vitamins help prevent cancer?
• Does exposure to certain chemicals increase the risk of getting a particular disease?

Epidemiologists also collaborate with biostatisticians in order to design and analyze studies so they can get the most information from them. Rather than work in a lab, epidemiologists often need no more than a desk and computer. However, the interdisciplinary field of molecular epidemiology is changing this, and many epidemiologists ask questions about how a particular gene can influence disease risk, rather than, or in addition to, a lifestyle exposure.

Roles in Research

Faculty member.

Faculty members usually have Ph.D.'s or M.D.'s and have gone through graduate school or medical school followed by several years of being a postdoctoral fellow or medical resident. A faculty member is the leader of their own lab or work group and determines the direction of the research in their group. Most faculty members spend a good deal of their time writing grant proposals and manuscripts, reading research papers, reviewing colleagues' manuscripts and grant proposals, thinking and talking with others about their research to gain new ideas, and mentoring the people in their group.

Faculty positions are usually very competitive to get and are often a result of hard work over many years. However, most faculty members love what they do and wouldn't trade it for anything.

Research Scientist

Shared resource specialist, technician and other support staff, administrative positions.

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What does a researcher do?

Would you make a good researcher? Take our career test and find your match with over 800 careers.

What is a Researcher?

A researcher is trained to conduct systematic and scientific investigations in a particular field of study. Researchers use a variety of techniques to collect and analyze data to answer research questions or test hypotheses. They are responsible for designing studies, collecting data, analyzing data, and interpreting the results. Researchers may work in a wide range of fields, including science, medicine, engineering, social sciences, humanities, and many others.

To become a researcher, individuals usually need to obtain a graduate degree in their chosen field of study. They may also need to gain experience working as an assistant or intern in a research setting before becoming a full-fledged researcher. Researchers may work in academic or industrial settings, or they may work independently as consultants or freelance researchers. Regardless of the setting, researchers play a vital role in advancing knowledge and finding solutions to real-world problems.

What does a Researcher do?

Researchers are essential to the advancement of knowledge in various fields, including science, technology, medicine, social sciences, and humanities. Their work involves conducting systematic investigations to gather data, analyze it, and draw meaningful conclusions. Through their research, they can identify new problems and challenges, develop innovative solutions, and test hypotheses to validate theories.

Researchers also play a critical role in improving existing practices and policies, identifying gaps in knowledge, and creating new avenues for future research. They provide valuable insights and information that can inform decision-making, shape public opinion, and drive progress in society.

Duties and Responsibilities The duties and responsibilities of researchers can vary depending on the field of study and the type of research being conducted. However, here are some common duties and responsibilities that researchers are typically expected to fulfill:

• Develop research proposals: Developing a research proposal typically involves identifying a research question or problem, reviewing the relevant literature, selecting appropriate research methods and techniques, and outlining the expected outcomes of the research. Researchers must also ensure that their proposal aligns with the funding agency's objectives and guidelines.
• Conduct literature reviews: Literature reviews involve searching for and reviewing existing research papers, articles, books, and other relevant publications to identify gaps in knowledge and to build upon previous research. Researchers must ensure that they are using credible and reliable sources of information and that their review is comprehensive.
• Collect and analyze data: Collecting and analyzing data is a key aspect of research. This may involve designing and conducting experiments, surveys, interviews, or observations. Researchers must ensure that their data collection methods are valid and reliable, and that their analysis is appropriate and accurate.
• Ensure ethical considerations: Research ethics involve ensuring that the research is conducted in a manner that protects the rights, welfare, and dignity of all participants, as well as the environment. Researchers must obtain informed consent from human participants, ensure that animal research is conducted ethically and humanely, and comply with relevant regulations and guidelines.
• Communicate research findings: Researchers must communicate their research findings clearly and effectively to a range of audiences, including academic peers, policymakers, and the general public. This may involve writing research papers, presenting at conferences, and producing reports or other materials.
• Manage research projects: Managing a research project involves planning, organizing, and coordinating resources, timelines, and budgets to ensure that the project is completed on time and within budget. Researchers must ensure that they have the necessary resources, such as funding, personnel, and equipment, and that they are managing these resources effectively.
• Collaborate with others: Collaboration is an important aspect of research, and researchers often work with other researchers, academic institutions, funding agencies, and industry partners to achieve research objectives. Collaboration can help to facilitate the sharing of resources, expertise, and knowledge.
• Stay up-to-date with developments in their field: Research is an evolving field, and researchers must stay up-to-date with the latest developments and trends in their field to ensure that their research remains relevant and impactful. This may involve attending conferences, workshops, and seminars, reading academic journals and other publications, and participating in professional development opportunities.

Types of Researchers There are many types of researchers, depending on their areas of expertise, research methods, and the types of questions they seek to answer. Here are some examples:

• Basic Researchers: These researchers focus on understanding fundamental concepts and phenomena in a particular field. Their work may not have immediate practical applications, but it lays the groundwork for applied research.
• Applied Researchers: These researchers seek to apply basic research findings to real-world problems and situations. They may work in fields such as engineering, medicine, or psychology.
• Clinical Researchers: These researchers conduct studies with human subjects to better understand disease, illness, and treatment options. They may work in hospitals, universities, or research institutes.
• Epidemiologists : These researchers study the spread and distribution of disease in populations, and work to develop strategies for disease prevention and control.
• Social Scientists: These researchers study human behavior and society, using methods such as surveys, experiments, and observations. They may work in fields such as psychology, sociology, or anthropology.
• Natural Scientists: These researchers study the natural world, including the physical, chemical, and biological processes that govern it. They may work in fields such as physics, chemistry, or biology.
• Data Scientists : These researchers use statistical and computational methods to analyze large datasets and derive insights from them. They may work in fields such as machine learning, artificial intelligence, or business analytics.
• Policy Researchers: These researchers study policy issues, such as healthcare, education, or environmental regulations, and work to develop evidence-based policy recommendations. They may work in government agencies, think tanks, or non-profit organizations.

What is the workplace of a Researcher like?

The workplace of a researcher can vary greatly depending on the field and area of study. Researchers can work in a variety of settings, including academic institutions, government agencies, non-profit organizations, and private companies.

In academic settings, researchers often work in universities or research institutions, conducting experiments and analyzing data to develop new theories and insights into various fields of study. They may also teach courses and mentor students in their area of expertise.

In government agencies, researchers may work on projects related to public policy, health, and safety. They may be responsible for conducting research to support the development of new regulations or programs, analyzing data to assess the effectiveness of existing policies, or providing expertise on specific issues.

Non-profit organizations often employ researchers to study social and environmental issues, such as poverty, climate change, and human rights. These researchers may conduct surveys and collect data to understand the impact of various programs and initiatives, and use this information to advocate for policy changes or other interventions.

Private companies also employ researchers, particularly in industries such as technology and healthcare. These researchers may be responsible for developing new products, improving existing technologies, or conducting market research to understand consumer preferences and behaviors.

Regardless of the setting, researchers typically spend a significant amount of time conducting research, analyzing data, and communicating their findings through presentations, reports, and publications. They may also collaborate with other researchers or professionals in their field, attend conferences and workshops, and stay up-to-date with the latest research and developments in their area of expertise.

Frequently Asked Questions

Academic writer vs researcher.

An academic writer is someone who produces written material for academic purposes, such as research papers, essays, and other scholarly works. Academic writers may work as freelance writers, editors, or as staff writers for academic institutions or publishers.

On the other hand, a researcher is someone who conducts original research to generate new knowledge or validate existing knowledge. Researchers may work in academic settings, government agencies, private companies, or non-profit organizations. They typically design and execute experiments, surveys, or other data collection methods, analyze the data, and draw conclusions based on their findings.

While there may be some overlap between the skills required for academic writing and research, they are distinct activities with different goals. Academic writers often rely on the research of others to support their arguments, while researchers generate new knowledge through their own experiments and data analysis. However, academic writers may also be researchers who write about their own research findings.

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Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

14k Accesses

Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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How to write a research plan: Step-by-step guide

Last updated

30 January 2024

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Today’s businesses and institutions rely on data and analytics to inform their product and service decisions. These metrics influence how organizations stay competitive and inspire innovation. However, gathering data and insights requires carefully constructed research, and every research project needs a roadmap. This is where a research plan comes into play.

There’s general research planning; then there’s an official, well-executed research plan. Whatever data-driven research project you’re gearing up for, the research plan will be your framework for execution. The plan should also be detailed and thorough, with a diligent set of criteria to formulate your research efforts. Not including these key elements in your plan can be just as harmful as having no plan at all.

Read this step-by-step guide for writing a detailed research plan that can apply to any project, whether it’s scientific, educational, or business-related.

• What is a research plan?

A research plan is a documented overview of a project in its entirety, from end to end. It details the research efforts, participants, and methods needed, along with any anticipated results. It also outlines the project’s goals and mission, creating layers of steps to achieve those goals within a specified timeline.

Without a research plan, you and your team are flying blind, potentially wasting time and resources to pursue research without structured guidance.

The principal investigator, or PI, is responsible for facilitating the research oversight. They will create the research plan and inform team members and stakeholders of every detail relating to the project. The PI will also use the research plan to inform decision-making throughout the project.

• Why do you need a research plan?

Create a research plan before starting any official research to maximize every effort in pursuing and collecting the research data. Crucially, the plan will model the activities needed at each phase of the research project.

Like any roadmap, a research plan serves as a valuable tool providing direction for those involved in the project—both internally and externally. It will keep you and your immediate team organized and task-focused while also providing necessary definitions and timelines so you can execute your project initiatives with full understanding and transparency.

External stakeholders appreciate a working research plan because it’s a great communication tool, documenting progress and changing dynamics as they arise. Any participants of your planned research sessions will be informed about the purpose of your study, while the exercises will be based on the key messaging outlined in the official plan.

Here are some of the benefits of creating a research plan document for every project:

Project organization and structure

Well-informed participants

All stakeholders and teams align in support of the project

Clearly defined project definitions and purposes

Distractions are eliminated, prioritizing task focus

Timely management of individual task schedules and roles

Costly reworks are avoided

• What should a research plan include?

The different aspects of your research plan will depend on the nature of the project. However, most official research plan documents will include the core elements below. Each aims to define the problem statement, devising an official plan for seeking a solution.

Specific project goals and individual objectives

Ideal strategies or methods for reaching those goals

Required resources

Descriptions of the target audience, sample sizes, demographics, and scopes

Key performance indicators (KPIs)

Project background

Research and testing support

Preliminary studies and progress reporting mechanisms

Cost estimates and change order processes

Depending on the research project’s size and scope, your research plan could be brief—perhaps only a few pages of documented plans. Alternatively, it could be a fully comprehensive report. Either way, it’s an essential first step in dictating your project’s facilitation in the most efficient and effective way.

• How to write a research plan for your project

When you start writing your research plan, aim to be detailed about each step, requirement, and idea. The more time you spend curating your research plan, the more precise your research execution efforts will be.

Account for every potential scenario, and be sure to address each and every aspect of the research.

Consider following this flow to develop a great research plan for your project:

Define your project’s purpose

Start by defining your project’s purpose. Identify what your project aims to accomplish and what you are researching. Remember to use clear language.

Thinking about the project’s purpose will help you set realistic goals and inform how you divide tasks and assign responsibilities. These individual tasks will be your stepping stones to reach your overarching goal.

Additionally, you’ll want to identify the specific problem, the usability metrics needed, and the intended solutions.

Know the following three things about your project’s purpose before you outline anything else:

What you’re doing

Why you’re doing it

What you expect from it

Identify individual objectives

With your overarching project objectives in place, you can identify any individual goals or steps needed to reach those objectives. Break them down into phases or steps. You can work backward from the project goal and identify every process required to facilitate it.

Be mindful to identify each unique task so that you can assign responsibilities to various team members. At this point in your research plan development, you’ll also want to assign priority to those smaller, more manageable steps and phases that require more immediate or dedicated attention.

Select research methods

Research methods might include any of the following:

User interviews: this is a qualitative research method where researchers engage with participants in one-on-one or group conversations. The aim is to gather insights into their experiences, preferences, and opinions to uncover patterns, trends, and data.

Field studies: this approach allows for a contextual understanding of behaviors, interactions, and processes in real-world settings. It involves the researcher immersing themselves in the field, conducting observations, interviews, or experiments to gather in-depth insights.

Card sorting: participants categorize information by sorting content cards into groups based on their perceived similarities. You might use this process to gain insights into participants’ mental models and preferences when navigating or organizing information on websites, apps, or other systems.

Focus groups: use organized discussions among select groups of participants to provide relevant views and experiences about a particular topic.

Diary studies: ask participants to record their experiences, thoughts, and activities in a diary over a specified period. This method provides a deeper understanding of user experiences, uncovers patterns, and identifies areas for improvement.

Five-second testing: participants are shown a design, such as a web page or interface, for just five seconds. They then answer questions about their initial impressions and recall, allowing you to evaluate the design’s effectiveness.

Surveys: get feedback from participant groups with structured surveys. You can use online forms, telephone interviews, or paper questionnaires to reveal trends, patterns, and correlations.

Tree testing: tree testing involves researching web assets through the lens of findability and navigability. Participants are given a textual representation of the site’s hierarchy (the “tree”) and asked to locate specific information or complete tasks by selecting paths.

Usability testing: ask participants to interact with a product, website, or application to evaluate its ease of use. This method enables you to uncover areas for improvement in digital key feature functionality by observing participants using the product.

Live website testing: research and collect analytics that outlines the design, usability, and performance efficiencies of a website in real time.

There are no limits to the number of research methods you could use within your project. Just make sure your research methods help you determine the following:

What do you plan to do with the research findings?

What decisions will this research inform? How can your stakeholders leverage the research data and results?

Recruit participants and allocate tasks

Next, identify the participants needed to complete the research and the resources required to complete the tasks. Different people will be proficient at different tasks, and having a task allocation plan will allow everything to run smoothly.

Prepare a thorough project summary

Every well-designed research plan will feature a project summary. This official summary will guide your research alongside its communications or messaging. You’ll use the summary while recruiting participants and during stakeholder meetings. It can also be useful when conducting field studies.

Ensure this summary includes all the elements of your research project. Separate the steps into an easily explainable piece of text that includes the following:

An introduction: the message you’ll deliver to participants about the interview, pre-planned questioning, and testing tasks.

Interview questions: prepare questions you intend to ask participants as part of your research study, guiding the sessions from start to finish.

An exit message: draft messaging your teams will use to conclude testing or survey sessions. These should include the next steps and express gratitude for the participant’s time.

Create a realistic timeline

While your project might already have a deadline or a results timeline in place, you’ll need to consider the time needed to execute it effectively.

Realistically outline the time needed to properly execute each supporting phase of research and implementation. And, as you evaluate the necessary schedules, be sure to include additional time for achieving each milestone in case any changes or unexpected delays arise.

For this part of your research plan, you might find it helpful to create visuals to ensure your research team and stakeholders fully understand the information.

Determine how to present your results

A research plan must also describe how you intend to present your results. Depending on the nature of your project and its goals, you might dedicate one team member (the PI) or assume responsibility for communicating the findings yourself.

In this part of the research plan, you’ll articulate how you’ll share the results. Detail any materials you’ll use, such as:

Presentations and slides

A project report booklet

A project findings pamphlet

Documents with key takeaways and statistics

Graphic visuals to support your findings

• Format your research plan

As you create your research plan, you can enjoy a little creative freedom. A plan can assume many forms, so format it how you see fit. Determine the best layout based on your specific project, intended communications, and the preferences of your teams and stakeholders.

Find format inspiration among the following layouts:

Written outlines

Narrative storytelling

Visual mapping

Graphic timelines

Remember, the research plan format you choose will be subject to change and adaptation as your research and findings unfold. However, your final format should ideally outline questions, problems, opportunities, and expectations.

• Research plan example

Imagine you’ve been tasked with finding out how to get more customers to order takeout from an online food delivery platform. The goal is to improve satisfaction and retain existing customers. You set out to discover why more people aren’t ordering and what it is they do want to order or experience. 

You identify the need for a research project that helps you understand what drives customer loyalty. But before you jump in and start calling past customers, you need to develop a research plan—the roadmap that provides focus, clarity, and realistic details to the project.

Here’s an example outline of a research plan you might put together:

Project title

Project members involved in the research plan

Purpose of the project (provide a summary of the research plan’s intent)

Objective 1 (provide a short description for each objective)

Objective 2

Objective 3

Proposed timeline

Audience (detail the group you want to research, such as customers or non-customers)

Budget (how much you think it might cost to do the research)

Risk factors/contingencies (any potential risk factors that may impact the project’s success)

Remember, your research plan doesn’t have to reinvent the wheel—it just needs to fit your project’s unique needs and aims.

Customizing a research plan template

Some companies offer research plan templates to help get you started. However, it may make more sense to develop your own customized plan template. Be sure to include the core elements of a great research plan with your template layout, including the following:

Introductions to participants and stakeholders

Background problems and needs statement

Significance, ethics, and purpose

Research methods, questions, and designs

Preliminary beliefs and expectations

Implications and intended outcomes

Realistic timelines for each phase

Conclusion and presentations

How many pages should a research plan be?

Generally, a research plan can vary in length between 500 to 1,500 words. This is roughly three pages of content. More substantial projects will be 2,000 to 3,500 words, taking up four to seven pages of planning documents.

What is the difference between a research plan and a research proposal?

A research plan is a roadmap to success for research teams. A research proposal, on the other hand, is a dissertation aimed at convincing or earning the support of others. Both are relevant in creating a guide to follow to complete a project goal.

What are the seven steps to developing a research plan?

While each research project is different, it’s best to follow these seven general steps to create your research plan:

Defining the problem

Identifying goals

Choosing research methods

Recruiting participants

Preparing the brief or summary

Establishing task timelines

Defining how you will present the findings

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Module 10: The Research Process—Finding and Evaluating Sources

What is research, learning objectives.

• Explain the essential components of research writing

At its most basic level, research is anything you have to do to find out something you didn’t already know. That definition might seem simple and obvious, but it contains some key assumptions that might not be as obvious. Understanding these assumptions is going to be essential to your success in this course (and in your life after college), so let’s look at them carefully.

Figure 1 . Research means searching for the answer to your research question and compiling the information you find in a useful and meaningful way.

First, research is about acquiring new information or new knowledge, which means that it always begins from a gap in your knowledge—that is, something you do not already know. More importantly, research is always goal-directed: that is, it always begins from a specific question you need to answer (a specific gap in your body of information that you need to fill) in order to accomplish some particular goal. This research question is the statement of the thing you don’t know that motivates your research.

Sometimes the answer to your question already exists in exactly the form you need. For example,

Question 1: Does Columbus, Ohio, have a commercial airport?

The answer to this turns out to be yes, and the time to find the answer is about ten seconds. A Google search of “airports in Ohio” produces a Wikipedia entry titled “List of airports in Ohio.” A quick glance at this document shows that Columbus does indeed have a commercial airport, and that it is one of the three largest airports in Ohio.

Question 2: Do any airlines offer direct flights from Kansas City to Columbus, Ohio?

The answer to this appears to be no, and the time to find the answer is about two minutes. Using Travelocity.com and searching for flights from MCI (Kansas City International Airport) to CMH (Port Columbus International Airport) gets the message “We’ve searched more than 400 airlines we sell and couldn’t find any flights from Kansas City (MCI) … [to] Columbus (CMH).” Doing the same search on Expedia.com and Orbitz.com yields the same answer. There appear to be no direct flights from Kansas City to Columbus, Ohio.

Often, however, the questions we need to have answered are more complicated, which means that the answer comes with some assembly required.

Question 3: What’s the best way to get from Kansas City to Columbus, Ohio?

To answer this question requires a two-step process of gathering information about travel options and then evaluating the results based on parameters not stated in the question. We already know that it is possible to fly to Columbus, although no direct flights are available. A quick look at a map shows that it is also a relatively straightforward drive of about 650 miles. That’s the information-gathering stage.

Now we have to evaluate the results based on things like cost, time and effort required, practicality given the purpose of the trip, and the personal preferences of the traveler. For a business traveler for whom shortest possible travel time is more important than lowest cost, the final decision may be very different than for a college student traveling with a large dog.

Although all three questions we listed above require information gathering, for the purposes of this course we are going to call questions like #1 and #2 “homework questions.” These are homework questions because you can find the answer just by going to a single reference source and looking it up. We will address the “research question” like #3 for which developing a fully functional answer requires both gathering relevant information and then assembling it in a meaningful way. In other words, a research question differs from a homework question because  research  is the process of finding the information needed to answer your research question and then deriving or building the answer from the information you found.

Research Writing

Some high school and first-year college writing courses use the term “research paper” or “research writing” to apply to any situation in which students use information from an outside source in writing a paper. The logic behind this is that if the writer has to go find information from a source, that action of going and finding information is similar to research, so it is convenient to call that kind of writing task a “research paper.” However, it is only true research if it starts from a question to which the writer genuinely does not know the answer and if the writer then develops or builds the answer to the question through gathering and processing information.

One way to consider this distinction is to think of research as the goal-directed process of gathering information and building the answer to a research question, and source-based writing to refer to the many other types of information gathering and source-based writing one might do.

Figure 2 . In true research-based writing, you begin with a research question and go hunt for the answer.

One important indicator of the difference between research and other source-based writing tasks is when in the process you develop the thesis (main point) of your paper. In a research project, you begin with a question, gather the data from which you will derive or build the answer to the question, build the answer, and then state your answer in a single sentence. This one-sentence statement of your answer to your research question then becomes your thesis statement and serves as the main point of your paper.

Any assignment you begin by developing a thesis that you then go out and gather information to support is source-based writing, but it is not technically “research” because it begins from the answer instead of the question.

Being aware of this distinction is helpful, as it can shape the way you approach your writing assignments, whether they be true research papers or source-based writing tasks. The work processes that lead to efficiency and success with research projects are different from the work processes you may have used successfully for other types of source-based papers. Both offer valuable learning experiences, but it is important to understand which type of assignment you are being asked to do so that you can plan your work.

Think of the most recent writing project you have done that required sources. Based on this definition, was it a research project or a source-based writing project?

We defined research as the physical process of gathering information plus the mental process of deriving the answer to your research question from the information you gather. Research writing , then, is the process of sharing the answer to your research question along with the evidence on which your answer is based, the sources you use, and your own reasoning and explanation. The essential components or building blocks of research writing are the same no matter what kind of question you are answering or what kind of reader you are assuming as you share your answer.

The Essential Building Blocks of Research Writing

These guidelines will help you as you approach research writing.

Step 1: Begin with a question to which you don’t know the answer and that can’t be answered just by going to the appropriate reference source. That is, begin from a research question, not a homework question.

• Decide what kind of information or data will be needed in order to build the answer to the question.
• Gather information and/or collect data.
• Work with the information/data to construct your answer.

Step 2: Engage in the  research process .

• Create a one-sentence answer to your research question. This will become the thesis statement/main point/controlling idea of your research paper.

Step 3: Share your answer to research questions in a way that makes it believable, understandable, and usable for your readers.

• Include plentiful and well-chosen examples from the data/information you gathered
• Indicate the validity of your data by accurately reporting your research method (field or lab research)
• Indicate the quality of your information by accurately citing your sources (source-based research)

homework question : a question for which a definite answer exists and can easily be found by consulting the appropriate reference source

research : the physical process of gathering information plus the mental process of deriving the answer to your research question from the information you gathered

research question : a question that can be answered through a process of collecting relevant information and then building the answer from the relevant information

research writing : the process of sharing the answer to your research question along with the evidence on which your answer is based, the sources you use, and your own reasoning and explanation

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• Starting the research process
• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

Prevent plagiarism. Run a free check.

Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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

Methodology

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

 Statistics

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

Research bias

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

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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What does a Researcher do?

A researcher is responsible for collating, organizing, and verifying necessary information for a specific subject. Researchers' duties include analyzing data, gathering and comparing resources, ensuring facts, sharing findings with the whole research team, adhering to required methodologies, performing fieldwork as needed, and keeping critical information confidential. Researchers must be knowledgeable about the current market trends and align findings with the research goals. A researcher must show strong communication skills , as well as strong attention to detail and time-management skills to meet deadlines under minimal supervision.

• Responsibilities
• Skills And Traits
• Comparisions
• Types of Researcher

Researcher responsibilities

A researcher's responsibilities include conducting research projects, collecting and analyzing data, and presenting findings in published papers and presentations. They use techniques such as molecular biology, genetics, and biochemistry to characterize evolutionary patterns of organisms, and develop and use computer vision-tracking systems. They also evaluate the sensitivity and specificity of market-ready rapid tests and communicate their findings through literature reviews, posters, and manuscripts.

Jan Levine , a Professor of Law and Director of the Legal Research & Writing Program at Duquesne University, emphasizes the importance of understanding how to plan a research effort, updating research to be timely, and using librarians and secondary sources of the law to expand the scope of their work.

Here are examples of responsibilities from real researcher resumes:

• Manage social media publications to spread awareness and notifications on Facebook.
• Perform cellular assays, DNA extractions, PCR, and sequencing to identify cellulase- producing soil bacteria.
• Implement data visualization tools by Java.
• Present research findings to select professors and professionals at scholar conferences.
• Master techniques in biomedical science research methods.
• Collaborate with doctorates in the field of immunology.
• Utilize CRISPR technology to genetically modify muscle stem cells.
• Develop auditing and monitoring tools for protocol and FDA compliance.
• Train in a clean room for lithography and etching techniques.
• Discover that CD98 is required for clonal expansion and adaptive immunity.
• Assist with patient recruitment efforts as approved per IRB and sponsor/CRO.
• Identify in vitro and in vivo biomarkers for patient selection and efficacy.
• Analyze protein binding and recognition of non-coding RNA in crRNA production stage CRISPR system.
• Analyze micro-wear polishes on Neanderthal stone tools using AFM, SEM and optical microscopy.
• Culture cancer cells, isolate RNA, design and perform multiple RT-PCR reactions for RNA quantification

Researcher skills and personality traits

We calculated that 12 % of Researchers are proficient in Python , Lab Equipment , and C++ . They’re also known for soft skills such as Observation skills , Communication skills , and Analytical skills .

We break down the percentage of Researchers that have these skills listed on their resume here:

Created computer vision-tracking system related to swarming behavior using Raspberry Pi technology and Python.

Learned proper laboratory etiquette and proper use of lab equipment in order to develop an understanding of crystallized proteins.

Created a C++ program to model molecular Bose-Einstein condensates Published in Physical Review A

Designed data analysis, sample collection and reporting processes to support the evaluation of ragweed pollen contributions to ambient particulate matter.

Developed user-friendly TLM measurement program in LabVIEW that resulted in 80-95% increase in time efficiency for semiconductor characterization data collection.

Conducted independent research into ultra-high R value thermal barriers for domestic home insulation and commercial applications.

Common skills that a researcher uses to do their job include "python," "lab equipment," and "c++." You can find details on the most important researcher responsibilities below.

Observation skills. To carry out their duties, the most important skill for a researcher to have is observation skills. Their role and responsibilities require that "medical scientists conduct experiments that require monitoring samples and other health-related data." Researchers often use observation skills in their day-to-day job, as shown by this real resume: "conducted on-site interviews, collected observations, developed coding booklets from data, organized data collection packets. "

Communication skills. Many researcher duties rely on communication skills. "medical scientists must be able to explain their research in nontechnical ways," so a researcher will need this skill often in their role. This resume example is just one of many ways researcher responsibilities rely on communication skills: "implemented multiple telosb motes communication(emitter, forwarder and base station), data collection in nesc. "

Most common researcher skills

The three companies that hire the most researchers are:

• Meta 57 researchers jobs
• Pearson 49 researchers jobs
• University of Washington 30 researchers jobs

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Researcher vs. postdoctoral associate.

A postdoctoral associate is responsible for researching to support scientific claims and theories by collecting evidence and information to answer scientific questions. Postdoctoral associates must have excellent communication skills , both oral and written, to interact with people and document investigation findings. They also utilize laboratory tools and equipment for scientific researches, conduct field investigations, and interview participants. A postdoctoral associate designs comprehensive research models to discuss results with the panel and the team efficiently and accurately.

There are some key differences in the responsibilities of each position. For example, researcher responsibilities require skills like "lab equipment," "conduct research," "linux," and "sociology anthropology." Meanwhile a typical postdoctoral associate has skills in areas such as "patients," "tip," "biomedical," and "crispr." This difference in skills reveals the differences in what each career does.

Researcher vs. Doctoral student

A doctoral fellow is a physician that has completed studies and receives a fellowship to cover his/her or her expenses while completing his/her or her medical dissertation. A doctor fellow undergoes this fellowship to get additional training for their chosen sub-specialty. During the fellowship period, a fellow can act as an attending physician or consultant physician with other physicians' direct supervision in the sub-specialty field.

Each career also uses different skills, according to real researcher resumes. While researcher responsibilities can utilize skills like "lab equipment," "conduct research," "sociology anthropology," and "research data," doctoral students use skills like "java," "protein expression," "scholar," and "gene expression."

Researcher vs. Doctoral fellow

A fellow's responsibility will depend on the organization or industry where one belongs. However, most of the time, a fellow's duty will revolve around conducting research and analysis, presiding discussions and attending dialogues, handle lectures while complying with the guidelines or tasks set by supervisors, and assist in various projects and activities. Furthermore, a fellow must adhere to the institution or organization's policies and regulations at all times, meet all the requirements and outputs involved, and coordinate with every person in the workforce.

There are many key differences between these two careers, including some of the skills required to perform responsibilities within each role. For example, a researcher is likely to be skilled in "lab equipment," "conduct research," "linux," and "sociology anthropology," while a typical doctoral fellow is skilled in "patients," "research projects," "cell biology," and "immunology."

Researcher vs. Fellow

Types of researcher.

• Graduate Research Student
• Research Fellow

Research Technician

Research scientist.

• Doctoral Fellow

Updated March 14, 2024

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

What a Researcher Does FAQs

Is a researcher a job, what does a researcher study, what is the role of a researcher, search for researcher jobs, what similar roles do.

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Taking Engagement to Task: The Nature and Functioning of Task Engagement Across Transitions

Daniel w. newton.

1 University of Missouri

Jeffery A. LePine

2 Arizona State University

Ji Koung Kim

3 Texas A&M University

Ned Wellman

John t. bush.

Author Note

Engagement is widely viewed as a motivational state that captures the degree that individuals apply their physical, cognitive, and emotional energies to their jobs, and that positively impacts job performance. However, this job-level view overlooks the possibility that engagement may vary across the different tasks that comprise a job, and that engagement in one task may influence engagement and performance in a subsequent task. In this article, we develop and test hypotheses that emerge from a task-level view of engagement and the general notion that there is “residual engagement” from a task that carries forward to a subsequent task. We propose that although task engagement (engagement in a specific task that comprises a broader role) positively spills over to influence task engagement and performance in a subsequent task, in part due to the transmission of positive affect, task engagement simultaneously engenders attention residue that impedes subsequent task engagement and performance. These predictions were supported in a study of 477 task transitions made by 20 crew members aboard NASA’s Human Exploration Research Analog (Study 1) and in a laboratory study of 346 participants who transitioned between a firefighting task and an assembly task (Study 2). Our investigation explains how engagement flows across tasks, illuminates a negative implication of engagement that has been masked by the predominant job-level perspective, and identifies completeness as a task attribute that reduces this negative consequence of engagement.

“I think that’s when mistakes happen is because you’re not fully engaged, and … you move from one thing to the next, to the next. It’s hard to keep your head in one game after the other after the other.” (International Space Station astronaut)

The extent to which members of organizations engage with their work is a critical determinant of their effectiveness (e.g., Morgan, 2017 ; Zenger & Folkman, 2017 ). Defined as the investment of one’s physical, cognitive, and emotional energies into the various activities that constitute one’s work role ( Kahn, 1990 ), engagement is beneficial to both individuals and organizations. For individuals, engagement is a positive motivational force that transmits the effects of a variety of individual and contextual factors to important job-relevant behaviors ( Rich, LePine, & Crawford, 2010 ; Salanova & Schaufeli, 2008 ; Schaufeli & Bakker, 2004 ). Indeed, individuals who invest more of their personal energies at work are more satisfied with their jobs and are viewed as performing their jobs more effectively ( Christian, Garza, & Slaughter, 2011 ). For organizations, employee engagement is positively associated with indicators of organization effectiveness such as customer loyalty, sales, and profitability ( Harter, Schmidt, Killham, & Agrawal, 2009 ). As such, scholars have proposed that an engaged workforce is a source of competitive advantage that allows organizations to gain an upper hand over their rivals ( Gruman & Saks, 2011 ; Macey, Schneider, Barbera, & Young, 2009 ).

Engagement researchers tend to presume that individuals’ engagement is relatively consistent across the tasks that comprise their jobs. As such, theories of engagement emphasize the investment of energies in individuals’ overall jobs, and the most frequently-used measures of engagement (e.g., Rich et al., 2010 ; Schaufeli, Salanova, González-Romá, & Bakker, 2002 ) capture how individuals attach themselves to their overall jobs or how they tend to immerse themselves in all activities that define their work roles ( Byrne, Peters, & Weston, 2016 ). Indeed, Rich and colleagues’ (2010) elaboration of Kahn’s (1990) engagement concept used the term “job engagement” to describe the construct, to make the job-level focus explicit. Although this job-level perspective of engagement has clearly taken root in the literature and yielded valuable insights, it is limited in its ability to fully capture the nature and implications of engagement in organizations today.

Many jobs today are multifaceted in that they comprise a number of very different tasks ( Cohen, 2013 ; Grant & Parker, 2009 ; Hasan, Ferguson, & Koning, 2015 ). As illustrated by our opening quote, the need to switch rapidly between work tasks may be relevant to engagement, and yet it is unclear how engagement functions in contexts with these temporal rhythms or time sensitivity. Although Kahn (1990) mentioned that engagement might vary throughout the course of the workday—a claim that has been supported indirectly by evidence that engagement fluctuates on a daily basis ( Fletcher, Bailey, & Gilman, 2018 ; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009 )—prior theorizing has not considered how engagement in the specific tasks that comprise one’s job could ebb or flow as individuals transition from one task to the next. Moreover, little is known about the extent to which the spillover of engagement across task transitions affects performance in subsequent tasks. Thus, a task-focused perspective on engagement may result in a more complete and relevant theoretical explanation of the phenomenon, and lead to practical insights related to issues such as task sequencing that could benefit employees who work in multifaceted jobs.

The objective of this manuscript is to expand our understanding of engagement by advancing a task-level view of the concept and its functioning. Specifically, we develop and test a model that explains how engagement in one task carries over to influence engagement in a subsequent task, and how this dynamic transfer of engagement influences performance at the task-level. Expanding on Kahn’s (1990) work, we propose that engagement in a task has both positive and negative consequences for engagement in a subsequent task. We propose a positive emotional pathway in which engagement in a task produces positive affect, which spills over to influence engagement in a subsequent task. We further propose a negative cognitive pathway in which engagement in a task causes individuals to experience attention residue —ruminative thinking about a prior task while engaged in a subsequent task ( Leroy, 2009 ; Leroy & Glomb, 2018 ; Leroy & Schmidt, 2016 )—which hinders engagement and performance in the subsequent task. We augment our theorizing with qualitative data provided by astronauts involved in missions aboard the International Space Station and crew members confined in an analog environment on the ground, who reported on their experiences transitioning between tasks.

We formally test our hypotheses in two studies. In Study 1, a field study with a sample of 477 task transitions experienced by 20 crew members in The National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog, we examine whether engagement in a prior task has a positive indirect effect on performance in a subsequent task through engagement in the subsequent task, and whether this indirect relationship may be partially offset by a negative indirect effect through attention residue. We then test whether activated positive affect accounts for the positive spillover of engagement, and whether task completion is a boundary condition that mitigates the effects of attention residue in a laboratory study of 346 participants transitioning between a group and individual task (Study 2).

The studies reported herein make several important theoretical contributions. First, we advance engagement theory by responding to calls to explore the nature and functioning of engagement at the task-level (e.g., Bakker, 2014 ; Sonnentag, 2011 ). Broadly, we advance the idea that engagement varies at the task-level, and that this “task engagement” flows across transitions to new tasks. More specifically, we develop and test hypotheses regarding how engagement experienced in a task may be related to performance in a subsequent task because it influences engagement in the subsequent task. In this respect, we show how individuals’ energies may flow from one task to another, and also how this flow can be disrupted. Second, and relatedly, we illuminate both positive and negative consequences of task engagement. Although job-level engagement has been shown to improve job performance ( Christian et al., 2011 ), we theorize and find that task-level engagement carries cognitive costs that can impair subsequent task performance. In doing so, we respond to calls to explore how disconnected cognitive energies may influence employee motivation (e.g., Randall, Oswald, & Beier, 2014 ). Third, our research provides an integrative account of the mechanisms responsible for the conveyance of personal energies during transitions to new tasks. On the one hand, we identify positive affect as a mechanism that is partially responsible for the beneficial spillover of task engagement. On the other hand, we confirm that attention residue is responsible for the negative impact of prior task engagement on subsequent task performance.

From a practical standpoint, our findings illustrate the potential benefits of understanding how task engagement influences subsequent task performance. Many individuals begin their workday with relatively unengaging tasks (e.g., checking email) that can have negative implications on their well-being (e.g., Kushlev & Dunn, 2015 ). Our results highlight the importance of individuals’ engaging in their work early in the day to reap the positive benefits from “residual engagement” on subsequent engagement and performance. Of course, residual engagement also involves attention residue, which limits these benefits and, consistent with Leroy’s (2009) work, can be managed by facilitating completion in a prior task in order to reduce the effect of task engagement on attention residue in contexts where time urgency exists. Our discussion centers on the application of these ideas to practices such as task scheduling, which could be enhanced to accentuate the positive spillover of engagement and avoid the dampening effect of attention residue on engagement and performance in subsequent tasks.

Theory and Hypotheses

Kahn (1990) characterized engagement as a holistic concept describing how individuals invest their personal energies in their work. Specifically, Kahn theorized that engagement occurs as individuals harness their physical, cognitive, and emotional energies to their work role performances. Individuals invest their physical energy when they apply bodily effort and intensity to their work. They invest their cognitive energy when they mentally focus their attention on their work role requirements. And finally, individuals invest their emotional energy when they care and are enthusiastic about their work. Meta-analyses have shown that individuals’ engagement in their jobs is positively associated with outcomes such as task and citizenship performance ( Christian et al., 2011 ).

Task-level Engagement

Empirical work has begun to examine how engagement might vary within one’s job. For example, Xanthopoulou et al. (2009) found that fast food workers’ daily engagement varied as a function of their perceived job and personal resources. As another example, Fletcher et al. (2018) found that daily engagement varied as a function of the meaningfulness of daily job tasks and whether resources were sufficient. Although these studies did not examine engagement at the task-level or across task transitions, they do provide evidence that engagement is not always stable for a given job as has been assumed by most prior research. In this manuscript, we further advance the idea that engagement varies within one’s job by offering a task-level view of engagement, which we believe is essential to understanding employee functioning and effectiveness in multifaceted jobs (e.g., Cohen, 2013 ; Sonnentag, 2012 ). To this end, we define task engagement as the degree to which individuals invest their physical, cognitive, and emotional energies into a specific task that composes part of their work role.

The notion of a within-job perspective of engagement and its value was alluded to by Kahn (1990 , p. 719) who wrote that “like using the zoom lens of a camera: a distant stationary image is brought close and revealed as a series of innumerable leaps of engagement and falls of engagement.” Although he stopped short of advancing a task-level engagement concept, Kahn acknowledged that “people are constantly bringing in and leaving out various depths of their selves during the course of their work days” (pp. 692–693). Kahn and others (e.g., Fletcher et al., 2018 ) have also argued that engagement may fluctuate as a function of factors that vary at the task-level. For example, Kreiner, Hollensbe, and Sheep (2006) noted that professional clergy might be engaged in meaningful tasks connected to caring for their flock, but less so for more mundane, functional tasks such as balancing the church budget.

Our task-level view of engagement proposes that individuals may apply different levels of their full selves to different tasks throughout the workday, and that this is reflected by the allocation of emotional, cognitive, and physical energies that vary across the tasks that are performed. Given that scholars have already acknowledged that the process of attaching and detaching one’s self from specific tasks may be influenced, in part, by differences in the characteristics of the particular tasks for which one is responsible ( Saks & Gruman, 2014 ), and because our research centers on the question of how this task-level view informs our understanding of effectiveness in jobs where these tasks are connected to each other, we focus on how the attachment and detachment of one’s self to a task influences the attachment of one’s self through a transition to a subsequent task.

Insights from NASA crew members

We developed the theoretical model we report below a priori based on the literatures on engagement ( Kahn, 1990 ; Rich et al., 2010 ; Shin & Grant, 2018 ), role transitions ( Culbertson, Mills, & Fullagar, 2012 ; Rodríguez-Muñoz, Sanz-Vergel, Demerouti, & Bakker, 2014 ; Rothbard, 2001 ), and attention residue ( Leroy, 2009 ; Leroy & Glomb, 2018 ; Leroy & Schmidt, 2016 ). However, to bring our theorizing to life and better situate it within our research context, we supplement the following section with illustrative quotes from a sample of 30 NASA crew members. The crew members consisted of astronauts conducting missions aboard the International Space Station (ISS) and astronaut-like crew members, who are often aspiring astronauts, in a confined ground-based analog environment (Human Exploration Research Analog; HERA) designed to simulate conditions in space. These environments exert significant time pressure on crew members, as their workday consisted primarily of highly divergent tasks and structured transitions between those tasks. We collected the quotes by applying the critical incident technique ( Flanagan, 1954 ) in 25 surveys administered during ISS missions, in which we asked crew members to recall a recent transition between two tasks and then describe the nature of the tasks, assess how they transitioned, and report any challenges they experienced transitioning. Additionally, within 10 days of the completion of the ISS and HERA missions, we conducted eight 30-minute semi-structured interviews. Given the limited number of data points, we did not achieve theoretical saturation ( O’Reilly, Paper, & Marx, 2012 ). However, we believe the quotes add dynamism and precision to our theorizing by illustrating the flow of engagement across task transitions in our research context and by highlighting the importance of the mechanisms and boundary condition in our conceptual model (i.e., positive affect, attention residue, and task 1 completion).

Spillover of Task Engagement

In many jobs, individuals are responsible for a series of different tasks or performance episodes ( Cohen, 2013 ; Ilgen & Hollenbeck, 1991 ). Our task-centric view of engagement recognizes that engagement may vary across different tasks, and that one’s level of engagement in a task is likely to predict task performance (e.g., Christian et al., 2011 ). However, we also propose that when individuals stop working on a task, the energies applied to that particular task may remain activated through the transition to a new task to influence performance in the subsequent task (e.g., D’Mello & Graesser, 2011 ). In short, an individual’s task engagement, or their allocation of personal energies in a task, may endure and impact their performance on a subsequent task. This concept, which we refer to as residual engagement , is supported by research which has established that motivationally relevant experiences in a task can spill over and influence performance in a subsequent task (e.g., Schmidt & DeShon, 2009 , 2010 ). Moreover, researchers have provided glimpses into how and why these effects might occur. For example, individuals’ physical activity at work has bearing on subsequent productivity ( Coulson, McKenna, & Field, 2008 ), individuals continue to think about a task even after transitioning to something else ( Levinson, Smallwood, & Davidson, 2012 ), and the duration of an emotional experience may last well beyond a trigger event ( Rothbard, 2001 ; Verduyn, Van Mechelen, & Tuerlinckx, 2011 ). Next, we develop hypotheses regarding mechanisms that could explain how, why, and under what conditions engagement in one task might influence performance in a subsequent task.

Positive affect

Researchers have theorized that engagement engenders and activates positive affect ( Bakker & Bal, 2010 ; Kahn, 1990 ; Rothbard, 2001 ), defined as the degree to which one experiences a positive mood and feelings ( Watson & Clark, 1997 ; Watson, Clark, & Tellegen, 1988 ). When individuals allocate and apply their physical, cognitive and emotional energies to a task, they come to appraise that task as being more personally important, which in turn activates positive emotional energy and fosters an inherently satisfying emotional state. For instance, Kahn (1990 , p. 701) described an engaged architect who subsequently expressed the “joy of creating designs both aesthetical and functional.” Consistent with Kahn’s description, numerous researchers have demonstrated that engagement activates positive feelings that make individuals feel happy, alert, and inspired (e.g., Culbertson et al., 2012 ; Rothbard, 2001 ; Salanova, Llorens, & Schaufeli, 2011 ). This line of thinking is also consistent with scholarly work theorizing that positive affect is an outcome of motivation (e.g., George & Brief 1996 ; Naylor, Pritchard, & Ilgen, 1980 ).

When a discrete event elicits an emotion, that emotion is relatively persistent and individuals continue to experience the emotion as it lingers ( Verduyn et al., 2011 ). In this vein, positive affect does not immediately dissipate once activated, but can influence individuals’ subsequent activities (e.g., Bledow, Schmitt, Frese, & Kühnel, 2011 ). Specifically, researchers have theorized that the presence of positive affect may facilitate an individual’s entry into a subsequent task ( Richardson & Taylor, 2012 ). Indeed, as Erez and Isen (2002) demonstrated, affect engendered in one context carries over to other contexts, such that individuals approach subsequent work with more motivation. Similarly, others have concluded that being engaged in a task may create an afterglow that influences subsequent tasks ( Isen & Reeve, 2005 ; Shin & Grant, 2018 ). Taken together, we argue that task engagement engenders the flow of continued positive thoughts and feelings ( Isen, Shalker, Clark, & Karp, 1978 ), which in turn produces an expanded reservoir of available energies ( Elsbach & Hargadon, 2006 ; Fredrickson, 2001 , 2004 ; Salanova, Bakker, & Llorens, 2006 ). Individuals can then draw upon and apply these enhanced personal energies to subsequent tasks and successfully accomplish the requirements of those tasks. Thus, a high level of engagement in one task can lead to positive “gain cycles and spirals” ( Salanova et al., 2011 ), such that the positive and invigorating effects of engagement influence subsequent task performance because they create positive emotions and feelings that carry over to influence subsequent task engagement (e.g., Erez & Isen, 2002 ; Fisher, 2003 ).

The positive emotional spillover we have discussed is illustrated in insights we obtained from NASA crew members. For example, one HERA crew member described how positive affect experienced when engaged in a task lingered throughout subsequent task work:

“There was one instance that I can remember that was MMSEV [multi-mission space exploration vehicle] transitioning to something else. I remember thinking—because I was like, ‘Oh my God, I actually do feel affected’ because I was so happy from the previous activity. And that was really kind of like my attitude carried over and that was about it. If I’m emotionally invested for whatever reason in something, that would have a lasting effect afterwards.”

Another HERA crew member noted:

“If anything spilled over for me it would be Robot and in terms of how I felt about Robot, I’ve said this a couple times: it was about 90 percent exhilaration and happiness and about 10 percent pure hate.”

In summary, we argue that task engagement may enhance performance in a subsequent task because it engenders positive affect, which in turn, promotes engagement in the subsequent task (e.g., Beal, Weiss, Barros, & MacDermid, 2005 ; Rich et al., 2010 ).

Hypothesis 1: Task 1 engagement is positively associated with task 2 performance. Hypothesis 2: Task 1 engagement has a positive indirect effect on task 2 performance through task 2 engagement. Hypothesis 3: Task 1 engagement has a positive indirect effect on task 2 performance through positive affect and task 2 engagement.

Attention Residue

Although the relationships we hypothesized in the previous section have not been directly articulated previously, they reflect the predominant view that engagement is inherently good and can result in a positive upward spiral ( Salanova et al., 2011 ). That is, engagement engenders positive feelings, which beget greater engagement. In this section, we identify a second mechanism through which task engagement may impact subsequent task engagement and performance. Importantly, this second mechanism serves to offset or mitigate the first, and thus, explains why the upward spiral of engagement is not limitless, or even assured. In essence, we argue that while task engagement, in general, may positively spill over from one task to another because it engenders positive affect, there may be negative cognitive consequences of task engagement that partially negate its benefits ( George, 2011 ; Sonnentag, 2011 ).

As Kahn (1990) suggested, highly engaged employees may at times experience reduced availability of energies and engagement in subsequent work. Individuals who experience high task engagement are intensely involved and absorbed in their task activities, and when confronted with an altogether new task may have difficulty letting go because it requires the decoupling of the self with personally meaningful efforts that are intrinsically satisfying. Engagement in a task reflects thinking deeply about the task, and when asked to transition to a new task, it may be difficult to switch this thinking off and redirect it to the new task. The implications of this process are that engagement in a task may inhibit the transfer of cognitive energies to a new task, thereby limiting the level of engagement in the new task. This argument is aligned with research suggesting that it is difficult for people to switch cognitive gears at work (e.g., Ancona & Chong, 1996 ; Freeman & Muraven, 2010 ; Louis & Sutton, 1991 ). This line of reasoning is also consistent with experiences relayed by participants in our interviews. For example, one ISS astronaut noted that after one particular task transition, “I kept thinking that I should have known better how to hook up the CMRS [Crew Medical Restraint System] and realized that I hadn’t done it in a very long time.” Similarly, a HERA crew member noted how they ruminated on engaging tasks after transitioning:

“Unpacking what went right and wrong on a mission and how we could do better in the next MMSEV, for example, or same thing with Robot, just could I have done that faster, could we have had better strategy? So it’s kind of still on my mind as I transition.”

The idea that individuals may not completely refocus their cognitive energies after a transition to a new task has been explored by Leroy and colleagues in their research on attention residue, which refers to persisting thoughts about a previous task after starting a new one ( Leroy, 2009 ; Leroy & Glomb, 2018 ; Leroy & Schmidt, 2016 ). Consistent with Leroy and colleagues’ findings, as well as our prior arguments, we argue that task engagement engenders attention residue, which in turn inhibits engagement in the subsequent task and hinders performance (e.g., Leroy, 2009 ; Leroy & Schmidt, 2016 ). This reasoning aligns with a long-standing notion that applying cognitive energy to a specific task soaks up cognitive resources and thereby limits the amount of cognitive energy individuals can allocate to other tasks to benefit task performances ( Harrison & Wagner, 2016 ; James, 1890 ; Kanfer & Ackerman, 1989 ). Indeed, research on attention residue and task transitions has shown that transitioning from an engaging task can create interference that leads to reduced performance in terms of slower reaction times and elevated error rates (e.g., Cellier & Eyrolle, 1992 ; Freeman & Muraven, 2010 ; Kiesel et al., 2010 ; Kühnel, Sonnentag, & Westman, 2009 ; Leroy, 2009 ; Leroy & Schmidt, 2016 ).

Hypothesis 4: Task 1 engagement is positively associated with attention residue. Hypothesis 5: Task 1 engagement has a negative indirect effect on task 2 performance through attention residue and task 2 engagement.

Task Completion

Thus far, we have argued that there are two offsetting paths through which task engagement influences subsequent task engagement and performance. On the one hand, engagement instills positive affect, which in turn fosters engagement and performance in the subsequent task. On the other hand, engagement engenders attention residue, which in turn inhibits engagement and performance in the subsequent task. With this framework in mind, one key to understanding how to manage engagement and performance in multifaceted jobs lies in identifying factors that could serve to mitigate the negative pathway attributed to attention residue. Here we argue that task completion, or the degree to which one perceives that a task has reached closure ( Leroy, 2009 ; Webster & Kruglanski, 1994 ), is likely to limit the attention residue individuals experience when they transition out of an engaging task.

We propose that task 1 engagement may produce the most attention residue when individuals perceive that they have left a task incomplete. Individuals who are engaged in a task couple their full selves to the task and are motivated to invest their energies to fully satisfy necessary task requirements ( Diefendorff & Chandler, 2011 ; Kahn, 1990 ; Lewin, 1935 ). A requirement to transition from an incomplete engaging task may result in a sharp tension and an accompanying thought process, which may amplify the effects of engagement in a task on attention residue and subsequent task engagement and performance ( Leroy, 2009 ; Zeigarnik, 1967 ). An individual in this dissatisfying situation may be left with a range of thoughts regarding implications of not finishing the engaging task and how best to catch up at some point in the future. As the level of attention residue from the first task increases, it is likely to inhibit the development of engagement in the subsequent task. The significance and implications of transitioning from an engaging but incomplete task were noted by several of the crew members in our sample. For example, one ISS astronaut noted after a task transition:

“I kept thinking about the first task because it had not been completed yet. But this second event is always time critical so you have to break off in the middle of the procedure to make the second event. This makes returning to the first event very hard. Especially because now you are behind the timeline.”

Similarly, a HERA crew member indicated:

“I would say if there was something at the end of the task, let’s say it was kind of unclear whether or not the task’s completed in its entirety, or if for some reason we had unclear instructions or something to that effect where it left it kind of open ended, then sometimes I might think back about, oh you know, we need to check on that.”

As is evident in these quotes, an engaging but incomplete task causes individuals to expend cognitive energy thinking about how they might pick up the incomplete task at a later time, making it difficult for them to engage with a subsequent task. These effects may be exacerbated by the time pressure individuals experience.

In contrast, we propose that task 1 engagement is less likely to result in attention residue when individuals perceive they have completed the task. As individuals fulfill requirements of an engaging task, they may perceive a sense of closure with the task, and consequently, are less likely to ruminate about the task, or plan new or different strategies for task completion. Instead, their personal resources are likely to be more available to invest in other tasks which comprise their broader work roles. This argument is consistent with Leroy’s (2009) work, which has shown that task completion reduces attention residue in contexts when individuals experience time pressure. Taken together, we hypothesize that:

Hypothesis 6: Task 1 completion moderates the positive relationship between task 1 engagement and attention residue such that the relationship is stronger when task 1 is incomplete and weaker when task 1 is complete. Hypothesis 7: Task 1 completion moderates the negative indirect relationship between task 1 engagement and task 2 performance through attention residue and task 2 engagement such that the indirect effect is stronger when task 1 is incomplete and weaker when task 1 is complete.

Overview of Studies

We tested our conceptual model across two studies. With Study 1, our goal was to establish whether the positive spillover of task engagement on subsequent task engagement and performance is offset by attention residue. In this study, we collected 477 task-transition-task episodes across five missions in a confined NASA isolation field environment and formally tested Hypotheses 1, 2, 4, and 5. With Study 2, we tested our entire conceptual model (Hypotheses 1–7) and accounted for some of the methodological limitations of the data gathered in Study 1. The second study involved 346 participants who participated in a laboratory study involving a firefighting simulation and a transition to an assembly task. Study 1 received Institutional Review Board approval under protocol #2668 [ Residual Engagement – Human Exploration Research Analog (HERA) ] from Arizona State University and protocol #1772 ( Understanding and Preventing Crew Member Task Entrainment ) from NASA. Study 2 received Institutional Review Board approval under protocol #2374 ( Residual Engagement Lab Experiment ) from Arizona State University.

Sample and procedure

We assessed the offsetting effect of attention residue on task engagement spillover by collecting data from NASA’s HERA isolation facility (the Human Exploration Research Analog) where crews of four lived together and performed tasks in a confined space-like environment without leaving for 30–45 days. Participants included 20 crew members from five missions. This NASA context provided an appropriate environment to test our hypotheses because crew members’ work schedule and time is highly programmed and regimented. Specifically, the crew members’ workday consists of the performance of a wide array of tasks and transitions between the tasks. Prior to each mission, NASA compiles a daily “playbook” or schedule for each crew member. The playbook is available to view throughout the “habitat” and creates time pressure for crew members by serving as a constant reminder to complete tasks in the allotted time period before beginning another task.

The focus of this study was task-transition-task episodes in which crew members worked on one task and then transitioned directly to a different task. We worked closely with NASA subject matter experts to identify episodes that could potentially vary in terms of the level of crew member engagement. For example, crew members performed simulated moon walks, rover landing tasks, emergency simulations, public outreach events, asteroid sampling analysis, seed growth projects, brine shrimp analysis, and general maintenance tasks to maintain the habitat. In order to make a conservative test of our hypotheses, we coordinated with NASA subject matter experts to balance task-transition-task episodes, such that crew members switched between engaging tasks, mundane tasks, or a combination of the two. In short, the types and combinations of tasks produced an ideal setting to examine the dynamic nature of task engagement that may exist in many types of jobs and ensured adequate variance on our independent variable—task 1 engagement. Moreover, some of the tasks were performed by crew members working alone (e.g., seed growth, system maintenance), whereas other tasks were performed with others (e.g., simulated moon walks, rover landing tasks). Crew members completed an average of 25 tasks per day, with the average task duration lasting approximately 30 minutes.

During the five missions (four of which consisted of 22 training days and 8 “rest” days, and one of which consisted of 32 training days and 13 “rest” days), we captured 477 task-transition-task episodes that crew members naturally performed during their work day. We also worked with NASA subject matter experts to select the episodes where (1) crew members transitioned immediately from one task to another (without a break period in between), and where (2) the first and second tasks were different enough to constitute a transition between different tasks (rather than two periods of performing the same task). Upon completion of a daily task 1-transition-task 2 episode, we administered a brief survey in which crew members rated their engagement in the first and second tasks, their attention residue in the initial task during the second task, and task performance. Crew members never reported on more than one task-transition-task episode per work day to protect against contamination with other transition episodes on the same day. Although we acknowledge limitations of the retrospective design (discussed later), we had to balance these concerns with constraints of the NASA mission and the necessity to study naturally occurring transitions. Administering surveys in the middle of a task-transition-task episode would have potentially disrupted the very flows of engagement we were attempting to study.

Task engagement

We measured crew members’ engagement in both task 1 and task 2. To fit our surveys in the time window allowed by NASA, we used 9 items (3 items each for physical, cognitive, and emotional engagement) that Crawford, LePine, and Buckman (2013) adapted from the Rich et al. (2010) job engagement scale. We further adapted the items by changing the referent experience from “job” to “task.” Sample items include “I exerted my full effort in the first task,” “I felt energetic working on the first task,” and “I concentrated completely on the first task.” Crew members rated their level of agreement on a 5-point Likert scale (1 = “ strongly disagree ” to 5 = “ strongly agree ”). Coefficient alpha was .96 for engagement in task 1, and .96 for engagement in task 2.

Attention residue

Leroy and Glomb (2018) recently validated a nine-item measure of attention residue. However, the limited time window allowed by NASA necessitated a shortened measure. Consequently, we developed and administered a three-item measure of attention residue. Prefaced by the phrase “while performing the second task,” our scale consists of the following items: “My mind kept on drifting back to the first task,” “I kept on thinking about the first task,” and “I thought about how to do the first task better.” Participants rated their level of agreement on a 5-point Likert scale (1 = “ strongly disagree ” to 5 = “ strongly agree ”). Coefficient alpha for this scale was .95. To examine the validity of our attention residue scale, we recruited 114 full-time Mturk workers [33% were female, 65% were Caucasian, average age was 36.0 ( SD = 11.8), and average years of work experience was 13.7 ( SD = 9.5)] and asked them to respond to our three-item measure and Leroy and Glomb’s nine-item measure. In a principal components factor analysis with varimax rotation conducted in SPSS v. 25, we found that two factors emerged with eigenvalues greater than 1.0 and which explained 75.6% of the variance. The first factor included all three of our items and six of Leroy and Glomb’s items. The second factor consisted solely of the three reverse coded items in Leroy and Glomb’s scale. Each item factor loading was statistically significant with values greater than .67, and no cross-loadings above .25. The correlation between Leroy and Glomb’s scale and our measure was .79; however, the correlation increased to .89 when removing the three reverse coded items. These findings provide us reasonable assurance that the two scales tap the same underlying construct.

Task 2 performance

We assessed participants’ performance in the second task using a 3-item scale developed by Aubé and Rousseau (2005) . Sample items for this scale include “Attained the assigned performance goals on the second task” and “Produced quality work on the second task.” Although we acknowledge the limitations of self-report performance ratings, in this context participants understood that the attainment of goals and the quality of outputs can be verified by the parties involved in the mission. All items were on a 5-point Likert scale ranging from 1 (“ strongly disagree” ) to 5 (“ strongly agree ”). Reliability of this scale was .82.

We controlled for crew members’ perceptions of performance on the first task using the same measure as task 2 performance. Controlling for task 1 performance is important because the extent to which individuals believe that they performed well (or poorly) on a task could relate to their task engagement and attention residue after transitioning to a different task. In addition, social bonds may develop among members of a group involved in a group task (e.g., Hollenbeck, Ellis, Humphrey, Garza, & Ilgen, 2011 ; Kelly & McGrath, 1985 ); thus, individuals who transition from a group-based task to an individual-based task may differ in their psychological experience, such as the proclivity to experience attention residue and engagement. To account for this possibility, we included three dummy variables to control for the different types of transitions. We chose individual task to individual task for the comparison group, and thus, the three transition dummy variables represented (a) individual task to crew task, (b) crew task to individual task, and (c) crew task to crew task. Finally, we included two temporal controls: the duration of the first task and the time of day a task-transition-task episode was completed (dummy coded as morning or afternoon). We reasoned that the time spent on a task could potentially introduce mind wandering ( Randall et al., 2014 ), and crew members could be more fatigued with tasks performed later in the day ( Hülsheger, 2016 ).

Confirmatory factor analysis

Descriptive statistics, correlations, and reliabilities for our Study 1 variables are presented in Table 1 . In order to verify the factor structure of our measurement model and further establish discriminant validity of our attention residue scale, we conducted a confirmatory factor analysis (CFA) using Mplus 7.4 ( Muthén & Muthén, 2015 ). We specified a CFA model with three latent factors (task engagement, attention residue, and task 2 performance), using individual items as indicators. Task engagement was modeled as a higher-order factor consisting of three lower-order dimensions: physical, cognitive, and emotional engagement ( Rich et al., 2010 ). In addition, as our model includes the same latent factor (engagement) measured across two tasks, we modeled task 1 engagement and excluded task 2 engagement in our CFA model. Results with task 2 engagement included and task 1 engagement excluded did not significantly alter the results of the CFA. Overall, our hypothesized three-factor model showed good fit to the data: χ 2 (84) = 185.98, p < .001; CFI = .99; RMSEA = .05; SRMR = .02. In addition, all factor loadings were statistically significant, the average variance explained was greater than .50 for each factor ( Fornell & Larcker, 1981 ), and the three-factor model fit the data better than alternative models that included one or two latent factors.

Descriptive Statistics, Correlations, and Reliabilities (Study 1)

Note . N = 477. Transition Type Dummy Coded (i.e., Individual to Team Transition, Team to Individual Transition, and Team to Team Transition); Time of Day Dummy Coded (1 = afternoon; 0 = morning).

Hypotheses testing

Although the primary focus of our theoretical model is on the flow of task engagement across task transitions, the hierarchical structure of our data (477 task-transition-task episodes nested within 20 individuals) necessitated that we control for the nesting of task transition episodes within crew members. Following guidelines provided by Raudenbush and Bryk (2002) , we first assessed whether sufficient level-2 variance (between-individuals) was present in our data. Specifically, we tested three null models for each of our dependent or endogenous variables of interest (attention residue, task 2 engagement, and task 2 performance). Results indicated that significant variance at level-2 (individual-level of analysis) existed for attention residue (τ 2 = .30, p = .003, ICC(1) = .31), task 2 engagement (τ 2 = .17, p < .001, ICC(1) = .22), and task 2 performance (τ 2 = .06, p = .003, ICC(1) = .13). We should note that although the 20 crew members that comprise our sample were nested in 5 crews, we did not account for crew-level nesting effects for two reasons. First, our three endogenous variables of interest had trivial levels of variance at the crew-level: task-2 engagement (τ 2 = .03, ICC(1) = .04), attention residue (τ 2 = .07, ICC(1) = .08), and task performance (τ 2 = .02, ICC(1) = .05). Second, the small number of crews did not afford us with sufficient statistical power to specify a three-level structural model. Accordingly, we specified a model in Mplus that accounted for nesting effects at the individual-level of analysis (level-2). As depicted in Figure 1 , our hypothesized path model suggests good fit to the data: χ 2 (6) = 5.48, p = .48; CFI = 1.00; RMSEA = .00; SRMR = .02 (for within-level of analysis) and SRMR = .00 (for between-level of analysis).

Structural Equation Model (Study 1). N = 477. Unstandardized path coefficients are presented; standard errors are in parenthesis. All paths with a solid line are significant at p < .05.

Results from our analysis do not support Hypothesis 1. That is, we did not find a positive relationship between task 1 engagement and task 2 performance ( b = −.07, p = .08). In contrast, Hypothesis 4, which predicts that task 1 engagement is positively related to attention residue, is supported ( b = .24, p < .001).

Hypothesis 2 predicts a positive indirect relationship between task 1 engagement and task 2 performance through task 2 engagement, and Hypothesis 5 predicts a negative indirect relationship between task 1 engagement and task 2 performance through attention residue and task 2 engagement. Because indirect effects represent the product of multiple path coefficients, and therefore are not normally distributed, we estimated the sampling distribution of the first (task 1 engagement → task 2 engagement) and second (task 2 engagement → task 2 performance) stage coefficients using a Monte Carlo simulation in R with 20,000 iterations (e.g., Bauer, Preacher, & Gil, 2006 ; MacKinnon, Fairchild, & Fritz, 2007 ; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002 ; Preacher, Zyphur, & Zhang, 2010 ) to test Hypothesis 2. We used this same Monte Carlo approach to estimate the sampling distribution of the first (task 1 engagement → attention residue), second (attention residue → task 2 engagement), and third (task 2 engagement → task 2 performance) stage coefficients to test Hypotheses 5. This approach provides bias-corrected confidence intervals for assessing the statistical significance of the indirect effects depicted in Figure 1 .

As shown in Table 2 , task 1 engagement has a positive indirect effect on task 2 performance through task 2 engagement ( indirect effect = .08, 95% CI = .052, .119), supporting Hypothesis 2. In other words, engagement in a task fosters engagement in a subsequent task, which, in turn, is positively associated with performance in the subsequent task. In support of Hypothesis 5, task 1 engagement has a negative indirect effect on task 2 performance through attention residue and task 2 engagement ( indirect effect = −.01, 95% CI = −.016, −.003). Stated more plainly, engagement in a task engenders attention residue, which negatively impacts subsequent task performance through subsequent task engagement.

Indirect Effects from Task 1 Engagement to Task 2 Performance (Study 1)

Note . N = 477. Unstandardized indirect effects, standard errors, and Monte Carlo bootstrapped (20,000) confidence intervals are reported.

We did not find support for our hypothesis that task 1 engagement and task 2 performance are positively related. However, the lack of a positive direct effect in our model, and the weak and non-significant zero-order relationship depicted in Table 1 are understandable in light of our hypothesis regarding the mitigating effects of attention residue. That is, the lack of an apparent relationship between task 1 engagement and task 2 performance may be explained by an indirect effect of attention residue that partially offsets the positive spillover of task 1 engagement to task 2 performance through task 2 engagement. In other words, it is not that task engagement is irrelevant to performance in a subsequent task; rather, task engagement has mixed implications with respect to its impact on performance in subsequent tasks.

Of course, Study 1 is subject to limitations. First, constraints imposed by the research setting limited our ability to temporally separate the measurement of our study variables and, therefore, our ability to draw causal inferences. Second, task performance was self-reported, which increases the risk of bias ( Podsakoff, MacKenzie, Lee, & Podsakoff, 2003 ). However, we were able to access a limited number of objective ratings of crew member performance (the number of objectives met during multi-mission space exploration flights). The correlation between self and objective performance ratings in 54 of the tasks was .49 ( p < .001), which is consistent with findings of prior research regarding the relationship between subjective and objective metrics of performance ( Bommer, Johnson, Rich, Podsakoff, & MacKenzie, 1995 ). Given that we controlled for self-rated prior performance, we are optimistic regarding the validity of the self-ratings in our study. Nevertheless, a study with non-same source ratings of performance would be valuable. Perhaps more importantly, although we found support for the idea that engagement in the first task fosters engagement in the second task, we did not fully explore why engagement in the two tasks were related, nor did we explore factors that might influence the strength of the explanatory pathways. In particular, a second study examining our hypotheses concerning the mediating role of positive affect and the moderating role of task completion could provide further insight into how and under what conditions task engagement might influence engagement and performance in a subsequent task, and how performance in multifaceted work could also be enhanced.

Study 2 was designed to build upon Study 1 in several ways. First, whereas Study 1 only tested a subset of our hypotheses, Study 2 tests our entire conceptual model. Second, although Study 1 occurred in a context where task transitions are frequent and critical to mission success, aspects of the design increased the likelihood of measurement bias and presented threats to internal validity. Thus, in Study 2 we used a laboratory study to increase the degree of control over the tasks and reduce potential concerns regarding common method variance and retrospective accounts of beliefs and feelings regarding prior tasks.

Participants

We conducted a laboratory study in which 364 undergraduate students at a large United States university participated in exchange for course credit. We excluded 18 participants who either expressed suspicions regarding the nature of our study or responded carelessly to the survey as identified by an even-odd consistency measure ( Meade & Craig, 2012 ). Our final sample was comprised of 346 participants. Participants’ average age was 22.81 years ( SD = 3.41) and 61.8% were male. 46.5% of participants were Caucasian, 29.2% were Asian, and 13.6% were Hispanic. 54.0% of participants reported that they were currently employed, with an average work experience of 4.06 years ( SD = 3.53).

Each session was comprised of a group of four participants. Each group was randomly assigned to either the task 1 incomplete condition or the task 1 complete condition. Upon arriving at our lab, participants completed a survey of individual differences. Based on results of Study 1 which indicated that the type of transition (e.g., crew to individual, individual to crew, individual to individual, crew to crew) explained little variance in our outcomes, we controlled for type of transition by design. That is, we structured the study such that all participants first performed a group task that was followed by an individual task. In introducing the study, the experimenter told participants that they would take part in a series of tasks—a group firefighting task and an independent assembly task of the firefighting truck—that were part of their larger firefighting job. We allotted 20 minutes to each task so that participants would sense a degree of urgency, consistent with our previous study.

Upon completion of the initial survey, the experimenter introduced C3Fire ( Johansson, Trnka, Granlund, & Götmar, 2010 ), a firefighting simulation, and informed the participants they would work on this computer simulation as a group for 20 minutes. We chose a firefighting task because it is meaningful and consequential to participants, and thus likely to engender task engagement that could spill over into a second task. Although there were elements of the task that necessitated some degree of interdependence and communication among participants, we programed the simulation in a way that emphasized distinct individual roles and the performance of those individual roles. Thus, the task was designed to promote sufficient individual-level variance to maintain the individual as the primary unit of theory and analysis. The experimenter conducted a brief training session (approximately 10 minutes) during which he explained the objective of the firefighting simulation and participants’ individual roles.

Participants in the C3Fire simulation extinguish as many forest wildfires as possible while also containing the fire and protecting landmarks such as houses, schools, and hospitals. Participants were randomly assigned to one of the following four roles within the simulation: fire chief, fire fighter, fire scout, and water carrier. The fire chief was responsible for coordinating crew actions and movements, and was the only member that could see the location of other crew members and the location of all active fires. The firefighter’s task was to put out and contain the fires. Although the firefighter could quickly extinguish fires, movement across terrain to other fires was slow. The fire scout, however, could move quickly across the terrain. Consequently, the fire scout’s duties were to respond to, contain, and extinguish distant fires. To facilitate quick movement, the scout’s compliment of equipment was small, and thus, was limited in firefighting capabilities. Finally, the water carrier was tasked with transporting water to other crew members so they could perform their task responsibilities. Members had distinct duties and communicated with each other during the simulation, but we programmed the task so that members spent the majority of their time performing their individual roles, which were quite similar in the level of complexity and required effort.

We manipulated task 1 completion after participants had reached 20 minutes of activity on the firefighting simulation. At that time, the experimenter told participants in the task 1 complete condition that the firefighting task simulation is complete. In the task 1 incomplete condition the experimenter told participants that they had not completed the firefighting task simulation, and that they would need to continue to fight remaining fires later in the session. Although participants did not receive individual performance feedback, participants in both conditions were shown the wildfire map of their simulation results so that they could assess their group’s performance in the task. Participants then answered a brief survey asking about their level of engagement and positive affect during the simulation.

Following this survey, participants were informed that “many jobs contain maintenance tasks—wherein equipment and materials must be maintained in order to retain their useful life. During the course of your firefighting work, you have driven over rough, burned out terrain—and the top portion of your fire truck has been damaged. Consequently, if you want to fight future fires, you must now maintain and repair your truck.” Participants were told that to simulate this aspect of the firefighting job, they would need to assemble a Lego fire truck. Participants were given a complete set of Legos and were told they could take 20 minutes to complete the task of building the truck according to the instructions, which we supplied (“truck maintenance task”). We chose a Lego task because it is similar to some of the assembly tasks performed by the crew members in Study 1 and provides a clear metric of performance. When participants completed the Lego truck, they informed the experimenter, who recorded how long it took the participant to finish the task (some participants finished the task early). After the 20 minutes were complete, participants who had not yet finished the truck maintenance task were asked to stop working.

Participants were then given a survey in which they rated their attention residue from task 1 (firefighting simulation) during task 2 (truck maintenance task) and also their engagement in task 2. During this time, the experimenter assessed the number of Lego pieces each participant had accurately and inaccurately assembled. Finally, participants provided demographic information, indicated whether they had suspicions about the study’s goals, and provided general open-ended comments about their reactions to the study. Upon completion of the final survey, participants were debriefed and dismissed. In all, each study lasted approximately 90 minutes.

We used the long form of Rich et al.’s (2010) engagement scale, which included the 9 items from Study 1, to assess participants’ engagement in the firefighting and truck maintenance tasks (e.g., Crane, Crawford, Buckman, & LePine, 2017 ). Example items include “I worked with high intensity on the (firefighting simulation/truck maintenance task),” “My heart was in the (firefighting simulation/truck maintenance task)”, “I paid a lot of attention to the (firefighting simulation/truck maintenance task).” Participants rated their level of agreement on a 5-point Likert scale (1 = “ strongly disagree ” to 5 = “ strongly agree” ). Reliability of this measure was .94 for engagement in task 1, and .95 for engagement in task 2. 1

We assessed participants’ attention residue with the three-item measure described in Study 1, and adapted it to pertain to the lab tasks. Participants rated their level of agreement on a 5-point Likert scale (1 = “ strongly disagree ” to 5 = “ strongly agree” ). Coefficient alpha was .79.

We measured participants’ positive affect during the firefighting simulation using 10 items from the PANAS-X ( Watson & Clark, 1994 ). Participants were instructed to indicate the extent to which they felt “interested,” “excited,” “strong,” “enthusiastic,” “proud,” “alert,” “inspired,” “determined,” “attentive,” and “active” during the firefighting simulation on a 5-point Likert scale (1 = “ very slightly or not at all ” to 5 = “ extremely” ). Coefficient alpha for this scale was .89.

We operationalized participants’ performance in the truck maintenance task by first calculating the number of Lego blocks they accurately assembled per minute. For instance, if a participant correctly put together a total of 60 blocks in 10 minutes, he/she received a score of 6. The truck maintenance task had a total of 77 Lego pieces, and participants’ average score was 4.68 ( SD = 1.65). In order to assist in the interpretability of indirect effects in our model, we standardized participants’ scores by conducting a z-score linear transformation. The mean score was 0 after transformation, with a standard deviation of 1.00.

Control variables

Although we randomly assigned participants to conditions, we measured rather than manipulated many of the variables in our model. Therefore, we included several statistical control variables to rule out alternative explanations and more accurately capture the relationships between our focal variables ( Bernerth & Aguinis, 2016 ). This approach is commonly adopted in studies with similar research designs (e.g., Chen & Mathieu, 2008 ; Wagner, Barnes, Lin, & Ferris, 2012 ). First, individuals with high levels of general mental ability (GMA) may have more cognitive energies at their disposal to invest in their tasks and may be better able to focus on current tasks (e.g., Kanfer & Ackerman, 1989 ; Randall et al., 2014 ). As such, we controlled for participants’ general mental ability by administering a six-minute general aptitude test we developed for settings where time constraints and participant fatigue is a concern. 2 We also controlled for participants’ trait positive affect ( Watson & Clark, 1994 ) as prior research indicates that the extent to which individuals experience positive affect in certain situations (i.e., state affect) is largely influenced by individuals’ general mood (i.e., trait affect; Nemanick & Munz, 1997 ; Watson, 1988 ). Furthermore, individuals’ dispositional willingness to try something different ( McCrae & Costa, 1997 ) may influence how effectively they transition between tasks and perform those tasks ( Dane, 2018 ), so we used 10 items from the international personality item pool to control for participants’ openness to experience ( Goldberg et al., 2006 ). Additionally, because individuals performed the first task in teams with specific roles where social bonds could develop ( Hollenbeck et al., 2011 ), we controlled for team membership as well as team role. Consistent with Study 1, we reasoned that participants’ performance on the first task could influence the degree to which they ruminated or experienced positive emotions with the first task that carried over to the second task. Consequently, we controlled for participants’ objective performance and perceptions of their performance on the firefighting task. Finally, we controlled for participants’ demographics (i.e., age and gender), as certain characteristics may potentially impact their familiarity with and performance on our experimental tasks (e.g., Blakemore & Centers, 2005 ).

Manipulation check

Descriptive statistics, correlations, and reliabilities for Study 2 are presented in Table 3 . As part of the survey participants completed when they rated their engagement and positive affect in the firefighting simulation, we also asked participants to rate the extent to which they had fully completed the firefighting simulation on a 5-point Likert scale (1 = “ strongly disagree ” to 5 = “ strongly agree ”). The mean of this manipulation check item was significantly higher for the task 1 complete condition ( M = 4.05, SD = 1.05) than for the task 1 incomplete condition ( M = 3.17, SD = 1.26; t (344) = 7.03, p < .001), suggesting that our manipulation worked as intended.

Descriptive Statistics, Correlations, and Reliabilities (Study 2)

Note . N = 332. Task 1 Completion coded 0 = Incomplete Condition; 1 = Complete Condition. Gender coded 1 = Male; 2 = Female. Team Role coded 1 = Fire Chief; 0 = Other Role.

As our sample for Study 2 had a nested structure (individual participants nested in teams), we first assessed whether significant between-team variance was present in our data. Consistent with Study 1, we conducted our Study 2 analysis in Mplus 7.4 ( Muthén & Muthén, 2015 ). Moreover, following the same procedure as in Study 1, we tested null models for the endogenous variables in our path model ( Raudenbush & Bryk, 2002 ). However, results indicated that significant between-team (level-2) variance does not exist for our endogenous variables of interest: task 2 engagement (τ 2 = .01, p = .78, ICC(1) = .02), attention residue (τ 2 = .00, p = .93, ICC(1) = .03), positive affect (τ 2 = .06, p = .12, ICC(1) = .11), and task 2 performance (τ 2 = .04, p = .40, ICC(1) = .05). As such, we specified our path model at the individual level of analysis.

As shown in Figure 2 , our hypothesized model provided good fit to the data: χ 2 (9) = 10.79, p = .29; CFI = 1.00; RMSEA = .03; SRMR = .02. In addition, our model replicated the results of Study 1 (Hypotheses 1, 2, 4, and 5). Hypothesis 1, which predicted a positive relationship between task 1 engagement and task 2 performance, was not supported ( b = −.04, p = .71). However, consistent with our predictions for Hypothesis 4, task 1 engagement had a positive relationship with attention residue during the second task ( b = .17, p = .03). To test the indirect effects of task 1 engagement on task 2 performance (i.e., Hypotheses 2, 3, and 5), we utilized the same approach as in Study 1. Specifically, we estimated the sampling distributions of the first, second, and third stage coefficients using a Monte Carlo simulation with 20,000 iterations, then calculated the boundaries of a bias-corrected 95% confidence interval ( Preacher et al., 2010 ). As reported in Table 4 , task 1 engagement had a positive indirect effect on task 2 performance via task 2 engagement ( indirect effect = .17, 95% CI = .071, .288), providing support for Hypothesis 2. In support of Hypotheses 3, task 1 engagement had a positive indirect effect on task 2 performance through positive affect and task 2 engagement ( indirect effect = .06, 95% CI = .006, .127). Finally, task 1 engagement had a negative indirect effect on task 2 performance via attention residue and task 2 engagement ( indirect effect = −.02, 95% CI = −.051, −.001), providing further support for Hypotheses 5.

Structural Equation Model (Study 2). N = 332. Unstandardized path coefficients are presented; standard errors are in parenthesis. All paths with a solid line are significant at p < .05.

Indirect Effects of Task 1 Engagement on Task 2 Performance (Study 2)

Note . N = 332. Unstandardized indirect effects, standard errors, and Monte Carlo bootstrapped (20,000) confidence intervals are reported.

Hypothesis 6 predicts that task completion moderates the positive relationship between task 1 engagement and attention residue such that this relationship is stronger when task 1 is incomplete and weaker when task 1 is complete. As shown in Figure 2 , the interaction of task 1 engagement and task 1 completion was significantly associated with attention residue ( b = −.31, p = .04). To assess the nature of the interaction, we conducted a simple slopes analysis ( Aiken & West, 1991 ) and plotted the interaction at “incomplete” and “complete” conditions of the moderator, as shown in Figure 3 . Providing further support for Hypothesis 6, the positive relationship between task 1 engagement and attention residue was stronger in the task 1 incomplete condition ( simple slope = .32, p = .01) than in the task 1 complete condition ( simple slope = .02, p = .86).

Task 1 Engagement and Task 1 Completion Predicting Attention Residue (Study 2)

Hypothesis 7 predicts that task 1 completion moderates the negative indirect effect from task 1 engagement to task 2 performance through attention residue and task 2 engagement such that the negative indirect effect is stronger when task 1 is incomplete and weaker when task 1 is complete. To test for conditional indirect effects (i.e., moderated mediation), we utilized the same approach as our analysis of indirect effects outlined above, but with the simple slopes at the complete and incomplete conditions replacing the coefficient for the first path of the indirect effect (task 1 engagement → attention residue; Preacher, Rucker, & Hayes, 2007 ). Results show that the indirect effect is negative when task 1 is incomplete ( indirect effect = −.04, 95% CI = .074, −.009), but non-significant when task 1 is complete ( indirect effect = −.00, 95% CI = −.026, .020). As an indication of moderated mediation and supporting Hypothesis 7, the conditional indirect effects are significantly different (Δ indirect effect = .04, 95% CI = .007, .086).

Study 2 results provide a more complete depiction of the dynamic effects of engagement in one task on engagement and performance in a subsequent task. We again did not find support for the direct relationship between task 1 engagement and task 2 performance. However, consistent with Study 1, our results suggest that this is a function of two offsetting pathways. That is, there is a positive spillover pathway, attributable to positive affect, which is partially offset by a negative pathway through attention residue. We also found that the debilitating effects of attention residue on subsequent engagement and performance are mitigated when a prior engaging task is viewed as being more complete. Thus, our findings illuminate the potential of managing attention residue so that the enhancing effects of task engagement on subsequent task engagement and performance can flow throughout a workday.

Overall Discussion

Across two studies, we develop and test a task-level view of engagement that explains how and under what conditions engagement in one task may spill over and be related to engagement and, by extension, performance in a subsequent task. In both studies, we found that task engagement positively cascades to a subsequent task when individuals transition from one task to another. However, we also found that task engagement can lead to attention residue that reduces the positive effect of task engagement on subsequent task engagement and performance. In Study 2, we specifically examine and find support for positive affect as the mechanism partially responsible for the positive spillover of engagement. Furthermore, task completion mitigates the effects of task 1 engagement on attention residue.

Theoretical Implications

The research reported in this article has several important implications. First, our view of engagement at the task-level provides a more nuanced and dynamic explanation of how engagement operates. By focusing our theoretical attention at the task-level, we were able to examine how engagement in a specific task influences the extent to which individuals invest their energies in a subsequent task. Our results underscore the critical role that task transitions play in understanding task engagement and task performance, primarily due to the residual engagement or spillover effect of engagement that we observed across transitions. This spillover of engagement further illuminates “the momentary ebbs and flows” of engagement over the course of daily performance episodes ( Kahn, 1990 , p. 693). In this respect, we not only respond to calls to explore engagement in specific tasks ( Bakker, 2014 ; Sonnentag, 2011 ), but also consider the dynamic nature of engagement across task transitions in a way that is more reflective of today’s multifaceted work environment.

Second, in contrast to the predominant view that engagement at the job level is largely beneficial in terms of general work performance (e.g., Christian et al., 2011 ), we find that viewing engagement at the task-level uncovers both positive and negative performance implications. Importantly, we find that the emotional and cognitive components of engagement may have differential effects at the task-level. Although the emotional component of engagement positively spills over to facilitate subsequent task engagement and performance, the cognitive component hinders subsequent task performance by producing attention residue. Our findings—that aspects of engagement may have some drawbacks when viewing how engagement unfolds from one task to the next—respond to scholars who have speculated that engagement may have previously unexplored costs ( George, 2011 ; Sonnentag, 2011 ). Moreover, attention residue provides an explanation for why there may be limits to engaged employees’ energies (e.g., Kim, Park, & Headrick, 2018 ) and what could otherwise be an upward spiral of engagement from one task to another. Indeed, to the extent that engagement in a task is viewed as something that is unequivocally positive with regard to the next task, the upward bounds could have no limits. Thus, the inclusion of attention residue to our understanding of the functioning of task engagement has ecological value and addresses an obvious logical gap with current theory. At the very least, our work presents a more balanced view of engagement that illuminates the performance implications of its bright and dark sides.

Third and relatedly, we provide a more fine-grained perspective of the psychological mechanisms that facilitate or hinder the transfer of individuals’ personal energies and produce these positive and negative effects across task transitions. By unpacking the dynamic nature of task engagement, we “specify more precisely the processes underlying micro role transitions” ( Ashforth, Kreiner, & Fugate, 2000 , p. 486) that occur between the tasks that comprise a work role. On the one hand, our results show that when individuals invest their energies in a task, their efforts generate positive affect which in turn facilitates increased engagement and performance in subsequent tasks. On the other hand, investing in one’s work can also produce attention residue. In contrast to positive affect, attention residue restricts subsequent task engagement and performance as individuals’ focus remains tethered to a previous task. Thus, due to positive affect and attention residue, task engagement appears to have mixed implications for individuals as the mechanisms differentially impact subsequent task engagement and subsequent task performance. By increasing our understanding of how positive affect and attention residue operate in tandem, we provide a view of the task transition phenomenon that is more cohesive. In sum, we provide greater theoretical insight regarding engagement in boundary-crossing activities at the task-level as well as the internal mechanisms that span multiple boundaries.

Finally, our research extends Leroy’s (2009) research on the workplace implications of attention residue. We replicate Leroy’s findings that experiencing attention residue from a prior task can impede performance on a subsequent task. However, we extend prior work by identifying task engagement as an attribute of task work that can intensify the experience of attention residue. These findings are very significant given that “very little is known about predictors of performance variability within persons who are still participating in the workplace” ( Sonnentag & Frese, 2012 , p. 569), particularly with respect to how engagement dynamically impacts performance. Our finding that the association between task 1 engagement and attention residue is weaker when individuals perceive that the prior task is completed is also consistent with Leroy’s (2009) finding that task completion reduces attention residue under conditions of high time pressure. However, the present research is the first to establish that the effects of completion are strong enough to offset the negative cognitive effects of high levels of task 1 engagement. These findings suggest that the beneficial effects of task completion in mitigating attention residue may be even stronger than has been suggested by previous research.

Limitations and Future Research

Although our studies have several theoretical implications, there are also limitations. First, we considered task engagement and its effects over the course of only one task transition episode, and we were unable to capture the far-reaching impact of positive affect and attention residue over multiple transitions. To provide a richer understanding of task engagement, future research could examine these effects over many performance episodes and transitions. This examination could lend additional insight into how individuals invest energies in specific tasks and even how they manage a state of flow over many episodes (e.g., Csikszentmihalyi, 1997 ). Relatedly, in Study 2 we manipulated a boundary condition rather than the independent variable which may reduce our ability to make casual claims. Although we considered manipulating task 1 engagement, we realized that doing so would actually manipulate antecedents of engagement such as task meaningfulness or one’s willingness to invest in the task ( Kahn, 1990 ). Given our Study 1 findings, we took as given that task 1 engagement would be related to task 2 engagement, and instead sought to parse out the mechanisms and boundary conditions that influenced this relationship. Thus, we control for alternative explanations and replicate our findings across two studies which gives us confidence in our theoretical model. In short, we see value in potential studies that track the ebbs and flows of task engagement over many transitions.

Second, our examination of positive affect may neglect other emotions, such as negative affect, that individuals experience across a task transition. Although our focus on positive affect is consistent with Kahn’s (1990) theorizing and empirical measures ( Rich et al., 2010 ), considering the role of negative affect calls to mind research on affect and multiple goal pursuit (e.g., Carver, 2003 ; Carver & Scheier, 1990 ). This research has proposed and found that experiencing negative affect while working on a task can cause individuals to invest more heavily in that task, because it provides a signal that additional resources are needed to complete the task effectively. This research also articulates how positive affect experienced in a task may cause individuals to invest fewer resources because it signals successful task accomplishment. Carver and colleagues’ perspective contrasts with our engagement-based arguments and findings that positive affect helps sustain engagement from task to task. Although our intention was not to compare the multiple goal pursuit perspective with Kahn’s (1990) work, an integration of these perspectives could be a worthwhile endeavor to develop a more nuanced theory of performance in multiple tasks. Such a theory could potentially propose circumstances that influence whether positive affect and negative affect lead to engagement or disengagement by virtue of spillover and goal-related discrepancy cognitions.

Third, there may be value in further examining task engagement and attention residue. Task similarity, or the degree to which a task requires comparable sets of personal energies, is one potential condition. Our study participants may have stayed absorbed in a previous task after switching due to similar energies being required and perceived similarity in our tasks (e.g., Ashforth et al., 2000 ). However, if two tasks are highly similar, attention residue might facilitate rather than hinder a task transition because individuals are in sync with the mode of the first task and would need few cognitive resources to adjust. An interdependence perspective may be one valuable lens to view task similarity (e.g., Bush, LePine, & Newton, 2018 ) to directly determine when switching between individual and team tasks is most beneficial or detrimental.

Considering effective task transitions also evokes task planning ( Earley, Wojnaroski, & Prest, 1987 ) and prospective task engagement wherein individuals look forward to an upcoming task. Although task anticipation may aid in preparing for and performing a future task, it may also occupy individuals’ attention and take their focus away from preceding tasks ( Leroy & Glomb, 2018 ) in a way that impairs previous task performance. In other words, we believe that an examination of how individuals anticipate upcoming tasks to the potential detriment of present tasks could be particularly important to explore. In probing these potential relationships, scholars should be aware that our measure of attention residue is narrower in scope than Leroy and Glomb’s (2018) measure, particularly if their task context involves a wider array of cognitive residue than our study contexts. In short, testing some of the boundary conditions that alter the nature of the relationships implied in our work could be fruitful avenues of inquiry.

Finally, because we focused exclusively on engagement at the task-level, we are unable to explore the implications of task engagement for individuals’ overall job engagement. Although our approach is consistent with the conventional view that tasks are nested within jobs ( Cohen, 2013 ; Ilgen & Hollenbeck, 1991 ; Pearlman, 1980 ), our research also raises the question of how engagement at the two levels is connected. This relationship might take several possible forms. For example, job engagement may reflect the average of individuals’ engagement across all of their tasks. Alternatively, job engagement may be more a function of individuals’ core tasks, the tasks that require the most time, or the tasks that are the most memorable. It may also be that job engagement is a function of the engagement experienced in the task immediately preceding the measurement of job engagement. This latter possibility is consistent with the notion of residual engagement, and has implications regarding when job engagement should be measured. Subsequent research that explores these and related possibilities by bridging the task-level and job-level aspects of engagement would further advance engagement theory.

Practical Implications

Our work has several implications for practice. Organizational leaders desire an engaged workforce (e.g., Morgan, 2017 ; Saks & Gruman, 2014 ), and our empirical findings offer some ideas as to how organizations may address employee task engagement. Individuals in organizations would do well to take part in intentional task planning that accentuates the positive effects of task engagement through positive affect and mitigates the negative effects of task engagement through attention residue. For example, many employees begin their workday by focusing their energies on tasks that are often less than engaging (e.g., checking email), and beginning with a less engaging task may not lead employees to start their day “off on the right foot.” Instead, our findings suggest that when individuals invest their energies in an engaging task then they not only experience positive feelings, but are also more engaged in a subsequent task and perform that subsequent task more effectively. Thus, organizations may find value in encouraging employees to deliberately plan and prioritize their workdays so that they begin with an engaging task and thereby reap the positive—and potentially multiplicative—benefits of task engagement in their subsequent task activities ( Salanova et al., 2011 ). Of course, our findings point to an important caveat in scheduling engaging tasks early in the day. Specifically, it may be important to select tasks where some degree of completeness can be reached prior to a task transition. Not being able to finish an engaging task elevates attention residue that dampens the positive effects from the engaging task. In some jobs—such as those involving project work—engaging tasks are often on-going, which makes “completing” the task difficult. In these cases, it may be possible to segment the work into smaller defined chunks (i.e., sets of related activities with defined objective) and setting aside time to achieve them so that a sense of closure can be reached and the positive effects of task engagement can be most pronounced.

Individuals who desire to perform effectively may need to balance how they allocate their energies across the many tasks that are required in their job. It may be helpful for individuals to identify an optimal number of tasks that they can transition between before the availability of their cognitive energies is spent (e.g., Kahn, 1990 ). Organizational leaders could be aware of the demands they put on their employees, and could help structure or support task activities in a way that maximizes the positive transfer of engagement across tasks. For example, organizations may find value in a mental thought exercise that enables employees to close the door mentally on a prior engaging task even if it is incomplete. As another example, instead of interrupting employees and immediately demanding their attention, managers could allow employees to wrap up a current task so that employees can be fully attentive to a manager’s request. Finally, managers may desire to temper their performance expectations on highly engaging tasks that occur back-to-back, or may find it valuable to help employees in these instances so that subsequent engagement and performance do not suffer.

We offer a task-level view of engagement to understand how the motivation pinned to the various tasks that comprise a multifaceted job are connected to each other and to performance. More plainly, we explain how task engagement influences performance in a subsequent task, through engagement in the subsequent task. We show that task engagement is associated with positive affect, and in turn, engagement and performance in a subsequent task. We also show that task engagement is associated with attention residue, which in turn, impedes subsequent task engagement and performance. Finally, we show that the negative effect of attention residue is ameliorated when a task is viewed as complete. In articulating the positive and negative pathways through which task engagement operates, we present a balanced view of engagement. Our moderator suggests how the dark side of engagement in multifaceted work can be managed.

Acknowledgments

This research is supported by The National Aeronautics and Space Administration (NASA), grant NNX15AK77G awarded to the first, second, and fourth authors.

Earlier versions of this article were presented at the annual meeting of the Academy of Management, the Association for Psychological Science annual convention, the Interdisciplinary Network of Group Research conference, and NASA’s Human Research Program Investigators’ Workshop.

1 Given the importance of distinguishing task engagement from job engagement for our theoretical arguments, we took an additional step to ensure that participants were rating their levels of engagement in each task . Specifically, we examined participants’ open-ended comments regarding the experiment, and counted the number of times participants mentioned the terms “task” and “job.” The word job came up in 7 participant responses (out of 346), and the term was used generically to refer to performance or how team members filled different roles in the firefighting task (e.g., “Overall our team did a really good job” and “Everyone should do their own job well in a group”). In contrast, the word task came up in 45 participant responses and was used most often in reference to the two experimental activities and switching between them (e.g., “completing a task and then working on a different task” or “switching from multiple tasks”). Combined with our experimental protocol that explicitly described the firefighting simulation and Lego truck assembly as different tasks that comprise the firefighter job, we are confident that our items accurately captured participants’ engagement at the task-level.

2 Our test is similar to the Wonderlic with respect to the types of items. We administered the test we developed, along with the Wonderlic, to 73 undergraduate students [81% were male, 60% were Caucasian, and their average age was 22.6 ( SD = 2.72)]. We alternated between administering our GMA test first and the Wonderlic first, and found that scores on our GMA test are correlated with scores on the Wonderlic at .74, which gives us assurance that our test is tapping participants’ GMA the same way the Wonderlic is. The magnitude of the correlation is about what we expect given research on alternative test forms reliability (e.g., Coyle, 2006 ). It is important to note that this correlation is commensurate or stronger than the correlation between scores on the Wonderlic and scores on other standardized tests that have been used as an indicator of GMA (e.g., Coyle, 2006 ; Coyle & Pillow, 2008 ; Frederick, 2005 ).

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Pokémon Go ‘It’s a Rocket World’ Special Research quest steps, rewards

Complete these research quests for a Shadow Groudon

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“It’s a Rocket World” is the latest Giovanni research quest in Pokémon Go.

Making its debut in late March 2024 as part of the “ World of Wonders: Taken Over ” event, it’s the usual Team Go Rocket research, which ends with an encounter and opportunity to catch Shadow Groudon .

“It’s a Rocket World” is part of a long line of Giovanni research quests, which started back in 2019 with “ A Troubling Situation .” If “It’s a Rocket World” is not appearing for you, first clear the most recent Giovanni quest in your quest log — the previous one was October 2023’s “ Showdown in the Shadows ” — to allow this new quest to become available.

‘It’s a Rocket World’ tasks and rewards

“It’s a Rocket World” Special Research will be available until the next Giovanni quest and highlighted Shadow Legendary encounter, which rolls out every few months. Once unlocked, there is no time limit to complete the quest.

Step 1 of 5

• Catch 15 Pokémon (5 Pinap Berries)
• Purify 2 shadow Pokémon (10 Poké Balls)
• Defeat 3 Team Go Rocket grunts (1 Mysterious Component)

Rewards: 1,500 XP, Aipom encounter

Step 2 of 5

• Catch 20 Pokémon (5 Pinap Berries)
• Purify 5 shadow Pokémon (10 Great Balls)
• Defeat 6 Team Go Rocket Grunts (3 Mysterious Components)

Rewards: 2,00 XP, Misdreavus encounter

Step 3 of 5

• Defeat the Team Go Rocket Leader Arlo (2,500 XP)
• Defeat the Team Go Rocket Leader Cliff (2,500 XP)
• Defeat the Team Go Rocket Leader Sierra (2,500 XP)

Rewards: 2,500 XP, 1 Super Rocket Radar

Step 4 of 5

• Find the Team Go Rocket Boss (10 Hyper Potions)
• Battle the Team Go Rocket Boss (10 Ultra Balls)
• Defeat the Team Go Rocket Boss (6 Max Revives)

Rewards : 3,000 XP, Croagunk encounter

You’ll be able to catch the final Shadow Pokémon in Giovanni’s team once you have used your Super Rocket Radar. This varies depending on when you play the quest; from March 2024 until when the next Giovanni quest debuts, this will be Shadow Groudon .

Step 5 of 5

• Claim reward (1,500 XP)

Rewards : 6,000 XP, 2 Silver Pinap Berries

Team Go Rocket Takeover event changes

As well as the above quest, the “ World of Wonders: Taken Over ” event is also host to a Team Go Rocket Takeover until March 31 at 11:59 p.m. in your local time , with the following changes:

• Boosted Team Go Rocket balloon spawns and PokéStop encounters
• Shadow Pokémon can forget Charged Attack Frustration with a Charged TM
• Rocket Grunts change their Shadow Pokémon line-ups
• Leaders Sierra, Cliff, and Arlo change their team line-ups
• Giovanni changes team line-up

12km Egg changes

Best of luck taking down Giovanni once again!

• Pokémon Go guides
• “World of Wonder” Special Research
• Wonder Ticket
• Ditto disguises

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What is artificial general intelligence (AGI)?

You’ve read the think pieces. AI—in particular, the generative AI (gen AI) breakthroughs achieved in the past year or so—is poised to revolutionize not just the way we create content but the very makeup of our economies and societies as a whole. But although gen AI tools such as ChatGPT may seem like a great leap forward, in reality they are just a step in the direction of an even greater breakthrough: artificial general intelligence, or AGI.

Get to know and directly engage with senior McKinsey experts on AGI

Aamer Baig is a senior partner in McKinsey’s Chicago office; Federico Berruti is a partner in the Toronto office; Ben Ellencweig is a senior partner in the Stamford, Connecticut, office; Damian Lewandowski is a consultant in the Miami office; Roger Roberts is a partner in the Bay Area office, where Lareina Yee is a senior partner;  Alex Singla  is a senior partner in the Chicago office and the global leader of QuantumBlack, AI by McKinsey;  Kate Smaje  and Alex Sukharevsky  are senior partners in the London office;   Jonathan Tilley is a partner in the Southern California office; and Rodney Zemmel is a senior partner in the New York office.

AGI is AI with capabilities that rival those of a human . While purely theoretical at this stage, someday AGI may replicate human-like cognitive abilities including reasoning, problem solving, perception, learning, and language comprehension. When AI’s abilities are indistinguishable from those of a human, it will have passed what is known as the Turing test , first proposed by 20th-century computer scientist Alan Turing.

But let’s not get ahead of ourselves. AI has made significant strides in recent years, but no AI tool to date has passed the Turing test. We’re still far from reaching a point where AI tools can understand, communicate, and act with the same nuance and sensitivity of a human—and, critically, understand the meaning behind it. Most researchers and academics believe we are decades away from realizing AGI; a few even predict we won’t see AGI this century (or ever). Rodney Brooks, a roboticist at the Massachusetts Institute of Technology and cofounder of iRobot, believes AGI won’t arrive until the year 2300 .

If you’re thinking that AI already seems pretty smart, that’s understandable. We’ve seen gen AI  do remarkable things in recent years, from writing code to composing sonnets in seconds. But there’s a critical difference between AI and AGI. Although the latest gen AI technologies, including ChatGPT, DALL-E, and others, have been hogging headlines, they are essentially prediction machines—albeit very good ones. In other words, they can predict, with a high degree of accuracy, the answer to a specific prompt because they’ve been trained on huge amounts of data. This is impressive, but it’s not at a human level of performance in terms of creativity, logical reasoning, sensory perception, and other capabilities . By contrast, AGI tools could feature cognitive and emotional abilities (like empathy) indistinguishable from those of a human. Depending on your definition of AGI, they might even be capable of consciously grasping the meaning behind what they’re doing.

The timing of AGI’s emergence is uncertain. But when it does arrive—and it likely will at some point—it’s going to be a very big deal for every aspect of our lives, businesses, and societies. Executives can begin working now to better understand the path to machines achieving human-level intelligence and making the transition to a more automated world.

Learn more about QuantumBlack, AI by McKinsey .

What is needed for AI to become AGI?

Here are eight capabilities AI needs to master before achieving AGI. Click each card to learn more.

How will people access AGI tools?

Today, most people engage with AI in the same ways they’ve accessed digital power for years: via 2D screens such as laptops, smartphones, and TVs. The future will probably look a lot different. Some of the brightest minds (and biggest budgets) in tech are devoting themselves to figuring out how we’ll access AI (and possibly AGI) in the future. One example you’re likely familiar with is augmented reality and virtual reality headsets , through which users experience an immersive virtual world . Another example would be humans accessing the AI world through implanted neurons in the brain. This might sound like something out of a sci-fi novel, but it’s not. In January 2024, Neuralink implanted a chip in a human brain, with the goal of allowing the human to control a phone or computer purely by thought.

A final mode of interaction with AI seems ripped from sci-fi as well: robots. These can take the form of mechanized limbs connected to humans or machine bases or even programmed humanoid robots.

What is a robot and what types of robots are there?

The simplest definition of a robot is a machine that can perform tasks on its own or with minimal assistance from humans. The most sophisticated robots can also interact with their surroundings.

Programmable robots have been operational since the 1950s. McKinsey estimates that 3.5 million robots are currently in use, with 550,000 more deployed every year. But while programmable robots are more commonplace than ever in the workforce, they have a long way to go before they outnumber their human counterparts. The Republic of Korea, home to the world’s highest density of robots, still employs 100 times as many humans as robots.

Introducing McKinsey Explainers : Direct answers to complex questions

But as hardware and software limitations become increasingly surmountable, companies that manufacture robots are beginning to program units with new AI tools and techniques. These dramatically improve robots’ ability to perform tasks typically handled by humans, including walking, sensing, communicating, and manipulating objects. In May 2023, Sanctuary AI, for example, launched Phoenix, a bipedal humanoid robot that stands 5’ 7” tall, lifts objects weighing as much as 55 pounds, and travels three miles per hour—not to mention it also folds clothes, stocks shelves, and works a register.

As we edge closer to AGI, we can expect increasingly sophisticated AI tools and techniques to be programmed into robots of all kinds. Here are a few categories of robots that are currently operational:

• Stand-alone autonomous industrial robots : Equipped with sensors and computer systems to navigate their surroundings and interact with other machines, these robots are critical components of the modern automated manufacturing industry.
• Collaborative robots : Also known as cobots, these robots are specifically engineered to operate in collaboration with humans in a shared environment. Their primary purpose is to alleviate repetitive or hazardous tasks. These types of robots are already being used in environments such as restaurant kitchens and more.
• Mobile robots : Utilizing wheels as their primary means of movement, mobile robots are commonly used for materials handling in warehouses and factories. The military also uses these machines for various purposes, such as reconnaissance and bomb disposal.
• Human–hybrid robots : These robots have both human and robotic features. This could include a robot with an appearance, movement capabilities, or cognition that resemble those of a human, or a human with a robotic limb or even a brain implant.
• Humanoids or androids : These robots are designed to emulate the appearance, movement, communicative abilities, and emotions of humans while continuously enhancing their cognitive capabilities via deep learning models. In other words, humanoid robots will think like a human, move like a human, and look like a human.

What advances could speed up the development of AGI?

Advances in algorithms, computing, and data  have brought about the recent acceleration of AI. We can get a sense of what the future may hold by looking at these three capabilities:

Algorithmic advances and new robotics approaches . We may need entirely new approaches to algorithms and robots to achieve AGI. One way researchers are thinking about this is by exploring the concept of embodied cognition. The idea is that robots will need to learn very quickly from their environments through a multitude of senses, just like humans do when they’re very young. Similarly, to develop cognition in the same way humans do, robots will need to experience the physical world like we do (because we’ve designed our spaces based on how our bodies and minds work).

The latest AI-based robot systems are using gen AI technologies including large language models (LLMs) and large behavior models (LBMs). LLMs give robots advanced natural-language-processing capabilities like what we’ve seen with generative AI models and other LLM-enabled tools. LBMs allow robots to emulate human actions and movements. These models are created by training AI on large data sets of observed human actions and movements. Ultimately, these models could allow robots to perform a wide range of activities with limited task-specific training.

A real advance would be to develop new AI systems that start out with a certain level of built-in knowledge, just like a baby fawn knows how to stand and feed without being taught. It’s possible that the recent success of deep-learning-based AI systems may have drawn research attention away from the more fundamental cognitive work required to make progress toward AGI.

• Computing advancements. Graphics processing units (GPUs) have made the major AI advances of the past few years possible . Here’s why. For one, GPUs are designed to handle multiple tasks related to visual data simultaneously, including rendering images, videos, and graphics-related computations. Their efficiency at handling massive amounts of visual data makes them useful in training complex neural networks. They also have a high memory bandwidth, meaning faster data transfer. Before AGI can be achieved, similar significant advancements will need to be made in computing infrastructure. Quantum computing  is touted as one way of achieving this. However, today’s quantum computers, while powerful, aren’t yet ready for everyday applications. But once they are, they could play a role in the achievement of AGI.

Growth in data volume and new sources of data . Some experts believe 5G  mobile infrastructure could bring about a significant increase in data. That’s because the technology could power a surge in connected devices, or the Internet of Things . But, for a variety of reasons, we think most of the benefits of 5G have already appeared . For AGI to be achieved, there will need to be another catalyst for a huge increase in data volume.

New robotics approaches could yield new sources of training data. Placing human-like robots among us could allow companies to mine large sets of data that mimic our own senses to help the robots train themselves. Advanced self-driving cars are one example: data is being collected from cars that are already on the roads, so these vehicles are acting as a training set for future self-driving cars.

What can executives do about AGI?

AGI is still decades away, at the very least. But AI is here to stay—and it is advancing extremely quickly. Smart leaders can think about how to respond to the real progress that’s happening, as well as how to prepare for the automated future. Here are a few things to consider:

• Stay informed about developments in AI and AGI . Connect with start-ups and develop a framework for tracking progress in AGI that is relevant to your business. Also, start to think about the right governance, conditions, and boundaries for success within your business and communities.
• Invest in AI now . “The cost of doing nothing,” says McKinsey senior partner Nicolai Müller , “is just too high  because everybody has this at the top of their agenda. I think it’s the one topic that every management board  has looked into, that every CEO  has explored across all regions and industries.” The organizations that get it right now will be poised to win in the coming era.
• Continue to place humans at the center . Invest in human–machine interfaces, or “human in the loop” technologies that augment human intelligence. People at all levels of an organization need training and support to thrive in an increasingly automated world. AI is just the latest tool to help individuals and companies alike boost their efficiency.
• Consider the ethical and security implications . This should include addressing cybersecurity , data privacy, and algorithm bias.
• Build a strong foundation of data, talent, and capabilities . AI runs on data; having a strong foundation of high-quality data is critical to its success.
• Organize your workers for new economies of scale and skill . Yesterday’s rigid organizational structures and operating models aren’t suited to the reality of rapidly advancing AI. One way to address this is by instituting flow-to-the-work models, where people can move seamlessly between initiatives and groups.
• Place small bets to preserve strategic options in areas of your business that are exposed to AI developments . For example, consider investing in technology firms that are pursuing ambitious AI research and development projects in your industry. Not all these bets will necessarily pay off, but they could help hedge some of the existential risk your business may face in the future.

Learn more about QuantumBlack, AI by McKinsey . And check out AI-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ Generative AI in operations: Capturing the value ,” January 3, 2024, Marie El Hoyek and  Nicolai Müller
• “ The economic potential of generative AI: The next productivity frontier ,” June 14, 2023, Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel
• “ What every CEO should know about generative AI ,” May 12, 2023, Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya
• “ An executive primer on artificial general intelligence ,” April 29, 2020, Federico Berruti , Pieter Nel, and Rob Whiteman
• “ Notes from the AI frontier: Applications and value of deep learning ,” April 17, 2018, Michael Chui , James Manyika , Mehdi Miremadi, Nicolaus Henke, Rita Chung, Pieter Nel, and Sankalp Malhotra
• “ Augmented and virtual reality: The promise and peril of immersive technologies ,” October 3, 2017, Stefan Hall and Ryo Takahashi

Want to know more about artificial general intelligence (AGI)?

Related articles.

An executive primer on artificial general intelligence

What every CEO should know about generative AI

Notes from the AI frontier: Applications and value of deep learning

‘Noisy’ autistic brains seem better at certain tasks. Here’s why neuroaffirmative research matters

PhD candidiate, University of Canberra

Associate professor, University of Canberra

Honours Graduate Student, University of Canberra

Disclosure statement

Jeroen van Boxtel receives funding from Australian Government through an Australian Research Council Discovery Project (project number DP220100406), the ACT government.

Jovana Acevska and Pratik Raul do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

University of Canberra provides funding as a member of The Conversation AU.

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Autism is a neurodevelopmental difference associated with specific experiences and characteristics.

For decades, autism research has focused on behavioural, cognitive, social and communication difficulties. These studies highlighted how autistic people face issues with everyday tasks that allistic (meaning non-autistic) people do not. Some difficulties may include recognising emotions or social cues.

But some research, including our own study, has explored specific advantages in autism. Studies have shown that in some cognitive tasks, autistic people perform better than allistic people. Autistic people may have greater success in identifying a simple shape embedded within a more complex design , arranging blocks of different shapes and colours , or spotting an object within a cluttered visual environment (similar to Where’s Wally?). Such enhanced performance has been recorded in babies as young as nine months who show emerging signs of autism.

How and why do autistic individuals do so well on these tasks? The answer may be surprising: more “neural noise”.

Read more: From deficits to a spectrum, thinking around autism has changed. Now there are calls for a 'profound autism' diagnosis

What is neural noise?

Generally, when you think of noise, you probably think of auditory noise, the ups and downs in the amplitude of sound frequencies we hear.

A similar thing happens in the brain with random fluctuations in neural activity. This is called neural noise.

This noise is always present, and comes on top of any brain activity caused by things we see, hear, smell and touch. This means that in the brain, an identical stimulus that is presented multiple times won’t cause exactly the same activity. Sometimes the brain is more active, sometimes less. In fact, even the response to a single stimulus or event will fluctuate continuously.

Neural noise in autism

There are many sources of neural noise in the brain. These include how the neurons become excited and calm again, changes in attention and arousal levels, and biochemical processes at the cellular level, among others. An allistic brain has mechanisms to manage and use this noise . For instance, cells in the hippocampus (the brain’s memory system) can make use of neural noise to enhance memory encoding and recall.

Evidence for high neural noise in autism can be seen in electroencephalography (EEG) recordings , where increased levels of neural fluctuations were observed in autistic children. This means their neural activity is less predictable, showing a wider range of activity (higher ups and downs) in response to the same stimulus.

In simple terms, if we imagine the EEG responses like a sound wave, we would expect to see small ups and downs (amplitude) in allistic brains each time they encounter a stimulus. But autistic brains seem to show bigger ups and downs, demonstrating greater amplitude of neural noise.

Many studies have linked this noisy autistic brain with cognitive, social and behavioural difficulties .

Read more: Most adults with autism can recognise facial emotions, almost as well as those without the condition

But could noise be a bonus?

The diagnosis of autism has a long clinical history . A shift from the medical to a more social model has also seen advocacy for it to be reframed as a difference, rather than a disorder or deficit. This change has also entered autism research. Neuroaffirming research can examine the uniqueness and strengths of neurodivergence.

Psychology and perception researcher David Simmons and colleagues at the University of Glasgow were the first to suggest that while high neural noise is generally a disadvantage in autism, it can sometimes provide benefits due to a phenomenon called stochastic resonance . This is where optimal amounts of noise can enhance performance . In line with this theory, high neural noise in the autistic brain might enhance performance for some cognitive tasks.

Our 2023 research explores this idea . We recruited participants from the general population and investigated their performance on letter-detection tasks. At the same time, we measured their level of autistic traits.

We performed two letter-detection experiments (one in a lab and one online) where participants had to identify a letter when displayed among background visual static of various intensities.

By using the static, we added additional visual noise to the neural noise already present in our participants’ brains. We hypothesised the visual noise would push participants with low internal brain noise (or low autistic traits) to perform better (as suggested by previous research on stochastic resonance). The more interesting prediction was that noise would not help individuals who already had a lot of brain noise (that is, those with high autistic traits), because their own neural noise already ensured optimal performance.

Indeed, one of our experiments showed people with high neural noise (high autistic traits) did not benefit from additional noise. Moreover, they showed superior performance (greater accuracy) relative to people with low neural noise when the added visual static was low. This suggests their own neural noise already caused a natural stochastic resonance effect, resulting in better performance.

It is important to note we did not include clinically diagnosed autistic participants, but overall, we showed the theory of enhanced performance due to stochastic resonance in autism has merits.

Read more: Autism is still underdiagnosed in girls and women. That can compound the challenges they face

Why this is important?

Autistic people face ignorance, prejudice and discrimination that can harm wellbeing . Poor mental and physical health, reduced social connections and increased “camouflaging” of autistic traits are some of the negative impacts that autistic people face.

So, research underlining and investigating the strengths inherent in autism can help reduce stigma, allow autistic people to be themselves and acknowledge autistic people do not require “fixing”.

The autistic brain is different. It comes with limitations, but it also has its strengths.

• Autism research
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Research: How Different Fields Are Using GenAI to Redefine Roles

• Maryam Alavi

Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

Partner Center

'Noisy' autistic brains seem better at certain tasks. Here's why neuroaffirmative research matters

Autism is a neurodevelopmental difference associated with specific experiences and characteristics.

For decades, autism research has focused on behavioral, cognitive, social and communication difficulties. These studies highlighted how autistic people face issues with everyday tasks that allistic (meaning non-autistic) people do not. Some difficulties may include recognizing emotions or social cues.

But some research, including our own study, has explored specific advantages in autism. Studies have shown that in some cognitive tasks, autistic people perform better than allistic people. Autistic people may have greater success in identifying a simple shape embedded within a more complex design , arranging blocks of different shapes and colors , or spotting an object within a cluttered visual environment (similar to Where's Wally?). Such enhanced performance has been recorded in babies as young as nine months who show emerging signs of autism.

How and why do autistic individuals do so well on these tasks? The answer may be surprising: more "neural noise."

What is neural noise?

Generally, when you think of noise, you probably think of auditory noise, the ups and downs in the amplitude of sound frequencies we hear.

A similar thing happens in the brain with random fluctuations in neural activity. This is called neural noise.

This noise is always present, and comes on top of any brain activity caused by things we see, hear, smell and touch. This means that in the brain, an identical stimulus that is presented multiple times won't cause exactly the same activity. Sometimes the brain is more active, sometimes less. In fact, even the response to a single stimulus or event will fluctuate continuously.

Neural noise in autism

There are many sources of neural noise in the brain. These include how the neurons become excited and calm again, changes in attention and arousal levels, and biochemical processes at the cellular level, among others. An allistic brain has mechanisms to manage and use this noise . For instance, cells in the hippocampus (the brain's memory system) can make use of neural noise to enhance memory encoding and recall.

Evidence for high neural noise in autism can be seen in electroencephalography (EEG) recordings , where increased levels of neural fluctuations were observed in autistic children. This means their neural activity is less predictable, showing a wider range of activity (higher ups and downs) in response to the same stimulus.

In simple terms, if we imagine the EEG responses like a sound wave, we would expect to see small ups and downs (amplitude) in allistic brains each time they encounter a stimulus. But autistic brains seem to show bigger ups and downs, demonstrating greater amplitude of neural noise.

Many studies have linked this noisy autistic brain with cognitive, social and behavioral difficulties .

But could noise be a bonus?

The diagnosis of autism has a long clinical history . A shift from the medical to a more social model has also seen advocacy for it to be reframed as a difference, rather than a disorder or deficit. This change has also entered autism research. Neuroaffirming research can examine the uniqueness and strengths of neurodivergence.

Psychology and perception researcher David Simmons and colleagues at the University of Glasgow were the first to suggest that while high neural noise is generally a disadvantage in autism, it can sometimes provide benefits due to a phenomenon called stochastic resonance . This is where optimal amounts of noise can enhance performance. In line with this theory, high neural noise in the autistic brain might enhance performance for some cognitive tasks.

Our 2023 research explores this idea . We recruited participants from the general population and investigated their performance on letter-detection tasks. At the same time, we measured their level of autistic traits.

We performed two letter-detection experiments (one in a lab and one online) where participants had to identify a letter when displayed among background visual static of various intensities.

By using the static, we added additional visual noise to the neural noise already present in our participants' brains. We hypothesized the visual noise would push participants with low internal brain noise (or low autistic traits) to perform better (as suggested by previous research on stochastic resonance). The more interesting prediction was that noise would not help individuals who already had a lot of brain noise (that is, those with high autistic traits), because their own neural noise already ensured optimal performance.

Indeed, one of our experiments showed people with high neural noise (high autistic traits) did not benefit from additional noise. Moreover, they showed superior performance (greater accuracy) relative to people with low neural noise when the added visual static was low. This suggests their own neural noise already caused a natural stochastic resonance effect, resulting in better performance.

It is important to note we did not include clinically diagnosed autistic participants, but overall, we showed the theory of enhanced performance due to stochastic resonance in autism has merits.

Why this is important?

Autistic people face ignorance, prejudice and discrimination that can harm well-being . Poor mental and physical health, reduced social connections and increased "camouflaging" of autistic traits are some of the negative impacts that autistic people face.

So, research underlining and investigating the strengths inherent in autism can help reduce stigma, allow autistic people to be themselves and acknowledge autistic people do not require "fixing."

The autistic brain is different. It comes with limitations, but it also has its strengths.

This article is republished from The Conversation under a Creative Commons license. Read the original article .

Provided by The Conversation

Pokemon GO It's a Rocket World - All Special Research Tasks And Rewards

Pokemon GO players can complete It's a Rocket World Special Research and get all the rewards by following this detailed guide.

Pokemon GO players that are participating in the World of Wonders: Taken Over event can access and complete It's a Rocket World Special Research story. The latest event offers numerous Research quests, and It's a Rocket World Special Research offers some of the most valuable in-game resources and Pokemon encounters. In addition to the Special Research, players get numerous Shadow raid opportunities, wild spawns, bonuses, and more.

Pokemon GO trainers can participate in the World of Wonders: Taken Over event from Wednesday, March 27, at 12 AM to Sunday, March 31, at 11:59 PM Local Time . Specific bonuses are active during the event’s runtime to help complete the event-exclusive elements. Trainers can participate in 4 different Research quests, each with various rewards and Pokemon encounters. Among them is It's a Rocket World Special Research which comprises numerous Steps. This guide details all the Steps, tasks, and rewards of It's a Rocket World Special Research quest.

Pokemon GO - All Ditto Disguises (March 2024)

Pokemon go: it's a rocket world special research tasks and rewards.

The Pokemon GO World of Wonders: Taken Over event offers It's a Rocket World Special Research with multiple Steps to complete and earn rewards. The Special Research has 5 Steps, each offering numerous tasks and rewards upon completion. The tasks range from catching numerous Pokemon, purifying Shadow Pokemon, defeating Team GO Rocket Grunts and Leaders, finding and defeating the Team GO Rocket Boss, and more.

Completing these tasks can reward players with items like Pinap Berry, a Mysterious Component, Great Balls, and more. The Special Research is also a great way to farm Pokemon GO XP and Stardust.