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6.3: Motivational Factors for Research

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• Scott T. Paynton & Laura K. Hahn with Humboldt State University Students
• Humboldt State University

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We think it is important to discuss the fact that human nature influences all research. While some researchers might argue that their research is objective, realistically, no research is totally objective. What does this mean? Researchers have to make choices about what to research, how they will conduct their research, who will pay for their research, and how they will present their research conclusions to others. These choices are influenced by the motives and material resources of researchers. The most obvious case of this in the physical sciences is research sponsored by the tobacco industry that downplays the health hazards associated with smoking (Frisbee & Donley). Intuitively, we know that certain motivations influence this line of research as it is an example of an extreme case of motivational factors influencing research. Realistically though, all researchers are motivated by certain factors that influence their research.

We will highlight three factors that motivate the choices we make when conducting communication research: 1) The intended outcomes, 2) theoretical preferences, and 3) methodological preferences.

Intended Outcomes

One question researchers ask while doing their research project is, “What do I want to accomplish with this research?” Three primary research goals are to increase understanding of a behavior or phenomenon, predict behavior, or create social change.

Case In Point

The evolution of anti­-drug commercials.

In 1987 an anti-­drug campaign, Your Brain on Drugs began to air on television. Wikipedia writes, “The first PSA, from 1987, showed a man who held up an egg and said, “This is your brain,” before picking up a frying pan and adding, “This is drugs.” He then cracks open the egg, fries the contents, and says, “This is your brain on drugs.” Finally he looks up at the camera and asks, “Any questions?”

After careful examination, researchers quickly discovered that this ad campaign was not effective, as it actually made the frying of an egg appealing, especially to those people who were watching the ad that were hungry! Thus, in 1998, they revised the PSA to make it more dramatic.

Scholars who study health campaigns are interested in finding the most effective ways to help get accurate health information to people so they can act on that information.

Here are some anti­-marijuana advertisements the millennial generation may be familiar with:

There is this anti­-marijuana commercial that tries to bring about the fears of smoking marijuana. Seth Stevenson states the commercial brings out two fears: “1) the fear that nonsmoker friends, or lovers, might find them tiresome and pathetic, and 2) the fear that they might be growing dependent on the drug.”

The same can be said for this video. In the video a pet dog tells what looks to be its teenage owner to quit smoking weed. This is supposed to evoke guilt upon the owner and carry out to the viewer. Both campaigns “effectively [pick] at both of these insecurities” that anti­-drug campaigns attempt to associate with smoking week.

Do you find any of these effective? Why or why not? Think of anti-­drug campaigns that would be effective for teens/young adults today.

A great deal of Communication research seeks understanding as the intended outcome of the research. As we gain greater understanding of human communication we are able to develop more sophisticated theories to help us understand how and why people communicate. One example might be research investigates the communication of registered nurses to understand how they use language to define and enact their professional responsibilities. Research has discovered that nurses routinely refer to themselves as “patient advocates” and state that their profession is unique, valuable, and distinct from being an assistant to physicians. Having this understanding can be useful for enacting change by educating physicians and nurses about the impacts of their language choices in health care.

A second intended outcome of Communication research is prediction and control . Ideas of prediction and control are taken from the physical sciences (remember our discussion of Empirical Laws theories in the last chapter?). Many Communication researchers want to use the results of their research to predict and control communication in certain contexts. This type of research can help us make communicative choices from an informed perspective. In fact, when you communicate, you often do so with the intention of prediction and control. Imagine walking on campus and seeing someone you would like to ask out. Because of your past experiences, you predict that if you say certain things to them in a certain way, you might have a greater likelihood that they will respond positively. Your predictions guide your behaviors in order to control the exchange at some level. This same idea motivates many Communication researchers to approach their research with the intention of being able to predict and control communication contexts. For example, in the article The Fear of Public Speaking , Sian Beilock, Ph.D explains prediction and control in action.

A third intended outcome of Communication research is positive critical/cultural change in the world. Scholars often perform research in order to challenge communicative norms and effect cultural and societal change. Research that examines health communication campaigns, for example, seeks to understand how effective campaigns are in changing our health behaviors such as using condoms to prevent sexually transmitted diseases or avoiding high fat foods. When it is determined that health campaigns are ineffective, researchers often suggest changes to health communication campaigns to increase their efficacy in reaching the people who need access to the information (Stephenson & Southwell).

As humans, researchers have particular goals in mind. Having an understanding of what they want to accomplish with their research helps them formulate questions and develop appropriate methodologies for conducting research that will help them achieve their intended outcomes.

Theoretical Preferences

Remember that theoretical paradigms offer different ways to understand communication. While it is possible to examine communication from multiple theoretical perspectives, it has been our experience that our colleagues tend to favor certain theoretical paradigms over others. Put another way, we all understand the world in ways that make sense to us.

Which theoretical paradigm(s) do you most align yourself with? How would this influence what you would want to accomplish if you were researching human communication? What types of communication phenomena grab your attention? Why? These are questions that researchers wrestle with as they put together their research projects.

Methodological Preferences

As you’ve learned, the actual process of doing research is called the methodology . While most researchers have preferences for certain theoretical paradigms, most researchers also have preferred methodologies for conducting research in which they develop increased expertise throughout their careers. As with theories, there are a large number of methodologies available for conducting research. As we did with theories, we believe it is easier for you to understand methodologies by categorizing them into paradigms. Most Communication researchers have a preference for one research paradigm over the others. For our purposes, we have divided methodological paradigms into 1) rhetorical methodologies, 2) quantitative methodologies, and 3) qualitative methodologies.

Contributions and Affiliations

• Survey of Communication Study. Authored by : Scott T Paynton and Linda K Hahn. Provided by : Humboldt State University. Located at : https://en.wikibooks.org/wiki/Survey_of_Communication_Study/Preface . License : CC BY-SA: Attribution-ShareAlike

Motivation: Introduction to the Theory, Concepts, and Research

• First Online: 03 May 2018

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• Paulina Arango 4  

Part of the book series: Literacy Studies ((LITS,volume 15))

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Motivation is a psychological construct that refers to the disposition to act and direct behavior according to a goal. Like most of psychological processes, motivation develops throughout the life span and is influenced by both biological and environmental factors. The aim of this chapter is to summarize research on the development of motivation from infancy to adolescence, which can help understand the typical developmental trajectories of this ability and its relation to learning. We will start with a review of some of the most influential theories of motivation and the aspects each of them has emphasized. We will also explore how biology and experience interact in this development, paying special attention to factors such as: school, family, and peers, as well as characteristics of the child including self-esteem, cognitive development, and temperament. Finally, we will discuss the implications of understanding the developmental trajectories and the factors that have an impact on this development, for both teachers and parents.

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Arango, P. (2018). Motivation: Introduction to the Theory, Concepts, and Research. In: Orellana García, P., Baldwin Lind, P. (eds) Reading Achievement and Motivation in Boys and Girls. Literacy Studies, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-75948-7_1

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Global Cognition

7 ways to improve your motivation to study (backed by science).

by Winston Sieck updated September 18, 2021

Just about everyone who has ever been in school knows what it feels like to sit in front of the computer, staring at a blank screen. Hoping their term paper would write itself.

Or tried reading a textbook only to find that they have read the same paragraph ten times and still don’t know what they read.

Or decided they would rather clean the clutter out from under their bed than study in the first place.

Bottom line, studying can be kind of a drag. When you have a hundred other things you would rather do and an overwhelming amount of work to do, it is hard to get started and even harder to finish.

Fortunately, there are some simple, scientifically proven ways you can find your motivation and keep it.

What is Motivation to Study?

Motivation comes from a Latin word that literally means “to move.” But what causes someone to be motivated to study has been a hot topic in the world of science.

Researchers believe that your motivation to study can either come from inside you or outside of you. You can be motivated by an internal drive to learn as much possible. Or, you might be motivated to study by an external reward like a good grade, or a great job, or someone promising you a car.

Recently, researchers have discovered that your motivation to study is rooted in lots of factors, many of which we have control over. Rory Lazowski of James Madison University and Chris Hulleman of the University of Virginia analyzed more than 70 studies into what motivates students in schools. They published their paper , “Motivation Interventions in Education: A Meta-Analytic Review, in the journal Review of Educational Research .

Lazowski and Hulleman found that a number of ways to improve motivation consistently yield positive results. Here, I describe seven of the techniques that you can most readily use on your own to power through your own study barriers, and move your learning forward.

1. Set Clear Goals

You may think to yourself, “My goal is to graduate and get a good job and be rich.” While that’s a fine ambition, by itself it probably won’t help you in school day-to-day.

In order to improve your motivation to study, your goals have to be a little closer to home. In fact, setting clear academic goals has been scientifically linked to higher grade point averages than students who set vague goals, like, “I’ll just do the best I can.”

Set a goal to earn an “A” on a particular test in a particular subject. Or, decide to learn everything you can about a concept because it will help you in the real world. Set a deadline for homework that will force you to finish a task before it is due so you can review it before handing it in. Whatever the goal is, be sure it is specific, relevant, and timely.

2. Don’t Just Shoot For Performance, Go For Mastery

There is nothing more frustrating than studying hard for a test only to get a grade that is less than what you were expecting. At that point, lots of students throw their hands in the air and say, “If this is what happens when I study, why study?”

Resist that urge.

The grades you receive on a test are examples of performance goals. If you set a goal to get an “A”, and stop there, you may only study the things that you think will be on the test, but not necessarily the things that will give you mastery of the concept.

Students who consistently strive for mastery , really learning what they are studying, almost always see their grades improve as a result.

Mastery goals also help with your motivation to study. If you want to learn everything there is to know, you are less likely to put off starting that process.

3. Take Responsibility for Your Learning

It’s tempting to blame your grades on other people. The teacher doesn’t like you. They never taught what you were tested on. Your homework assignment doesn’t apply. When you blame others for your performance, you are more likely to do poorly on tests, assignments and projects.

Taking responsibility for your own learning can make a world of difference when it comes to getting yourself motivated to study. Recognizing that you are in charge of what you learn can help you start studying, but it can also keep you going when other distractions threaten to take your attention away.

Next time you are tempted to stop in the middle of an assignment and do something else, pause. Take a breath. Then, say out loud, “No one is going to learn this for me.” You might be surprised at how hearing those words affect your focus.

4. Adopt a Growth Mindset

Some people still believe that you’re either born smart (or not). And there’s not much you can do about it. However, research has shown that successful people tend to believe that intelligence is something you build up over your life. These folks have a growth mindset.

When your intelligence is challenged by hard assignments or difficult concepts, people with a growth mindset tend to think, “I don’t know this yet, but if I work hard, I will learn it.”

Researchers found that believing your brain can get stronger when you tackle hard things not only improves your mastery of what you are learning, it also improves your grades and increases your motivation to study.

The next time you are faced by a blank screen or hard textbook chapter remember, “I don’t know this yet, but if I work hard, I will learn it.”

5. Find the Relevance

If you ever want to annoy your math teacher, tell them algebra has no relevance in the real world. Alternatively, try to figure out how what you are studying relates to your life. Studies have shown that high school students who were asked to write down how their subject matter related to their everyday life saw a significant jump in their GPA.

Before you start studying, try jotting down a few ways this information will come in handy in the future. Making this connection will help you see value in what you are doing and get you started on an assignment or topic.

Sometimes, the connection between what you are learning and how it applies to your life is not easy to see. Try searching the web for applications of your topic to help you see the real-life relevance of what you are learning.

6. Imagine Your Future Self

Imagine what your life will be like in 10 years. Are you successful? Do you have a great career that you love? Are you living in the best city in the world?

Now, imagine how you are going to get there.

Some people automatically connect the school work they are doing now with getting into a good college or training program that will lead to their desired future. Other students have difficulty making that connection.

Having the ability to imagine your future self is a skill that has been shown to improve motivation to study. It has also been linked to higher grades, lower cases of truancy and fewer discipline problems in school.

Next time you are faced with a particularly daunting assignment, close your eyes and picture what you want your life to be like. Then, recognize that in order to have the life you want, you have to do the assignment in front of you.

7. Reaffirm Your Personal Values

What do you value most? What are the two or three most important qualities you can possibly develop? Do you strive to be honest in everything you do? Do you value kindness? Is success the most important value in your life?

Taking a few minutes now and again to reaffirm your values by writing in a journal or meditating about them can help you focus your efforts in other areas of your life.

If you value family over everything, your ability to take care of your family will motivate you to study and do well in school. If you value honesty, you will never feel inclined to cheat on a test, but will work hard to study.

Ultimately, finding the motivation to study is less about going on a treasure hunt and more about changing the way you think about learning. Even implementing a few of these seven tips can help you stay focused and keep going.

Image Credit: PublicDomainPictures

Lazowski, R. A., & Hulleman, C. S. (2016). Motivation interventions in education: A meta-analytic review. Review of Educational research , 86(2), 602-640. DOI: 10.3102/0034654315617832

Study Smarter

Build your study skills with thinker academy.

About Winston Sieck

Dr. Winston Sieck is a cognitive psychologist working to advance the development of thinking skills. He is founder and president of Global Cognition, and director of Thinker Academy .

Reader Interactions

October 2, 2018 at 4:59 pm

Thanks for sharing this post. I plan to share it with my students this week. We’re implementing some growth mindset and mindfulness practices this year. This will be a good reinforcement of some of those ideas and will provide some new insight as well. I think it will be well-received. I’ve been pleasantly surprised at how open they’ve been to these ideas so far. Thanks again.

October 2, 2018 at 5:24 pm

That’s great, Tony. Excellent to hear the success you’re having with these ideas in your class. Thanks for stopping by..

October 25, 2021 at 12:51 pm

Thanks for posting this . I felt it after reading it and I think that if I prepare it today tomarow will be good . From this I’ll stay motivated .

October 2, 2018 at 6:54 pm

Thank greatly for this post. I’m studying at college at 45yrs ,sometimes want to give up studying but you came along with this great post. Great assurance and encouragement for young and old students alike.

Will have to share with my students as well,

kind regards,

clotilda Claudia Harry Solomon islands.

October 2, 2018 at 7:14 pm

Yep, we all need a little motivation boost at any age. Way to keep learning, Clotilda.

November 16, 2018 at 12:08 am

Thanks for providing a resource for our children to grow in knowledge. Seems that no matter what the age, we all struggle with these issues.

November 17, 2018 at 4:39 pm

No doubt, Michael! Managing motivation is a life-long skill we can teach our kids. Good to see you here – thanks for stopping by..

October 6, 2020 at 4:23 am

Thank you so much for motivating, the point you are mentioned such as set goal and go for mastery, be responsibility for learning, etc. all these points are really very helpful and they are very useful for study thank you so much for sharing

February 3, 2021 at 5:18 am

Thank you! Without following all of these steps, it’s hard to have any significant academic success, I think. It helps me not to lose motivation with step-by-step planning: I divide the global goal into several small short-term goals and achieving even minimal results makes me happy and motivates me to try harder. Of course, there are also bad periods, when I feel exhausted and overwhelmed. But a little rest allows me to get back on track.

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Essay 1.1 What Is Motivation, Where Does It Come from, and How Does It Work?

• Published: January 2023
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Motivation is the process that drives, selects, and directs goals and behaviors. Motivation typically arises out of the person’s needs, and it then comes to life through the person’s specific goals. In this essay, the authors examine the concept of “needs” as the crucible from which motivated behavior arises because all individuals are born with needs that jump-start the goal-oriented, motivated behaviors that are critical to survival and thriving. These are both physical needs (such as hunger and thirst) and psychological needs (such as the need for social relationships, optimal predictability, and competence). The aim of motivation is therefore to bring about a desired (need, goal) state. Motivation underlies and organizes all aspects of a person’s psychology. As it does so, motivation “glues” a person together as a functioning individual in their culture and context.

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

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The Importance of Students’ Motivation for Their Academic Achievement – Replicating and Extending Previous Findings

Ricarda steinmayr.

1 Department of Psychology, TU Dortmund University, Dortmund, Germany

Anne F. Weidinger

Malte schwinger.

2 Department of Psychology, Philipps-Universität Marburg, Marburg, Germany

Birgit Spinath

3 Department of Psychology, Heidelberg University, Heidelberg, Germany

Associated Data

The datasets generated for this study are available on request to the corresponding author.

Achievement motivation is not a single construct but rather subsumes a variety of different constructs like ability self-concepts, task values, goals, and achievement motives. The few existing studies that investigated diverse motivational constructs as predictors of school students’ academic achievement above and beyond students’ cognitive abilities and prior achievement showed that most motivational constructs predicted academic achievement beyond intelligence and that students’ ability self-concepts and task values are more powerful in predicting their achievement than goals and achievement motives. The aim of the present study was to investigate whether the reported previous findings can be replicated when ability self-concepts, task values, goals, and achievement motives are all assessed at the same level of specificity as the achievement criteria (e.g., hope for success in math and math grades). The sample comprised 345 11th and 12th grade students ( M = 17.48 years old, SD = 1.06) from the highest academic track (Gymnasium) in Germany. Students self-reported their ability self-concepts, task values, goal orientations, and achievement motives in math, German, and school in general. Additionally, we assessed their intelligence and their current and prior Grade point average and grades in math and German. Relative weight analyses revealed that domain-specific ability self-concept, motives, task values and learning goals but not performance goals explained a significant amount of variance in grades above all other predictors of which ability self-concept was the strongest predictor. Results are discussed with respect to their implications for investigating motivational constructs with different theoretical foundation.

Introduction

Achievement motivation energizes and directs behavior toward achievement and therefore is known to be an important determinant of academic success (e.g., Robbins et al., 2004 ; Hattie, 2009 ; Plante et al., 2013 ; Wigfield et al., 2016 ). Achievement motivation is not a single construct but rather subsumes a variety of different constructs like motivational beliefs, task values, goals, and achievement motives (see Murphy and Alexander, 2000 ; Wigfield and Cambria, 2010 ; Wigfield et al., 2016 ). Nevertheless, there is still a limited number of studies, that investigated (1) diverse motivational constructs in relation to students’ academic achievement in one sample and (2) additionally considered students’ cognitive abilities and their prior achievement ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). Because students’ cognitive abilities and their prior achievement are among the best single predictors of academic success (e.g., Kuncel et al., 2004 ; Hailikari et al., 2007 ), it is necessary to include them in the analyses when evaluating the importance of motivational factors for students’ achievement. Steinmayr and Spinath (2009) did so and revealed that students’ domain-specific ability self-concepts followed by domain-specific task values were the best predictors of students’ math and German grades compared to students’ goals and achievement motives. However, a flaw of their study is that they did not assess all motivational constructs at the same level of specificity as the achievement criteria. For example, achievement motives were measured on a domain-general level (e.g., “Difficult problems appeal to me”), whereas students’ achievement as well as motivational beliefs and task values were assessed domain-specifically (e.g., math grades, math self-concept, math task values). The importance of students’ achievement motives for math and German grades might have been underestimated because the specificity levels of predictor and criterion variables did not match (e.g., Ajzen and Fishbein, 1977 ; Baranik et al., 2010 ). The aim of the present study was to investigate whether the seminal findings by Steinmayr and Spinath (2009) will hold when motivational beliefs, task values, goals, and achievement motives are all assessed at the same level of specificity as the achievement criteria. This is an important question with respect to motivation theory and future research in this field. Moreover, based on the findings it might be possible to better judge which kind of motivation should especially be fostered in school to improve achievement. This is important information for interventions aiming at enhancing students’ motivation in school.

Theoretical Relations Between Achievement Motivation and Academic Achievement

We take a social-cognitive approach to motivation (see also Pintrich et al., 1993 ; Elliot and Church, 1997 ; Wigfield and Cambria, 2010 ). This approach emphasizes the important role of students’ beliefs and their interpretations of actual events, as well as the role of the achievement context for motivational dynamics (see Weiner, 1992 ; Pintrich et al., 1993 ; Wigfield and Cambria, 2010 ). Social cognitive models of achievement motivation (e.g., expectancy-value theory by Eccles and Wigfield, 2002 ; hierarchical model of achievement motivation by Elliot and Church, 1997 ) comprise a variety of motivation constructs that can be organized in two broad categories (see Pintrich et al., 1993 , p. 176): students’ “beliefs about their capability to perform a task,” also called expectancy components (e.g., ability self-concepts, self-efficacy), and their “motivational beliefs about their reasons for choosing to do a task,” also called value components (e.g., task values, goals). The literature on motivation constructs from these categories is extensive (see Wigfield and Cambria, 2010 ). In this article, we focus on selected constructs, namely students’ ability self-concepts (from the category “expectancy components of motivation”), and their task values and goal orientations (from the category “value components of motivation”).

According to the social cognitive perspective, students’ motivation is relatively situation or context specific (see Pintrich et al., 1993 ). To gain a comprehensive picture of the relation between students’ motivation and their academic achievement, we additionally take into account a traditional personality model of motivation, the theory of the achievement motive ( McClelland et al., 1953 ), according to which students’ motivation is conceptualized as a relatively stable trait. Thus, we consider the achievement motives hope for success and fear of failure besides students’ ability self-concepts, their task values, and goal orientations in this article. In the following, we describe the motivation constructs in more detail.

Students’ ability self-concepts are defined as cognitive representations of their ability level ( Marsh, 1990 ; Wigfield et al., 2016 ). Ability self-concepts have been shown to be domain-specific from the early school years on (e.g., Wigfield et al., 1997 ). Consequently, they are frequently assessed with regard to a certain domain (e.g., with regard to school in general vs. with regard to math).

In the present article, task values are defined in the sense of the expectancy-value model by Eccles et al. (1983) and Eccles and Wigfield (2002) . According to the expectancy-value model there are three task values that should be positively associated with achievement, namely intrinsic values, utility value, and personal importance ( Eccles and Wigfield, 1995 ). Because task values are domain-specific from the early school years on (e.g., Eccles et al., 1993 ; Eccles and Wigfield, 1995 ), they are also assessed with reference to specific subjects (e.g., “How much do you like math?”) or on a more general level with regard to school in general (e.g., “How much do you like going to school?”).

Students’ goal orientations are broader cognitive orientations that students have toward their learning and they reflect the reasons for doing a task (see Dweck and Leggett, 1988 ). Therefore, they fall in the broad category of “value components of motivation.” Initially, researchers distinguished between learning and performance goals when describing goal orientations ( Nicholls, 1984 ; Dweck and Leggett, 1988 ). Learning goals (“task involvement” or “mastery goals”) describe people’s willingness to improve their skills, learn new things, and develop their competence, whereas performance goals (“ego involvement”) focus on demonstrating one’s higher competence and hiding one’s incompetence relative to others (e.g., Elliot and McGregor, 2001 ). Performance goals were later further subdivided into performance-approach (striving to demonstrate competence) and performance-avoidance goals (striving to avoid looking incompetent, e.g., Elliot and Church, 1997 ; Middleton and Midgley, 1997 ). Some researchers have included work avoidance as another component of achievement goals (e.g., Nicholls, 1984 ; Harackiewicz et al., 1997 ). Work avoidance refers to the goal of investing as little effort as possible ( Kumar and Jagacinski, 2011 ). Goal orientations can be assessed in reference to specific subjects (e.g., math) or on a more general level (e.g., in reference to school in general).

McClelland et al. (1953) distinguish the achievement motives hope for success (i.e., positive emotions and the belief that one can succeed) and fear of failure (i.e., negative emotions and the fear that the achievement situation is out of one’s depth). According to McClelland’s definition, need for achievement is measured by describing affective experiences or associations such as fear or joy in achievement situations. Achievement motives are conceptualized as being relatively stable over time. Consequently, need for achievement is theorized to be domain-general and, thus, usually assessed without referring to a certain domain or situation (e.g., Steinmayr and Spinath, 2009 ). However, Sparfeldt and Rost (2011) demonstrated that operationalizing achievement motives subject-specifically is psychometrically useful and results in better criterion validities compared with a domain-general operationalization.

Empirical Evidence on the Relative Importance of Achievement Motivation Constructs for Academic Achievement

A myriad of single studies (e.g., Linnenbrink-Garcia et al., 2018 ; Muenks et al., 2018 ; Steinmayr et al., 2018 ) and several meta-analyses (e.g., Robbins et al., 2004 ; Möller et al., 2009 ; Hulleman et al., 2010 ; Huang, 2011 ) support the hypothesis of social cognitive motivation models that students’ motivational beliefs are significantly related to their academic achievement. However, to judge the relative importance of motivation constructs for academic achievement, studies need (1) to investigate diverse motivational constructs in one sample and (2) to consider students’ cognitive abilities and their prior achievement, too, because the latter are among the best single predictors of academic success (e.g., Kuncel et al., 2004 ; Hailikari et al., 2007 ). For effective educational policy and school reform, it is crucial to obtain robust empirical evidence for whether various motivational constructs can explain variance in school performance over and above intelligence and prior achievement. Without including the latter constructs, we might overestimate the importance of motivation for achievement. Providing evidence that students’ achievement motivation is incrementally valid in predicting their academic achievement beyond their intelligence or prior achievement would emphasize the necessity of designing appropriate interventions for improving students’ school-related motivation.

There are several studies that included expectancy and value components of motivation as predictors of students’ academic achievement (grades or test scores) and additionally considered students’ prior achievement ( Marsh et al., 2005 ; Steinmayr et al., 2018 , Study 1) or their intelligence ( Spinath et al., 2006 ; Lotz et al., 2018 ; Schneider et al., 2018 ; Steinmayr et al., 2018 , Study 2, Weber et al., 2013 ). However, only few studies considered intelligence and prior achievement together with more than two motivational constructs as predictors of school students’ achievement ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). Kriegbaum et al. (2015) examined two expectancy components (i.e., ability self-concept and self-efficacy) and eight value components (i.e., interest, enjoyment, usefulness, learning goals, performance-approach, performance-avoidance goals, and work avoidance) in the domain of math. Steinmayr and Spinath (2009) investigated the role of an expectancy component (i.e., ability self-concept), five value components (i.e., task values, learning goals, performance-approach, performance-avoidance goals, and work avoidance), and students’ achievement motives (i.e., hope for success, fear of failure, and need for achievement) for students’ grades in math and German and their GPA. Both studies used relative weights analyses to compare the predictive power of all variables simultaneously while taking into account multicollinearity of the predictors ( Johnson and LeBreton, 2004 ; Tonidandel and LeBreton, 2011 ). Findings showed that – after controlling for differences in students‘ intelligence and their prior achievement – expectancy components (ability self-concept, self-efficacy) were the best motivational predictors of achievement followed by task values (i.e., intrinsic/enjoyment, attainment, and utility), need for achievement and learning goals ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). However, Steinmayr and Spinath (2009) who investigated the relations in three different domains did not assess all motivational constructs on the same level of specificity as the achievement criteria. More precisely, students’ achievement as well as motivational beliefs and task values were assessed domain-specifically (e.g., math grades, math self-concept, math task values), whereas students’ goals were only measured for school in general (e.g., “In school it is important for me to learn as much as possible”) and students’ achievement motives were only measured on a domain-general level (e.g., “Difficult problems appeal to me”). Thus, the importance of goals and achievement motives for math and German grades might have been underestimated because the specificity levels of predictor and criterion variables did not match (e.g., Ajzen and Fishbein, 1977 ; Baranik et al., 2010 ). Assessing students’ goals and their achievement motives with reference to a specific subject might result in higher associations with domain-specific achievement criteria (see Sparfeldt and Rost, 2011 ).

Taken together, although previous work underlines the important roles of expectancy and value components of motivation for school students’ academic achievement, hitherto, we know little about the relative importance of expectancy components, task values, goals, and achievement motives in different domains when all of them are assessed at the same level of specificity as the achievement criteria (e.g., achievement motives in math → math grades; ability self-concept for school → GPA).

The Present Research

The goal of the present study was to examine the relative importance of several of the most important achievement motivation constructs in predicting school students’ achievement. We substantially extend previous work in this field by considering (1) diverse motivational constructs, (2) students’ intelligence and their prior achievement as achievement predictors in one sample, and (3) by assessing all predictors on the same level of specificity as the achievement criteria. Moreover, we investigated the relations in three different domains: school in general, math, and German. Because there is no study that assessed students’ goal orientations and achievement motives besides their ability self-concept and task values on the same level of specificity as the achievement criteria, we could not derive any specific hypotheses on the relative importance of these constructs, but instead investigated the following research question (RQ):

RQ. What is the relative importance of students’ domain-specific ability self-concepts, task values, goal orientations, and achievement motives for their grades in the respective domain when including all of them, students’ intelligence and prior achievement simultaneously in the analytic models?

Materials and Methods

Participants and procedure.

A sample of 345 students was recruited from two German schools attending the highest academic track (Gymnasium). Only 11th graders participated at one school, whereas 11th and 12th graders participated at the other. Students of the different grades and schools did not differ significantly on any of the assessed measures. Students represented the typical population of this type of school in Germany; that is, the majority was Caucasian and came from medium to high socioeconomic status homes. At the time of testing, students were on average 17.48 years old ( SD = 1.06). As is typical for this kind of school, the sample comprised more girls ( n = 200) than boys ( n = 145). We verify that the study is in accordance with established ethical guidelines. Approval by an ethics committee was not required as per the institution’s guidelines and applicable regulations in the federal state where the study was conducted. Participation was voluntarily and no deception took place. Before testing, we received written informed consent forms from the students and from the parents of the students who were under the age of 18 on the day of the testing. If students did not want to participate, they could spend the testing time in their teacher’s room with an extra assignment. All students agreed to participate. Testing took place during regular classes in schools in 2013. Tests were administered by trained research assistants and lasted about 2.5 h. Students filled in the achievement motivation questionnaires first, and the intelligence test was administered afterward. Before the intelligence test, there was a short break.

Ability Self-Concept

Students’ ability self-concepts were assessed with four items per domain ( Schöne et al., 2002 ). Students indicated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree) how good they thought they were at different activities in school in general, math, and German (“I am good at school in general/math/German,” “It is easy to for me to learn in school in general/math/German,” “In school in general/math/German, I know a lot,” and “Most assignments in school/math/German are easy for me”). Internal consistency (Cronbach’s α) of the ability self-concept scale was high in school in general, in math, and in German (0.82 ≤ α ≤ 0.95; see Table 1 ).

Means ( M ), Standard Deviations ( SD ), and Reliabilities (α) for all measures.

Task Values

Students’ task values were assessed with an established German scale (SESSW; Subjective scholastic value scale; Steinmayr and Spinath, 2010 ). The measure is an adaptation of items used by Eccles and Wigfield (1995) in different studies. It assesses intrinsic values, utility, and personal importance with three items each. Students indicated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree) how much they valued school in general, math, and German (Intrinsic values: “I like school/math/German,” “I enjoy doing things in school/math/German,” and “I find school in general/math/German interesting”; Utility: “How useful is what you learn in school/math/German in general?,” “School/math/German will be useful in my future,” “The things I learn in school/math/German will be of use in my future life”; Personal importance: “Being good at school/math/German is important to me,” “To be good at school/math/German means a lot to me,” “Attainment in school/math/German is important to me”). Internal consistency of the values scale was high in all domains (0.90 ≤ α ≤ 0.93; see Table 1 ).

Goal Orientations

Students’ goal orientations were assessed with an established German self-report measure (SELLMO; Scales for measuring learning and achievement motivation; Spinath et al., 2002 ). In accordance with Sparfeldt et al. (2007) , we assessed goal orientations with regard to different domains: school in general, math, and German. In each domain, we used the SELLMO to assess students’ learning goals, performance-avoidance goals, and work avoidance with eight items each and their performance-approach goals with seven items. Students’ answered the items on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree). All items except for the work avoidance items are printed in Spinath and Steinmayr (2012) , p. 1148). A sample item to assess work avoidance is: “In school/math/German, it is important to me to do as little work as possible.” Internal consistency of the learning goals scale was high in all domains (0.83 ≤ α ≤ 0.88). The same was true for performance-approach goals (0.85 ≤ α ≤ 0.88), performance-avoidance goals (α = 0.89), and work avoidance (0.91 ≤ α ≤ 0.92; see Table 1 ).

Achievement Motives

Achievement motives were assessed with the Achievement Motives Scale (AMS; Gjesme and Nygard, 1970 ; Göttert and Kuhl, 1980 ). In the present study, we used a short form measuring “hope for success” and “fear of failure” with the seven items per subscale that showed the highest factor loadings. Both subscales were assessed in three domains: school in general, math, and German. Students’ answered all items on a 4-point scale ranging from 1 (does not apply at all) to 4 (fully applies). An example hope for success item is “In school/math/German, difficult problems appeal to me,” and an example fear of failure item is “In school/math/German, matters that are slightly difficult disconcert me.” Internal consistencies of hope for success and fear of failure scales were high in all domains (hope for success: 0.88 ≤ α ≤ 0.92; fear of failure: 0.90 ≤ α ≤ 0.91; see Table 1 ).

Intelligence

Intelligence was measured with the basic module of the Intelligence Structure Test 2000 R, a well-established German multifactor intelligence measure (I-S-T 2000 R; Amthauer et al., 2001 ). The basic module of the test offers assessments of domain-specific intelligence for verbal, numeric, and figural abilities as well as an overall intelligence score (a composite of the three facets). The overall intelligence score is thought to measure reasoning as a higher order factor of intelligence and can be interpreted as a measure of general intelligence, g . Its construct validity has been demonstrated in several studies ( Amthauer et al., 2001 ; Steinmayr and Amelang, 2006 ). In the present study, we used the scores that were closest to the domains we investigated: overall intelligence, numerical intelligence, and verbal intelligence (see also Steinmayr and Spinath, 2009 ). Raw values could range from 0 to 60 for verbal and numerical intelligence, and from 0 to 180 for overall intelligence. Internal consistencies of all intelligence scales were high (0.71 ≤ α ≤ 0.90; see Table 1 ).

Academic Achievement

For all students, the school delivered the report cards that the students received 3 months before testing (t0) and 4 months after testing (t2), at the end of the term in which testing took place. We assessed students’ grades in German and math as well as their overall grade point average (GPA) as criteria for school performance. GPA was computed as the mean of all available grades, not including grades in the nonacademic domains Sports and Music/Art as they did not correlate with the other grades. Grades ranged from 1 to 6, and were recoded so that higher numbers represented better performance.

Statistical Analyses

We conducted relative weight analyses to predict students’ academic achievement separately in math, German, and school in general. The relative weight analysis is a statistical procedure that enables to determine the relative importance of each predictor in a multiple regression analysis (“relative weight”) and to take adequately into account the multicollinearity of the different motivational constructs (for details, see Johnson and LeBreton, 2004 ; Tonidandel and LeBreton, 2011 ). Basically, it uses a variable transformation approach to create a new set of predictors that are orthogonal to one another (i.e., uncorrelated). Then, the criterion is regressed on these new orthogonal predictors, and the resulting standardized regression coefficients can be used because they no longer suffer from the deleterious effects of multicollinearity. These standardized regression weights are then transformed back into the metric of the original predictors. The rescaled relative weight of a predictor can easily be transformed into the percentage of variance that is uniquely explained by this predictor when dividing the relative weight of the specific predictor by the total variance explained by all predictors in the regression model ( R 2 ). We performed the relative weight analyses in three steps. In Model 1, we included the different achievement motivation variables assessed in the respective domain in the analyses. In Model 2, we entered intelligence into the analyses in addition to the achievement motivation variables. In Model 3, we included prior school performance indicated by grades measured before testing in addition to all of the motivation variables and intelligence. For all three steps, we tested for whether all relative weight factors differed significantly from each other (see Johnson, 2004 ) to determine which motivational construct was most important in predicting academic achievement (RQ).

Descriptive Statistics and Intercorrelations

Table 1 shows means, standard deviations, and reliabilities. Tables 2 –4 show the correlations between all scales in school in general, in math, and in German. Of particular relevance here, are the correlations between the motivational constructs and students’ school grades. In all three domains (i.e., school in general/math/German), out of all motivational predictor variables, students’ ability self-concepts showed the strongest associations with subsequent grades ( r = 0.53/0.61/0.46; see Tables 2 –4 ). Except for students’ performance-avoidance goals (−0.04 ≤ r ≤ 0.07, p > 0.05), the other motivational constructs were also significantly related to school grades. Most of the respective correlations were evenly dispersed around a moderate effect size of | r | = 0.30.

Intercorrelations between all variables in school in general.

Intercorrelations between all variables in German.

Intercorrelations between all variables in math.

Relative Weight Analyses

Table 5 presents the results of the relative weight analyses. In Model 1 (only motivational variables) and Model 2 (motivation and intelligence), respectively, the overall explained variance was highest for math grades ( R 2 = 0.42 and R 2 = 0.42, respectively) followed by GPA ( R 2 = 0.30 and R 2 = 0.34, respectively) and grades in German ( R 2 = 0.26 and R 2 = 0.28, respectively). When prior school grades were additionally considered (Model 3) the largest amount of variance was explained in students’ GPA ( R 2 = 0.73), followed by grades in German ( R 2 = 0.59) and math ( R 2 = 0.57). In the following, we will describe the results of Model 3 for each domain in more detail.

Relative weights and percentages of explained criterion variance (%) for all motivational constructs (Model 1) plus intelligence (Model 2) plus prior school achievement (Model 3).

Beginning with the prediction of students’ GPA: In Model 3, students’ prior GPA explained more variance in subsequent GPA than all other predictor variables (68%). Students’ ability self-concept explained significantly less variance than prior GPA but still more than all other predictors that we considered (14%). The relative weights of students’ intelligence (5%), task values (2%), hope for success (4%), and fear of failure (3%) did not differ significantly from each other but were still significantly different from zero ( p < 0.05). The relative weights of students’ goal orientations were not significant in Model 3.

Turning to math grades: The findings of the relative weight analyses for the prediction of math grades differed slightly from the prediction of GPA. In Model 3, the relative weights of numerical intelligence (2%) and performance-approach goals (2%) in math were no longer different from zero ( p > 0.05); in Model 2 they were. Prior math grades explained the largest share of the unique variance in subsequent math grades (45%), followed by math self-concept (19%). The relative weights of students’ math task values (9%), learning goals (5%), work avoidance (7%), and hope for success (6%) did not differ significantly from each other. Students’ fear of failure in math explained the smallest amount of unique variance in their math grades (4%) but the relative weight of students’ fear of failure did not differ significantly from that of students’ hope for success, work avoidance, and learning goals. The relative weights of students’ performance-avoidance goals were not significant in Model 3.

Turning to German grades: In Model 3, students’ prior grade in German was the strongest predictor (64%), followed by German self-concept (10%). Students’ fear of failure in German (6%), their verbal intelligence (4%), task values (4%), learning goals (4%), and hope for success (4%) explained less variance in German grades and did not differ significantly from each other but were significantly different from zero ( p < 0.05). The relative weights of students’ performance goals and work avoidance were not significant in Model 3.

In the present studies, we aimed to investigate the relative importance of several achievement motivation constructs in predicting students’ academic achievement. We sought to overcome the limitations of previous research in this field by (1) considering several theoretically and empirically distinct motivational constructs, (2) students’ intelligence, and their prior achievement, and (3) by assessing all predictors at the same level of specificity as the achievement criteria. We applied sophisticated statistical procedures to investigate the relations in three different domains, namely school in general, math, and German.

Relative Importance of Achievement Motivation Constructs for Academic Achievement

Out of the motivational predictor variables, students’ ability self-concepts explained the largest amount of variance in their academic achievement across all sets of analyses and across all investigated domains. Even when intelligence and prior grades were controlled for, students’ ability self-concepts accounted for at least 10% of the variance in the criterion. The relative superiority of ability self-perceptions is in line with the available literature on this topic (e.g., Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ; Steinmayr et al., 2018 ) and with numerous studies that have investigated the relations between students’ self-concept and their achievement (e.g., Möller et al., 2009 ; Huang, 2011 ). Ability self-concepts showed even higher relative weights than the corresponding intelligence scores. Whereas some previous studies have suggested that self-concepts and intelligence are at least equally important when predicting students’ grades (e.g., Steinmayr and Spinath, 2009 ; Weber et al., 2013 ; Schneider et al., 2018 ), our findings indicate that it might be even more important to believe in own school-related abilities than to possess outstanding cognitive capacities to achieve good grades (see also Lotz et al., 2018 ). Such a conclusion was supported by the fact that we examined the relative importance of all predictor variables across three domains and at the same levels of specificity, thus maximizing criterion-related validity (see Baranik et al., 2010 ). This procedure represents a particular strength of our study and sets it apart from previous studies in the field (e.g., Steinmayr and Spinath, 2009 ). Alternatively, our findings could be attributed to the sample we investigated at least to some degree. The students examined in the present study were selected for the academic track in Germany, and this makes them rather homogeneous in their cognitive abilities. It is therefore plausible to assume that the restricted variance in intelligence scores decreased the respective criterion validities.

When all variables were assessed at the same level of specificity, the achievement motives hope for success and fear of failure were the second and third best motivational predictors of academic achievement and more important than in the study by Steinmayr and Spinath (2009) . This result underlines the original conceptualization of achievement motives as broad personal tendencies that energize approach or avoidance behavior across different contexts and situations ( Elliot, 2006 ). However, the explanatory power of achievement motives was higher in the more specific domains of math and German, thereby also supporting the suggestion made by Sparfeldt and Rost (2011) to conceptualize achievement motives more domain-specifically. Conceptually, achievement motives and ability self-concepts are closely related. Individuals who believe in their ability to succeed often show greater hope for success than fear of failure and vice versa ( Brunstein and Heckhausen, 2008 ). It is thus not surprising that the two constructs showed similar stability in their relative effects on academic achievement across the three investigated domains. Concerning the specific mechanisms through which students’ achievement motives and ability self-concepts affect their achievement, it seems that they elicit positive or negative valences in students, and these valences in turn serve as simple but meaningful triggers of (un)successful school-related behavior. The large and consistent effects for students’ ability self-concept and their hope for success in our study support recommendations from positive psychology that individuals think positively about the future and regularly provide affirmation to themselves by reminding themselves of their positive attributes ( Seligman and Csikszentmihalyi, 2000 ). Future studies could investigate mediation processes. Theoretically, it would make sense that achievement motives defined as broad personal tendencies affect academic achievement via expectancy beliefs like ability self-concepts (e.g., expectancy-value theory by Eccles and Wigfield, 2002 ; see also, Atkinson, 1957 ).

Although task values and learning goals did not contribute much toward explaining the variance in GPA, these two constructs became even more important for explaining variance in math and German grades. As Elliot (2006) pointed out in his hierarchical model of approach-avoidance motivation, achievement motives serve as basic motivational principles that energize behavior. However, they do not guide the precise direction of the energized behavior. Instead, goals and task values are commonly recruited to strategically guide this basic motivation toward concrete aims that address the underlying desire or concern. Our results are consistent with Elliot’s (2006) suggestions. Whereas basic achievement motives are equally important at abstract and specific achievement levels, task values and learning goals release their full explanatory power with increasing context-specificity as they affect students’ concrete actions in a given school subject. At this level of abstraction, task values and learning goals compete with more extrinsic forms of motivation, such as performance goals. Contrary to several studies in achievement-goal research, we did not demonstrate the importance of either performance-approach or performance-avoidance goals for academic achievement.

Whereas students’ ability self-concept showed a high relative importance above and beyond intelligence, with few exceptions, each of the remaining motivation constructs explained less than 5% of the variance in students’ academic achievement in the full model including intelligence measures. One might argue that the high relative importance of students’ ability self-concept is not surprising because students’ ability self-concepts more strongly depend on prior grades than the other motivation constructs. Prior grades represent performance feedback and enable achievement comparisons that are seen as the main determinants of students’ ability self-concepts (see Skaalvik and Skaalvik, 2002 ). However, we included students’ prior grades in the analyses and students’ ability self-concepts still were the most powerful predictors of academic achievement out of the achievement motivation constructs that were considered. It is thus reasonable to conclude that the high relative importance of students’ subjective beliefs about their abilities is not only due to the overlap of this believes with prior achievement.

Limitations and Suggestions for Further Research

Our study confirms and extends the extant work on the power of students’ ability self-concept net of other important motivation variables even when important methodological aspects are considered. Strength of the study is the simultaneous investigation of different achievement motivation constructs in different academic domains. Nevertheless, we restricted the range of motivation constructs to ability self-concepts, task values, goal orientations, and achievement motives. It might be interesting to replicate the findings with other motivation constructs such as academic self-efficacy ( Pajares, 2003 ), individual interest ( Renninger and Hidi, 2011 ), or autonomous versus controlled forms of motivation ( Ryan and Deci, 2000 ). However, these constructs are conceptually and/or empirically very closely related to the motivation constructs we considered (e.g., Eccles and Wigfield, 1995 ; Marsh et al., 2018 ). Thus, it might well be the case that we would find very similar results for self-efficacy instead of ability self-concept as one example.

A second limitation is that we only focused on linear relations between motivation and achievement using a variable-centered approach. Studies that considered different motivation constructs and used person-centered approaches revealed that motivation factors interact with each other and that there are different profiles of motivation that are differently related to students’ achievement (e.g., Conley, 2012 ; Schwinger et al., 2016 ). An important avenue for future studies on students’ motivation is to further investigate these interactions in different academic domains.

Another limitation that might suggest a potential avenue for future research is the fact that we used only grades as an indicator of academic achievement. Although, grades are of high practical relevance for the students, they do not necessarily indicate how much students have learned, how much they know and how creative they are in the respective domain (e.g., Walton and Spencer, 2009 ). Moreover, there is empirical evidence that the prediction of academic achievement differs according to the particular criterion that is chosen (e.g., Lotz et al., 2018 ). Using standardized test performance instead of grades might lead to different results.

Our study is also limited to 11th and 12th graders attending the highest academic track in Germany. More balanced samples are needed to generalize the findings. A recent study ( Ben-Eliyahu, 2019 ) that investigated the relations between different motivational constructs (i.e., goal orientations, expectancies, and task values) and self-regulated learning in university students revealed higher relations for gifted students than for typical students. This finding indicates that relations between different aspects of motivation might differ between academically selected samples and unselected samples.

Finally, despite the advantages of relative weight analyses, this procedure also has some shortcomings. Most important, it is based on manifest variables. Thus, differences in criterion validity might be due in part to differences in measurement error. However, we are not aware of a latent procedure that is comparable to relative weight analyses. It might be one goal for methodological research to overcome this shortcoming.

We conducted the present research to identify how different aspects of students’ motivation uniquely contribute to differences in students’ achievement. Our study demonstrated the relative importance of students’ ability self-concepts, their task values, learning goals, and achievement motives for students’ grades in different academic subjects above and beyond intelligence and prior achievement. Findings thus broaden our knowledge on the role of students’ motivation for academic achievement. Students’ ability self-concept turned out to be the most important motivational predictor of students’ grades above and beyond differences in their intelligence and prior grades, even when all predictors were assessed domain-specifically. Out of two students with similar intelligence scores, same prior achievement, and similar task values, goals and achievement motives in a domain, the student with a higher domain-specific ability self-concept will receive better school grades in the respective domain. Therefore, there is strong evidence that believing in own competencies is advantageous with respect to academic achievement. This finding shows once again that it is a promising approach to implement validated interventions aiming at enhancing students’ domain-specific ability-beliefs in school (see also Muenks et al., 2017 ; Steinmayr et al., 2018 ).

Data Availability

Ethics statement.

In Germany, institutional approval was not required by default at the time the study was conducted. That is, why we cannot provide a formal approval by the institutional ethics committee. We verify that the study is in accordance with established ethical guidelines. Participation was voluntarily and no deception took place. Before testing, we received informed consent forms from the parents of the students who were under the age of 18 on the day of the testing. If students did not want to participate, they could spend the testing time in their teacher’s room with an extra assignment. All students agreed to participate. We included this information also in the manuscript.

Author Contributions

RS conceived and supervised the study, curated the data, performed the formal analysis, investigated the results, developed the methodology, administered the project, and wrote, reviewed, and edited the manuscript. AW wrote, reviewed, and edited the manuscript. MS performed the formal analysis, and wrote, reviewed, and edited the manuscript. BS conceived the study, and wrote, reviewed, and edited the manuscript.

Conflict of Interest Statement

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

Funding. We acknowledge financial support by Deutsche Forschungsgemeinschaft and Technische Universität Dortmund/TU Dortmund University within the funding programme Open Access Publishing.

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What is Goal Setting and How to Do it Well

Perhaps you know exactly what you want to achieve, but have no idea how to get there.

That’s where goal setting comes in. Goals are the first step towards planning for the future, and play a fundamental role in the development of skills in various facets of life, from work to relationships and everything in between. They are the target at which we aim our proverbial arrow.

Understanding the importance of goals and the techniques involved in setting achievable goals paves the way for success.

In the words of Pablo Picasso:

Our goals can only be reached through a vehicle of a plan, in which we must fervently believe, and upon which we must vigorously act. There is no other route to success.

Before you continue, we thought you might like to download our three Goal Achievement Exercises for free . These detailed, science-based exercises will help you or your clients create actionable goals and master techniques to create lasting behavior change.

This Article Contains:

What is goal setting, why is goal setting important, key principles of goal setting, 8 interesting facts on goal setting, research and studies, how and why goal setting works, what skills does it require, an outline for personal goal setting, 3 descriptions of goal setting in practice, 3 goal-setting pdfs, how often should we review goals, how can we best achieve goals we have set, 7 tips and strategies, a take-home message.

Goal setting is a powerful motivator, the value of which has been recognized in an abundance of clinical and real-world settings for over 35 years.

‘Goals,’ are “ the object or aim of an action, for example, to attain a specific standard of proficiency, usually within a specified time limit .” (Locke & Latham, 2002, p. 705) They are the level of competence that we wish to achieve and create a useful lens through which we assess our current performance.

Goal setting is the process by which we achieve these goals. The importance of the goal-setting process should not go unappreciated. According to Lock (2019) “ Every person’s life depends on the process of choosing goals to pursue; if you remain passive you are not going to thrive as a human being. ”

Goal-setting theory (Locke & Latham, 1984) is based on the premise that conscious goals affect action (Ryan, 1970) and conscious human behavior is purposeful and regulated by individual goals. Simply put, we must decide what is beneficial to our own welfare, and set goals accordingly.

Why do some people perform better on tasks than others? According to Ryan (1970), if individuals are equal in ability and knowledge, then the cause must be motivational .

The theory states that the simplest and most direct motivational explanation of why some people perform better than others is due to disparate performance goals, implying that setting and adjusting goals can significantly impact performance.

Initially, research into goal setting attempted to ascertain how the level of intended achievement (goal) is related to the actual level of achievement (performance) in an organizational setting (Locke & Latham, 1990).

Goal setting increases employee motivation and organizational commitment (Latham, 2004). Additionally, goals affect the intensity of our actions and our emotions. The more difficult and valued a goal is, the more intense our efforts will be in order to attain it, and the more success we experience following achievement (Latham & Locke, 2006).

Through the experience of success and the positive emotions that accompany it, confidence and belief in our own abilities grow. Schunk (1985) found that participation in goal setting encourages a search for new strategies to aid success. Finding novel ways to utilize our skills and push our abilities increases task-relevant knowledge while enhancing self-efficacy and self-confidence .

Goal setting involves planning for the future. MacLeod, Coates & Hetherton (2008) found that goal setting and skill-oriented planning significantly improved subjective wellbeing in those who took part in a goal-setting intervention program. Thinking positively about the future bolsters our ability to create goals and consider the actions required to achieve them.

The capacity to plan positively impacts our perceived control over goal outcomes and our future (Vincent, Boddana, & MacLeod, 2004). Furthermore, goal setting and achievement can promote the development of an internal locus of control.

While individuals with an external locus of control believe that both positive and negative outcomes are the result of external influences, those with an internal locus of control believe that success is determined by their own actions and skills.

1. Commitment

Commitment refers to the degree to which an individual is attached to the goal and their determination to reach it – even when faced with obstacles. Goal performance is strongest when people are committed, and even more so when said goals are difficult (Locke & Latham, 1990).

Once they’re committed, if an individual discovers their performance is inadequate, they are likely to increase their effort or change their strategy in order to attain it (Latham & Locke, 2006).

When we are less committed to goals – particularly more challenging goals – we increase the likelihood of giving up.

A number of factors can influence our commitment levels (Miner, 2005). Namely, the perceived desirability of a goal and the perceived ability of achieving it. To be successful, you must possess the desire and a comprehensive understanding of what is required to achieve your goal.

Specific goals put you on a direct course. When a goal is vague, it has limited motivational value. Goal clarity is positively related to overall motivation and satisfaction in the workplace (Arvey et al., 1976).

Set clear, precise and unambiguous goals that are implicit and can be measured. When a goal is clear in your mind, you have an improved understanding of the task at hand. You know exactly what is required and the resulting success is a further source of motivation.

3. Challenging

Goals must be challenging yet attainable. Challenging goals can improve performance through increased self-satisfaction, and the motivation to find suitable strategies to push our skills to the limit (Locke & Latham, 1990). Conversely, goals that are not within our ability level may not be achieved, leading to feelings of dissatisfaction and frustration.

We are motivated by achievement and the anticipation of achievement. If we know a goal is challenging yet believe it is within our abilities to accomplish, we are more likely to be motivated to complete a task (Zimmerman et al., 1992).

4. Task complexity

Miner (2005) suggested that overly complex tasks introduce demands that may mute goal-setting effects. Overly complex goals that lie out of our skill level may become overwhelming and negatively impact morale, productivity, and motivation.

The timescale for such goals should be realistic. Allowing sufficient time to work toward a goal allows opportunities to reassess the goal complexity, while reviewing and improving performance. Even the most motivated of people can become disillusioned if the task’s complexity is too great for their skills.

5. Feedback

Goal setting is more effective in the presence of immediate feedback (Erez, 1977). Feedback – including internal feedback – helps to determine the degree to which a goal is being met and how you are progressing.

Unambiguous feedback ensures that action can be taken if necessary. If performance falls below the standard required to achieve a goal, feedback allows us to reflect upon our ability and set new, more attainable, goals. When such feedback is delayed, we cannot evaluate the effectiveness of our strategies promptly, leading to a potential reduction in the rate of progress (Zimmerman, 2008).

When we perceive our progress towards a goal as adequate, we feel capable of learning new skills and setting more challenging future goals.

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• Setting goals and reflecting upon them improves academic success. Around 25% of students who enroll in 4-year university courses do not complete their studies – common explanations for this include a lack of clear goals and motivation. Goal-setting intervention programs have been shown to significantly improve academic performance (Morisano, Hirsh, Peterson, Pihl, & Shore, 2010).
• Goals are good for motivation and vice versa. Most definitions of motivation incorporate goals and goal setting as an essential factor. For example, “ Motivation is the desire or want that energizes and directs goal-oriented behavior .” (Kleinginna & Kleinginna, 1981).
• Goal setting is associated with achieving the optimal conditions for flow state . Setting clear goals that are both challenging yet within your skill level is a powerful contributor to finding yourself in ‘the zone’.
• An optimistic approach to goal setting can aid success. Research into goal-setting among students indicates that factors such as hope and optimism have a significant impact on how we manage our goals (Bressler, Bressler, & Bressler, 2010).
• Goals that are both specific and difficult lead to overall improved performance. Comparisons between the effect of non-specific goals such as “I will try to do my best” and specific, challenging goals suggest that people do not tend to perform well when trying to ‘do their best’. A vague goal is compatible with multiple outcomes, including those lower than one’s capabilities (Locke, 1996).
• People with high efficacy are more likely to set challenging goals and commit to them. Individuals who sustain belief in their abilities under the pressure of challenging goals tend to maintain or even increase their subsequent goals, thereby making improvements to ensuing performances. Conversely, individuals who lack this confidence have a tendency to lower their goals (making them easier to achieve) and decrease their future efforts (Locke, 1996).
• Social influences are a strong determinant in goal choice. While the impact of social influences on goal achievement may diminish with increased task-specific knowledge, social influences remain a strong determinant of goal choice (Klein, Austin & Cooper, 2008).
• Goal setting is a more powerful motivator than monetary incentives alone. Latham and Locke (1979) found goal setting to be the major mechanism by which other incentives affect motivation. Within the workplace, money was found most effective as a motivator when the rewards offered were contingent on achieving specific objectives.

The setting of clear goals is more likely to close the gap between current ability and the desired objectives. With this in mind, let’s look at some of the research related to goal setting.

Goal setting in teams

The increasing prevalence of team-based structures in the workplace encouraged research in goal setting within teams. Such research indicated structural differences between goal setting for individuals and for groups (Locke & Latham, 2013).

Kozlowski and Klein (2000) suggested that while the effectiveness of individual and team goals may look similar when considering the final outcomes, the structure of the goal-setting construct is very different.

In team-based structures, individuals must engage in interpersonal interaction and various other processes in order to accomplish the team’s goal. Kristof-Brown and Stevens (2001) examined how perceived team mastery and performance goals affected individual outcome. Their findings suggested that agreement on team performance goals elicited greater individual satisfaction and contributions, regardless of goal strength.

Goal setting in virtual teams

Within virtual teams (workgroups in which members collaborate remotely), designing interactions that encourage the setting of goals leads to the achievement of shared mental models (Powell, Piccoli, & Ives, 2004). The addition of intermediate goals in addition to final goals, and clearly articulating them, significantly improved task performance within virtual groups (Kaiser, Tuller, & McKowen, 2000).

Research by Powell, et al. (2004) suggested that virtual groups should employ someone who is responsible for sharing goal-critical information, known as a caretaker. The inclusion of a ‘caretaker’ ensures each virtual team member’s efforts are aligned with those of the group, that there is role clarity, and that each teammate’s contribution advances the team toward its goals.

Goals and academia

The setting of educational goals in academia ensures learners have an unequivocal understanding of what is expected, which in turn aids concentration on the attainment of their goals (Hattie & Timperly, 2007).

Reis and McCoach (2000) suggested that specific characteristics are commonly associated with academic underachievement. These include low motivation, low self-regulation, and low goal valuation. For children, self-regulation and motivation are affected by perceived goal and achievement values. When a goal is valued, children are more likely to engage in, expend more effort on, and perform better on the task

Further research by McCoach and Siegle (2003) found that valuing a goal was a necessary prerequisite to one’s motivation to self-regulate and to achieve in a scholastic environment. Additionally, students’ beliefs in their efficacy for self-regulated learning influenced the academic goals they set for themselves and their final academic achievement (Zimmerman, 2008).

Neurological rehabilitation

Goal setting is at the core of many neurological rehabilitation therapies. Holliday, Ballinger, & Playford (2007) explored how in-patients with neurological impairments experienced goal setting and identified the issues that underpin individual experiences of goal setting.

Their findings suggested that within rehabilitative healthcare professions, it is vital that patients understand what is expected of them in order to ensure goal setting is a meaningful activity.

Goal setting in physical therapy

Goal setting is a traditional method used within the practice of physical therapy. Cott and Finch (1991) examined the potential use of goal setting in improving and measuring physical therapy effectiveness. The study suggested that active participation by the patient in the goal-setting process is of primary importance to the attainment of goals.

That is, inclusion in the formation of goals rather than having them externally imposed is imperative.

A complete guide to goal setting – The Art of Improvement

When done correctly, goal setting is effective and often critical to success. Goals give us direction by focusing attention on goal-relevant behavior and away from irrelevant tasks (Zimmerman, Bandura, & Martinez-Pons, 1992). Miner (2005) suggested that goal setting works through three basic propositions:

• Goals energize performance through the motivation to expend the required effort in line with the difficulty of the task.
• Goals motivate people to persist in activities over time.
• Goals direct people’s attention to relevant behaviors and away from behaviors which are irrelevant or detrimental to the achievement of the task.

As previously discussed goals that are specific and challenging lead to higher levels of performance. Locke and Latham (1990) suggested that these types of goal strategies work more effectively for the following reasons:

• Specific and challenging goals are associated with higher self-efficacy (the belief in our own skills and abilities).
• They require higher performance and more effort to elicit a sense of satisfaction.
• Specific goals are less ambiguous in terms of what constitutes good performance.
• Challenging goals are more likely to result in outcomes that are valued by the individual.
• They encourage a tendency to persist with a task for longer.
• The more specific and challenging the goal is, the more attention an individual will dedicate to it, often utilizing skills that have previously gone unused.
• They motivate individuals to search for better strategies and to plan ahead.

The good news is they can be learned and developed through practice. If you cannot achieve the goals you have set, it is possible that the problem lies in one or more of these areas:

The old adage ‘ fail to plan, plan to fail ’ is applicable to successful goal achievement. Low-quality planning negatively affects performance in relation to goals (Smith, Locke, & Barry, 1990). Planning and organizational skills are integral to the goal achievement process. Through proper planning, we can prioritize and maintain focus on the task at hand, while avoiding extraneous distractions that can draw us away from the end goal.

Self-motivation

Without the desire to achieve, our attempts at goal setting are doomed to fail. Motivation to achieve a goal encourages us to develop new techniques and skills in order to succeed (Locke, 2001). In more challenging circumstances, the motivation to keep going is a powerful contributor to goal attainment.

Time management

Time management is a useful skill across many facets of life including goal setting. While setting goals is commonly considered being a specific time management behavior (Macan, Shahani, Dipboye, & Phillips, 1990), time management is also required to successfully accomplish a goal. If we do not properly consider the timescale required to attain a goal, we will inevitably fail.

Additionally, the time we allocate to planning our goals directly impacts task performance – the more time spent on the planning stage, the more likely we are to succeed (Smith, Locke, & Barry, 1990).

Flexibility

Inevitably, at some point, things aren’t going to go as planned. Having the flexibility to adapt to barriers, the perseverance to sustain your efforts and to carry on in the face of adversity is essential to reaching your goal.

Self-regulation

An individual needs to regulate and manage their own emotions in order to promote their own personal and social goals. With developed Emotional Intelligence comes the ability to efficiently consider and describe motivational goals, aims, and missions (Mayer, 2004).

Commitment and Focus

If we are not committed to our goals, goal setting will not work (Locke, 2001). It is imperative that goals are important and relevant on a personal level, and that we know we are capable of attaining, or at the very least making substantial progress towards, a goal.

The following outline will help focus your attention on the personal goal-setting process and guide you in the right direction for successful personal goal attainment.

Set three goals

It might be tempting to approach goal setting with gusto, and while enthusiasm is a good thing it is important not to rush into too much too soon. By limiting the number of goals you initially set there is less chance that you will become overwhelmed by the tasks ahead. Setting just a few initial goals will allow you to make a start on the journey while avoiding the negative emotions that accompany failure.

As you begin to achieve your objectives, try setting more challenging, longer-term goals to push your abilities even further. Once your goals are set, remember to review them regularly. When you begin the goal-setting process it may be beneficial to revisit your progress daily or weekly depending on the goal.

Focus on short-term goals

Initially, it is better to set short-term and more realistic goals. Setting short-term goals such as “ I will learn to make pancakes by next week ” enables more frequent opportunities to review and acknowledge the achievement of goals. More frequent experiences of success result in greater positive emotions and increased motivation to set additional goals or a combination of short, medium and long-term goals.

Make your goals positive

Reframe negative goals such as “ I want to stop eating so much junk food ” into more positive terms like “ I want to feel healthy and will change my diet in order to do so ”. With negative goals, the initial motivation often comes from a place of negativity. For example, “ I want to stop eating so much junk food because I feel unattractive. ” These negative connotations can lead to self-criticism and de-motivation.

Failure to achieve a positive goal is viewed as an indication that while we may have failed at least we are still on the right path.

1. Psychological health

Goal setting is a robust method of support for positive mental health (Rose & Smith, 2018).

When considering the goals you would like to achieve in relation to psychological health, think about what you want to change and how you want to go about changing it. Achieving goals in any aspect of life can boost self-esteem and self-efficacy, leading to improvements in  confidence and wellbeing.

Janet has been thinking about her wellbeing and wants to make changes to improve her mental health. Within this area, goals such as “ I want to be happier ” are too vague and will create barriers to achievement. Janet settles on the more specific goals of “ I will do one thing every day that makes me happy ”. This is much more realistic and can easily be reviewed.

2. Relationships

Canevello and Crocker (2011) suggested that goals contribute to the cycles of responsiveness between people and improve relationship quality. Interpersonal goal setting allows us to create higher quality relationships characterized by improved responsiveness that ultimately enhance relationship quality for everyone involved.

Toby decides he wants to spend more time with his family, after thinking about how he can do this he feels that the problem may be related to the many late nights he has been spending at work. Toby decides, “ I will make sure I am home from work every night before the children go to bed ”.

While this may seem like a specific goal, there is still much ambiguity. What if he has to work late in order to meet a deadline? Both he and his children will feel disappointed and frustrated with this outcome.

After reviewing his goal, Toby makes some alterations: “ I will make sure I am home from work 2 days a week so that I can see the children before bedtime ”. By adding specifics, he has made his goal more achievable and measurable. On reviewing his goal progress, Toby might then decide to change his goal to three times per week if experience tells him this is attainable.

3. Financial

Money, or lack thereof, can massively influence our mental health and wellbeing. It is impossible to know what life will throw at you – illness, redundancy, unexpected expenditure.

In this category, like many others, short term, smaller goals are often more likely to result in success. Perhaps you have debt that you want freedom from or even just a rainy day savings fund. Whatever your financial goal, small positive steps to taking control of your finances can make a big impact.

Jenny has been thinking about her finances and decides she wants to start building her savings. Rather than setting the vague goal, “ I want to save money, ” she thinks in more detail about her objective and sets the goal “ I will save $500 in the next 8 weeks. ” By making the goal more specific and measurable, Jenny has improved the likelihood of actually achieving her goal.

The goal can now be reviewed when she decides to and it will be clear if she is on track.

In the 1980s, business coaches Graham Alexander, Alan Fine, and Sir John Whitmore developed the GROW goal setting model, which has become a very influential and effective coaching framework (Nguyen, 2018).

The core of the model relies on four pillars:

– Goals Setting clear goals that align with our core values is important for increasing engagement with actions that will make those goals a reality.

– Reality Being aware of our current state in relation to our goals, including what’s working well, as well as the possible barriers (e.g., excuses, fears, weaknesses), is key for making positive changes aligned with our goals.

– Options Acknowledging the possible routes for action, our own strengths, as well as our available resources (e.g., peer support) can help us use our options to get back on track when faced with obstacles.

– Way forward Motivation, commitment, and accountability towards making positive changes now are crucial in getting us started on our journey towards achieving our goals.

Many revisions of this model have been suggested since it was first developed, such as adding the “Tactics” and “Habits” components (GROWTH). However, the core model remains the same and is used across various contexts, including workplaces, couples, families, and the individual level.

This PDF : ‘ Workbook for Goal-setting and Evidence-based Strategies for Success ’ provides an abundance of exercises and worksheets to teach the reader the best practices for designing, pursuing and achieving important goals.

Compiled by Caroline Adams Miller, MAPP, author of ‘ Creating Your Best Life: The Ultimate Life List Guide ’, the 90+ page workbook provides a structured approach to guide readers towards successful goal setting.

This workbook/guide draws input from a number of areas, including work on “ flourishing ” from positive psychology founding father, Dr. Martin Seligman . It presents a thorough 6-theme process which guides readers to successful goal setting and provides an in-depth review of the underlying psychology.

Anxiety Canada’s PDF ‘ Guide for Goal Setting ’ provides a simple but effective guide on how to identify, set, and achieve realistic goals. The guide handily breaks down the process into easy-to-follow steps while prompting readers to view their future prospects in a positive light.

In brief, the guide is broken down into five steps:

• Identify your goals with a focus on being realistic and specific.
• Break down these goals into smaller steps.
• Identify potential obstacles between you and your goals.
• Build a schedule and allow adequate time to pursue goals.

The guide is a really great overview of goal-setting practices and represents a fantastic starting point if you’re keen to jump right into the practice of goal-setting.

The University of Exeter’s PDF , ‘ Goal Setting ’ for the physically impaired, was compiled by BABCP-accredited Cognitive Behavioral Psychotherapist, Dr. Paul Farrand and associate research fellow – Joanne Woodford. The guide focuses on goal setting for individuals facing physical health problems.

Alongside goal-setting advice, the guide contains worksheets for tracking their progress.

While some goals can be achieved relatively quickly, others take time, patience and lasting motivation to continue. The frequency with which goals should be reviewed is very much dependent on the goal itself. What is more certain is that you should plan to review your goals regularly.

If, for example, you have set yourself smaller milestones to reach on the route to your final goal, it may be prudent to review these on a weekly basis. Being aware of your progress allows opportunities to alter your actions and goals so as not to undermine the hard work you have already put in.

Perhaps things aren’t quite going as planned, regular reviews allow you to reflect on the difficulty of the goals you have set. Is the goal more challenging than you expected? What can you improve upon to attain it?

Regular goal reviews ensure the goal is still relevant – is this still what you want to achieve? If you do not ‘check in’ on your progress, you can lose sight of your ultimate aim which will result in disappointment, frustration and less motivation to attain it than when you first began your journey.

Time-based goals such as learning a new language can take months or even years to complete. When working towards these types of long term goals, it is a good idea to break them down into more manageable targets that can be reviewed weekly.

Essentially, reviewing your goals ensures that you are monitoring your progress in relation to successes and failures. It gives you the chance to analyze the good and bad, so that you can regroup, build on that knowledge, and improve future goal setting strategies.

Have you ever made a grand New Year’s resolution only to find that by the middle of January, you’ve given up or forgotten all about it? You may have set yourself a goal that was too general, ambitious, or impersonal. Incorporating healthy goal-setting techniques is an excellent way to tackle these issues.

Pick goals that are S.M.A.R.T.

The S.M.A.R.T. protocol offers a guide to help steer you towards setting goals that are suited to your abilities, timely, and measurable. If you are unsure of the goal-setting process, the S.M.A.R.T framework offers a sense-check to ensure your goals are the best they can be.

– Specific

Be as specific as possible when setting goals. Look at the what, why, where, when and how of a goal. What do I want to achieve? How will I get there? When should I have achieved this goal by?

– Measurable

Having a goal which can be quantified makes it a lot easier to track your progress.

– Achievable/Attainable

The goals we set need to be grounded in reality lest we set ourselves up for disappointment.

– Relevant

Focus more intently on the subjective ‘why’. Is the goal something you actually want to achieve, or does it stem from external pressure?

– Time-specific

Create a clear yet achievable timescale. Deadlines maximize the reward versus time component. Be explicit about the time span or deadline. For example, change ‘end of summer’ to a specific date for improved clarity.

Write down your goals

It may seem like an unnecessary additional effort, but there is value in putting pen to paper. Write down your goals and think carefully about the steps involved to get there. The very act of writing something down improves recall (Naka & Naoi, 1995), and having a physical reminder of what you want to achieve means you can check-in and review it at any time.

Put a plan into action and review it regularly

Consider the timescale in which you wish to achieve your target. If your goal is a particularly challenging one, break it down into smaller, more manageable goals that culminate in attaining your main goal.

Rather than saying “I want a promotion”, consider the smaller steps that will help get you to that goal, “In the next 4 weeks I will commit to taking on a project I haven’t tried before”. Whatever you decide, ensure it is right for you.

Keep it specific and review your progress often

How we articulate goals to ourselves is integral to the outcome of our efforts. Rather than a blanket statement, more specific goals will be much more effective. Rethink your objectives by presenting them in more specific terms, then build on that.

Reward yourself for your successes, but don’t punish yourself for failure

This doesn’t mean rewarding yourself with chocolate when you attain a healthy eating goal, rather an internal pat on the back. Acknowledge your success and revel in the positive emotions that accompany it.

It is important to be resilient in the face of adversity. Reassess your goals and make alterations when you feel it is necessary to do so.

It’s great to shoot for the stars, but goal setting is more about what you can realistically accomplish rather than an idealistic vision of what you hope you can achieve.

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To culminate this extensive guide on goal-setting, we leave you with a final list of tips and strategies.

1. Brainstorm

Consider what you want to accomplish and be specific in your goals. Really think about your core values and what outcome you are reaching for and write them down. Clear goals will ensure a comprehensive understanding of what is required in order to achieve them. Take the time to really reflect on what you want.

2. Create a ‘goal tree’

This logical thinking process tool is an excellent way to maintain focus on your goal while considering the strategy you might use to achieve it. The very top of the tree is the end goal – your mission statement. On the next level are a maximum of five objectives that are critical to attaining your main goal.

Under the objectives are the necessary conditions required to achieve each one. A goal tree is like a map to success, over time each step is color coded as it is completed, meaning that you can easily review your progress at a glance.

3. Be optimistic but realistic

If you set an unrealistic goal, it may well discourage you from continuing with your endeavor.

4. Evaluate your goals and reflect upon them

Feedback is superior to no feedback, and self-generated feedback is more powerful than externally generated feedback (Ivancevich & McMahon, 1982).

After setting your goal, feedback is the best way to assess how well you are doing. Try setting up a schedule where you can ‘check-in’ on your progress every week. Do you need to reassess and redefine your goal?

5. Intermittent reinforcement

Intermittent reinforcement involves interspersing easier, more achievable goals among more challenging, difficult goals (Martin & Pear, 2019). The completion of each smaller goal becomes rewarding in and of itself, thus delivering the positive effect of success at regular intervals.

6. Tell others about your goals

When we share our goals we are more inclined to exhibit accountability and strengthened commitment. If you tell a friend about a goal you have set, how will you feel if they ask about it and you haven’t been working towards it?

7. Believe in your abilities

Believe in your abilities, but know that it’s OK if things aren’t going to plan. Reevaluating our progress and rethinking goals is all part of the process. Remember that any progress towards your goal is a good thing.

We all have the capacity to adapt and to achieve our personal expectations. Through goal setting, we raise the bar in relation to our own potential and push ourselves to achieve things we only hoped were possible.

Have you incorporated any goal-setting techniques to help you on your way to success? Or maybe you are tempted to make a start on your own plan? How are you going to turn your goal setting into goal getting? Let us know in the comments below.

We hope you enjoyed reading this article. Don’t forget to download our three Goal Achievement Exercises for free .

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3 Goal Achievement Exercises Pack

• Open access
• Published: 29 November 2023

The relationship between social support and academic engagement among university students: the chain mediating effects of life satisfaction and academic motivation

• Chunmei Chen 1 ,
• Fei Bian 2 &
• Yujie Zhu 3  

BMC Public Health volume  23 , Article number:  2368 ( 2023 ) Cite this article

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Metrics details

University students’ academic engagement has a significant impact on their academic performance and career development.

In order to explore the influential mechanisms of social support on university students’ academic engagement and the mediating role of academic motivation and life satisfaction, this study used the Adolescent Social Support Scale, University Students’ Academic Engagement Scale Questionnaire, Adolescent Student Life Satisfaction Scale and University Students’ Academic Motivation Questionnaire, to conduct a questionnaire survey and empirical analysis on 2106 Chinese university students.

(1) social support significantly and positively predicts academic engagement; (2) social support influences academic engagement through the mediating effect of life satisfaction; (3) social support influences academic engagement through the mediating effect of academic motivation; (4) life satisfaction and academic motivation play a chain mediating role in the effect of social support on academic engagement.

Conclusions

This study contributes to understanding the underlying mechanisms of the relationship between social support and academic engagement, which in turn provides insights for universities and the departments concerned to make measures to improve the level of university students’ academic engagement.

Peer Review reports

Introduction

According to the Ministry of Education of the People’s Republic of China, as of 2022, there was a total of 3,013 higher education institutions in the country. Among them, there were 1,239 general undergraduate schools with a total of 19,656,400 students enrolled [ 1 ]. As a main force for buiding the country, university students however more and more commonly lack academic engagement in learning. This might pose a risk to the cultivation of undergraduate talents in China [ 2 ]. Considering that the level of students’ academic engagement in higher education institutions is increasingly recognized as a valid indicator of institutional excellence [ 3 ]. Thus, one key factor to higher education development is to improve university students’ academic engagement, which in turn enhances the quality of talent cultivation in undergraduate universities [ 4 ]. Academic engagement reflects the quality of students’ participation, investment, commitment into and recognition with schools and related activities to improve students’ performance [ 5 ]. It is the extent to which students are committed to schools and are motivated to learn [ 6 ]. Only when students are actively engaged in the learning process can they have meaningful and lasting learning experiences [ 7 , 8 ]. Academic engagement encompasses the behavioral (e.g., participation in academic and social activities), affective (comprised of students’ attitudes, interests, and values), and cognitive (e.g., motivational goals and the application of learning strategies, etc.) dimensions of an individual’s engagement in the learning process [ 9 ]. More specifically, academic engagement includes students attending classes, completing assignments, interacting with peers and instructors, and enrolling and participating in extracurricular activities [ 10 ]. Academic engagement also refers to the time and efforts that students invest into the activities with educational purposes [ 11 ]. Academic engamenment is typically characterized as vitality (representing energy, willingness, and persistence in the face of difficulties), dedication (understanding the meaning of the work, being enthusiastic, inspired, and proud of the work), and absorption (focusing on the work) [ 12 ]. High levels of academic engagement are necessary for students’ success in universities [ 13 , 14 ]. Scholars have confirmed that university students’ learning effectiveness depends on their academic engagement [ 15 ]. Their academic engagement is a better predictor to students’ learning outcomes such as critical thinking, cognitive activities, and reading and writing skills [ 16 ]. Research into university students’ academic engagement can help reduce dissatisfaction, avoid boredom, increase motivation to participate in school-related activities, and improve their achievement level [ 17 , 18 ]. University students’ academic engagement has become a topic of wide interest and discussion in the academic community. However, Studies in Fujian and Zhejiang provinces of of China have found that the overall level of university students’ academic engagement is on an average or even moderately low level [ 19 , 20 ]. It has been found that situational awareness, academic motivation, affective input, cognitive input, behavioral input and learning gain constitute a learning input mechanism that influences and constrains each other [ 21 ]. Based on 134,178 undergraduate students from 311 universities in China, a study survey has used the self-system model of motivational development as a theoretical framework.to examine the internal and external influencing mechanisms of university students’ academic engagement [ 22 ]. Another investigation of the factors influencing the lack of academic engagement of some university students in four higher education institutions in Jiangxi Province has found that there is a significant positive correlation between active cooperation, learning attitude, family factors, teaching management, school support and university students’ academic engagement [ 23 ]. In addition, scholars have explored the impact of instructor support strategies on university students’ academic engagement in online learning. University students’ academic engagement may be related to social support, life satisfaction, and academic motivation [ 24 ]. What factors are associated with university students’ academic engagement? How do these factors affect academic engagement? This study aims to investigate the relationship between university students’ academic engagement and these factors and the mechanism of their influence.

Relationship between social support and learning engagement

Social support refers to the social and psychological support that an individual receives or perceives in the environment, such as respect, care and help [ 25 ]. Psychological support refers mainly to the emotional and evaluative support provided [ 26 ], whereas non-psychological support refers mainly to instrumental and material support [ 27 ]. Evaluation support is the most common and frequently occurring social support among university students. Social support for classroom evaluation is positively correlated with academic engagement [ 28 ]. In addition, technology-supported learning environments promote greater classroom participation [ 29 ]. Students’ need for relevance or belonging is viewed as the extent to which students feel accepted and supported by teachers and peers [ 30 ]. This is even more important at the university level because students are often faced with the need to establish and maintain new relationships as their transition from high school to universities [ 31 ]. According to Hernandez et al. (2021), social support refers to any assistance and help provided to someone by others. Positive relationships are built around the provision of these supports. In educational settings, the groups that give social support are usually teachers, peers and parents. Through their research, they find that social support has a positive impact on university students’ academic engagement [ 32 ]. Guardian support helps students to be engaged in learning and achieve better academic success [ 33 ]. Moreover, good relationships with peers and teachers increase the likelihood that adolescents would demonstrate higher level of behavioral engagement in the classroom [ 34 ]. In particular, the social support provided by teachers that students find accessible plays a key role in the maintenance and development of students’ academic engagement [ 35 ]. Social support is positively related to academic engagement [ 36 , 37 , 38 ]. In summary, hypothesis H 1 is proposed.

There is a significant effect of social support on learning engagement.

Mediating effects of life satisfaction

Life satisfaction is an important cognitive measure of subjective well-being, which refers to an individual’s overall cognitive assessment of his or her life situation most of the time or over a certain period of time according to the criteria chosen by himself or herself. It is an important parameter of an individual’s life quality in a given society since it offers an overall perception and evaluation of life quality on a positive-to-negative continuum, and is an important parameter of the quality of life of individuals in a given society [ 39 ]. According to Restubog et al. (2010), social support is a form of social capital that is acquired through social interactions in various human groups. General social support from family, friends, and significant others can contribute to the development of an individual’s life satisfaction [ 40 ]. The relationship between different sources of social support (family, peers, and teachers) and life satisfaction among 1,133 Korean adolescents is investigated. It is found that the level of social support is one of the core factors of life satisfaction, which is positively correlated with life satisfaction [ 41 ]. It has been established that social support is a buffer against psychological distress [ 42 ], a factor influencing mental health [ 43 , 44 ], and a protective mechanism for life satisfaction [ 45 ], which is positively correlated with life satisfaction. In addition, life satisfaction is an important factor in mental and psychological well-being and is related to the individual’s perception of cheerfulness, which is significantly and positively correlated with students’ academic engagement [ 46 ]. This is in line with previous studies [ 47 , 48 ]. Students with higher life satisfaction across dimensions (environment, family, school, peers, self) are more academically engaged [ 49 ]. Students’ life satisfaction affects their academic engagement. In summary, hypothesis H 2 is proposed:

Life satisfaction mediates the relationship between social support and academic engagement.

The mediating effect of academic motivation

Academic motivation is the internal motivation that directly pushes students to learn, and it has an initiating, maintaining and orienting effect on learning. The nature and intensity of academic motivation directly affects the direction, progress and outcomes of university students’ learning [ 39 ]. Hsieh (2014) has pointed out that motivation consists of two dimensions: internal and external motivation. Internal motivations mainly refers to the students’ engagement in learning driven by challenges, curiosity and knowledge acquisition. External motivations represents external incentives such as grades, rewards and competitions with or evaluations from others [ 50 ]. There is a positive correlation between students’ academic motivation and the support they receive from their parents, teachers, and friends [ 51 ]. The sense of social support received from teacher-student and family relationships compensates for students’ daily low moods and thus positively affects their internal motivation to learn [ 52 ]. Camacho et al. (2021) have put forward that teachers’ social support perceived by students is a significant predictor of students’ motivation. The higher the teacher’s social support is, the lower students’ academic motivation declines [ 53 ]. It has been shown that students with higher level of social support have higher level of academic motivation [ 54 ]. In addition, students’ motivation has an impact on their academic engagement. Motivation is what drives students’ behavior, and engagement is explained through specific manifestations of students’ behavior or motivation [ 55 ]. Specific motivational structures can uniquely predict engagement [ 56 , 57 ]. Numerous studies have confirmed that academic motivation is positively correlated with academic engagement [ 36 , 58 , 59 , 60 ]. In summary, hypothesis H 3 is proposed.

Academic motivation mediates the relationship between social support and academic engagement.

Chain-mediated effects of life satisfaction and academic motivation

Sense of social support can help adolescents connect their goals with those of others so that they begin to gain a shared understanding of how the world works. Students with a higher sense of social support show a higher level of life satisfaction [ 61 ]. Many students’ life satisfaction reduces their life satisfaction because they are disappointed in social relationships and the constructed social support system is not strong [ 62 ]. High life satisfaction allows flexibility and autonomy for young people to pursue higher education, as well as providing them the possibility of attending various events and opportunities for personal development. Life satisfaction is a positive predictor of increased academic motivation [ 63 ]. Previous study arrives at a similar view. Students with high life satisfaction tend to believe that relying on their own abilities and behaviors can largely reduce academic stress and activate academic motivation. They always have a full enthusiasm and passion for learning [ 64 ]. Numerous studies have confirmed that life satisfaction is positively correlated with academic motivation [ 65 , 66 , 67 ]. The higher the university students’ life satisfaction is, the higher their level of academic motivation becomes. Students with high level of academic motivation do not give up easily when they encounter difficulties in their studies, instead they usually sollutions, which enables them to acquire the behavioral and cognitive strategies, information, and emotional energy demanded for task re-engagement. Therefore,. these students have high academic engagement [ 68 ]. In summary, hypothesis H 4 is proposed:

Life satisfaction and academic motivation chain mediate the effect of social support on academic engagement among university students.

This study constructed a chain mediating model to examine the effects of social support on academic engagement and the mediating role of life satisfaction and academic motivation between the two in the group of university students, with a view to providing guidance for improving university students’ academic engagement.

Research methodology

Research subjects.

Convenient sampling method was used to select subjects from several universities in China (Jimei University, Xiamen Institute of Technology, Xiamen Medical College, Guangdong University of Petrochemical Technology, etc.) to conduct a questionnaire survey, and 2,106 valid questionnaires were collected and sorted out. The age of the subjects ranged from 17 to 23 years old (M = 20.16 years old, SD = 1.31). The basic characteristics of the sample are shown in Table  1 .

Research instruments

Adolescent social support scale.

The Adolescent Social Support Scale was developed by Ye et al. in 2008 [ 39 ]. The scale includes the social support resources that the respondent receives and his/her utilization of the available resources. The Adolescent Social Support Scale is a self-report scale including three dimensions: subjective support, objective support, and support utilization, with a total of 17 entries on a five-point scale. All item scores were averaged after reverse scoring of the reverse questions, with higher mean values indicating stronger individual social support. The KMO value of the questionnaire was 0.972 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.924.

University students’ academic engagement scale questionnaire

The University Students’ Academic Engagement Scale Questionnaire was developed by Wang (2014) [ 69 ] in his doctoral dissertation. The scale contains five dimensions: active learning, teacher-student interaction, peer interaction, deep cognitive strategies and enthusiasm for learning. The first three dimensions belong to behavioral engagement, while the last two belong to cognitive and affective engagement, respectively. The questionnaire contains 22 items on a five-point scale. The scores of all items were averaged after reverse scoring the reverse questions, with higher mean values indicating higher individual academic engagement. The KMO value of the questionnaire was 0.972 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.932.

Adolescent student life satisfaction scale

The life satisfaction scale for adolescent students was developed by Zhang and He in 2004 [ 39 ]. Based on the Multidimensional Life Satisfaction Scale for Adolescents (MLSA) developed by Huebner (1994) [ 70 ], the scale was adapted to measure adolescent students’ learning and life. The life satisfaction scale for adolescent students is a self-report scale consisting of 6 dimensions of friendship, family, academics, freedom, school, and environment with 36 entries on a 5-point scale. The scores of all items were averaged after reverse scoring of the reverse questions, with higher mean values indicating higher individual life satisfaction. The KMO value of the questionnaire was 0.969 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.916.

University students’ academic motivation questionnaire

The academic motivation questionnaire for university students was developed by Tian and Pan in 2006 [ 39 ]. The questionnaire was based on Ozupal’s theoretical model of achievement motivation. The scale contains 4 dimensions of interest in knowledge, competence pursuit, reputation acquisition, and altruistic orientation, with 34 entries on a 5-point scale. The scores of all items were averaged after the reverse scoring of the reverse questions, with higher values indicating stronger individual academic motivation. The KMO value of the questionnaire was 0.976 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.93.

Research procedures

Descriptive statistics and Pearson correlation analysis were performed in this study using SPSS 26.0. In order to ensure the accuracy of the results, the variance inflation factor (VIF) method was used in the study for the covariance test (if VIF > 10, it means that there is a serious covariance problem between the variables, and the corresponding variables need to be excluded). Meanwhile, the study used model 6 in the process plug-in prepared by Hayes (2017) [ 71 ] for chained mediation effect analysis and tested the significance of the mediation effect using the bias-corrected percentile Bootstrap method. It was considered statistically significant if the 99% confidence interval did not contain a value of zero [ 72 ]. In addition, before analyzing the data, a common method bias test was performed using the Harman single-factor test [ 73 ].

Common method bias test

When the self-report method was used to collect data, the issue of common method bias may arise. Therefore, the common method bias test was performed using the Harman single-factor test. The results showed that there were eight principal components with eigenvalues greater than 1. The first principal component explained 34% of the variance, which was below the critical criterion of 40%. Therefore, there was no serious common method bias in this study.

Descriptive statistics and correlation analysis of the variables

Table  2 presented the mean and standard deviation of academic engagement, life satisfaction, social support, and academic motivation, as well as the Pearson product difference correlation coefficients between the variables. All the correlations between the variables all reached the significance level and could be further analyzed.

Relationship between social support and academic engagement: a chain mediating model

Path coefficient analysis.

The above analysis indicated a significant correlation between the variables and possible covariance. Therefore, before the effects were tested, the predictor variables in the equations were standardized and diagnosed for covariance. The results showed that the variance inflation factors for all the predictor variables (3.182, 3.277 and 1.865) were less than 5. Therefore, the data used in this study did not have serious covariance problems and were suitable for further tests of mediation effect. The process plug-in developed by Hayes was used to assess the 95% confidence interval (CI) for the mediating effect of life satisfaction and academic motivation in the effect of social support on students’ academic engagement (bootstrap sample size of 5000). The results of the chained mediation modeling were shown in Fig.  1 ; Table  3 .

The results showed that social support significantly and positively predicted academic engagement (β = 0.707, p  < 0.001). With the addition of the mediating variables of life satisfaction and academic motivation, social support still significantly and positively predicted academic engagement, but with a significantly lower effect size (β = 0.061, p  < 0.001). In addition, social support significantly and positively predicted life satisfaction (β = 0.643, p  < 0.001) and academic motivation (β = 0.388, p  < 0.001); life satisfaction significantly and positively predicted academic engagement (β = 0.757, p  < 0.001); and academic motivation significantly and positively predicted academic engagement (β = 0.246, p  < 0.001).

Mediation effect test

Further testing for mediating effects (see Table  4 ) found that the Bootstrap 95% CI intervals for the total indirect effects of life satisfaction and academic motivation in the effect of social support on academic engagement did not include zero. This indicated that life satisfaction and academic motivation were mediating variables in the effect of social support on academic engagement. Moreover, the total effect of social support on academic engagement was 0.707, with a direct effect of 0.061, accounting for 8.6% of the total effect, and a total indirect effect of 0.646, accounting for 91.4% of the total effect. This mediating effect was mainly composed of the following three paths:

Social Support -> Life Satisfaction -> Academic Engagement [95% CI = (0.432, 0.552), Boot SE = 0.025], with a mediating effect of 0.487, which accounted for 68.9% of the total effect, and Hypothesis 2 was supported;

Social Support -> Academic Motivation -> Academic Engagement [95% CI = (0.071, 0.130), Boot SE = 0.013], with a mediating effect of 0.095, which accounted for 13.4% of the total effect, Hypothesis 3 was supported;

Social Support -> Life Satisfaction -> Academic Motivation -> Academic Engagement [95% CI = (0.040, 0.101), Boot SE = 0.015], with a mediating effect of 0.064, which accounted for 9.1% of the total effect, and Hypothesis 4 was supported.

The effect of social support on academic engagement

The results of this study showed that social support positively predicts academic engagement, i.e., the group of university students with more social support will have higher degree of academic engagement, and conversely, the group of university students with less social support will have lower degree of academic engagement. This was consistent with the conclusions drawn from existing research. Social support can evoke positive psychological and behavioral responses by providing individuals with solutions to problems [ 74 ]. Students who receive a greater sense of social support are generally more likely to feel personally connected to the learning environment, to experience positive emotions in the classroom, and to actively use adaptive cognitive strategies for learning and participate better in learning tasks [ 37 ]. Students with more social support are better integrated into their support network and the university academic environment, thus increasing their academic achievement [ 75 ]. According to the social support theory, the provision of emotional, material, and informational support can enhance an individual’s ability and willingness to engage in specific behaviors [ 76 ]. When students receive more interactive support, they are more competent to deal with learning-related issues. Existing study shows the similar views [ 77 ]. These students believe that they can depend on their own ability to achieve their academic goals and see their studies as meaningful. For this reason, they can better persist in their studies and continue on a positive academic path [ 78 ]. Social support from parents and teachers helps develop students’ academic self-efficacy, which in turn promotes their academic engagement [ 79 ]. The social support from parents rises students’ awareness of the importance of education, promotes their desire for education and influences their academic attitudes and beliefs [ 80 ]. The social support from teachers can enhance students’ pursuit of mastery goals and interest in academic tasks, thus promoting their engagement in educational activities [ 81 ]. For example, teachers’ interaction with students during classroom activities, providing clear guidance and timely feedback to students can provide students with a sense of social support [ 32 ]. This helps deepen students’ sense of recognition with their schools and their understanding of learning value, at the same time regulating their stress and anxiety. For this reason, students’ academic engagement is positively influenced by teachers’ social support. Teachers with strong student-teacher relationships are more engaged with their students. They are able to employ strategies that engage students in deeper learning, which increases student engagement in academic activities [ 82 ]. In addition, other scholars have studied students’ online learning and reached similar conclusions. Students who receive more supportive responses and assistance for online learning invest more subjective efforts and self-regulated learning strategies in their learning activities, which increase their level of academic engagement [ 36 ].

The chain mediation model (Note: *** p  < 0.001)

Mediating effect of life satisfaction

The results of this study showed that life satisfaction plays a partial mediating role between social support and academic engagement. That is, the group of university students with more social support has higher level of life satisfaction, and thus the degree of their academic engagement will be higher. This is similar to the conclusions reached by existing studies. Lin et al. (2020) note that people’s perception of social support protects them from negative outcomes and makes them less vulnerable to mental illness [ 83 ]. Additionally, individuals with more social support can more effectively cope with challenges to physical health and are more likely to overcome obstacles. For example, students who receive more support from teachers more experience joy, interest, and hope in learning and thus less anxiety, depression, or despair [ 84 , 85 ]. In the face of stress, frustration, and challenges, such students are able to actively utilize the available social resources as an alternative method to overcome emotional imbalances [ 86 ]. Social support can help individuals cope with learning demands and provides them with stronger beliefs and motivation to adapt to their learning [ 43 ]. A study of Chilean university students has found that plummeting levels of social support exacerbated the frustration of adolescent students, thereby reducing their life satisfaction. This leads to students to be prone to pessimism about learning and not to believe their ability to meet the expectations of their teachers and parents on learning, thus decreasing academic engagement [ 87 ]. More social support helps individuals better adapt to the new and changing social environments, reduce stress-induced tension, and thus enable students to experience higher level of life satisfaction [ 40 , 42 ]. Social support enhances emotional resilience in students. Close relationships with friends help adolescents better control their emotions, provide them with emotional support, and encourage self-expression and self-discovery in a stable environment, thus increasing individuals’ life satisfaction [ 43 ]. At the same time, a sense of social support can help bring about positive self-focused thinking. Social support from significant sources leads individuals to generate positive self-evaluations and develop other related personal traits, such as positive self-focus and self-concept, and subsequently leads them to have positive life experiences (e.g., life satisfaction) [ 88 ]. The higher the level of students’ life satisfaction is, the more engaged they are in learning. Social support from teachers and peers, as well as parents, can help students’ meet the basic needs for competence (i.e., a sense of effectiveness and mastery of learning), autonomy (i.e., freedom or ownership in learning), and relevance (i.e., a sense of belonging and a sense of connection to teachers and peers). Students tend to be emotionally, behaviorally, and cognitively engaged in a learning task when teachers support their independent learning [ 89 ]. When these basic needs are met, students’ life satisfaction improves and thus their engagement in academic activities is promoted [ 90 ]. A study has confirmed that more social support for adolescents would correspondingly increase their life satisfaction. They will have a more positive attitude towards their lives and be more interested in their academic and school activities, thus focusing more on their academic tasks [ 46 ]. A study of students with dyslexia has found that this type of students were exposed to fewer resources of professional tutors, leaving them with little knowledge about the options that they can have to receive substantial help. When students receive lower social support, their life satisfaction is accordingly affected. They often experience secondary emotional and motivational barriers, which in turn reduces their academic engagement [ 91 ]. In conclusion, the social support that university students receive can help them cope with the challenges from their studies, meet their basic needs and reduce negative psychological impact, thus enhancing their life satisfaction. The higher their life satisfaction is, the more they are inclined to engage in their studies.

Mediating effects of academic motivation

The results of this study showed that academic motivation plays a partial mediating role between social support and academic engagement. That is, the more the social support is, the stronger the academic motivation is, and thus the higher degree of the academic engagement becomes. This finding has been confirmed by existing studies. With today’s students facing increasing levels of anxiety, parents, teachers, and other educational professionals can help students equip with coping strategies to anxiety, thereby promoting their mental health and ultimately their academic motivation [ 92 ]. Ryan & Deci (2020), based on self-determination theory, argue that authentic, warm, and supportive environments provided by teachers and peers help students meet basic psychological needs (e.g., relatedness, etc.), which increases internal motivation to learn [ 89 ]. Students tend to have stronger academic motivation when they perceive that their teachers and peers clearly communicate expectations and values that are consistent with their own interests and provide resources and assistance, including emotional support, that are needed to fulfill those expectations and values [ 93 ]. Previous studies hold the similar view [ 94 , 95 ]. A study of English language learning among university students has found that teachers who provide social support to their students tend to create learner-centered environments and attempt to understand the emotional state of their learners, which increases students’ academic motivation. This can help students deal with challenging problems and reduce their mental stress [ 96 ]. Students who receive a higher sense of social support from their peers tend to have clearer plans and higher expectations for their own academics because of the positive motivational feedback they feel, thus promoting their own learning progress [ 97 ]. Academic engagement begins when students actively acquire knowledge based on internal motivation to learn and activate the cognitive processes required for successful problem solving [ 98 ]. Students with high levels of internal motivation are more aware of themselves and standardize their study plans, which increases the amount of effort they put into their studies [ 99 ]. Students with high degree of academic motivation may take advantage of learning opportunities in pursuit of their learning goals in order to perform better than others, rather than simply to acquire knowledge, thus increasing their degree of academic engagement [ 100 ]. This kind of students tends to become more resilient in their motivation. They are able to adjust their academic strategies in accordance with their own learning situations, take the initiative and focus on learning, and thus have a high degree of academic engagement [ 101 ]. They are able to adopt more adaptive coping strategies (particularly strategizing, help-seeking, and self-encouragement) when faced with difficult challenges, which in turn increases the degree of academic engagement [ 102 , 103 ]. Students’ motivated persistence promotes the adoption of academic strategies and provides feedback on their learning outcomes, which in turn is more conducive to subsequent academic engagement [ 104 ]. Individuals receive various types of information through social support, including the fact that they believe to be appreciated and liked by others and the fact that they believe to be valued and a part of a social network, which fully mobilizes the individual’s academic motivation and enables them to engage in learning tasks spontaneously [ 105 ]. When students are supported by their teachers, they are more likely to attend class and develop close relationships with their teachers, which increases their academic motivation. This in turn helps students to increase their self value, social self-esteem and the feeling that they can take control of their lives, which in turn promotes their level of academic engagement [ 106 ]. The more social support university students receive, the more positive feedback and incentives they receive, the stronger their level of academic motivation is. And with their increased motivation level, they are more inclined to engage in academic learning.

Chain mediation effect of life satisfaction and academic motivation

This study found that life satisfaction and academic motivation have a close relationship, and the two constitute an intermediate link in the influence path of social support -> life satisfaction -> academic motivation -> academic engagement, which has a chain mediation effect in the influence of social support on academic engagement. That is, university students with more social support have higher level of life satisfaction, which leads to stronger academic motivation and consequently higher degree of academic engagement. Social support is a buffer for university students when they are faced withf stress and adversity. Social support allows individuals to feel cared, valued, and even to have a contact in case of an emergency [ 107 ]. It helps individuals to reassess stressors as less threatening, which in turn facilitates the development of problem-solving strategies. Therefore, social support has a positive predictive effect on life satisfaction [ 108 ]. Students with high life satisfaction have the confidence to make necessary efforts to succeed, persevere in achieving their academic goals and are able to overcome the setbacks they face [ 48 , 109 ]. Students who receive social support are able to increase their life satisfaction by solving problems in a positive manner, finding appropriate ways to improve the current situation or to prevent the stressful events from recurring in the future and by encouraging themselves to regulate their emotions constructively. These coping styles allow students to return to academic activities with new energy and strategies for approaching tasks, improving the learning environment and thus greatly increasing the degree of student academic engagement [ 110 ]. Meanwhile, pre-service teachers are more likely to learn effectively in classrooms where teacher educators provide clear instructions, instrumental support and constructive feedback, support learning autonomy, and promote collaborative learning. When pre-service teachers’ needs for competence or effective learning are met, they are internally motivated to learn and adopt better knowledge integration strategies, and thus are more likely to be cognitively, emotionally, and behaviorally engaged in learning [ 58 ]. Opdenakker (2021) suggest that when students feel socially supported, they are able to gain a sense of identity in their interactions with the social environment, have more opportunities to express and expand their abilities, and thus aspire to further academic development, which fully mobilizes academic motivation that in turn increases their attention and concentration on classroom activities [ 111 ]. In addition, a number of studies have confirmed that life satisfaction is positively correlated with academic motivation. high level of life satisfaction can correspondingly increase students’ basic academic expectations and balance negative emotions such as academic irritation through, for example, rational self-regulation [ 112 ]. This kind of students are able to promote the ability to understand and regulate their own and others’ emotions and can better overcome frustrations encountered in their academic life, thus showing stronger academic motivation [ 113 ]. This is in line with existing studies. Individuals with higher life satisfaction are able to engage in more stable self-regulation and can better internalize external demands, which in turn helps individuals to acquire greater learning autonomy and better learning outcomes with stronger academic motivation [ 67 ]. Students with higher life satisfaction possess skills that make them feel empowered to achieve their goals, take control of their lives and take responsibility for their outcomes, and thus have more motivation to learn [ 114 ]. These students tend to face life with full positive emotions. This facilitates to promote meta-cognitive thinking and the use of creative learning strategies to achieve their goals [ 115 ]. They are able to enhance their academic motivation in environments characterized by a sense of safety and closeness, allowing them to persist and engage in selected tasks for longer periods of time, which in turn promotes positive learning outcomes [ 116 ]. Therefore, the mediating roles of life satisfaction and academic motivation should be given consideration when exploring the mechanisms by which social support influences academic engagement is explored.

This study examined the mechanism of how social support’s influences on university students’ academic engagement, and the chain mediating role of life satisfaction and academic motivation in the mechanism. This study found (1) social support significantly and positively predicts academic engagement; (2) social support influences academic engagement through the mediating effect of life satisfaction; (3) social support influences academic engagement through the mediating effect of academic motivation; (4) life satisfaction and academic motivation play a chain mediating role in the effect of social support on academic engagement.

These findings can help understand the inner mechanism of the relationship between social support and academic engagement, which in turn provides insights for universities and the departments concerned to improve the level of university students’ academic engagement. Our society should better understand the social support that students receive from teachers, peers, and guardians and build supportive learning environments as equal as possible for them [ 117 ]. Universities should try their best to create a comfortable accommodation environment, a safe food environment and a free and harmonious interpersonal atmosphere for students, and offer colorful extracurricular activities, so as to improve the life satisfaction of university students. At the same time, universities can help students understand the significance of learning and clarify their career development path through relevant courses and lectures, thus stimulating their academic motivation, reducing learning burnout, and enabling them to be more actively engaged in learning. On a daily basis, teachers should make efforts to improve the quantity and quality of social support provided and to facilitate interactions between students [ 118 ]. Students get more appreciation and praise from teachers and peers, which enhances their self-efficacy, stimulates their interest in learning, and makes them better complete learning tasks. Parents should also give more positive guidance to university students, provide them with appropriate financial and mental support for their studies, and help them overcome all kinds of obstacles encountered in the process of learning. Through the concerted efforts of all parties, we can jointly improve the level of university students’ academic engagement.

Contributions, limitations and prospects

Contributions.

There have been many researches on university students’ social support, which is of great significance to their healthy development. Social support is a buffer against stress [ 119 ], which can improve an individual’s psychological state [ 120 ], and increase an individual’s perception of his or her own value [ 121 ]. Social support also provides university students with a sense of security and competence [ 122 ]. Among them, there are also many studies concerned with the influence of social support on university students’ academic engagement [ 36 , 37 ]. However, there are relatively fewer separate investigations into the mediating roles of life satisfaction [ 110 ] and academic motivation [ 111 ] in this process, and extremely few ones that have simultaneously explored the chain mediating role of the two in the influence of social support on academic engagement. This study systematically and comprehensively explores the influence mechanism of social support on university students’ academic engagement. This study to a certain extent enriches the theoretical research on the influence mechanism of university students’ social support on university students’ academic engagement, which is instructive for the subsequent research. At the same time, the conclusions drawn from this study also provide references for universities and the departments concerned to improve the degree of university students’ academic engagement at the practical level, which in turn can promote the quality of talent cultivation in undergraduate universities.

Limitations and prospects

This study investigated the influence mechanism the social support on university students’ academic engagement by surveying 2,106 university students from different undergraduate universities across Chins through the principle of convenience sampling. The study has certain contributions at both the theoretical and practical levels. Of course, this study still has some limitations. First, the sample was limited by the cross-sectional data sources and remains deficient in the confirmatory nature of the causal inferences of the variables. Due to the constraints on the authors’ time and energy, only one collection of data was conducted in this study. In subsequent research, longitudinal studies can be conducted in context, with multiple collections of data tracking the development of the mechanism by which social support influences universities students’ academic engagement over time. Second, there may be selection bias and potential threats in the case of convenience sampling. Future research could adopt the method of multiple data collections. Finally, due to time constraint, the questionnaire was administered without intervention. The follow-up study could add appropriate interventions into the questionnaire process.

Data availability

The raw data supporting the conclusions of this article will be available from Chunmei Chen ([email protected]) on resonable requests.

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Acknowledgements

The authors would like to thank the participants for their involvement in this study. The authors would also like to take this opportunity to express their sincere acknowledgment to Jiancai Xu, Mingqun Que and Fangxiao Hao for their help.

2022 Guangdong Province Education Science Planning Project (Project No. 2022GXJK105); CSTVE and New - Era TVET Institute of China 2022 Annual Key Project (Project No. SZ22B05).

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CC designed the study and wrote the manuscript. FB and CC analyzed the data. FB, CC and YZ collected the data. CC, FB and YZ modified the manuscript. FB supervised the development of research and provided funding support. All authors contributed to the article and approved the submitted version.

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Chen, C., Bian, F. & Zhu, Y. The relationship between social support and academic engagement among university students: the chain mediating effects of life satisfaction and academic motivation. BMC Public Health 23 , 2368 (2023). https://doi.org/10.1186/s12889-023-17301-3

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• Stress management

Positive thinking: Stop negative self-talk to reduce stress

Positive thinking helps with stress management and can even improve your health. Practice overcoming negative self-talk with examples provided.

Is your glass half-empty or half-full? How you answer this age-old question about positive thinking may reflect your outlook on life, your attitude toward yourself, and whether you're optimistic or pessimistic — and it may even affect your health.

Indeed, some studies show that personality traits such as optimism and pessimism can affect many areas of your health and well-being. The positive thinking that usually comes with optimism is a key part of effective stress management. And effective stress management is associated with many health benefits. If you tend to be pessimistic, don't despair — you can learn positive thinking skills.

Understanding positive thinking and self-talk

Positive thinking doesn't mean that you ignore life's less pleasant situations. Positive thinking just means that you approach unpleasantness in a more positive and productive way. You think the best is going to happen, not the worst.

Positive thinking often starts with self-talk. Self-talk is the endless stream of unspoken thoughts that run through your head. These automatic thoughts can be positive or negative. Some of your self-talk comes from logic and reason. Other self-talk may arise from misconceptions that you create because of lack of information or expectations due to preconceived ideas of what may happen.

If the thoughts that run through your head are mostly negative, your outlook on life is more likely pessimistic. If your thoughts are mostly positive, you're likely an optimist — someone who practices positive thinking.

The health benefits of positive thinking

Researchers continue to explore the effects of positive thinking and optimism on health. Health benefits that positive thinking may provide include:

• Increased life span
• Lower rates of depression
• Lower levels of distress and pain
• Greater resistance to illnesses
• Better psychological and physical well-being
• Better cardiovascular health and reduced risk of death from cardiovascular disease and stroke
• Reduced risk of death from cancer
• Reduced risk of death from respiratory conditions
• Reduced risk of death from infections
• Better coping skills during hardships and times of stress

It's unclear why people who engage in positive thinking experience these health benefits. One theory is that having a positive outlook enables you to cope better with stressful situations, which reduces the harmful health effects of stress on your body.

It's also thought that positive and optimistic people tend to live healthier lifestyles — they get more physical activity, follow a healthier diet, and don't smoke or drink alcohol in excess.

Identifying negative thinking

Not sure if your self-talk is positive or negative? Some common forms of negative self-talk include:

• Filtering. You magnify the negative aspects of a situation and filter out all the positive ones. For example, you had a great day at work. You completed your tasks ahead of time and were complimented for doing a speedy and thorough job. That evening, you focus only on your plan to do even more tasks and forget about the compliments you received.
• Personalizing. When something bad occurs, you automatically blame yourself. For example, you hear that an evening out with friends is canceled, and you assume that the change in plans is because no one wanted to be around you.
• Catastrophizing. You automatically anticipate the worst without facts that the worse will happen. The drive-through coffee shop gets your order wrong, and then you think that the rest of your day will be a disaster.
• Blaming. You try to say someone else is responsible for what happened to you instead of yourself. You avoid being responsible for your thoughts and feelings.
• Saying you "should" do something. You think of all the things you think you should do and blame yourself for not doing them.
• Magnifying. You make a big deal out of minor problems.
• Perfectionism. Keeping impossible standards and trying to be more perfect sets yourself up for failure.
• Polarizing. You see things only as either good or bad. There is no middle ground.

Focusing on positive thinking

You can learn to turn negative thinking into positive thinking. The process is simple, but it does take time and practice — you're creating a new habit, after all. Following are some ways to think and behave in a more positive and optimistic way:

• Identify areas to change. If you want to become more optimistic and engage in more positive thinking, first identify areas of your life that you usually think negatively about, whether it's work, your daily commute, life changes or a relationship. You can start small by focusing on one area to approach in a more positive way. Think of a positive thought to manage your stress instead of a negative one.
• Check yourself. Periodically during the day, stop and evaluate what you're thinking. If you find that your thoughts are mainly negative, try to find a way to put a positive spin on them.
• Be open to humor. Give yourself permission to smile or laugh, especially during difficult times. Seek humor in everyday happenings. When you can laugh at life, you feel less stressed.
• Follow a healthy lifestyle. Aim to exercise for about 30 minutes on most days of the week. You can also break it up into 5- or 10-minute chunks of time during the day. Exercise can positively affect mood and reduce stress. Follow a healthy diet to fuel your mind and body. Get enough sleep. And learn techniques to manage stress.
• Surround yourself with positive people. Make sure those in your life are positive, supportive people you can depend on to give helpful advice and feedback. Negative people may increase your stress level and make you doubt your ability to manage stress in healthy ways.
• Practice positive self-talk. Start by following one simple rule: Don't say anything to yourself that you wouldn't say to anyone else. Be gentle and encouraging with yourself. If a negative thought enters your mind, evaluate it rationally and respond with affirmations of what is good about you. Think about things you're thankful for in your life.

Here are some examples of negative self-talk and how you can apply a positive thinking twist to them:

Practicing positive thinking every day

If you tend to have a negative outlook, don't expect to become an optimist overnight. But with practice, eventually your self-talk will contain less self-criticism and more self-acceptance. You may also become less critical of the world around you.

When your state of mind is generally optimistic, you're better able to handle everyday stress in a more constructive way. That ability may contribute to the widely observed health benefits of positive thinking.

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• Forte AJ, et al. The impact of optimism on cancer-related and postsurgical cancer pain: A systematic review. Journal of Pain and Symptom Management. 2021; doi:10.1016/j.jpainsymman.2021.09.008.
• Rosenfeld AJ. The neuroscience of happiness and well-being. Child and Adolescent Psychiatric Clinics of North America. 2019;28:137.
• Kim ES, et al. Optimism and cause-specific mortality: A prospective cohort study. American Journal of Epidemiology. 2016; doi:10.1093/aje/kww182.
• Amonoo HL, et al. Is optimism a protective factor for cardiovascular disease? Current Cardiology Reports. 2021; doi:10.1007/s11886-021-01590-4.
• Physical Activity Guidelines for Americans. 2nd ed. U.S. Department of Health and Human Services. https://health.gov/paguidelines/second-edition. Accessed Oct. 20, 2021.
• Seaward BL. Essentials of Managing Stress. 4th ed. Burlington, Mass.: Jones & Bartlett Learning; 2021.
• Seaward BL. Cognitive restructuring: Reframing. Managing Stress: Principles and Strategies for Health and Well-Being. 8th ed. Burlington, Mass.: Jones & Bartlett Learning; 2018.
• Olpin M, et al. Stress Management for Life. 5th ed. Cengage Learning; 2020.
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What is cloud computing?

With cloud computing, organizations essentially buy a range of services offered by cloud service providers (CSPs). The CSP’s servers host all the client’s applications. Organizations can enhance their computing power more quickly and cheaply via the cloud than by purchasing, installing, and maintaining their own servers.

The cloud-computing model is helping organizations to scale new digital solutions with greater speed and agility—and to create value more quickly. Developers use cloud services to build and run custom applications and to maintain infrastructure and networks for companies of virtually all sizes—especially large global ones. CSPs offer services, such as analytics, to handle and manipulate vast amounts of data. Time to market accelerates, speeding innovation to deliver better products and services across the world.

What are examples of cloud computing’s uses?

Get to know and directly engage with senior mckinsey experts on cloud computing.

Brant Carson is a senior partner in McKinsey’s Vancouver office; Chandra Gnanasambandam and Anand Swaminathan are senior partners in the Bay Area office; William Forrest is a senior partner in the Chicago office; Leandro Santos is a senior partner in the Atlanta office; Kate Smaje is a senior partner in the London office.

Cloud computing came on the scene well before the global pandemic hit, in 2020, but the ensuing digital dash  helped demonstrate its power and utility. Here are some examples of how businesses and other organizations employ the cloud:

• A fast-casual restaurant chain’s online orders multiplied exponentially during the 2020 pandemic lockdowns, climbing to 400,000 a day, from 50,000. One pleasant surprise? The company’s online-ordering system could handle the volume—because it had already migrated to the cloud . Thanks to this success, the organization’s leadership decided to accelerate its five-year migration plan to less than one year.
• A biotech company harnessed cloud computing to deliver the first clinical batch of a COVID-19 vaccine candidate for Phase I trials in just 42 days—thanks in part to breakthrough innovations using scalable cloud data storage and computing  to facilitate processes ensuring the drug’s safety and efficacy.
• Banks use the cloud for several aspects of customer-service management. They automate transaction calls using voice recognition algorithms and cognitive agents (AI-based online self-service assistants directing customers to helpful information or to a human representative when necessary). In fraud and debt analytics, cloud solutions enhance the predictive power of traditional early-warning systems. To reduce churn, they encourage customer loyalty through holistic retention programs managed entirely in the cloud.
• Automakers are also along for the cloud ride . One company uses a common cloud platform that serves 124 plants, 500 warehouses, and 1,500 suppliers to consolidate real-time data from machines and systems and to track logistics and offer insights on shop floor processes. Use of the cloud could shave 30 percent off factory costs by 2025—and spark innovation at the same time.

That’s not to mention experiences we all take for granted: using apps on a smartphone, streaming shows and movies, participating in videoconferences. All of these things can happen in the cloud.

Learn more about our Cloud by McKinsey , Digital McKinsey , and Technology, Media, & Telecommunications  practices.

How has cloud computing evolved?

Going back a few years, legacy infrastructure dominated IT-hosting budgets. Enterprises planned to move a mere 45 percent of their IT-hosting expenditures to the cloud by 2021. Enter COVID-19, and 65 percent of the decision makers surveyed by McKinsey increased their cloud budgets . An additional 55 percent ended up moving more workloads than initially planned. Having witnessed the cloud’s benefits firsthand, 40 percent of companies expect to pick up the pace of implementation.

The cloud revolution has actually been going on for years—more than 20, if you think the takeoff point was the founding of Salesforce, widely seen as the first software as a service (SaaS) company. Today, the next generation of cloud, including capabilities such as serverless computing, makes it easier for software developers to tweak software functions independently, accelerating the pace of release, and to do so more efficiently. Businesses can therefore serve customers and launch products in a more agile fashion. And the cloud continues to evolve.

Introducing McKinsey Explainers : Direct answers to complex questions

Cost savings are commonly seen as the primary reason for moving to the cloud but managing those costs requires a different and more dynamic approach focused on OpEx rather than CapEx. Financial-operations (or FinOps) capabilities  can indeed enable the continuous management and optimization of cloud costs . But CSPs have developed their offerings so that the cloud’s greatest value opportunity is primarily through business innovation and optimization. In 2020, the top-three CSPs reached $100 billion  in combined revenues—a minor share of the global $2.4 trillion market for enterprise IT services—leaving huge value to be captured. To go beyond merely realizing cost savings, companies must activate three symbiotic rings of cloud value creation : strategy and management, business domain adoption, and foundational capabilities.

What’s the main reason to move to the cloud?

The pandemic demonstrated that the digital transformation can no longer be delayed—and can happen much more quickly than previously imagined. Nothing is more critical to a corporate digital transformation than becoming a cloud-first business. The benefits are faster time to market, simplified innovation and scalability, and reduced risk when effectively managed. The cloud lets companies provide customers with novel digital experiences—in days, not months—and delivers analytics absent on legacy platforms. But to transition to a cloud-first operating model, organizations must make a collective effort that starts at the top. Here are three actions CEOs can take to increase the value their companies get from cloud computing :

• Establish a sustainable funding model.
• Develop a new business technology operating model.
• Set up policies to attract and retain the right engineering talent.

How much value will the cloud create?

Fortune 500 companies adopting the cloud could realize more than $1 trillion in value  by 2030, and not from IT cost reductions alone, according to McKinsey’s analysis of 700 use cases.

For example, the cloud speeds up design, build, and ramp-up, shortening time to market when companies have strong DevOps (the combination of development and operations) processes in place; groups of software developers customize and deploy software for operations that support the business. The cloud’s global infrastructure lets companies scale products almost instantly to reach new customers, geographies, and channels. Finally, digital-first companies use the cloud to adopt emerging technologies and innovate aggressively, using digital capabilities as a competitive differentiator to launch and build businesses .

If companies pursue the cloud’s vast potential in the right ways, they will realize huge value. Companies across diverse industries have implemented the public cloud and seen promising results. The successful ones defined a value-oriented strategy across IT and the business, acquired hands-on experience operating in the cloud, adopted a technology-first approach, and developed a cloud-literate workforce.

Learn more about our Cloud by McKinsey and Digital McKinsey practices.

What is the cloud cost/procurement model?

Some cloud services, such as server space, are leased. Leasing requires much less capital up front than buying, offers greater flexibility to switch and expand the use of services, cuts the basic cost of buying hardware and software upfront, and reduces the difficulties of upkeep and ownership. Organizations pay only for the infrastructure and computing services that meet their evolving needs. But an outsourcing model  is more apt than other analogies: the computing business issues of cloud customers are addressed by third-party providers that deliver innovative computing services on demand to a wide variety of customers, adapt those services to fit specific needs, and work to constantly improve the offering.

What are cloud risks?

The cloud offers huge cost savings and potential for innovation. However, when companies migrate to the cloud, the simple lift-and-shift approach doesn’t reduce costs, so companies must remediate their existing applications to take advantage of cloud services.

For instance, a major financial-services organization  wanted to move more than 50 percent of its applications to the public cloud within five years. Its goals were to improve resiliency, time to market, and productivity. But not all its business units needed to transition at the same pace. The IT leadership therefore defined varying adoption archetypes to meet each unit’s technical, risk, and operating-model needs.

Legacy cybersecurity architectures and operating models can also pose problems when companies shift to the cloud. The resulting problems, however, involve misconfigurations rather than inherent cloud security vulnerabilities. One powerful solution? Securing cloud workloads for speed and agility : automated security architectures and processes enable workloads to be processed at a much faster tempo.

What kind of cloud talent is needed?

The talent demands of the cloud differ from those of legacy IT. While cloud computing can improve the productivity of your technology, it requires specialized and sometimes hard-to-find talent—including full-stack developers, data engineers, cloud-security engineers, identity- and access-management specialists, and cloud engineers. The cloud talent model  should thus be revisited as you move forward.

Six practical actions can help your organization build the cloud talent you need :

• Find engineering talent with broad experience and skills.
• Balance talent maturity levels and the composition of teams.
• Build an extensive and mandatory upskilling program focused on need.
• Build an engineering culture that optimizes the developer experience.
• Consider using partners to accelerate development and assign your best cloud leaders as owners.
• Retain top talent by focusing on what motivates them.

How do different industries use the cloud?

Different industries are expected to see dramatically different benefits from the cloud. High-tech, retail, and healthcare organizations occupy the top end of the value capture continuum. Electronics and semiconductors, consumer-packaged-goods, and media companies make up the middle. Materials, chemicals, and infrastructure organizations cluster at the lower end.

Nevertheless, myriad use cases provide opportunities to unlock value across industries , as the following examples show:

• a retailer enhancing omnichannel  fulfillment, using AI to optimize inventory across channels and to provide a seamless customer experience
• a healthcare organization implementing remote heath monitoring to conduct virtual trials and improve adherence
• a high-tech company using chatbots to provide premier-level support combining phone, email, and chat
• an oil and gas company employing automated forecasting to automate supply-and-demand modeling and reduce the need for manual analysis
• a financial-services organization implementing customer call optimization using real-time voice recognition algorithms to direct customers in distress to experienced representatives for retention offers
• a financial-services provider moving applications in customer-facing business domains to the public cloud to penetrate promising markets more quickly and at minimal cost
• a health insurance carrier accelerating the capture of billions of dollars in new revenues by moving systems to the cloud to interact with providers through easier onboarding

The cloud is evolving  to meet the industry-specific needs of companies. From 2021 to 2024, public-cloud spending on vertical applications (such as warehouse management in retailing and enterprise risk management in banking) is expected to grow by more than 40 percent annually. Spending on horizontal workloads (such as customer relationship management) is expected to grow by 25 percent. Healthcare and manufacturing organizations, for instance, plan to spend around twice as much on vertical applications as on horizontal ones.

Learn more about our Cloud by McKinsey , Digital McKinsey , Financial Services , Healthcare Systems & Services , Retail , and Technology, Media, & Telecommunications  practices.

What are the biggest cloud myths?

Views on cloud computing can be clouded by misconceptions. Here are seven common myths about the cloud —all of which can be debunked:

• The cloud’s value lies primarily in reducing costs.
• Cloud computing costs more than in-house computing.
• On-premises data centers are more secure than the cloud.
• Applications run more slowly in the cloud.
• The cloud eliminates the need for infrastructure.
• The best way to move to the cloud is to focus on applications or data centers.
• You must lift and shift applications as-is or totally refactor them.

How large must my organization be to benefit from the cloud?

Here’s one more huge misconception: the cloud is just for big multinational companies. In fact, cloud can help make small local companies become multinational. A company’s benefits from implementing the cloud are not constrained by its size. In fact, the cloud shifts barrier to entry skill rather than scale, making it possible for a company of any size to compete if it has people with the right skills. With cloud, highly skilled small companies can take on established competitors. To realize the cloud’s immense potential value fully, organizations must take a thoughtful approach, with IT and the businesses working together.

For more in-depth exploration of these topics, see McKinsey’s Cloud Insights collection. Learn more about Cloud by McKinsey —and check out cloud-related job opportunities if you’re interested in working at McKinsey.

Articles referenced include:

• “ Six practical actions for building the cloud talent you need ,” January 19, 2022, Brant Carson , Dorian Gärtner , Keerthi Iyengar, Anand Swaminathan , and Wayne Vest
• “ Cloud-migration opportunity: Business value grows, but missteps abound ,” October 12, 2021, Tara Balakrishnan, Chandra Gnanasambandam , Leandro Santos , and Bhargs Srivathsan
• “ Cloud’s trillion-dollar prize is up for grabs ,” February 26, 2021, Will Forrest , Mark Gu, James Kaplan , Michael Liebow, Raghav Sharma, Kate Smaje , and Steve Van Kuiken
• “ Unlocking value: Four lessons in cloud sourcing and consumption ,” November 2, 2020, Abhi Bhatnagar , Will Forrest , Naufal Khan , and Abdallah Salami
• “ Three actions CEOs can take to get value from cloud computing ,” July 21, 2020, Chhavi Arora , Tanguy Catlin , Will Forrest , James Kaplan , and Lars Vinter

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