research articles of time management

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Time Management Is About More Than Life Hacks

• Erich C. Dierdorff

Your productivity hinges on these three skills.

There is certainly no shortage of advice — books and blogs, hacks and apps — all created to boost time management with a bevy of ready-to-apply tools. Yet, the frustrating reality for individuals trying to improve their time management is that tools alone won’t work. You have to develop your time management skills in three key areas: awareness, arrangement, and adaptation. The author offers evidence-based tactics to improve in all three areas.

Project creep, slipping deadlines, and a to-do list that seems to get longer each day — these experiences are all too common in both life and work. With the New Year’s resolution season upon us, many people are boldly trying to fulfill goals to “manage time better,” “be more productive,” and “focus on what matters.” Development goals like these are indeed important to career success. Look no further than large-scale surveys that routinely find time management skills among the most desired workforce skills, but at the same time among the rarest skills to find.

• Erich C. Dierdorff is a professor of management and entrepreneurship at the Richard H. Driehaus College of Business at DePaul University and is currently an associate editor at  Personnel Psychology.

Partner Center

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Study Protocol

Effects of time management interventions on mental health and wellbeing factors: A protocol for a systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Applied Psychology, University College Cork, Cork, Ireland

Contributed equally to this work with: Aoife Bourke, Sarah Foley, Zelda Di Blasi

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

• Anna Navin Young, 
• Aoife Bourke, 
• Sarah Foley, 
• Zelda Di Blasi

• Published: March 11, 2024
• https://doi.org/10.1371/journal.pone.0288887
• Peer Review
• Reader Comments

Poor employee mental health and wellbeing are highly prevalent and costly. Time-related factors such as work intensification and perceptions of time poverty or pressure pose risks to employee health and wellbeing. While reviews suggest that there are positive associations between time management behavior and wellbeing, there is limited rigorous and systematic research examining the effectiveness of time management interventions on wellbeing in the workplace. A thorough review is needed to synthesize time management interventions and their effectiveness to promote employee mental health and wellbeing.

A systematic search will be conducted using the following databases: PsychINFO via OVID (1806-Present), Web of Science, Scopus via Elsevier (1976-Present), Academic Search Complete (EBSCO), Cochrane Library via Wiley (1992-Present), and MEDLINE via OVID (1946-Present). The review will include experimental and quasi-experimental studies that evaluate the effects of time management interventions on wellbeing outcomes on healthy adults in a workplace context. Only studies in English will be included. Two authors will independently perform the literature search, record screening, data extraction, and quality assessment of each study included in the systematic review and meta-analysis. Data will be critically appraised using the Cochrane risk-of-bias tools. Depending on the data, a meta-analysis or a narrative synthesis will be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in the development of this protocol. The protocol has been registered in PROSPERO (CRD4202125715).

This review will provide systematic evidence on the effects of time management interventions on wellbeing outcomes in the workplace. It will contribute to our understanding of how time management approaches may help to address growing concerns for employee mental health and wellbeing.

Citation: Young AN, Bourke A, Foley S, Di Blasi Z (2024) Effects of time management interventions on mental health and wellbeing factors: A protocol for a systematic review. PLoS ONE 19(3): e0288887. https://doi.org/10.1371/journal.pone.0288887

Editor: Collins Atta Poku, Kwame Nkrumah University of Science and Technology, GHANA

Received: October 13, 2023; Accepted: February 23, 2024; Published: March 11, 2024

Copyright: © 2024 Young et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Each year, the consequences of poor employee mental health and wellbeing cost the global economy an estimated $1 trillion [ 1 ]. In 2022, the U.S. Surgeon General raised the issue of workplace wellbeing to national prominence [ 2 ]. Time is a critical factor to consider in understanding the current mental health and wellbeing challenges observed in the workplace. In the European Union’s 2022 Occupational Safety and Health survey, nearly half of respondents reported that severe time pressure and work overload contributed to increased work stress [ 3 ]. Research over the last few decades indicates that work intensification, referring to both the increased pace and increased amount of work, impairs employee wellbeing, health, and motivation [ 4 – 6 ].

Additionally, research on time poverty, or the perception of not having enough time, finds this temporal perception is detrimental to self-assessed mental health and health, emotional wellbeing, work-family conflict, physical activity, life satisfaction, perceived work performance, concentration at work, and turnover intentions [ 7 – 13 ]. Time poverty can also increase stress and stress-related symptoms including headaches, sleep disturbances, and musculoskeletal pains [ 9 , 11 , 14 ].

Time management interventions

Time management interventions are the most common time-focused interventions implemented in the workplace and may support employee mental health and wellbeing by addressing experiences and impacts of time poverty and work intensification. Definitions of time management vary across the literature, often including components related to goal and priority setting, planning, structuring, organizing, and evaluation [ 15 – 19 ]. Time management interventions consequently vary depending on which definition of time management has been adopted [ 15 , 16 ].

There is currently some evidence to suggest that time management interventions can improve wellbeing, however there are limitations with this research [ 15 – 17 ]. For example, a non-systematic review identified 35 time management studies using self-report questionnaires, diaries, and experiments published between 1954 and 2005 [ 15 ]. The authors reported that time management was positively related to perceived control of time, job satisfaction, and health, and negatively related to factors such as emotional exhaustion, role overload, and work-family conflict. This review identified several methodological limitations within the time management literature. First, the majority of study participants were university students, limiting the results’ relevance in a workplace context [ 15 ]. Second, a variety of time management definitions were used across studies, with some studies not providing any definition. Further, ten different self-report questionnaires were used to measure time management behaviors. The lack of transparent and consistent operationalization indicates strong heterogeneity, making it difficult to know whether ‘time management’ is being evaluated consistently across the literature [ 15 ]. Third, only eight of the 35 studies evaluated time management interventions, indicating a limited body of experimental research [ 15 ]. However, these experiments generally found that time management training increased self-reported time management skills and academic and job performance.

A recent comprehensive meta-analysis of 158 studies (n = 53,957) found time management (assessed based on studies using a quantitative measure of time management) to increase wellbeing, particularly life satisfaction, more than academic and job performance [ 16 ]. This meta-analysis further highlighted the limitations identified in the previous non-systematic literature review. First, a majority of studies used cross-sectional designs, thus limiting the relational conclusions that can be drawn between time management and wellbeing outcomes. Second, a majority of studies involved university students and time management was significantly less impactful for worker populations compared to student samples [ 16 ]. Third, there are limited experimental studies done to evaluate the effectiveness of time management interventions. And, finally, there is a lack of clarity, consistency, and generalizability across what is being conducted as a time management intervention [ 16 ].

The meta-analysis addressed the question of whether time management works, revealing that time management may primarily enhance wellbeing opposed to performance [ 16 ]. However, the question remains whether time management interventions (and which interventions) work to improve wellbeing. A review and synthesis of the time management intervention literature is needed to understand the current state of the field and further provide foundations for future research, development, and application of consistent, valid, and generalizable time management interventions. This is the focus and contribution of this systematic review.

Aim of the review

The aim of this proposed review is to synthesize experimental and quasi-experimental studies that evaluated the effectiveness of a time management intervention on wellbeing outcomes among healthy adults in a workplace context. As the need for effective interventions grows alongside rising concern for workplace mental health and wellbeing, this review will contribute to our understanding of whether time management interventions may be integrated into impactful solutions. The proposed review aims to answer the following questions:

• Do time management interventions improve mental health and wellbeing outcomes among healthy working adults?
• What are the characteristics of effective time management interventions?
• The primary objective is to critically synthesize the effectiveness of time management interventions on wellbeing among healthy adults in the workplace.
• The secondary objective of the review is to investigate the types and characteristics of time management interventions that have been conducted in experimental settings.
• The final objective is to evaluate the quality of the evidence.

Methods and analysis

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines were adhered to in the development of this protocol [ 20 , 21 ]. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021257157). The systematic review will be carried out following the PRISMA-P checklist ( S1 Table ) [ 22 ] and the Cochrane Handbook for Systematic Reviews of Interventions guidelines [ 23 ].

Types of studies

The acronym PICO (Population, Intervention, Comparison, Outcomes) guided the inclusion and exclusion criteria for the systematic review ( Table 1 ) [ 24 ]. This review will include randomised controlled trials and quasi-experiments (controlled, non-randomised, and pre/post-intervention studies). Non-experimental studies, including literature reviews, case reports, qualitative, correlational, and cross-sectional studies, will be excluded from the review. Included articles will be written in the English language.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0288887.t001

Types of participants

The review will include studies that involve healthy (non-clinical) adult participants in an organisational or educational context.

Patient and public involvement.

As this research is based on previously published data, participants were not directly involved or recruited for this study. Participant consent for publication of this research is not required.

Types of interventions

Studies will be eligible for inclusion if they evaluated the delivery of a time management intervention aimed at enhancing at least one wellbeing-related outcome. The review will include studies that involved one intervention (single component) or two or more interventions (multicomponent). The intervention must be explicitly referred to as a time management intervention, though the review will not limit study inclusion to a specific definition of time management.

Types of outcome measures

The primary outcomes will be self-reported wellbeing-related outcomes, including life satisfaction, stress, anxiety, exhaustion, burnout, and depression. Studies will only be included in the review if they reported at least one wellbeing-related psychological outcome measure as assessed pre-intervention and post-intervention.

Search method

The search strategy will be carried out through six specialized and general electronic databases from inception for this review: Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, PsycInfo, Web of Science, Scopus, Academic Search Complete, and Cochrane Library Central. A range of words related to ‘time management’ and ‘wellbeing’ will be searched ( Table 2 ). The search will aim to identify published experimental and quasi-experimental studies that evaluated a time management intervention in relation to at least one wellbeing-related outcome. The detailed search strategy was developed by the research team in consultation with a Faculty Librarian. The search will be limited to studies published in the English language. The decision to include only studies published in English results from limited resources and the language constraints of our review team. As the aim of this systematic review is to evaluate rigorous experimental studies unpublished grey literature will not be included. The review will include studies published up until 1 July 2023.

https://doi.org/10.1371/journal.pone.0288887.t002

Manual searches of references will be conducted in relevant papers, including the reference lists of any studies assessed for inclusion in the review, in attempts to identify any additional eligible studies. PROSPERO and the Cochrane Library will also be searched for any systematic reviews planned or completed that relate to this review. The reference lists of a recent meta-analysis [ 16 ] and a previous time management literature review [ 15 ] will also be manually searched.

Study selection

The first and second authors will independently screen papers, first by title and abstract and then by full text. Data will be extracted using a data extraction form and recorded in a shared spreadsheet. Both the extraction form and spreadsheet have been designed for the purposes of this review. Any conflicts which arise in the screening and extraction stages will be resolved through discussion or further involvement of a third researcher (ZDB). A flow diagram will present a record of study screening following the PRISMA-P guidelines. Excluded studies, and their reason for exclusion, will be documented within the flow diagram.

Data extraction process

The data extraction form has been designed by ANY to record data from studies during the full-text review stage.

The following information will be included in data extraction:

• Country of origin, author(s), and year of publication
• Study method: design (e.g., experimental and quasi-experimental)
• Sample: number of participants, age, gender, and other demographic characteristics
• Context: Workplace, educational environment
• Type of intervention: single or multi-component
• Delivery form
• Session duration (number of sessions and duration of each session)
• Control group(s)
• Number of participants at follow up and overall retention rates.
• Mean/SD, p-value, and effect size
• Outcome measures used

Missing data.

The authors will attempt to contact study authors in the case of missing or incomplete information. The available data will be analysed as reported should study authors be unavailable.

Risk of bias assessment

In accordance with the Cochrane Handbook, the Cochrane risk-of-bias tool will be used to assess the methodological quality of the included studies. Randomised controlled trials will be assessed using the Risk of Bias II tool (ROB II), while quasi-experimental and nonrandomised trials will be assessed using the ROBINS I tool. Assessment will include methods of randomisation and intervention allocation. Risk of bias will be independently conducted by the first and second author and inter-rater reliability will be calculated using the kappa coefficient. In the case of disagreements, a discussion with a third reviewer (ZDB) will be used to reach a consensus. Study authors will be contacted in the case of insufficient information. The risk of bias assessments will result in a classification of low risk, some concerns, or high risk.

Data synthesis

Adhering to Cochrane guidelines [ 25 ], ANY will lead the authors’ conduction of a narrative synthesis. The authors will address any conflicting interpretations that arise during the narrative synthesis through discussion until a consensus is reached. The narrative synthesis will be structured around the included studies, the types of time management interventions and topics used, and the intervention outcomes. The characteristics and components of included interventions will also be analyzed and reported. Wellbeing outcomes will be reported along with the measures used in each study. The authors will calculate the percentage of studies that included each intervention and outcome element. Overall, the narrative synthesis will integrate these findings to provide a comprehensive overview of the current evidence of the effectiveness of time management interventions on workplace wellbeing. This will involve a summary of what the included studies reveal regarding effective time management intervention structures, topics, modes of delivery, and outcomes.

A limited scope for meta-analysis is anticipated due to the range of outcomes measured, measurement types, and the small number of existing trials. Where studies have used the same intervention, comparator, and outcomes measures, a random-effects meta-analysis will be conducted with the pooled results.

Depending on the data gathered, subgroup analyses may be conducted to examine the effects of the type of intervention (single component or multicomponent) and duration of intervention.

The aim of this study is to provide a comprehensive overview of the factors influencing effectiveness of time management interventions aimed at enhancing mental health and wellbeing, based on the evidence of experimental and quasi-experimental studies.

Considering the rise in mental health and wellbeing issues in the workplace and reported time poverty, and despite the popularity of time management tools, little is known about the effectiveness of time management interventions, and what elements of time management are particularly useful.

Effective time management interventions have the potential to promote mental health and wellbeing. However, the history of time management highlights limited evidence-based, empirically evaluated strategies for enhancing time management in work and educational settings [ 15 – 17 ]. The findings of this review are expected to provide an overview of time management interventions that have been conducted using a robust trial design, and their corresponding wellbeing outcomes.

The review will contribute to evaluating these time management interventions from a health and wellbeing perspective, and provide guidance for HR professionals, leaders, and health professionals regarding the current landscape of evidence-based time management interventions and how they may be adopted to support employee wellbeing. Findings from the systematic review will be synthesized and disseminated for relevant stakeholders to promote evidence-based wellbeing initiatives in the workplace.

Supporting information

S1 table. prisma-p checklist..

https://doi.org/10.1371/journal.pone.0288887.s001

• View Article
• Google Scholar
• PubMed/NCBI
• 11. Zuzanek J.Work, leisure, time-pressure and stress. In: Haworth JT, Veal AJ, editors. Work and Leisure. London: Routledge; 2004. p. 123–144.
• 19. Hellsten LM. What do we know about time management? A review of the literature and a psychometric critique of instruments assessing time management. In: Stoilov T, editor. Time Management. London: IntechOpen; 2012. P. 3–28. https://doi.org/10.5772/37248
• 24. McKenzie JE, Brennan SE, Ryan RE, Thomson HJ, Johnston RV, Thomas J. Chapter 3: Defining the criteria for including studies and how they will be grouped for the synthesis. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019). Cochrane; 2019. Available from: https://www.training.cochrane.org/handbook .
• 25. Ryan R. Data synthesis and analysis. Cochrane Consumers and Communication Review Group. 2013. Available from: https://cccrg.cochrane.org/sites/cccrg.cochrane.org/files/uploads/AnalysisRestyled.pdf .

To read this content please select one of the options below:

Please note you do not have access to teaching notes, a review of the time management literature.

Personnel Review

ISSN : 0048-3486

Article publication date: 13 February 2007

The purpose of this article is to provide an overview for those interested in the current state‐of‐the‐art in time management research.

Design/methodology/approach

This review includes 32 empirical studies on time management conducted between 1982 and 2004.

The review demonstrates that time management behaviours relate positively to perceived control of time, job satisfaction, and health, and negatively to stress. The relationship with work and academic performance is not clear. Time management training seems to enhance time management skills, but this does not automatically transfer to better performance.

Research limitations/implications

The reviewed research displays several limitations. First, time management has been defined and operationalised in a variety of ways. Some instruments were not reliable or valid, which could account for unstable findings. Second, many of the studies were based on cross‐sectional surveys and used self‐reports only. Third, very little attention was given to job and organizational factors. There is a need for more rigorous research into the mechanisms of time management and the factors that contribute to its effectiveness. The ways in which stable time management behaviours can be established also deserves further investigation.

Practical implications

This review makes clear which effects may be expected of time management, which aspects may be most useful for which individuals, and which work characteristics would enhance or hinder positive effects. Its outcomes may help to develop more effective time management practices.

Originality/value

This review is the first to offer an overview of empirical research on time management. Both practice and scientific research may benefit from the description of previous attempts to measure and test the popular notions of time management.

• Time measurement
• Job satisfaction
• Performance management

Claessens, B.J.C. , van Eerde, W. , Rutte, C.G. and Roe, R.A. (2007), "A review of the time management literature", Personnel Review , Vol. 36 No. 2, pp. 255-276. https://doi.org/10.1108/00483480710726136

Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

College Students’ Time Management: a Self-Regulated Learning Perspective

• Review Article
• Published: 27 October 2020
• Volume 33 , pages 1319–1351, ( 2021 )

Cite this article

• Christopher A. Wolters   ORCID: orcid.org/0000-0002-8406-038X 1 &
• Anna C. Brady 1  

28k Accesses

69 Citations

10 Altmetric

Explore all metrics

Despite its recognized importance for academic success, much of the research investigating time management has proceeded without regard to a comprehensive theoretical model for understanding its connections to students’ engagement, learning, or achievement. Our central argument is that self-regulated learning provides the rich conceptual framework necessary for understanding college students’ time management and for guiding research examining its relationship to their academic success. We advance this larger purpose through four major sections. We begin by describing work supporting the significance of time management within post-secondary contexts. Next, we review the limited empirical findings linking time management and the motivational and strategic processes viewed as central to self-regulated learning. We then evaluate conceptual ties between time management and processes critical to the forethought, performance, and post-performance phases of self-regulated learning. Finally, we discuss commonalities in the antecedents and contextual determinants of self-regulated learning and time management. Throughout these sections, we identify avenues of research that would contribute to a greater understanding of time management and its fit within the framework of self-regulated learning. Together, these efforts demonstrate that time management is a significant self-regulatory process through which students actively manage when and for how long they engage in the activities deemed necessary for reaching their academic goals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Examining the relations of time management and procrastination within a model of self-regulated learning

Willing, able, and engaged: roles of action-state orientation, intrinsic academic motivation, and time management on academic engagement

Are Mastery-Oriented College Students Better Time Managers?

Explore related subjects.

• Artificial Intelligence

Adams, G. A., & Jex, S. M. (1999). Relationships between time management, control, work family conflict, and strain. Journal of Occupational Health Psychology, 4 (1), 72–77.

Google Scholar  

Aeon, B., & Aguinis, H. (2017). It’s about time: new perspectives and insights on time management. Academy of Management Perspectives, 31 , 309–330.

Ames, C. (1992). Classrooms: goals, structures, and student motivation. Journal of Educational Psychology, 84 , 261–271.

Asikainen, H., & Gijbels, D. (2017). Do students develop towards more deep approaches to learning during studies? A systematic review on the development of students’ deep and surface approaches to learning in higher education. Educational Psychology Review, 29 , 205–234.

Balduf, M. (2009). Underachievement among college students. Journal of Advanced Academics, 20 , 274–294.

Banahan, L., & Mullendore, R. (2014). Navigating the first college year. A guide for parents and families . Columbia: University of South Carolina, National Resource Center for The First-Year Experience and Students in Transition.

Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4 , 359–373.

Bartels, J. M., Magun-Jackson, S., & Ryan, J. J. (2010). Dispositional approach-avoidance achievement motivation and cognitive self-regulated learning: the mediation of achievement goals. Individual Differences Research, 8 , 97–110.

Basila, C. (2014). Good time management and motivation level predict student academic success in college on-line courses. International Journal of Cyber Behavior, Psychology and Learning, 4 , 45–52.

Bembenutty, H. (2009). Academic delay of gratification, self-regulation of learning, gender differences, and expectancy-value. Personality and Individual Differences, 46 , 347–352.

Beuhler, R., Griffin, D., & Peetz, J. (2010). The planning fallacy: cognitive, motivational, and social origins. Advances in Experimental Social Psychology, 43 , 1–62.

Bidjerano, T., & Dai, D. Y. (2007). The relationship between the big-five model of personality and self-regulated learning strategies. Learning and Individual Differences, 17 , 69–81.

Boekaerts, M. (1996). Self-regulated learning at the junction of cognition and motivation. European Psychologist, 1 , 100–112.

Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: a perspective on assessment and intervention. Applied Psychology: An International Review, 54 , 199–231.

Bond, M. J., & Feather, N. T. (1988). Some correlates of structure and purpose in the use of time. Journal of Personality and Social Psychology, 55 , 321–329.

Britton, B. K., & Glynn, S. M. (1989). Mental management and creativity: a cognitive model of time management for intellectual productivity. In J. Glover, R. Ronning, & C. Reynolds (Eds.), Handbook of creativity (pp. 429–440). New York: Plenum.

Britton, B. K., & Tesser, A. (1991). Effects of time-management practices on college grades. Journal of Educational Psychology, 83 , 405–410.

Buehler, R., & Griffin, D. (2015). When plans lead to optimistic forecasts. In M. D. Mumford & M. Frese (Eds.), The psychology in planning in organizations: research and applications (pp. 31–57). New York: Routledge.

Burlison, J. D., Murphy, C. S., & Dwyer, W. O. (2009). Evaluation of the Motivated Strategies for Learning Questionnaire for predicting academic performance in college students of varying scholastic aptitude. College Student Journal, 43 , 1313–1323.

Burnette, J., O’Boyle, E., VanEpps, E., Pollack, J., & Finkel, E. (2013). Mind-sets matter: a meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139 (3), 655–701.

Burt, C. D., Weststrate, A., Brown, C., & Champion, F. (2010). Development of the time management environment (TiME) scale. Journal of Managerial Psychology, 25 , 649–668.

Butler, D., & Winne, P. (1995). Feedback and self-regulated learning: a theoretical synthesis. Review of Educational Research, 65 , 245–281.

Cano, F. (2006). An in-depth analysis of the Learning and Study Strategies Inventory (LASSI). Educational and Psychological Measurement, 66 , 1023–1038.

Capdeferro, N., Romero, M., & Barberà, E. (2014). Polychronicity: review of the literature and a new configuration for the study of this hidden dimension of online learning. Distance Education, 35 , 294–310.

Cassidy, S. (2011). Self-regulated learning in higher education: identifying key component processes. Studies in Higher Education, 36 , 989–1000.

Chang, A., & Nguyen, L. T. (2011). The mediating effects of time structure on the relationships between time management behaviour, job satisfaction, and psychological well-being. Australian Journal of Psychology, 63 , 187–197.

Choi, B. (2016). How people learn in an asynchronous online learning environment: the relationships between graduate students’ learning strategies and learning satisfaction. Canadian Journal of Learning and Technology, 42 , 1–15.

Chuderski, A. (2016). Time pressure prevents relational learning. Learning and Individual Differences, 49 , 361–365.

Claessens, B. J. C., van Eerde, W., Rutte, C. G., & Roe, R. A. (2007). A review of the time management literature. Personnel Review, 36 , 255–276.

Conti, R. (2001). Time flies: investigating the connection between intrinsic motivation and the experience of time. Journal of Personality, 69 (1), 1–26.

Corno, L., Cronbach, L. J., Kupermintz, H., Lohman, D. F., Mandinach, E. B., Porteus, A. W., & Talbert, J. E. (2002). Remaking the concept of aptitude: extending the legacy of Richard E Snow . Mahwah: Lawrence Erlbaum Associates Publishers.

Crede, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: the third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3 (6), 425–453.

Crede, M., & Phillips, L. A. (2011). A meta-analytic review of the Motivated Strategies for Learning Questionnaire. Learning and Individual Differences, 21 , 337–346.

Crede, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: a meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80 , 272–295.

Csikszentmihalyi, M. (1996). Creativity: flow and the psychology of discovery and invention . New York: HarperCollins Publishers.

Dembo, M. H., & Eaton, M. J. (2000). Self-regulation of academic learning in middle-level schools. The Elementary School Journal, 100 , 473–490.

Dent, A., & Koenka, A. (2016). The relation between self-regulated learning and academic achievement across childhood and adolescence: a meta-analysis. Educational Psychology Review, 28 , 425–474.

Díaz-Morales, J. F., Ferrari, J. R., & Cohen, J. R. (2008). Indecision and avoidant procrastination: the role of morningness—eveningness and time perspective in chronic delay lifestyles. The Journal of General Psychology, 135 (3), 228–240.

Donker, A., de Boer, H., Kostons, D., Dignath van Ewijk, C., & van der Werf, M. (2014). Effectiveness of learning strategy instruction on academic performance: a meta-analysis. Educational Psychology Review, 11 , 1–26.

Douglas, H. E., Bore, M., & Munro, D. (2016). Coping with university education: the relationships of time management behaviour and work engagement with the five factor model aspects. Learning and Individual Differences, 45 , 268–274.

Doumen, S., Broeckmans, J., & Masui, C. (2014). The role of self-study time in freshmen’s achievement. Educational Psychology, 34 , 385–402.

Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40 , 117–128.

Duncheon, J. C., & Tierney, W. G. (2013). Changing conceptions of time: implications for educational research and practice. Review of Educational Research, 83 , 236–272.

Dunlosky, J., & Ariel, R. (2011). Self-regulated learning and the allocation of study time. In B. Ross (Ed.), Psychology of learning and motivation (Vol. 54, pp. 103–140). San Diego: Academic Press.

Dunning, D., Heath, C., & Suls, J. (2004). Flawed self-assessments. Psychological Science in the Public Interest, 5 (3), 69–106.

Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: the MASRL model. Educational Psychologist, 46 , 6–25.

Eilam, B., & Aharon, I. (2003). Students’ planning in the process of self-regulated learning. Contemporary Educational Psychology, 28 , 304–334.

Ferrari, J. R., & Díaz-Morales, J. F. (2007). Procrastination: different time orientations reflect different motives. Journal of Research in Personality, 41 , 707–714.

Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: the forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41 , 232–244.

Flanigan, A. E., & Kiewra, K. A. (2018). What college instructors can do about student cyber-slacking. Educational Psychology Review, 30 , 585–597.

Francis-Smythe, J. A., & Robertson, I. T. (1999a). On the relationship between time management and time estimation. British Journal of Psychology, 90 , 333–347.

Francis-Smythe, J. A., & Robertson, I. T. (1999b). Time-related individual differences. Time & Society, 8 , 273–292.

Fromme, K., Corbin, W., & Kruse, M. (2008). Behavioral risks during the transition from high school to college. Developmental Psychology, 44 (5), 1497–1504.

Gable, P. A., & Poole, B. D. (2012). Time flies when you’re having approach-motivated fun: effects of motivational intensity on time perception. Psychological Science, 23 (8), 879–886.

Gevers, J. M., Rutte, C. G., & Van Eerde, W. (2006). Meeting deadlines in work groups: implicit and explicit mechanisms. Applied Psychology, 55 , 52–72.

Green, P., & Skinner, D. (2005). Does time management training work? An evaluation. International Journal of Training and Development, 9 , 124–139.

Gulec, M., Selvi, Y., Boysan, M., Aydin, A., Oral, E., & Aydin, E. F. (2013). Chronotype effects on general well-being and psychopathology levels in healthy young adults. Biological Rhythm Research, 44 , 457–468.

Guzman, G., Goldberg, T., & Swanson, H. (2018). A meta-analysis of self-monitoring on reading performance of K-12 students. School Psychology Quarterly, 33 (1), 160–168.

Hadwin, A., & Oshige, M. (2011). Self-regulation, coregulation, and socially shared regulation: exploring perspectives of social in self-regulated learning theory. Teachers College Record, 113 , 240–264.

Hafner, A., Stock, A., Pinneker, L., & Strohle, S. (2014). Stress prevention through a time management training intervention: an experimental study. Educational Psychology, 34 , 403–416.

Hahn, C., Cowell, J. M., Wiprzycka, U. J., Goldstein, D., Ralph, M., Hasher, L., & Zelazo, P. D. (2012). Circadian rhythms in executive function during the transition to adolescence: the effect of synchrony between chronotype and time of day. Developmental Science, 15 (3), 408–416.

Harkin, B., Webb, T. L., Chang, B. P. I., Prestwich, A., Conner, M., Kellar, I., et al. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142 (2), 198–229.

Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: a meta-analysis. Review of Educational Research, 66 , 99–136.

Haynes, N., Comer, J., & Hamilton-Lee, M. (1988). Gender and achievement status differences on learning factors among Black high school students. Journal of Educational Research, 81 , 233–237.

Hensley, L. C., Wolters, C. A., Won, S., & Brady, A. C. (2018). Academic probation, time management, and time use in a college success course. Journal of College Reading and Learning, 48 , 105–123.

Hicks, T., & Heastie, S. (2008). High school to college transition: a profile of the stressors, physical and psychological health issues that affect the first-year on-campus college students. Journal of Cultural Diversity, 15 (3), 143–147.

Hilbrecht, M., Zuzanek, J., & Mannell, R. C. (2008). Time use, time pressure and gendered behavior in early and late adolescence. Sex Roles, 58 , 342–357.

Hofer, B. K., Yu, S. L., & Pintrich, P. R. (1998). Teaching college students to be self-regulated learners. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: from teaching to self-reflective practice (pp. 57–85). New York: Guilford Press.

Horstmanshof, L., & Zimitat, C. (2007). Future time orientation predicts academic engagement among first-year university students. British Journal of Educational Psychology, 77 (Pt 3), 703–718.

Huie, F. C., Winsler, A., & Kitsantas, A. (2014). Employment and first-year college achievement: the role of self-regulation and motivation. Journal of Education and Work, 27 , 110–135.

Husman, J., & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34 , 113–125.

Kanfer, R., Frese, M., & Johnson, R. (2017). Motivation related to work: a century of progress. Journal of Applied Psychology, 102 (3), 338–355.

Kantrowitz, T. M., Grelle, D. M., Beaty, J. C., & Wolf, M. B. (2012). Time is money: polychronicity as a predictor of performance across job levels. Human Performance, 25 , 114–137.

Kauderer, S., & Randler, C. (2013). Differences in time use among chronotypes in adolescents. Biological Rhythm Research, 44 , 601–608.

Kaufman-Scarborough, C., & Lindquist, J. D. (1999). Time management and polychronicity: comparisons, contrasts, and insights for the workplace. Journal of Managerial Psychology, 14 , 288–312.

Keating, D. P. (2012). Cognitive and brain development in adolescence. Enfance, 64 , 267–279.

Kesici, Ş., Baloğlu, M., & Deniz, M. E. (2011). Self-regulated learning strategies in relation with statistics anxiety. Learning and Individual Differences, 21 , 472–477.

Kim, K. R., & Seo, E. H. (2015). The relationship between procrastination and academic performance: a meta-analysis. Personality and Individual Differences, 82 , 26–33.

Kitsantas, A., Winsler, A., & Huie, F. (2008). Self-regulation and ability predictors of academic success during college: a predictive validity study. Journal of Advanced Academics, 20 , 42–68.

Koch, C. J., & Kleinmann, M. (2002). A stitch in time saves nine: behavioural decision-making explanations for time management problem. European Journal of Work and Organizational Psychology, 11 , 199–217.

Komarraju, M., Karau, S. J., & Schmeck, R. R. (2009). Role of the Big Five personality traits in predicting college students’ academic motivation and achievement. Learning and Individual Differences, 19 , 47–52.

Konig, C. J., & Waller, M. J. (2010). Time for reflection: a critical examination of polychronicity. Human Performance, 23 , 173–190.

Kooij, D. T. A. M., Kanfer, R., Betts, M., & Rudolph, C. W. (2018). Future time perspective: a systematic review and meta-analysis. Journal of Applied Psychology, 103 (8), 867–893.

Krumrei-Mancuso, E. J., Newton, F. B., Kim, E., & Wilcox, D. (2013). Psychosocial factors predicting first-year college student success. Journal of College Student Development, 54 , 247–266.

Landrum, R. E., Turrisi, R., & Brandel, J. M. (2006). College students’ study time: course level, time of semester, and grade earned. Psychological Reports, 98 (3), 675–682.

Liborius, P., Bellhauser, H., & Schmitz, B. (2017). What makes a good study day? An intraindividual study on university students’ time investment by means of time-series analyses. Learning and Instruction.

Linnenbrink-Garcia, L., & Patall, E. (2016). Motivation. In L. Corno & E. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 91–1030). New York: Routledge.

Liu, O. L., Rijmen, F., MacCann, C., & Roberts, R. (2009). The assessment of time management in middle-school students. Personality and Individual Differences, 47 , 174–179.

Locke, E., & Latham, G. (2019). The development of goal setting theory: a half-century retrospective. Motivation Science, 5 , 93–105.

Loureiro, F., & Garcia-Marques, T. (2015). Morning or evening person? Which type are you? Self-assessment of chronotype. Personality and Individual Differences, 86 , 168–171.

Lynch, D. J. (2010). Application of online discussion and cooperative learning strategies to online and blended college courses. College Student Journal, 44 , 920–927.

Macan, T. H. (1994). Time management: test of a process model. Journal of Applied Psychology, 79 , 381–391.

Macan, T. H., Shahani, C., Dipboye, R. L., & Phillips, A. P. (1990). College students’ time management: correlations with academic performance and stress. Journal of Educational Psychology, 82 , 760–768.

MacCann, C., & Roberts, R. D. (2010). Do time management, grit, and self-control relate to academic achievement independently of conscientiousness? In R. E. Hicks (Ed.), Personality and individual differences: current directions (pp. 79–90). Bowen Hills: Australian Academic Press.

MacCann, C., Fogarty, G. J., & Roberts, R. D. (2012). Strategies for success in education: time management is more important for part-time than full-time community college students. Learning and Individual Differences, 22 , 618–623.

McCrae, R., & Lockenhoff, C. (2010). Self-regulation and the five-factor model of personality traits. In R. Hoyle (Ed.), Handbook of personality and self-regulation (pp. 143–168). New York: Wiley.

McInerney, D., & King, R. (2018). Culture and self-regulation in educational contexts. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 485–502). New York: Routledge.

Meeuwisse, M., Born, M. P., & Severiens, S. E. (2013). Academic performance differences among ethnic groups: do the daily use and management of time offer explanations? Social Psychology of Education, 16 , 599–615.

Melancon, J. G. (2002). Reliability, structure, and correlates of Learning and Study Strategies Inventory scores. Educational and Psychological Measurement, 62 , 1020–1027.

Miele, D. B., & Scholer, A. A. (2018). The role of metamotivational monitoring in motivation regulation. Educational Psychologist, 53 , 1–21.

Miller, R. B., & Brickman, S. J. (2004). A model of future-oriented motivation and self-regulation. Educational Psychology Review, 16 , 9–33.

Muis, K. R. (2007). The role of epistemic beliefs in self-regulated learning. Educational Psychologist, 42 , 173–190.

Nelson, T., & Narens, L. (1990). Metamemory: a theoretical framework and some new findings. In G. H. Bower (Ed.), The psychology of learning and motivation (pp. 125–173). New York: Academic Press.

Nonis, S., Teng, J., & Ford, C. (2005). A cross-cultural investigation of time management practices and job outcomes. International Journal of Intercultural Relations, 29 , 409–428.

Nonis, S. A., Philhours, M. J., & Hudson, G. I. (2006). Where does the time go? A diary approach to business and marketing students’ time use. Journal of Marketing Education, 28 , 121–134.

Olaussen, B. S., & Bråten, I. (1998). Identifying latent variables measured by the Learning and Study Strategies Inventory (LASSI) in Norwegian college students. The Journal of Experimental Education, 67 , 82–96.

Olejnik, S., & Nist, S. L. (1992). Identifying latent variables measured by the Learning and Study Strategies Inventory (LASSI). The Journal of Experimental Education, 60 , 151–159.

Panadero, E. (2017). A review of self-regulated learning: six models and four directions for research. Frontiers in Psychology, 8 , 1–28.

Panadero, E., Brown, G., & Strijbos, J. (2016). The future of student self-assessment: a review of known and unknowns and potential directions. Educational Psychology Review, 28 (803), 830.

Panek, E. (2014). Left to their own devices: college students’ “guilty pleasure” media use and time management. Communication Research, 41 , 561–577.

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educational Psychologist, 37 , 91–105.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego: Academic.

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16 , 385–407.

Pintrich, P. R., & Zusho, A. (2007). Student motivation and self-regulated learning in the college classroom. In R. P. Perry & J. C. Smart (Eds.), The scholarship of teaching and learning in higher education: an evidence based perspective (pp. 731–810). New York: Springer.

Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire. Educational and Psychological Measurement, 53 , 801–813.

Pintrich, P. R., Wolters, C. A., & Baxter, G. P. (2000). Assessing metacognition and self-regulated learning. In G. Schraw & J. Impara (Eds.), Issues in the measurement of metacognition (pp. 43–97). Lincoln: Buros Institute of Mental Measurement.

Plant, E. A., Ericsson, K. A., Hill, L., & Asberg, K. (2005). Why study time does not predict grade point average across college students: implications of deliberate practice for academic performance. Contemporary Educational Psychology, 30 , 96–116.

Prevatt, F., Petscher, Y., Proctor, B. E., Hurst, A., & Adams, K. (2006). The revised Learning and Study Strategies Inventory: an evaluation of competing models. Educational and Psychological Measurement, 66 (3), 448–458.

Pychyl, T. A., Morin, R. W., & Salmon, B. R. (2000). Procrastination and the planning fallacy: an examination of the study habits of university students. Journal of Social Behavior and Personality, 15 , 135–150.

Ranellucci, J., Hall, N. C., & Goetz, T. (2015). Achievement goals, emotions, learning, and performance: a process model. Motivation Science, 1 , 98–120.

Rhodes, M., & Tauber, S. (2011). The influence of delaying judgments of learning on metacognitive accuracy: a meta-analytic review. Psychological Bulletin, 137 (1), 131–148.

Richards, J. H. (1987). Time management—a review. Work & Stress, 1 , 73–78.

Rickert, N. P., Meras, I. L., & Witkow, M. R. (2014). Theories of intelligence and students’ daily self-handicapping behaviors. Learning and Individual Differences, 36 , 1–8.

Roskes, M., Elliot, A. J., Nijstad, B. A., & De Dreu, C. K. (2013). Time pressure undermines performance more under avoidance than approach motivation. Personality and Social Psychology Bulletin, 39 , 803–813.

Rytkonen, H., Parpala, A., Lindblom-Ylanne, S., Virtanen, V., & Postareff, L. (2012). Factors affecting bioscience students’ academic achievement. Instructional Science, 40 , 241–256.

Sanders, L., Reedy, D., & Frizell, M. (Eds.). (2018). Learning centers in the 21 st century: a modern guide for learning assistance professionals in higher education . Bentonville: AR. Iona Press.

Sansgiry, S. S., Bhosle, M., & Sail, K. (2006). Factors that affect academic performance among pharmacy students. American Journal of Pharmaceutical Education, 70 , 1–9.

Schell, K. L., & Conte, J. M. (2008). Associations among polychronicity, goal orientation, and error orientation. Personality and Individual Differences, 44 , 288–298.

Schraw, G. (2006). Knowledge: structures and processes. In P. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 245–263). Mahwah: Erlbaum Associates.

Schunk, D. H., & Zimmerman, B. J. (1997). Social origins of self-regulatory competence. Educational Psychologist, 32 , 195–208.

Schunk, D. H., & Zimmerman, B. J. (Eds.). (1998). Self-regulated learning: from teaching to self-reflective practice . New York: Guilford Press.

Sen, S., & Yilmaz, A. (2016). Devising a structural equation model of relationships between preservice teachers’ time and study environment management, effort regulation, self-efficacy, control of learning beliefs, and metacognitive self-regulation. Science Education International, 27 , 301–316.

Shaunessy-Dedrick, E., Suldo, S. M., Roth, R. A., & Fefer, S. A. (2015). Students’ perceptions of factors that contribute to risk and success in accelerated high school courses. The High School Journal, 98 , 109–137.

Shell, D. F., & Husman, J. (2001). The multivariate dimensionality of personal control and future time perspective beliefs in achievement and self-regulation. Contemporary Educational Psychology, 26 (4), 481–506.

Simons, J., Vansteenkiste, M., Lens, W., & Lacante, M. (2004). Placing motivation and future time perspective theory in a temporal perspective. Educational Psychology Review, 16 , 121–139.

Sinatra, G., Kienhues, D., & Hofer, B. (2014). Addressing challenges to public understanding of science: epistemic cognition, motivated reasoning, and conceptual change. Educational Psychologist, 49 , 123–138.

Sirois, F. M. (2014). Procrastination and stress: exploring the role of self-compassion. Self and Identity, 13 , 128–145.

Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go. Psychological Bulletin, 137 (3), 421–442.

Sitzmann, T., Ely, K., Brown, K., & Bauer, K. (2010). Self-assessment of knowledge: a cognitive learning or affective measure? Academy of Management Learning & Education, 9 , 169–191.

Snow, R. (1989). Cognitive-conative aptitude interactions in learning. In R. Kanfer, P. Ackerman, & R. Cudeck (Eds.), Abilities, motivation, and methodology: the Minnesota symposium on learning and individual differences (pp. 435–474). Hillsdale: Erlbaum Associates.

Snow, R. E., Corno, L., & Jackson III, D. (1996). Individual differences in affective and conative functions. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 243–310). New York: Macmillan Library Reference.

Soderstrom, N. C., & Bjork, R. A. (2014). Testing facilitates the regulation of subsequent study time. Journal of Memory and Language, 73 , 99–115.

Steel, P. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133 (1), 65–94.

Stevens, T., & Tallent-Runnels, M. K. (2004). The learning and study strategies inventory-high school version: issues of factorial invariance across gender and ethnicity. Educational and Psychological Measurement, 64 , 332–346.

Strunk, K. K., Cho, Y., Steele, M. R., & Bridges, S. L. (2013). Development and validation of a 2× 2 model of time-related academic behavior: procrastination and timely engagement. Learning and Individual Differences, 25 , 35–44.

Tanner, J. R., Stewart, G., Maples, G. M., & Totaro, M. W. (2009). How business students spend their time—do they really know? Research in Higher Education Journal, 3 , 1–9.

Terenzini, P., Rendon, L., Upcraft, M. L., Millar, S., Allison, K., Gregg, P., & Jalomo, R. (1994). The transition to college: diverse students, diverse stories. Research in Higher Education, 35 , 57–73.

Trueman, M., & Hartley, J. (1996). A comparison between the time-management skills and academic performance of mature and traditional-entry university students. Higher Education, 32 , 199–215.

Truschel, J., & Reedy, D. L. (2009). National survey—what is a learning center in the 21st century? Learning Assistance Review, 14 , 9–22.

Tsai, H. C., & Liu, S. H. (2015). Relationships between time-management skills, Facebook interpersonal skills and academic achievement among junior high school students. Social Psychology of Education, 18 , 503–516.

U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse (2016). Studies of interventions for students in developmental education intervention report: first year experience courses for students in developmental education . Retrieved from http://whatworks.ed.gov . Accessed 20 Jan 2020.

Urdan, T., & Schoenfelder, E. (2006). Classroom effects on student motivation: goal structures, social relationships, and competence beliefs. Journal of School Psychology, 44 , 331–349.

Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: critical review of the literature and future directions. Review of Educational Research, 78 , 751–796.

van Den Hurk, M. (2006). The relation between self-regulated strategies and individual study time, prepared participation and achievement in a problem-based curriculum. Active Learning in Higher Education, 7 , 155–169.

van der Meer, J., Jansen, E., & Torenbeek, M. (2010). ‘It’s almost a mindset that teachers need to change’: first-year students’ need to be inducted into time management. Studies in Higher Education, 35 , 777–791.

van Eerde, W. (2015). Time management and procrastination. In M. D. Mumford & M. Frese (Eds.), The psychology of planning in organizations: research and applications (pp. 312–333). New York: Routledge.

Wagner, P., Schober, B., & Spiel, C. (2008). Time students spend working at home for school. Learning and Instruction, 18 , 309–320.

Weiner, B. (2005). Motivation from an attribution perspective and the social psychology of perceived competence. In A. Elliot & C. Dweck (Eds.), Handbook of competence and motivation (pp. 73–84). New York: Guildford.

Weinstein, C. E., Palmer, D. R., & Acee, T. W. (2016). LASSI user’s manual (3th ed.). Clearwater: H & H Pub.

Winne, P. H. (1995). Inherent details in self-regulated learning. Educational Psychologist, 30 , 173–187.

Winne, P. H., & Baker, R. (2013). The potentials of educational data mining for researching metacognition, motivation, and self-regulated learning. Journal of Educational Data Mining, 5 , 1–8.

Winne, P., & Hadwin, A. (1998). Studying as self-regulated learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah: Erlbaum.

Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. Motivation and self-regulated learning: theory, research, and applications (pp. 297–314). New York: Taylor & Francis.

Winne, P., & Perry, N. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). San Diego: Academic Press.

Witkow, M. R. (2009). Academic achievement and adolescents’ daily time use in the social and academic domains. Journal of Research on Adolescence, 19 , 151–172.

Wolters, C. A. (2003). Regulation of motivation: evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38 , 189–205.

Wolters, C. A. (2011). Regulation of motivation: contextual and social aspects. Teachers College Record, 113 , 265–283.

Wolters, C. A., & Gonzalez, A. L. (2008). Classroom climate and motivation: a step toward integration. Advances in Motivation and Achievement: Social Psychological Influences, 15 , 493–519.

Wolters, C. A., & Hoops, L. D. (2015). Self-regulated learning interventions for motivationally disengaged college students. In T. L. Cleary (Ed.), Self-regulated learning interventions with at-risk youth: Enhancing adaptability, performance, and well-being (pp. 67–88). Washington: American Psychological Association.

Wolters, C., & Taylor, D. (2012). A self-regulated learning perspective on student engagement. In S. L. Christenson, A. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 635–651). New York: Springer.

Wolters, C. A., & Won, S. (2018). Validity and the use of self-report questionnaires to assess self-regulated learning. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 307–322). New York: Routledge.

Wolters, C. A., Won, S., & Hussain, M. (2017). Examining the relations of time management and procrastination within a model of self-regulated learning. Metacognition and Learning, 12 , 381–399.

Won, S., & Yu, S. L. (2018). Relations of perceived parental autonomy support and control with adolescents’ academic time management and procrastination. Learning and Individual Differences, 61 , 205–215.

Woolfolk, A. E., & Woolfolk, R. L. (1986). Time management: an experimental investigation. Journal of School Psychology, 24 , 267–275.

Xie, K., Heddy, B. C., & Greene, B. A. (2019). Affordances of using mobile technology to support experience-sampling method in examining college students’ engagement. Computers & Education, 128 , 183–198.

Xu, J. (2008). Models of secondary school students’ interest in homework: a multilevel analysis. American Educational Research Journal, 45 , 1180–1205.

Xu, J. (2010). Predicting homework time management at the secondary school level: a multilevel analysis. Learning and Individual Differences, 20 , 34–39.

Xu, J., Du, J., & Fan, X. (2013). “Finding our time”: predicting students’ time management in online collaborative groupwork. Computers & Education, 69 , 139–147.

Xu, J., Yuan, R., Xu, B., & Xu, M. (2014). Modeling students’ time management in math homework. Learning and Individual Differences, 34 , 33–42.

Yamada, M., Goda, Y., Matsuda, T., Saito, Y., Kato, H., & Miyagawa, H. (2016). How does self-regulated learning relate to active procrastination and other learning behaviors? Journal of Computing in Higher Education, 28 , 326–343.

Young, D. G., & Hopp, J. M. (2014). 2012-2013 national survey of first-year seminars: exploring high-impact practices in the first college year (Research report No. 4) . Columbia: University of South Carolina.

Zimbardo, P., & Boyd, J. (1999). Putting time in perspective: a valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77 , 1271–1288.

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81 , 329–339.

Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation: theory, research, and applications (pp. 13–29). San Diego: Academic Press.

Zimmerman, B. J., & Risemberg, R. (1997). Becoming a self-regulated writer: a social cognitive perspective. Contemporary Educational Psychology, 22 , 73–101.

Zimmerman, B. J., & Schunk, D. (2012). Motivation: an essential dimension of self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: theory, research and applications (pp. 1–30). New York: Routledge.

Zimmerman, B. J., Greenberg, D., & Weinstein, C. E. (1994). Self-regulating academic study time: a strategy approach. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: issues and educational applications (pp. 181–199). Hillsdale: Erlbaum.

Zusho, A. (2017). Toward an integrated model of student learning in the college classroom. Educational Psychology Review, 29 , 301–324.

Zusho, A., & Pintrich, P. (2003). Skill and will: the role of motivation and cognition in the learning of college chemistry. International Journal of Science Education, 25 , 1081–1094.

Download references

Author information

Authors and affiliations.

Dennis Learning Center, The Ohio State University, 250B Younkin Success Building, 1640 Neil Ave., Columbus, OH, 43201, USA

Christopher A. Wolters & Anna C. Brady

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Christopher A. Wolters .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Wolters, C.A., Brady, A.C. College Students’ Time Management: a Self-Regulated Learning Perspective. Educ Psychol Rev 33 , 1319–1351 (2021). https://doi.org/10.1007/s10648-020-09519-z

Download citation

Published : 27 October 2020

Issue Date : December 2021

DOI : https://doi.org/10.1007/s10648-020-09519-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Self-regulated learning
• Time management
• Postsecondary students
• Find a journal
• Publish with us
• Track your research

Apr 16 Time Management: What is it, who has it, and can you improve it?

by Althea Need Kaminske

COVID-19 has produced a number of challenges for teachers, students, and parents. We have collected a number of resources to help with at home and distance learning here . In today’s post I want to talk about another challenge that results from having our daily routines and work/study environments upended: time management.

Even under ideal circumstances, time management can be challenging. As an academic advisor I deal a lot with first year students who struggle to manage their time when they transition from high school to college/university. Even motivated, dedicated students who did well in high school can struggle to manage their time once they get to university. Students who were able to get to school by 7:30am in high school, maintain good grades,and participate in sports and other extracurriculars suddenly find themselves failing out of their 9:30am biology class because they overslept. 

There are, of course, many things that are unique about this transition at this time of life for many students. However, when I talk with students about the differences between how they spent their time in high school and how they spend their time now, one key difference seems to be structure. Before coming to university their time was highly structured and managed, or co-managed, by other people. Parents helped them to wake up on time, schools provided consistent daily routines, and even part-time jobs and extracurriculars helped to structure their days and weeks. When they get to university they find themselves with swathes of unstructured time that they have to manage on their own. Yes, there are classes, but the schedule can vary wildly day to day. Not only that, the social support network that had been carefully built over a lifetime - parents, family, friends, teachers, coaches - has all but disappeared and they have to start from scratch. 

This situation is somewhat analogous to what many of us are experiencing now: a change in our daily routines, environment, and social structure that make managing our time more challenging. So what does the research on time management look like?

What is time management?

Image from Pixabay

Time management can refer to a wide range of behaviors and can look very different from person to person. There are lots of different systems and approaches to time management. Making a to-do list, keeping a planner, setting event reminders in your phone, setting goals, prioritizing tasks, and marking events on a calendar can all be part of time management. It’s therefore a little tricky to define time management. However, in general when we talk about time management we are referring to a skill that involves assessing your use of time, planning ahead, and monitoring your activities and use of time (1) .

Are there differences between people who are good at managing their time and those who are not?

Effective time management is linked to a number of personality traits. People with better time management skills tend to have higher self-control, which means that they are able to control and refrain from acting on impulses (2). People who are better at managing their time also tend to be high in self-efficacy, which is your belief in your ability to handle challenges and complete tasks successfully  (3, 4). Time management is also linked with conscientiousness, which is a personality trait that describes someone who sets a high priority on completing obligations and doing tasks well (5). However, it should be noted that all of these studies on time management used surveys and self-reports. This means that we cannot determine causality. For instance, we do not know if higher self-efficacy leads to better time management or if better time management leads to higher self-efficacy - or if there is some other factor that influences both of these things. But, these studies do indicate that there are reliable differences between people who are good at managing their time and those who are not.

Interestingly, some of these same factors (high self-control and self-efficacy) are often linked with higher levels of satisfaction and happiness (3, 6, 7, 8). Again, while we know these relationships exist, it’s difficult to determine causality. Are people happier because they have higher self-efficacy (or, by extension, time management)? Or does happiness lead to better self-efficacy and time management? It’s hard to say. But it is reasonable to assume that managing your time can help reduce your stress and help you to be more productive. So that leads to the next question: Can you improve your time management or is this just a trait that some lucky people have and some people don’t?

Can you improve your time management?

The literature on time management interventions is somewhat mixed (1). Some studies have found improvements in time management after an intervention. For example, Burrus, Jackson, Holtzman, and Roberts (2017) found an improvement in time management after an intervention for high school students who were low in time management skills (9). In this quasi-experiment half of the students were assigned to be in the intervention condition and half were in the control condition. The intervention involved an assessment of time management skills, feedback on their individual skills (e.g. if a student scored low on goal setting they were told “Remind yourself regularly of your goals. Goals are an investment in your future.”), and homework assignments over the course of 5 weeks. Homework assignments involved goal setting exercises, accounting for how they spend their time, and learning how to use a planner. Students who were in the control condition (and did not receive the intervention) took the assessment of time management skills, but did not receive feedback or homework. One month after the intervention ended all students were rated on their time management skills by their academic advisors. Importantly, the academic advisors did not know which students received the time management training and which students did not. Burrus and colleagues (2017) found that time management improved after the intervention, but only for students who scored low on time management to begin with. 

However, other studies have found no improvement in time management after an intervention. For example, Macan (1996) studied the effects of a time management training program on employees’ time management behaviors at a social service agency (10). All of the participants completed a time management survey at the beginning of the study. Half of the participants then attended a 2-day seminar that covered topics such as: goal setting, setting priorities, overcoming procrastination, desk organization, and dealing with interruptions. The other half did not attend the seminar. Four to five months after the seminar participants filled out the time management survey again. Participants in the seminar did not report engaging in time management behaviors after the training. However, they did report that they felt more in control of their time after the training.

There are a lot of differences between these two studies. They examined different populations: high school students versus employees at a social service agency. The interventions are different: feedback on behaviors and homework over a 5 week period versus a two day seminar. And the assessment delay: one month after the interventions versus four to five months. These differences highlight the complexity of research on time management and why it is difficult to make recommendations on how to improve time management. 

Can you improve your time management? Probably, but it seems that not all interventions are successful and it may depend on what you consider an improvement. Even though the Macan (1996) study did not find an improvement in time management behaviors, those who completed the training did report feeling more in control of their time which is valuable in and of itself. What does seem likely based on these studies is that any improvement is not going to happen overnight. Like any skill, it will most likely involve deliberate and consistent practice to improve - and it is a skill worth improving. Better time management is linked to improved academic outcomes and general stress relief (1, 11). 

Sadly, it seems there is no silver-bullet to improving your time management. While the literature is still mixed on how to improve your time management skills, there is no shortage of advice and tips on how to deal with procrastination and time management. We have collected some of these in previous digests on procrastination and time management .

Claessens, B. J. C., van Eerde, W., Rutte, C. G., & Roe, R. A. (2005). A review of the time management literature. Personnel Review , 36 (2), 255-276. DOI 10.1108/00483480710726136

Osgood, J. M., McNally, O., & Talerico, G. (2017). The personality of a “good test taker”: Self-control and mindfulness predict good time-management when taking exams. International Journal of Psychology and Educational Studies , 4 (3), 12-21.

Donnelly, D., Kovar, S. E., & Fisher, D. G. (2019). The mediating effects of time management on accounting students’ perception of time pressure, satisfaction with the major, and academic performance. Journal of Accounting & Finance , 19 (9), 46-63.

Boysan, M. & Kiral, E. (2017). Associations between procrastination, personality, perfectionism, self-esteem, and locus of control. British Journal of Guidance and Counselling , 45 (3), 284-296. http://dx.doi.org/10.1080/03069885.2016.1213374

Douglas, H. E., Bore, M. & Munro, D. (2016). Coping with university education: The relationships of time management behavior and work engagement with the five factor model aspects. Learning and Individual Differences , 45 (1), 268-274.

Hofmann, W., Luhmann, M., Fisher, R. R., Vohs, K. D., & Baumeister, R. F. (2014). Yes, But are they happy? Effects of trait self-control on affective well-being and life satisfaction. Journal of Personality , 82 (4), 265-277. https://doi.org/10.1111/jopy.12050

Cheung, T. T., Gillebaart, M., Kroese, F., & De Ridder, D. (2014). Why are people with high self-control happier? The effect of trait self-control on happiness as mediated by regulatory focus. Frontiers in Psychology , 5 , 722. doi: 10.3389/fpsyg.2014.00722

Caprara, G. V. & Steca, P. (2005). Affective and social self-regulatory efficacy beliefs as determinants of positive thinking and happiness. European Psychologist , 10 (4), 275-286. DOI 10.1027/1016-9040.10.4.275

Burrus, J. Jackson, T. Holtzman, S., & Roberts, R. D. (2017). Teaching high school students to manage time: The development of an intervention. Improving Schools , 20 (2), 101-112.

Maccan, T. H. (1996). Time management training: Effects on time behaviors, attitudes, and job performance. The Journal of Psychology , 130 (3), 229-236.

Macan, T. F., Shahani, C., Dipboye, R. L., & Phillips, A. P. (1990). College students’ time management: Correlations with academic performance and stress. Journal of Educational Psychology , 82 (4), 760-768. DOI 10.17220/ijpes.2017.03.002

Apr 30 Digest #143: Using Podcasts

Apr 9 Book Review: How Learning Happens (Kirschner & Hendrick)

We apologize for the inconvenience...

To ensure we keep this website safe, please can you confirm you are a human by ticking the box below.

If you are unable to complete the above request please contact us using the below link, providing a screenshot of your experience.

https://ioppublishing.org/contacts/

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Time management: a realistic approach

Affiliation.

• 1 Indiana University School of Medicine, 550 North University Boulevard, Indianapolis, IN 46202, USA. [email protected]
• PMID: 19467489
• DOI: 10.1016/j.jacr.2008.11.018

Realistic time management and organization plans can improve productivity and the quality of life. However, these skills can be difficult to develop and maintain. The key elements of time management are goals, organization, delegation, and relaxation. The author addresses each of these components and provides suggestions for successful time management.

PubMed Disclaimer

Similar articles

• How to work smarter, not harder. [No authors listed] [No authors listed] Natl Med Leg J. 1998;9(2):4-5. Natl Med Leg J. 1998. PMID: 10568931 No abstract available.
• Ten time-management tips for family physicians. Crosby JW. Crosby JW. CMAJ. 2004 Mar 16;170(6):949-50. doi: 10.1503/cmaj.1021781. CMAJ. 2004. PMID: 15023917 Free PMC article. No abstract available.
• Getting organized in a complex health care world. Feuer L. Feuer L. Case Manager. 2004 Jul-Aug;15(4):21-3. doi: 10.1016/j.casemgr.2004.05.003. Case Manager. 2004. PMID: 15247890 No abstract available.
• Office management: personnel issues overview. Andrew DM. Andrew DM. Urol Clin North Am. 2005 Aug;32(3):253-62, v. doi: 10.1016/j.ucl.2005.03.008. Urol Clin North Am. 2005. PMID: 16036136 Review. No abstract available.
• The conundrum of office-based clinical trials. Scott LD. Scott LD. Am J Gastroenterol. 2007 Feb;102(2):232-5. doi: 10.1111/j.1572-0241.2006.01027.x. Am J Gastroenterol. 2007. PMID: 17311645 Review. No abstract available.
• How to accomplish mission-driven work for emergency medicine educators. Comer JK, Cueva J, McCafferty L, Silvestre J, Reyes M, Naicker B, Gottlieb M, Schnapp B. Comer JK, et al. AEM Educ Train. 2024 May 16;8(3):e10966. doi: 10.1002/aet2.10966. eCollection 2024 Jun. AEM Educ Train. 2024. PMID: 38765711 No abstract available.
• Work-Life Balance: Keep the Cycle Moving - Find a Purpose, Set Priorities, and Manage Time Well Then Reassess and Reset. Saini A. Saini A. Front Pediatr. 2016 Jan 5;3:118. doi: 10.3389/fped.2015.00118. eCollection 2015. Front Pediatr. 2016. PMID: 26779467 Free PMC article. Review. No abstract available.
• Search in MeSH

LinkOut - more resources

Full text sources.

• Elsevier Science
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Acute Crit Care
• v.37(2); 2022 May

The effect of time management education on critical care nurses’ prioritization: a randomized clinical trial

Fatemeh vizeshfar.

1 Community Based Psychiatric Care Research Center, School of Nursing and Midwifery, Shiraz University of Medical Sciences, Shiraz, Iran

Mahnaz Rakhshan

Fatemeh shirazi, roya dokoohaki.

2 Department of Nursing, School of Nursing and Midwifery, Community Based Psychiatric Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Nurses are at the forefront of patient care, and time management skills can increase their ability to make decisions faster. This study aimed to assess the effect of a time management workshop on prioritization and time management skills among nurses of emergency and intensive care units.

This randomized clinical trial was performed with 215 nurses. The educational intervention about time management was held in the form of a workshop for the intervention group. The time management questionnaire was completed by both groups before, immediately after, and 3 months after the intervention.

Most participants were female (n=191, 88%), with a mean age of 31.82 years (range, 22–63 years). Additionally, the participants’ work experience ranged from 1 to 30 years (mean±standard deviation, 8.00±7.15 years). After the intervention, the mean score of time management increased significantly in the intervention group, but no significant difference was observed in this regard in the control group. The results also revealed a significant difference between the intervention and control groups regarding the mean score of time management 3 months after the intervention (P<0.001).

Conclusions

Time management training helped nurses adjust the time required to perform and prioritize various tasks.

INTRODUCTION

Currently, with the increasing growth of information and businesses and, consequently, the increase in responsibilities and the resulting stress, the importance of proper time management is becoming more and more apparent. Time management refers to a set of behaviors for the optimal organization and division of time [ 1 ]. This set of behaviors leads to better use of time and increased productivity and increases the likelihood of achieving predetermined goals [ 2 ]. These behaviors include gaining skills in the areas of goal setting, prioritization, and planning as well as finding ways to reduce the waste of time [ 3 ]. Applying these skills is more important among positions with high workloads and responsibilities such as nursing. Nurses, who are an integral part of the healthcare system, face a heavy workload on a daily basis [ 4 ]. This workload, time constraints, and the need for making decisions in a limited time necessitate the application of time management skills [ 2 , 3 , 5 ]. Proper implementation of these skills facilitates the work of nurses and allows them to perform their tasks more intelligently. Additionally, implementation of these skills not only leads to the provision of better and timely care for patients, but can also reduce nurses’ work stress and increase their quality of life [ 2 ]. Nurses in intensive care units (ICUs) and emergency departments, in particular, need to implement these skills properly as they are exposed to long lasting job stressors and the challenges of dealing with critically ill patients, heavy workloads, complications, unforeseen events, and shortage of time. Overcoming these challenges requires time management skills, timely and correct decision-making, prioritization, and familiarity with different devices [ 6 - 8 ]. The persistence of these challenges can cause stress and numerous physical and mental traumas for nurses [ 8 , 9 ]. One of the problems in implementing time management skills is that they are not taken seriously and are not properly implemented. One study found that in an eight-hour work shift, 31% of the nurses’ time was spent on direct patient care, 45% on indirect care, and the remaining 24% on outpatient and personal work [ 10 ]. Personal and non-clinical work are important factors that waste nurses’ time and reduces their performance efficiency [ 11 ]. These include non-clinical or personal tasks such as unscheduled appointments, frequent phone calls, inadequate day-to-day assignments, not having a weekly or daily schedule, waiting for meetings, and the inability to say no [ 12 ].

Despite the importance of nurses’ understanding of time management and the need for proper training in these skills, there is a lack of studies on the impact of time management training courses. Thus, the present study aimed to assess the effect of a time management workshop on the prioritization and time management skills among nurses working in emergency and ICUs.

MATERIALS AND METHODS

Design and setting.

The present study was a single-blind, randomized clinical trial with pre- and post-test design. This study was performed in the emergency and adult ICUs in the largest specialized and sub-specialized referral center in southern Iran (Nemazee Hospital, Shiraz, Iran). The emergency department of this hospital includes triage and screening departments, a cardiopulmonary resuscitation room, surgical emergency, acute wards I and II, and eight internal medicine, heart, and neurology wards. The adult ICU also includes an emergency ICU, two neurosurgery ICUs, a heart surgery ICU, a general ICU, and two internal medicine adult ICUs.

Participants

At the time of the study, 500 nurses were working in the emergency ward and adult ICUs. All the nurses working in these wards who met the inclusion criteria were invited to participate in the research. The inclusion criteria were working in the ICU or emergency ward as a nurse for more than 6 months, having at least a Bachelor’s degree, and not having participated in other training programs on time management. The exclusion criteria were reluctance to participate in the study, change of ward during the study, and long-term leaves such as maternity leave.

Data Collection

Considering α=5% and β=0.15 and using similar studies [ 13 ], a 171-subject sample size was estimated for this study. Considering the probability of loss, 200 nurses were selected using stratified random sampling. Each ward was considered a class. Based on the research population (500 nurses) and the approximate equality of nurses in the emergency ward and ICU (nearly 250 people in each unit) in proportion to the volume of each category, 107 emergency nurses and 108 ICU nurses were randomly selected to participate in the study via lottery. Among these nurses, 108 were allocated to the intervention group and 107 to the control group) ( Figure 1 ).

Consolidated standards of reporting trials (CONSORT) flow diagram of participant enrollment.

The study data were collected using a questionnaire consisting of two parts. The first part included demographic and occupational information of the participants such as age, sex, education level, marital status, ward, work experience, and workload (working more than required). The second part of the questionnaire was the Macan Time Management Behavior Questionnaire, which was used in the pre- and post-tests. This questionnaire was developed by Macan and colleagues in 1990, and was chosen due to the inclusion of a list of common concepts for time management behaviors as well as being validated and user-friendly. This questionnaire contained 39 items divided into six dimensions, namely goal setting (having specific goals at the beginning of a work shift), goal and activity prioritization (identifying the goals and tasks that are more important and have to be done sooner), operational planning (the extent to which goals are achieved), delegation (assigning some tasks to other colleagues in order to reduce workload and take care of more important tasks), communication management (controlling unnecessary harassment and conversation), and meeting management (attending and finishing meetings at the appointed time). The questions were both positive and negative and could be answered using a 5-point Likert scale ranging from very high to very low. Thus, the score of the questionnaire could range from 30 to 195 [ 14 , 15 ]. The validity and reliability of this questionnaire have been confirmed in various studies. Accordingly, the alpha coefficients for the subscales ranged from 0.5 to 0.9 [ 16 ]. Other researchers have also reported on the acceptable validity and reliability of this questionnaire [ 17 ]. In addition, the validity and reliability of this questionnaire have been confirmed in several studies in Iran [ 18 , 19 ].

Intervention

The study was conducted from the second half of July 2018 to January 2019. After selecting the participants and randomly assigning them to the intervention and control groups, informed consent forms were completed by all the participants. Before the intervention (time management workshops), both groups were required to complete the Time Behavior Management Questionnaire. For the intervention group, a time management training session was held in the form of a four-hour training workshop on different dimensions of time management behaviors. With the help of the educational supervisor, the intervention group was divided into three groups based on work shifts, and the time of the workshop was announced to all intervention groups. Because of the large number of intervention groups and the nurses’ rotation program that complicated the participation of some staff on certain dates, one of the researchers held the workshop in three shifts with the same content during 1 month. If a participant was not able to attend the workshop on their specified day for any reason, he/she could attend a workshop held on 1 of the other 2 other days. One of the researchers who had a PhD in nursing education and 25 years of experience in teaching nursing management courses as well as holding workshops in various fields of nursing management arranged the workshop for the intervention group. The content of the workshop included the definition of time management and its importance, practical tips on time management behaviors, time-saving techniques such as a daily activity chart, how to set and prioritize goals based on the prioritization formula, how to implement goals, to whom and how to delegate, how to deal with people who seek out nurses unnecessarily or entrust nurses with extra work that wastes time, using dead times, and managing phone calls. At the end of each section, a short practical task in the form of a case was given to the participants. At the end of the workshop, a scenario about time management of an ICU nurse was presented and the participants and the instructor (who was one of the researchers) discussed the nurse’s time management problems and strategies. Additionally, the material was summarized with the help of the participants.

It should be mentioned that both intervention and control groups received their routine continuous medical educational programs. Immediately after the end of the workshop and 3 months later, both groups were required to complete the post-test time management behaviors questionnaire. In observance of ethical principles, the control group received the educational content in the form of an educational booklet after the end of the study.

Ethical Considerations

The present study was approved by the Ethics Committee of University of Medical Sciences (No. IR.SUMS.REC.1395.46). It was also registered in Iranian Registry of Clinical Trials (IRCT) with the registration number IRCT2016080927216N4 on October 31, 2016. All necessary permissions for conducting the research were obtained from the relevant administrators and all methods were performed in accordance with the relevant guidelines and regulations. Furthermore, a session was held after the selection of the participants for explaining the study objectives and procedures. Written informed consent forms were also taken from all the participants.

Data Analysis

After the last post-test, the data were coded and entered into IBM SPSS ver. 22.0 (IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test confirmed the normality of the data. Therefore, repeated measures were used for comparing the study groups. In addition, the independent t test and chi-square test were used to compare the two groups with regard to demographic and occupational variables as well as quantitative and qualitative data. Pearson’s and Spearman’s correlation coefficients were used to determine the relationship between the mean score of time management behaviors and quantitative and qualitative demographic and occupational variables. A P<0.05 was considered statistically significant.

Most of the participants (n=191, 88%) were female, with the mean±standard deviation (SD) age of 31.82±8.02 years (range, 22–63 years; median, 30; interquartile range, 25–37). In addition, 117 participants (54.4%) were single and 205 (95.3%) had Bachelor’s degrees. The participants’ work experience ranged from 1 to 30 years (mean±SD, 8.00±7.15 years). Besides, 184 participants (85.6%) worked normal shifts and 31 (14.4%) did more shifts than their duties ( Table 1 ).

Comparison of the general characteristics of the intervention and control groups

Values are presented as number (%) unless otherwise indicated.

SD: standard deviation.

Before the intervention, there was no statistically significant difference between the two groups regarding the mean score for time management behavior (P<0.1). After the intervention and 3 months later, however, statistically significant differences were found in the total score of time management and the three dimensions of goal setting, prioritization of goals and activities, and operational planning. On the other hand, no statistically significant differences were observed between the two groups regarding the three dimensions of delegation, communication management, and meeting management.

After the intervention, the intervention group’s total mean±SD score of time management increased from 120.38±6.06 to 132.67±7.23 (P<0.001). However, the control group’s mean score changed from 126.06±7.60 before the intervention to 126.71±6.46 after that, which was not statistically significant. Three months after the intervention, a significant difference was observed between the intervention (130.56±6.85) and control (124.95±6.61) groups concerning the total mean score of time management (P<0.001). Comparison of the two groups with respect to the total score as well as the mean scores of the model constructs at the three time points has been presented in Table 2 .

Comparison of the two groups regarding the mean scores of time management behaviors

Values are presented as mean±standard deviation.

Pearson’s and Spearman’s correlation coefficients were used to assess the relationship between the mean score of time management behaviors and demographic and occupational variables. The results revealed no statistically significant relationship between any of the demographic and occupational variables and the mean score of time management behaviors (P>0.05).

This clinical trial demonstrated that participating in the time management training workshop had a significant and positive effect on the time management and prioritization skills of the nurses in the emergency ward and ICUs. There are controversial opinions about the effect of time management training. In a similar study, a significant increase was observed in the implementation of time management skills in the areas of time control and organization among head nurses, while this difference was not significant in terms of time mechanics and goal setting skills [ 20 ]. In another study, passing these trainings did not lead to an overall significant difference. These controversies might be attributed to such factors as less frequent training courses, intensive schedule of each course, interference of nurses’ shifts, and not repeating these sessions [ 13 ]. In a previous review, of the 32 studies on time management, only seven evaluated the effects of training courses, only four of which showed a significant difference in this regard [ 21 ]. In another study, the time management training program had a positive effect on the job satisfaction of head nurses [ 22 ].

In the questionnaire used in the present study, the items were organized into six sections, namely goal setting skills, prioritization of goals and activities, operational planning, delegation, communication management, and meeting management. It should be noted that this questionnaire is one of the most valid instruments in the field of time management [ 17 ]. The current study results revealed a significant difference between the intervention and control groups regarding goal setting skills, prioritization of goals and activities, and operational planning. Other dimensions were found to be nonsignificant in other studies [ 20 ]. For example, the areas of goal setting and prioritization of goals were not significantly related, which was explained by continuous training of these skills at lower levels of education and continuous education as well as integration with daily activities [ 20 ]. The target community in the current research was nurses of emergency wards and ICUs, and thus lack of significant associations in the three areas of delegation, communication management, and meeting management could be justified by the fact that nurses in these vital wards considered their main responsibility to be direct communication with patients. They believed that managing communications and meetings and delegating authority were not as important and were the duties of head nurses or other staff. In this study, the lowest scores of both intervention and control groups were related to the goal setting dimension before intervention (intervention, 16.3; control, 15.81), which showed a significant increase after intervention (intervention, 19.27; control, 15.27). Both groups scored relatively higher from the beginning in the areas of goal prioritization and operational planning (intervention, 23.5 and 25.52, respectively; control, 23.1 and 25.41, respectively). After intervention, an increase in these scores was observed in the intervention group (intervention, 25 and 28.41, respectively; control, 23.3 and 26.93, respectively). The effect of training also remained after 3 months (goal setting, 19.3; prioritization, 25.2; and operational planning, 28.9), indicating the high quality of the training as well as the nurses’ eagerness and active participation.

The mean score of the intervention group increased from 128.36 before participating in the training course to 139.67 immediately after participating in the course and 136.22 3 months later, which was significantly higher compared to the control group. Possible factors influencing the final results included the large number of participants in the study, holding multiple courses to cover the interference of work shifts, making use of capable teachers, and active participation of nurses in the courses. Apart from the role of participating in training courses, other factors could also affect time management skills. Researchers in the present study made genuine attempts to control these factors as much as possible. These factors have been explored in various studies on nurses’ use of these skills; factors such as work experience and personal characteristics have been shown to affect time management [ 13 , 20 ]. Since there are differing views on the role of sex, age, and level of education [ 13 , 23 , 24 ], further research on the issue is warranted.

Implications for Nursing Management

The use of practical methods of time management can be the basis for reviewing the guidelines and instructions for treatment and care. Time management and prioritization are important aspects of ensuring effective patient care in ICUs. Time management techniques are learnable, and nurses may experience lower stress levels while performing their duties on time when they are aware of these techniques.

This study sought to assess the effect a time management workshop on the time management skills of nurses who work in the emergency ward and ICU. The results demonstrated that time management training enhanced the nurses’ knowledge of these skills, which could reduce the time required to perform various tasks. Nurses were able to make good use of their limited time in a work shift by learning how to prioritize tasks; plan operational activities; delegate tasks in non-specialist cases; manage communication with patients, colleagues, physicians, and patient companions; and manage sessions. Effective management also resulted in an increase in patient and nurse satisfaction.

Limitations

The present study had some limitations. It was performed only in ICUs and emergency wards in one hospital using a workshop intervention. Thus, the effectiveness of other educational methods such as virtual training is recommended for study because of the limited time nurses have for time management training. Further research in several hospitals is recommended. In the current study, the scores of the study questionnaire ranged from 30 to 195, and the mean scores of the nurses were moderate. Hence, it is necessary to provide a refresher training program to determine the factors affecting time management behaviors and how to control them with quantitative and qualitative methods, so that nurses can overcome problems through time management. Furthermore, future studies are recommended to evaluate the effectiveness of the training course on nurses’ performance.

KEY MESSAGES

▪ Time management is an important part of effective patient care in intensive care units.

▪ Time management techniques are learnable.

▪ Time management training helped nurses adjust the time required to perform and prioritize various tasks.

Acknowledgments

This research was financially supported by Shiraz University of Medical Sciences (grant No. 11202). The authors would like to express their sincerest gratitude to Shiraz University of Medical Sciences for financially supporting the research.

The authors would also like to thank Ms. A. Keivanshekouh at the Research Consultation Center (RCC) of Shiraz University of Medical Sciences for improving the use of English in the manuscript.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conceptualization: FV, MR, RD. Data curation: FV, FS, RD. Formal analysis: FS. Funding acquisition: FV. Methodology: FV, MR. Visualization: FS. Writing–original draft: FV, RD. Writing–review & editing: MR, FS.

What employees say matters most to motivate performance

The past few years have been a confounding time in performance management. Disruptions of long-standing workplace norms have led many employees to rethink their expectations of employers  regarding remote work, employee burnout, and work–life balance. Compounding these challenges, an inflationary economy and a slower hiring market have put pressure on employers to “do more” with the talent they already have.

Organizations have responded to this volatility by seeking new formulas to motivate talent, including rethinking their approaches to performance management. Of course, tweaking performance management is not new: McKinsey’s prepandemic research  found that most companies had made at least one major change to their approaches in the prior 18 months. But recently, we have seen companies consider more sweeping changes. Some have streamlined goal setting and formal review processes, separated performance and compensation conversations, or simply done away with ratings altogether.

Yet as organizations weigh changes to performance management, it’s difficult to understand what will yield the highest ROI. Leaders are often forced to rely on anecdotal case studies and success stories from others’ experiences to help boost employee motivation to perform. While a plethora of books and articles have been published on the topic in recent years, a “data desert” remains, with a lack of quantitative insights derived from what employees say most inspires and motivates them.

McKinsey’s 2024 performance management survey

Our survey of more than 1,000 employees across the globe sought to shed light on what matters most to employees and offer a new fact base for employers to weigh varied performance management methodologies (see sidebar, “McKinsey’s 2024 performance management survey”). We tested a range of options to understand employee perceptions, including approaches to goals, performance reviews, ongoing development, and rewards.

The survey responses in some cases confirm what intuition has long suggested. In other cases, responses indicate ways to tailor performance management to the unique needs of an organization. Overall, the responses point to essential areas of focus as organizations weigh performance management redesigns. New data helps to better identify options most worthy of investment, based on sources of employee motivation.

A consistent and clearly articulated performance management framework wins the day

The most resonant overall survey finding was this: performance management is most effective when it features strong, consistent internal logic that employees understand.

In recent years, some companies have shifted away from results-based performance management goals and metrics in favor of measures that balance what an employee achieved with how they achieved it. The rationale is partly to make employees feel they are assessed in a more holistic way that considers external factors that contributed to their ability to deliver on a result. The holistic approach also measures how well employees adhered to company cultural norms and leadership expectations. However, the survey results revealed that respondents did not view results-based assessments particularly negatively. Instead, what worked less well were systems without clear and easily understood structures, which respondents viewed as significantly less motivating and fair.

These findings stress that when it comes to building the overall framework for performance management, consistency and simplicity win the day (Exhibit 1). Approaching each element of performance management separately had a lower effect on motivation to perform. Instead, the way the four pillars work together made a difference for respondents. Approaches with a coherent, connected framework across goal setting, performance reviews, feedback, and rewards correlated with the highest motivation to perform. Each company can design a fit-for-purpose approach tailored to the needs of its organization , ensuring core elements are well connected and articulated to employees.

Goal setting has impact when goals are measurable and clearly linked to company priorities

Goal setting has long been accepted as a critical tool for improving performance. The survey puts some hard data behind the decision to invest time and energy into goal setting: 72 percent of respondents cited it as a strong motivator. However, the “what” and “how” of setting those goals are less definitively understood. The survey results shed light on both questions.

What makes an effective goal? The survey revealed that employees felt more motivated when their performance goals included a mix of both individual and team-level goals and when their goals were clearly linked to their company’s goals. Respondents also reported feeling more motivated by goals that felt measurable (Exhibit 2).

However, the survey also suggests that just as important as the content of a goal is the process by which it is set. Employees tended to be more motivated and perceive the performance management approach as fair when they were involved in the process and the goals were updated throughout the year to align with team and company priorities.

These findings suggest high ROI when managers spend time throughout the year counseling employees on updates to align goals with current business priorities and articulating the connection between individual and team goals.

Performance reviews with skilled managers are crucial to employee performance

As employers meet evolving employee expectations, many have rethought their approaches to performance reviews by focusing on changes to ratings. Some have shifted from numerical scales (for example, one through five) to word-based systems (for example, from “underperforming” to “exceeds expectations”) or have done away with ratings altogether.

But the survey showed that different ratings scales (for example, those that measured results versus behavior) yielded negligible differences in how much motivation employees reported.

There was also no significant difference between receiving no rating and receiving a rating on a two-point scale (such as a “pass or fail”) or a three- or five-point scale.

Instead, the survey responses suggest employers may be overemphasizing ratings frameworks and overlooking the criticality of how ratings are given. Our survey indicated employees were significantly more motivated by performance reviews when they were offered by a skilled manager and reflected the individual achievement of a performance goal. This was especially true when managers were involved in setting goals and, therefore, well informed when it came time to assess performance (Exhibit 3).

Investments in manager training to foster meaningful development discussions pay dividends

The survey data also shows how big a difference ongoing development discussion outside the review cycle can make. Only 21 percent of respondents who had no development conversations felt motivated by their companies’ performance management, compared with 77 percent of those who received ongoing feedback.

When it comes to providing feedback, manager training is critical, given that nearly 25 percent of survey respondents said their managers or feedback providers did not have sufficient skills or capabilities to conduct their performance reviews. At large companies (with 10,000 to 50,000 employees), 34 percent of respondents cited this lack of skills. Large companies in particular could designate more power and resources to middle managers —traditionally the most passionate and capable coaches within an organization.

But how can employers empower managers without creating excessive workloads? Because both managers and employees often find the process of providing and receiving feedback taxing , some companies try to limit these exchanges to only once a year. However, given the decisive benefit of regular feedback that our survey revealed, a better approach may be to equip managers with the right tools. Generative AI can make it easier for managers to deliver better feedback —for example, by synthesizing insights from the colleagues who work closely with an employee.

Rewards that include nonfinancial incentives provide a boost

Money matters, of course. But the survey also suggests that, as work–life expectations continue to shift, nonfinancial rewards, like opportunities for upskilling or professional development, can play an increasingly important role in performance management strategies.

The survey showed a strong relationship between employers’ use of both financial and nonfinancial rewards and employees’ perceptions of their personal motivation and performance improvement. The survey also shed light on a unique distinction: employees were more likely to perceive that their organizations’ performance management systems were improving company performance overall when nonfinancial rewards were used. Taken together, these findings suggest that nonfinancial rewards can serve as a critical booster for the success of a cohesive performance management system (Exhibit 4).

Previous McKinsey research has found that nonfinancial incentives should appeal to five sources of meaning : society, client, company, team, and self. These findings align with abundant social science research. 1 Jena McGregor, “What companies get wrong about motivating their people,” Washington Post , November 25, 2016.   Nonfinancial incentives could include an immediate manager’s praise, a step-up opportunity to lead a high-profile project, greater autonomy, or more workplace flexibility.

Nonfinancial incentives, like other aspects of an effective approach, should be both frequent and explicitly tied to desired behaviors. They can be used to reward progress toward large, company-wide goals; small, private goals specific to individual employees; or career moves, among other things. Thoughtful deployment of these rewards can help reinforce elements from across the four pillars of a cohesive system.

Economic volatility and shifting workplace norms have sparked many employers’ renewed interest in creating the right performance management formula. Our survey suggests that cohesive overall design and effective execution are the most important focus points.

As organizations consider their approaches across the four performance management pillars—goal setting, performance reviews, ongoing feedback, and rewards—we urge them to pay close attention to the “what” and the “how” to motivate and inspire employees.

Asmus Komm is a partner in McKinsey’s Hamburg office; Brooke Weddle is a senior partner in the Washington, DC, office, where Vivian Breaux is an associate partner; Dana Maor is a senior partner in the Tel Aviv office; and Katharina Wagner is an associate partner in the Berlin office.

The authors wish to thank Karla Martinez and Katherine Boorstein for their contributions to this article.

This article was edited by Katy McLaughlin, an executive editor in the Southern California office.

Explore a career with us

Related articles.

In the spotlight: Performance management that puts people first

This time it’s personal: Shaping the ‘new possible’ through employee experience

Harnessing the power of performance management

Making Good Friends

Finding the right career.

• Self-Care Tips to Prioritize Your Mental Health
• Self-Esteem: How to Feel Good About Yourself
• How to Break Bad Habits and Change Negative Behaviors
• Imposter Syndrome: Causes, Types, and Coping Tips

Personality Types, Traits, and How it Affects Mental Health

• Online Therapy: Is it Right for You?
• Mental Health
• Health & Wellness
• Children & Family
• Relationships

Are you or someone you know in crisis?

• Bipolar Disorder
• Eating Disorders
• Grief & Loss
• Personality Disorders
• PTSD & Trauma
• Schizophrenia
• Therapy & Medication
• Exercise & Fitness
• Healthy Eating
• Well-being & Happiness
• Weight Loss
• Work & Career
• Illness & Disability
• Heart Health
• Learning Disabilities
• Family Caregiving
• Teen Issues
• Communication
• Emotional Intelligence
• Love & Friendship
• Domestic Abuse
• Healthy Aging
• Alzheimer’s Disease & Dementia
• End of Life
• Meet Our Team

Why volunteer?

Benefit 1: volunteering connects you to others, benefit 2: volunteering is good for your mind and body, benefit 3: volunteering can advance your career, benefit 4: volunteering brings fun and fulfillment to your life, how to find the right volunteer opportunity, getting the most out of volunteering, volunteering and its surprising benefits.

Volunteering can help you make friends, learn new skills, advance your career, and even feel happier and healthier. Learn how to find the right volunteer opportunity for you.

With busy lives, it can be hard to find time to volunteer. However, the benefits of volunteering can be enormous. Volunteering offers vital help to people in need, worthwhile causes, and the community, but the benefits can be even greater for you, the volunteer. The right match can help you to find friends, connect with the community, learn new skills, and even advance your career.

Giving to others can also help protect your mental and physical health. It can reduce stress, combat depression, keep you mentally stimulated, and provide a sense of purpose. While it’s true that the more you volunteer, the more benefits you’ll experience, volunteering doesn’t have to involve a long-term commitment or take a huge amount of time out of your busy day. Giving in even simple ways can help those in need and improve your health and happiness.

Benefits of volunteering: 4 ways to feel healthier and happier

• Volunteering connects you to others.
• Volunteering is good for your mind and body.
• Volunteering can advance your career.
• Volunteering brings fun and fulfillment to your life.

One of the more well-known benefits of volunteering is the impact on the community. Volunteering allows you to connect to your community and make it a better place. Even helping out with the smallest tasks can make a real difference to the lives of people, animals, and organizations in need. And volunteering is a two-way street: It can benefit you and your family as much as the cause you choose to help. Dedicating your time as a volunteer helps you make new friends, expand your network, and boost your social skills.

Make new friends and contacts

One of the best ways to make new friends and strengthen existing relationships is to commit to a shared activity together. Volunteering is a great way to meet new people, especially if you are new to an area. It strengthens your ties to the community and broadens your support network, exposing you to people with common interests, neighborhood resources, and fun and fulfilling activities.

Increase your social and relationship skills

While some people are naturally outgoing, others are shy and have a hard time meeting new people. Volunteering gives you the opportunity to practice and develop your social skills, since you are meeting regularly with a group of people with common interests. Once you have momentum, it’s easier to branch out and make more friends and contacts.

Volunteering as a family

Children watch everything you do. By giving back to the community, you’ll show them firsthand how volunteering makes a difference and how good it feels to help other people and animals and enact change. It’s also a valuable way for you to get to know organizations in the community and find resources and activities for your children and family.

Volunteering provides many benefits to both mental and physical health.

Volunteering helps counteract the effects of stress, anger, and anxiety. The social contact aspect of helping and working with others can have a profound effect on your overall psychological well-being. Nothing relieves stress better than a meaningful connection to another person. Working with pets and other animals has also been shown to improve mood and reduce stress and anxiety.

Volunteering combats depression. Volunteering keeps you in regular contact with others and helps you develop a solid support system, which in turn protects you against depression.

Volunteering makes you happy . By measuring hormones and brain activity, researchers have discovered that being helpful to others delivers immense pleasure. Human beings are hard-wired to give to others. The more we give, the happier we feel.

[Read: Cultivating Happiness]

Volunteering increases self-confidence. You are doing good for others and the community, which provides a natural sense of accomplishment. Your role as a volunteer can also give you a sense of pride and identity. And the better you feel about yourself, the more likely you are to have a positive view of your life and future goals.

Volunteering provides a sense of purpose. Older adults, especially those who have retired or lost a spouse, can find new meaning and direction in their lives by helping others. Whatever your age or life situation, volunteering can help take your mind off your own worries, keep you mentally stimulated, and add more zest to your life.

Volunteering helps you stay physically healthy. Studies have found that those who volunteer have a lower mortality rate than those who do not. Older volunteers tend to walk more, find it easier to cope with everyday tasks, are less likely to develop high blood pressure, and have better thinking skills. Volunteering can also lessen symptoms of chronic pain and reduce the risk of heart disease.

Speak to a Licensed Therapist

BetterHelp is an online therapy service that matches you to licensed, accredited therapists who can help with depression, anxiety, relationships, and more. Take the assessment and get matched with a therapist in as little as 48 hours.

I have limited mobility—can I still volunteer?

People with disabilities or chronic health conditions can still benefit greatly from volunteering. In fact, research has shown that adults with disabilities or health conditions ranging from hearing and vision loss to heart disease, diabetes or digestive disorders all show improvement after volunteering.

Whether due to a disability, a lack of transportation, or time constraints, many people choose to volunteer their time via phone or computer. In today’s digital age, many organizations need help with writing, graphic design, email, and other web-based tasks. Some organizations may require you to attend an initial training session or periodical meetings while others can be conducted completely remotely. In any volunteer situation, make sure that you are getting enough social contact, and that the organization is available to support you should you have questions.

If you’re considering a new career, volunteering can help you get experience in your area of interest and meet people in the field. Even if you’re not planning on changing careers, volunteering gives you the opportunity to practice important skills used in the workplace, such as teamwork, communication, problem solving, project planning, task management, and organization. You might feel more comfortable stretching your wings at work once you’ve honed these skills in a volunteer position first.

Teaching you valuable job skills

Just because volunteer work is unpaid does not mean the skills you learn are basic. Many volunteering opportunities provide extensive training. For example, you could become an experienced crisis counselor while volunteering for a women’s shelter or a knowledgeable art historian while donating your time as a museum docent.

[Read: Finding the Right Career]

Volunteering can also help you build upon skills you already have and use them to benefit the greater community. For instance, if you hold a successful sales position, you can raise awareness for your favorite cause as a volunteer advocate, while further developing and improving your public speaking, communication, and marketing skills.

Gaining career experience

Volunteering offers you the chance to try out a new career without making a long-term commitment. It is also a great way to gain experience in a new field. In some fields, you can volunteer directly at an organization that does the kind of work you’re interested in. For example, if you’re interested in nursing, you could volunteer at a hospital or a nursing home.

Your volunteer work might also expose you to professional organizations or internships that could benefit your career.

When it comes to volunteering, passion and positivity are the only requirements

While learning new skills can be beneficial to many, it’s not a requirement for a fulfilling volunteer experience. Bear in mind that the most valuable assets you can bring to any volunteer effort are compassion, an open mind, a willingness to pitch in wherever needed, and a positive attitude.

Volunteering is a fun and easy way to explore your interests and passions. Doing volunteer work you find meaningful and interesting can be a relaxing, energizing escape from your day-to-day routine of work, school, or family commitments. Volunteering also provides you with renewed creativity, motivation, and vision that can carry over into your personal and professional life.

[Read: Building Better Mental Health]

Many people volunteer in order to make time for hobbies outside of work as well. For instance, if you have a desk job and long to spend time outdoors, you might consider volunteering to help plant a community garden, walk dogs for an animal shelter, or help out at a children’s camp.

There are numerous volunteer opportunities available. The key is to find a position that you would enjoy and are capable of doing. It’s also important to make sure that your commitment matches the organization’s needs. Ask yourself the following:

• Would you like to work with adults, children, animals, or remotely from home?
• Do you prefer to work alone or as part of a team?
• Are you better behind the scenes or do you prefer to take a more visible role?
• How much time are you willing to commit?
• What skills can you bring to a volunteer job?
• What causes are important to you?

Consider your interests

You will have a richer and more enjoyable volunteering experience if you first take some time to identify your goals and interests. Think about why you want to volunteer. What would you enjoy doing? The opportunities that match both your goals and your interests are most likely to be fun and fulfilling.

What are your volunteering goals?

To find a volunteer position that’s right for you, look for something that matches your personality, skills, and interests. Ask yourself if there is something specific you want to do or achieve as a volunteer.

For example, you might want to:

• Improve your neighborhood.
• Meet new people with different outlooks or experiences.
• Try something new.
• Do something rewarding with your spare time.
• See new places or experience a different way of living.
• Try a new type of work that you might want to pursue as a full-time job.
• Expand on your interests and hobbies.

Consider several volunteer possibilities

Don’t limit yourself to just one organization or one specific type of job. Sometimes an opportunity looks great on paper, but the reality is quite different. Try to visit different organizations and get a feel for what they are like and if you click with other staff and volunteers.

Where to find volunteer opportunities

• Community theaters, museums, and monuments.
• Libraries or senior centers.
• Service organizations such as Lions Clubs or Rotary Clubs.
• Local animal shelters, rescue organizations, or wildlife centers.
• Youth organizations, sports teams, and after-school programs.
• Historical restorations, national parks, and conservation organizations.
• Places of worship such as churches or synagogues.
• Online directories and other resources (see below).

How much time should you volunteer?

Volunteering doesn’t have to take over your life to be beneficial. In fact, research shows that just two to three hours per week, or about 100 hours a year, can confer the most benefits—to both you and your chosen cause. The important thing is to volunteer only the amount of time that feels comfortable to you. Volunteering should feel like a fun and rewarding hobby, not another chore on your to-do list.

You’re donating your valuable time, so it’s important that you enjoy and benefit from your volunteering. To make sure that your volunteer position is a good fit:

Ask questions. You want to make sure that the experience is right for your skills, your goals, and the time you want to spend. Sample questions for your volunteer coordinator might address your time commitment, if there’s any training involved, who you will be working with, and what to do if you have questions during your experience.

Make sure you know what’s expected. You should be comfortable with the organization and understand the time commitment. Consider starting small so that you don’t over commit yourself at first. Give yourself some flexibility to change your focus if needed.

Don’t be afraid to make a change. Don’t force yourself into a bad fit or feel compelled to stick with a volunteer role you dislike. Talk to the organization about changing your focus or look for a different organization that’s a better fit.

If volunteering overseas, choose carefully. Some volunteer programs abroad can cause more harm than good if they take much-needed paying jobs away from local workers. Look for volunteer opportunities with reputable organizations.

Enjoy yourself. The best volunteer experiences benefit both the volunteer and the organization. If you’re not enjoying yourself, ask yourself why. Is it the tasks you’re performing? The people you’re working with? Or are you uncomfortable simply because the situation is new and unfamiliar? Pinpointing what’s bothering you can help you decide how to proceed.

VolunteerMatch  – Find opportunities that match your volunteer interests, from location to type of work. (VolunteerMatch)

Idealist  – Find volunteer opportunities in your local area or internationally. (Idealist)

National and Community Service  – Federal organization offering volunteer positions across the U.S. (National Service)

Volunteer  – Directory of environmental volunteer opportunities. (Volunteer.gov)

U.S. Peace Corps  – Offers volunteer opportunities overseas and includes a  50 Plus  division. (Peace Corps)

American Red Cross  – Volunteer in any of the Red Cross’s key service areas. (Red Cross)

More Information

• Simple Changes, Big Rewards - A Practical, Easy Guide for Healthy, Happy Living. (Harvard Medical School Special Health Report)
• The Health Benefits of Volunteering: Recent Research (PDF) - Research on the benefits of volunteering, especially for seniors. (Corporation for National and Community Service)
• The many ways volunteering is good for your heart - Includes resources for finding volunteer positions. (Harvard Health Publications)
• 10 Tips on Volunteering Wisely - Tips to make the most of your volunteering experience. (Network for Good)
• Carr, D. C., Kail, B. L., & Rowe, J. W. (2018). The Relation of Volunteering and Subsequent Changes in Physical Disability in Older Adults. The Journals of Gerontology: Series B , 73(3), 511–521. Link
• Kim, E. S., Whillans, A. V., Lee, M. T., Chen, Y., & VanderWeele, T. J. (2020). Volunteering and Subsequent Health and Well-Being in Older Adults: An Outcome-Wide Longitudinal Approach. American Journal of Preventive Medicine , 59(2), 176–186. Link
• Lawton, R. N., Gramatki, I., Watt, W., & Fujiwara, D. (2021). Does Volunteering Make Us Happier, or Are Happier People More Likely to Volunteer? Addressing the Problem of Reverse Causality When Estimating the Wellbeing Impacts of Volunteering. Journal of Happiness Studie , 22(2), 599–624. Link
• Okun, M. A., Yeung, E. W., & Brown, S. (2013). Volunteering by older adults and risk of mortality: A meta-analysis. Psychology and Aging , 28(2), 564–577. Link
• Salt, E., Crofford, L. J., & Segerstrom, S. (2017). The Mediating and Moderating Effect of Volunteering on Pain and Depression, Life Purpose, Well-Being, and Physical Activity. Pain Management Nursing , 18(4), 243–249. Link

More in Well-being & Happiness

Tips for meeting people and making meaningful connections

How to choose or change career paths and find job satisfaction

Tips to staying healthy as you get older

Self-Care Tips

Strategies to improve your mental health

Self-Esteem

How to feel good about yourself

How to Break Bad Habits

Tips for changing negative behaviors

Imposter Syndrome

What to do when you feel like a fraud at work, school, or in relationships

Your personality impacts your health, mood, and relationships. Here’s what you need to know.

Professional therapy, done online

BetterHelp makes starting therapy easy. Take the assessment and get matched with a professional, licensed therapist.

Help us help others

Millions of readers rely on HelpGuide.org for free, evidence-based resources to understand and navigate mental health challenges. Please donate today to help us save, support, and change lives.

• Open access
• Published: 27 August 2024

Effect of self-directed versus traditional learning model on nurses’ airway management competencies and patients’ airway-related incidents

• Sameh Elhabashy 1 &
• Amen Moawad 2  

BMC Nursing volume  23 , Article number:  599 ( 2024 ) Cite this article

Metrics details

Introduction

Self-directed learning (SDL) stands as a contemporary approach to learning, offering efficient and sustainable strategies for enhancing knowledge and practices. Given the pivotal role of nurses in ensuring patient safety and care effectiveness, this study aims to assess the impact of the SDL model compared to the traditional learning model (TLM) on elevating nurses’ airway management (AM) competencies and minimizing airway-related incidents.

Methodology

The study employed an experimental research design using a posttest-only control group structure within a two-group comparison framework. Seventy-two nurses participated, with 35 in the study group and 37 in the control group at the Obstetrics and Gynecology Hospital affiliated with Cairo University, Egypt. The trial was carried out between February 2020 and July 2021. Following an assessment of SDL readiness for the intervention group, they received SDL model training based on Knowles’ SDL principles, while the control group received TLM. The primary endpoint was a significant elevation in nurses’ airway management competency, with the secondary outcome being a significant decrease in airway-related incidents reported by nurses. Competency assessments occurred immediately after completion of the intervention and again three months later.

A statistically significant difference was observed between the control and intervention groups regarding their practice and knowledge scores, with p-values of 0.02 and < 0.01, respectively. Additionally, the clinically relevant difference between control and intervention groups was evidenced by the effect size (ES) Cohen’s d in both practices and knowledge levels (-0.56 and − 1.55, respectively). A significant difference was also noted between the first post-assessment and the paired second post-assessment concerning nurses’ knowledge and practices among control and intervention groups, as indicated by the paired t-test with p  < .01. Over three months, the intervention group reported 18 airway incidents, while the control group reported 24, with no statistically significant difference (> 0.05).

The SDL model significantly enhanced nurses’ competencies in AM compared to the TLM. However, the efficacy of both learning models diminishes over time. Although nurses who underwent SDL model reported fewer airway incidents compared to those who received TLM approach of learning, no statistically significant difference was detected.

Trial registration

The study has been registered with Clinical Trials.gov under the registration number (NCT04244565) on 28/01/2020.

Peer Review reports

Airway-related incidents are prevalent, critical, and intricate events that pose a significant risk to patient safety but can be avoided [ 1 ]. Airway-related incidents encompass any condition that impairs the patency of a patient’s airway, either partially or entirely. These incidents may originate from various causes, such as foreign bodies, injury, or infection [ 2 ]. The number of studies on airway-related incidents in hospitals is limited. However, it has been found that 7.04% of ICU patients experienced such incidents [ 3 ]. According to the UK National Reporting and Learning Centre, 82% of airway-related incidents occurred in patients with an endotracheal tube (ETT). Out of these incidents, 25% resulted in the patient’s death [ 4 ]. Airway management (AM) is crucial to ensuring patient safety [ 5 ]. In addition, AM encompasses nursing measures to maintain airway patency and minimize aspiration risk [ 6 ]. These actions include suctioning, oral care, basic motions such as head-tilt and chin-lift, securing the ETT, and monitoring and caring for the cuff pressure of the ETT.

Nurses play a significant role in ensuring the patient’s airways are functioning properly, particularly in emergency and critical care settings [ 7 ]. Various nursing-related malpractices can lead to airway-related incidents in patients [ 2 , 8 ]. Nurses in developing countries lack knowledge and practice regarding AM [ 7 ]. According to prior research, 54.9% of nurses possess inadequate knowledge in this area [ 7 ]. Furthermore, nurses’ average knowledge score in AM was less than 50% of the highest possible score [ 9 ]. These findings demonstrate the significance of educating nurses to enhance their knowledge and practices, thereby ensuring their ability to deliver safe and qualified care. The field of nursing, including AM and other forms of care, is experiencing rapid progress due to advancements in knowledge and technology. Therefore, nurses need to be encouraged to find and utilize an appropriate learning model that can effectively achieve the concept of continuing education [ 10 , 11 ].

Although nursing care has experienced significant transformations in the past thirty years, the approaches utilized in clinical training for nurses have remained unchanged [ 12 ]. The traditional learning model (TLM) in clinical teaching is a familiar and effective method of learning for nurses. It includes activities such as lectures, skills laboratory training, and supervised clinical experience. One benefit of this strategy is the opportunity to assist nurses by implementing the principles they have learned in class or skill lab to patient care. In addition, nurses feel more secure due to their awareness and familiarity [ 13 ]. Conversely, the transition from TLM to clinical instruction hinders the development of effective critical thinking skills and limits flexibility [ 14 ].

Self-directed learning (SDL) is a contemporary learning technique that provides adults with efficient and sustainable strategies for acquiring knowledge. The concept of SDL is stated in the Adult Learning Theory. This theory confirms that people are pragmatic and focused on solving problems. Their learning is mainly influenced by clinical experiences rather than passive methods [ 15 ]. SDL was conducted, and its impact on nursing students and practicing nurses who are actively involved in continuing education was assessed [ 16 ]. Moreover, it is a process in which the instructors play a facilitating role while learners actively determine their own learning needs and goals, allocate resources, and engage in self-reflection and evaluation. SDL offers various benefits, such as enhanced self-independence, confidence, sustainable learning, and autonomy [ 15 ]. Although SDL can be beneficial, there is a growing recognition that SDL is not universally applicable to all learners and circumstances. Additionally, a considerable proportion of nurses who were unfamiliar with the SDL method indicated a preference for and felt more confident with the traditional teacher-centred approach, which they commonly encountered during their postgraduate education [ 17 ].

In order to maximize the benefits, it is essential to implement SDL in a methodical manner. SDL encompasses four fundamental stages: readiness for autonomous learning, establishment of learning objectives, active participation in the learning process, and assessment of learning outcomes [ 18 , 19 ]. SDL requires nurses and educators to meet specific requirements or perform certain functions. Engaging in communication about their respective perceptions is mutually advantageous for both parties. Nurses are responsible for evaluating their preparedness to gain knowledge, setting learning goals, supervising the learning process, displaying self-motivation, reevaluating and adjusting objectives as needed, and seeking guidance from the instructor when necessary. Instructors possess a range of duties, including establishing a collaborative learning atmosphere, motivating and guiding nurses in their learning endeavours, facilitating the learning process, being available for consultations when necessary, and acting as advisors rather than formal instructors [ 20 ].

In Egypt, nurses primarily participate in in-service learning activities through TLMs to improve their skills and reduce errors in practice. However, there is limited use of modern learning approaches like SDL, and there is a dearth of research on the impact and challenges of implementing SDL among Egyptian nurses. Additionally, patient care places a high emphasis on the use of AM techniques. Therefore, this study aims to assess the impact of the SDL Model in comparison to TLM on elevating nurses’ AM competencies and minimizing airway-related incidents. In order to achieve this objective, the following research hypotheses were formulated: (1) Nurses enrolled in the self-directed learning model (µ1) demonstrate higher scores in airway management practices compared to those adopting TLMs (µ2), H1: µ1 > µ2. (2) Nurses enrolled in the SDL model (µ1) exhibit higher scores in airway management knowledge than their counterparts adopting TLMs (µ2), H1: µ1 > µ2. (3) Intensive care units wherein nurses are enrolled in the SDL model (µ1) are projected to have a lower occurrence of airway-related incidents among patients compared to units utilizing TLMs (µ2), H1: µ1 < µ2.

Theoretical framework

Due to the correlation between inadequate nurses’ knowledge and skills regarding AM [ 7 , 9 ] and the significant increase in airway-related incidents [ 21 ], it is necessary to improve nurses’ competencies through a contemporary and sustainable approach to learning. In the current study, we adopted the SDL model based on Knowles’ principles (1975) [ 22 ](Fig.  1 ) to assess its application effect on nurses’ competencies compared to the TLM. We aimed to address contextual factors that improve the safety and quality of care.

A proposed conceptual framework

Trial design

The current study utilized a prospective open-label parallel 1:1 experimental research design employing a posttest-only control group structure within a two-group comparison framework. Pretest was not implemented to avoid a testing threat. The study was carried out between February 2020 and July 2021 and adhered to the guidelines outlined in the Consolidated Standards of Reporting Trials (CONSORT). In addition, the study was registered with Clinical Trials.gov (Registration # NCT04244565) on 28/01/2020, and Official IRB approval for the study’s execution was obtained at the Faculty of Nursing, Cairo University (CU), Egypt (approval # 2020-52). The study’s objectives, methods, potential risks, and benefits were thoroughly explained to the participants, allowing nurses sufficient review time. Participation was voluntary and had no impact on the participants’ performance appraisal. Moreover, participants had the right to withdraw from the study at any time without any repercussions on their professional evaluations. Withdrawn participants received equivalent treatment to those who remained in the regular study. Data was encoded to guarantee the anonymity and confidentiality of the subjects. It should be noted that the data collected were exclusively utilized for the specified research purpose and were not repurposed for any other purposes.

Participants sample and setting

The research was carried out at the Obstetrics and Gynecology Hospital affiliated with Cairo University, Egypt. The architectural construction of selected units consists of pairs of sections, with each pair comprising two sections. These two sections within each pair have comparable numbers of beds, patient flow, equipment, and working nurses. Each pair was divided into two sections, with one section assigned to the control group and the other to the intervention group. This method ensured that the groups were comparable and reduced the influence of potential confounding variables related to the setting. A total of 72 voluntary participants were recruited for study inclusion, all of whom were included in the final analysis. The sample size was determined using G* Power software V.3.1.9.4 (Psychonomic Society, Madison, Wisconsin, USA) with independent t-tests, α = 0.05, Power (1-β) = 0.80, balanced allocation ratio 1:1, and Effect size = 0.65 which is consistent with the effect sizes reported in previous studies [ 23 , 24 ]. The participants were divided into 35 in the study group and 37 in the control group. All selected nurses met the following inclusion criteria: hold their current position for at least one month and have a minimum of two years of critical care experience. Nurses who had plans to resign within the next six months or have been involved in any educational programs related to AM in the past six months were excluded.

Randomization and allocation

The eligible nurses were randomly selected using a simple random sample method utilizing the computer-based program Statistical Package for Social Sciences (SPSS) V.23.0 (IBM, New York) after obtaining the sampling frame, including the pool of eligible participants. Subsequently, a sequential, random allocation was carried out for both the intervention and control groups. In order to prevent contamination between the intervention and control groups, measures were taken to ensure that each group operated in distinct sections throughout the study period. In addition, the process of randomization and allocation was carried out by an uninvolved third party.

The Primary endpoint: The primary outcome was a significant elevation in nurses’ airway management competency indicated by nurses’ knowledge and practices, measured by the Airway Management Structured Questionnaire (AMSQ) and Airway Management Structured Observational Checklist (AMSOC) at the endpoint-2nd post-assessment (3 months after receiving SDL).

The secondary endpoint: The secondary outcome was a significant decrease in airway-related incidents reported during the three-month duration of patient care after receiving the SDL. The incidents were measured using Patient Safety Incident Reports (PSIR).

Measurement tools

The Self-directed Learning Readiness Scale (SDLRS) was developed by Guglielmino in 1977 [ 25 ]. It is a widely used self-reported tool [ 26 ] designed to measure individuals’ perceptions of their ability and readiness to participate in SDL. It consists of 58 items, divided into eight factors. Participants respond to each item using a 5-point Likert scale, with scores ranging from “1 = almost never true of me” to “5 = almost always true of me.” The overall score varies from 58 to 290, with higher values reflecting a higher perceived level of preparedness for SDL. Individuals with a score above 226 are deemed to be above average and prepared for SDL [ 27 ]. The scale demonstrates satisfactory internal consistency, as evidenced by a Cronbach’s Alpha ranging from 0.71 to 0.88 [ 28 ]. Furthermore, the scale collects demographic data, including participant sex, years of experience, staff category, and educational level. We chose the SDLRS for our study because it uses simple and straightforward language, making it easier for participants with limited English skills to understand. Additionally, we provide clear instructions in Arabic and on-demand assistance, enhancing the participants’ sense of ease and guaranteeing easy access to the tool. Finally, this tool is in the public domain, thus necessitating no permission.

The Airway Management Nurses’ Knowledge Questionnaire (AMNKQ) is a tool that was developed by reviewing previous research [ 7 , 29 ]. It is a self-reported tool that was used to assess nurses’ knowledge regarding AM. The questionnaire comprises 20 multiple-choice questions, with options for true and false. Each correct answer is given one point, while an incorrect answer receives zero points. Each student’s scores were summed to calculate the total score, with a maximum possible score of 20.

The Airway Management Nurses’ Practices Checklist (AMNPC) is a checklist designed to monitor the nurses’ practices regarding AM-related nursing practices. A third-party evaluator collected the data, we selected a team of experienced third-party evaluators who underwent comprehensive training to familiarise themselves with the (AMNPC). The training included a detailed overview of the tool, scoring criteria, and case studies. Following the initial training, raters participated in calibration sessions, which involved practice evaluations using standardised video recordings and consensus discussions to align their scoring interpretations. Also, ongoing calibration sessions and periodic reviews were conducted to maintain consistency. The tool consists of 150 steps. Those who successfully and accurately complete a step will receive a score of 2. Those who did complete a step received a score of 1. Those who incorrectly completed a step received a score of zero. The scores of each nurse were aggregated for interpretation, with the maximum score being 300.

The Patient Safety Incident Reports (PSIR): It is an adopted tool developed by the UK National Patient Safety Agency in 2019 for assessing airway-related incidents reported by nurses [ 30 ]. Specifically, it focuses on events associated with airway obstruction, injury, or aspiration. The data collected pertains to the type of incident, its causes, and the frequency of occurrence. A third-party evaluator regularly asked the participants to document any airway-related incidents encountered.

Reliability and validity

Content and scope validity for the developed second and third tools were determined utilizing the Lawshe method [ 31 ]. The tool was reviewed by a panel of five experts in medicine and nursing. After calculating each item’s content validity ratio (CVR), AMNKQ and AMNPC content validity index (CVI) were 0.94 and 0.95, respectively. Before the main study, a pre-test pilot study was conducted with 12 nurses from the same setting to assess the feasibility, acceptability, and internal consistency of the tools. Internal consistency (Cronbach’s alpha) was measured to evaluate the scale’s reliability. The Cronbach’s alpha for AMNPC and AMNKQ were 0.77 and 0.75, respectively, indicating a satisfactory internal consistency.

Self-directed learning for the intervention group

The SDL model was conducted to teach the intervention group of working nurses the concept and competencies of AM. After the assessment of nurses’ readiness to utilize the SDL approach by Tool 1, Knowles’ SDL principles (1975) [ 22 ](Fig.  1 ) were implemented as follows: The participants engaged in a deliberation process to formulate and establish a prearranged course of action. This plan involved defining learning objectives, organizing outlines, arranging activities in a specific order, and setting a timeline for completing the activities within one month. In addition, the participants were provided with a diverse selection of EBP resources and learning materials. These resources were reviewed by nursing experts, and each participant chose the ones that aligned with their preferred learning styles and preferences. Examples of such resources include the Egyptian Knowledge Bank and the book library. The clinical instructor serves as a facilitator, providing regular feedback to both clinical instructors and peers. In addition, participants actively engaged in self-reflection about this learning experience.

Traditional learning model for the control group

The control group of nurses learned the concept of AM and related nursing competencies using the regular clinical teaching approach familiar to the participants. A one-month plan was implemented during regular working hours. We divided the control group into two equal subgroups, each receiving pre-scheduled integrated lectures on the theoretical foundations of AM. There were a total of four lectures, each lasting two hours. The lectures were held once a week and participants received written handouts. The scientific content was adopted from different reviews of the literature [ 4 , 6 ]. After each driven lecture, the researcher gave a supportive four-hour clinical application in a clinical setting utilizing the “see one-do one” clinical learning method. Furthermore, clinical supervision and guidance were given to the participants to ensure their ability to apply acquired knowledge in a real clinical setting.

Procedures of data collection

After the implementation was completed, the follow-up and evaluation process began. This involved monitoring the occurrence of airway-related incidents reported by nurses in both the control and study-selected units on a daily basis for a continuous three-month period using (tool 4). Subsequently, the proficiency and methodologies of the nurses in the control group were assessed on two occasions. The initial assessment took place immediately after the completion of the implementation, while the second assessment occurred three months later to gauge the extent to which the education and training had been retained. This evaluation was conducted using tools 2 and 3. In contrast, self-evaluation was utilized among the study group using tools (2, 3), and it was observed and rated by someone else (peer colleague). Peer evaluations were conducted anonymously, cross-verified by multiple peers, and regular feedback sessions were held to ensure consistency and objectivity in the evaluations. Also, peer raters underwent comprehensive training, practice sessions, consensus meetings, and ongoing calibration to ensure consistency and reliability. The study group was also evaluated two times, similar to the control group. Finally, the researcher conducted a comparative analysis between the SDL results in the study group and the baseline data collected from the control group that underwent traditional ongoing learning practices.

Data analysis

The study of both descriptive and inferential statistics was conducted using IBM SPSS Statistics version 23.0, IBM in New York. The data were presented using mean and standard deviation (SD). The data collected was assessed for normality using Shapiro-Wilk’s test and box plots. In order to examine the statistical differences between the control and intervention groups, an independent samples t-test was conducted with a significance level set at P  < .05. Furthermore, the clinical significance was assessed using Cohen’s d effect size, where an effect size (ES)  ≥  0.4 was considered to be clinically meaningful [ 32 ]. In addition, an analysis of covariance (ANCOVA) was utilized to examine potential confounding factors that may have a statistical influence on the study’s dependent variable, with groups being considered as fixed factors.

A total of 91 nurses were assessed for their eligibility to partake in the present study. Out of these, 72 nurses were successfully enrolled and remained participants until the 2nd post-assessment, which marked the last stage of analysis, as reported in Fig.  2 . The female participants accounted for 93% of the total, with 36.1% falling within the age range of 30 to 40 and 44.4% having accumulated 10 to 20 years of experience. In addition. 69.5% of the participants were diploma nurses. No significant difference was found between the intervention and control groups in terms of demographic features (Table  1 ).

CONSORT flow chart shows subjects’ participation flow

A statistically significant difference was found between the control and intervention groups regarding their practice score during both the first and second points of assessments, with p-values of 0.01 and 0.02, respectively. The intervention group demonstrated the highest practice score (251.05 ± 12.37) during the initial assessment out of a maximum score of 300. Conversely, the control group had the lowest practice score (240.05 ± 10.36) during the subsequent assessment. In addition, the knowledge score was found to be significantly different between the control and intervention groups, either at the first or second assessment points, with p-values of 0.01 and < 0.01, respectively. The intervention group exhibited the highest scores in nurses’ knowledge during the initial assessment, with a mean score of 17.60 ± 0.94 out of a total of 20. The control group achieved the lowest knowledge score in the second assessment point, with a mean score of (13.62 ± 1.47). However, the overall nurses’ practices and knowledge scores were found to be statistically significantly different between the control and intervention groups with p  < .01. Moreover, the difference found between control and intervention groups was clinically relevant, as evidenced by the effect size (ES) Cohen’s d in both practices and knowledge level (-0.56 and − 1.55, respectively) (Table  2 ). This result indicates significant differences between groups [ 31 ].

A significant difference was found between the immediate first post-assessment and the paired second (three months later) post-assessment concerning nurses’ knowledge and practices among control and intervention groups, as indicated by the paired t-test with p  < .01 (Table  3 ). This difference occurs due to a significant decrease in the mean scores of nurses in the second assessment compared to the first assessment.

The intervention group reported 18 airway-related incidents over three months, whereas the control group reported 24 incidents. Nevertheless, the observed difference was not statistically significant. The most prevalent type of airway-related incidents reported by both the control and intervention groups of nurses was airway injury, accounting for a total of 20 occurrences (47.6%). Conversely, the least frequent airway-related incident was aspiration, which was reported by both groups a total of seven times (16.6%) over a three-month period (Table  4 ).

Covariance analysis was conducted to examine the relationship between nurses’ practice scores at the second point (dependent variable) and participants’ groups (fixed variable). The results showed that nurses’ practice scores at the first point were a significant confounding variable (f = 688.96, p  < .01). After adjusting for covariates, such as nurses’ demographic characteristics, nurses’ knowledge, and airway-related incidents, there was no statistically significant impact on the nurses’ practice scores at the second point. In addition, the relationships between groups and study-dependent variables did not demonstrate a statistically significant effect, as indicated by low values of partial eta squared (< 0.02), as shown in Table  5 . This finding suggests that the tested variables have a relatively minor impact on the dependent variable being examined.

The current study is the first in Egypt to examine the impact of utilizing the SDL model compared to TLM among working nurses, focusing on enhancing their competencies regarding AM. The findings presented both clinical and statistical evidence to support the hypothesis that the self-directed clinical learning model enhances nurses’ practices and knowledge levels, leading to improved competency compared to the TLM. Moreover, the improvement was found at two different time points: the first was immediately after the implementation of the intervention, and the second was three months later, serving as a follow-up point.

Nurses may favor SDL due to its capacity to provide flexibility in terms of time and learning modalities. This adaptability can be particularly beneficial for working nurses with varying schedules and commitments. In addition, nurses may feel they take control of their learning process. Additionally, the nurses’ readiness to engage in SDL activity was emphasized as a prerequisite to participate in the study’s intervention group. Those nurses who exhibit readiness for SDL are more likely to demonstrate higher motivation levels to continue learning and enhance their capabilities [ 33 ]. The current study’s findings are consistent with the literature that states SDL is more effective than TLM in increasing nurses’ knowledge and practices [ 34 , 35 ].

The knowledge and practice scores of the nurses in both intervention and control groups decreased over time, three months after the initial assessment following the educational content. A plausible explanation is that the duration for which acquired knowledge and skills are retained tends to decrease over time. Nevertheless, the lack of supervision and the heavy workload can also contribute to this result. This finding is consistent with previous research findings [ 36 ]. The difficulty in maintaining nurses’ competencies can be attributed to nurses’ attitudes, reluctance to change, lack of motivation, and inadequate dedication. This discovery aligns with prior research [ 37 , 38 ].

The intervention group of nurses reported a lower incidence of airway-related incidents in their setting compared to the control group, based on the participants’ reports. Although the statistical significance was not observed, the results imply that the utilization of SDL may have a comparable impact to TLM in decreasing airway-related accidents, which serves as a reliable indicator of providing safe care. Furthermore, the minimal decrease in airway-related incidents may be attributed to a higher level of nurses’ knowledge and practices in the intervention group compared to the control group. Several researchers highlight the significance of training nurses as a highly effective approach to ensuring the maintenance of patients’ airways and the prevention of related complications [ 7 , 21 ]. However, the occurrence of airway-related incidents is influenced by various factors beyond nurses’ competency, including patient-related aspects such as consciousness level, the accessibility of airway-related equipment and supplies, and the involvement of medical staff.

The reported airway-related incidents indicate that airway injury was the most frequently occurring type, while aspiration was the least common. This finding aligns with a previous prospective study that examined airway incidents over a period of six months [ 39 ]. Airway injuries are common among critically ill patients due to airway maintenance procedures such as suctioning, endotracheal intubation, and the use of airway patency equipment. Many other research has evidenced the high frequency of airway injuries [ 40 , 41 ].

The current study implies that the nurses’ practice at the first point substantially influenced their practice scores at the second point. It suggests that interventions or improvements in practice may need to be targeted at an early stage to have a sustainable impact by keeping nurses motivated and emphasizing adequate supervision. Organizational commitment and support are essential, along with effective leadership, to keep nurses encouraged to continue refining their practices [ 42 ]. However, nurses’ demographic characteristics and knowledge level were not given significant emphasis as confounding variables in the nurses’ practice when the participants’ groups were considered fixed variables. The influence of SDL on nurses’ practice outcomes may be more significantly influenced by other prominent factors such as workload, motivation, learning style, and environmental factors. Furthermore, studies have demonstrated that problem-solving ability, self-efficacy, learning attitude, and learning interest were evidenced as factors affecting SDL implications; other studies suggest that collectively, both external and internal elements should be considered while implementing SDL [ 43 , 44 ].

In order to improve the quality of nursing care and maintain patients’ safety, the SDL model demonstrated a positive effect on nurses’ competencies in AM, as indicated by the improvement in their knowledge and practices compared to the TLM. However, the models of learning that the nurses used did not significantly affect the reported airway-related incidents. Therefore, to achieve a significant result in reducing airway-related incidents, it is necessary to manage and regulate various additional factors, including environmental conditions, staffing levels, equipment availability, and collaboration with other staff. Given the study’s findings that both the intervention and control groups experienced a decline in their knowledge and practices over time, it is crucial to examine the factors that hinder nurses from consistently applying what they have learned. It is necessary to identify an appropriate strategy, such as adequate supervision, to ensure the long-term sustainability of nurses’ performance. Furthermore, the observed improvement in nurses’ proficiency resulting from the SDL, as compared to the TLM, was not influenced by the nurses’ demographic characteristics. Consequently, the SDL approach can be beneficial for all nurses, irrespective of their differences. Finally, the investigators suggest refining nurses’ competencies using the SDL model, particularly for those who exhibit readiness. Besides, it is essential to establish collaborative learning environments with other team members to improve overall patient outcomes.

Given that the frequency of airway-related incidents in this study is solely based on participant reports, the primary concern is the possibility of underreporting bias. This bias stems from the potential reluctance of nurses to report or disclose airway-related incidents. The study was conducted in only one healthcare setting; therefore, examining the study variables in different settings is recommended to ensure generalization. The current study was an “open-label” study, as participants were informed about their group assignment (intervention or control), potentially impacting their behavior or responses. A blinding methodological approach is recommended in future research.

Data availability

The tools utilized for data collection, SDLRS, AMNKQ, AMNPC, and PSIR, in addition to the raw data of this study, are available from the corresponding author upon request.

Nguyen LHP, Bank I, Fisher R, Mascarella M, Young M. Managing the airway catastrophe: longitudinal simulation-based curriculum to teach airway management. J Otolaryngol - Head Neck Surg. 2019;48:10. https://doi.org/10.1186/s40463-019-0332 .

Article   PubMed   PubMed Central   Google Scholar  

Holbery N, Newcombe P. Emergency nursing at a glance. Wiley; 2016.

Chatterjee A, Islam S, Divatia JV. Airway accidents in an intensive care unit. 2003. https://doi.org/10.5005/ijccm-8-1-36

Higgs A, Cook TM, McGrath BA. Airway management in the critically ill: the same, but different. BJA: Br J Anaesth. 2016;117 suppl1:i5–9. https://doi.org/10.1093/bja/aew055 .

Article   Google Scholar  

Carney N, Totten AM, Cheney T, Jungbauer R, Neth MR, Weeks C, et al. Prehospital Airway Management: a systematic review. Prehospital Emerg Care. 2021;26:716–27. https://doi.org/10.1080/10903127.2021.1940400 .

da Silva LFM, Pascoal LM, Nunes SFL, de Sousa Freire VEC, de Araújo Almeida AG, Gontijo PVC, et al. Ineffective Airway Clearance in Surgical patients: evaluation of nursing interventions and outcomes. Int J Nurs Knowl. 2019;30:251–6. https://doi.org/10.1111/2047-3095.12242 .

Article   PubMed   Google Scholar  

Nigatu M, Debebe F, Tuli W. Assessment of Knowledge, Practice, and Associated Factors towards Airway and Breathing Management among nurses Working in the Emergency Departments of Selected Public Hospitals in Addis Ababa, Ethiopia: a cross-sectional study. Open Access Emerg Med. 2022;14:235–47. https://doi.org/10.2147/OAEM.S366218 .

Hassan AMA. Effect of Educational Program on Nurses’ practice Regarding Care of Adult Patients with Endotracheal Tube. Port said scientific journal of nursing. 2018;5:142–69. doi.10.21608/pssjn.2018.33320.

Sultan M, Hussien Ahmed H, Seliman A, Morsy AM, Sultan WYA, Elshamy MA. Impact of a designed head trauma nursing management protocol on critical Care nurses’ Knowledge and practices at Emergency Hospital Mansoura University. J Am Sci. 2014;10:1545–1003.

Google Scholar  

Spaliaras J, Streiff A, Mann G, Straker T. Teaching and training in airway management: time to evaluate the current model? Airway. 2019;2:28. https://doi.org/10.4103/ARWY.ARWY_12_19 .

Ni C, Hua Y, Shao P, Wallen GR, Xu S, Li L. Continuing education among Chinese nurses: a general hospital-based study. Nurse Educ Today. 2014;34:592–7. https://doi.org/10.1016/j.nedt.2013.07.013 .

Niederhauser V, Schoessler M, Gubrud-Howe PM, Magnussen L, Codier E. Creating innovative models of clinical nursing education. J Nurs Educ. 2012;51:603–8. https://doi.org/10.3928/01484834-20121011-02 .

Luhanga FL. Learning in the traditional faculty supervised teaching model: part 1–The nursing students’ perspective’. J Nurs Educ Pract. 2017;8:89–102. https://doi.org/10.5430/jnep.v8n3p89 .

Hill L, Williams EP. Contemporary models for clinical nursing education. 2017.

Ghiyasvandian S, Malekian M, Cheraghi MA. Iranian clinical nurses’ activities for Self-Directed Learning: a qualitative study. Glob J Health Sci. 2015;8:48–58. https://doi.org/10.5539/gjhs.v8n5p48 .

Montin L, Koivisto J-M. Effectiveness of self-directed learning methods compared with other learning methods in nursing education related to nursing students’ or registered nurses’ learning outcomes: a systematic review protocol. JBI Database Syst Rev Implement Rep. 2014;12:1–8. https://doi.org/10.11124/jbisrir-2014-532 .

Levett-Jones TL. Self-directed learning: implications and limitations for undergraduate nursing education. Nurse Educ Today. 2005;25:363–8. https://doi.org/10.1016/j.nedt.2005.03.003 .

Kim R, Olfman L, Ryan T, Eryilmaz E. Leveraging a personalized system to improve self-directed learning in online educational environments. Comput Educ. 2014;70:150–60. https://doi.org/10.1016/j.compedu.2013.08.006 .

Murad MH, Varkey P. Self-directed learning in health professions education. Annals Acad Med Singap. 2008;37:580.

University of Waterloo. Self-Directed Learning: A Four-Step Process. Centre for Teaching Excellence. 2020. https://uwaterloo.ca/centre-for-teaching-excellence/catalogs/tip-sheets/self-directed-learning-four-step-process . Accessed 25 Jan 2024.

Miranda FBG, Alves Pereira-Junior G, Mazzo A. Competences in the training of nurses to assist the airway of adult patients in urgency and emergency situations. Rev Lat Am Enfermagem. 2021;29. https://doi.org/10.1590/1518-8345.3380.3434 .

Knowles MS. Self-directed learning: A guide for learners and teachers. 1975.

Noh G-O, Kim DH. Effectiveness of a self-directed learning program using blended coaching among nursing students in clinical practice: a quasi-experimental research design. BMC Med Educ. 2019;19:225. https://doi.org/10.1186/s12909-019-1672-1 .

Choi E, Lindquist R, Song Y. Effects of problem-based learning vs. traditional lecture on Korean nursing students’ critical thinking, problem-solving, and self-directed learning. Nurse Educ Today. 2014;34:52–6. https://doi.org/10.1016/j.nedt.2013.02.012 .

Guglielmino LM. Development of the self-directed learning readiness scale. University of Georgia; 1977.

Adenuga BO, Wilson J. Self-directed learning readiness and learning style preferences of adult learners. 1989.

Guglielmino PJ, Guglielmino LM. An examination of the relationship between self-directed learning readiness and job performance in a major utility. Guglielmino and Associates: Unpublished research report; 1982.

Chen S, Fan J. Validation of the psychometric properties of the Self-Directed Learning Readiness Scale. Nurs Open. 2023;10:1639–46. https://doi.org/10.1002/nop2.1418 .

Nshutiyukuri C, Rosemary Bhengu B, Gishoma D. An assessment of nurses’ knowledge, attitude and practice of emergency care related to road traffic accident victims at three selected hospitals in Rwanda. 2020. https://doi.org/10.1016/j.afjem.2020.03.003

National Health Service (NHS). Patient safety incident reports, NHS-UK. 2019. https://webarchive.nationalarchives.gov.uk/ukgwa/20200501110913/https://improvement.nhs.uk/resources/national-patient-safety-incident-reports-25-september-2019/ . Accessed 30 Jan 2024.

Lawshe CH. A quantitative approach to content validity. Pers Psychol. 1975;28:563–75.

Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale NJ Erlbaum; 1988.

Chen L, Tang X-J, Liu Q, Zhang X. Self-directed learning: alternative for traditional classroom learning in undergraduate ophthalmic education during the COVID-19 pandemic in China. Heliyon. 2023;9. https://doi.org/10.1016/j.heliyon.2023.e15632 .

Lee S, Kim DH, Chae SM. Self-directed learning and professional values of nursing students. Nurse Educ Pract. 2020;42:102647. https://doi.org/10.1016/j.nepr.2019.102647 .

Murad MH, Coto-Yglesias F, Varkey P, Prokop LJ, Murad AL. The effectiveness of self‐directed learning in health professions education: a systematic review. Med Educ. 2010;44:1057–68. https://doi.org/10.1111/j.1365-2923.2010.03750.x .

García Botero G, Questier F, Zhu C. Self-directed language learning in a mobile-assisted, out-of-class context: do students walk the talk? Comput Assist Lang Learn. 2019;32:71–97. https://doi.org/10.1080/09588221.2018.1485707 .

Chu T-L, Wang J, Monrouxe L, Sung Y-C, Kuo C, Ho L-H, et al. The effects of the flipped classroom in teaching evidence based nursing: a quasi-experimental study. PLoS ONE. 2019;14:e0210606. https://doi.org/10.1371/journal.pone.0210606 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Mannix J, Faga P, Beale B, Jackson D. Towards sustainable models for clinical education in nursing: an on-going conversation. Nurse Educ Pract. 2006;6:3–11. https://doi.org/10.1016/j.nepr.2005.05.004 .

Endlich Y, Beckmann LA, Choi S-W, Culwick MD. A prospective six-month audit of airway incidents during anaesthesia in twelve tertiary level hospitals across Australia and New Zealand. Anaesth Intensive Care. 2020;48:389–98. https://doi.org/10.1177/0310057X20945325 .

Brodsky MB, Levy MJ, Jedlanek E, Pandian V, Blackford B, Price C, et al. Laryngeal injury and upper airway symptoms after oral endotracheal intubation with mechanical ventilation during critical care: a systematic review. Crit Care Med. 2018;46:2010. https://doi.org/10.1097/CCM.0000000000003368 .

Cook TM, MacDougall-Davis SR. Complications and failure of airway management. Br J Anaesth. 2012;109 suppl_1:i68–85. https://doi.org/10.1093/bja/aes393

Fleiszer AR, Semenic SE, Ritchie JA, Richer M-C, Denis J-L. An organizational perspective on the long-term sustainability of a nursing best practice guidelines program: a case study. BMC Health Serv Res. 2015;15:535. https://doi.org/10.1186/s12913-015-1192-6 .

Wong FMF, Tang ACY, Cheng WLS. Factors associated with self-directed learning among undergraduate nursing students: a systematic review. Nurse Educ Today. 2021;104:104998. https://doi.org/10.1016/j.nedt.2021.104998 .

Ramli N, Muljono P, Afendi FM. External factors, Internal Factors and Self-Directed Learning readiness. J Educ Elearn Res. 2018;5:37–42. https://doi.org/10.20448/journal.509.2018.51.37.42 .

Download references

Acknowledgements

The authors acknowledge the support of the hospital’s chief executive officer and nurse director where the research was conducted.

The authors disclosed no funding was received for conducting this research.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

Author information

Authors and affiliations.

Faculty of Nursing, Cairo University, Cairo, 11562, Egypt

Sameh Elhabashy

Aswan Heart Centre – Magdi Yacoub Foundation, Aswan, Egypt

Amen Moawad

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, S.E.; methodology, S.E.,and A.M.; Data collection, S.E.; investigation and formal analysis, S.E. and A.M.; writing—review and editing, S.E., and A.M.; All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sameh Elhabashy .

Ethics declarations

Ethics approval and informed consent to participate.

Before conducting the study, the authors obtained ethical permission from the Research Ethics Committee for Human Subject Research at the Faculty of Nursing, Cairo University, Egypt (approved # 2020-52). All participants in this study voluntarily agreed to participate and provided written informed consent after being fully instructed about the study’s objectives, potential hazards, and possible benefits. The study was implemented in compliance with local laws in Egypt, also, the protection of participants’ rights and safety was ensured. All procedures followed the standards and restrictions outlined in the Declaration of Helsinki. Each participant was assigned an exclusive identification number to ensure their identity. Additionally, confidentiality was assured.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Elhabashy, S., Moawad, A. Effect of self-directed versus traditional learning model on nurses’ airway management competencies and patients’ airway-related incidents. BMC Nurs 23 , 599 (2024). https://doi.org/10.1186/s12912-024-02232-0

Download citation

Received : 30 January 2024

Accepted : 06 August 2024

Published : 27 August 2024

DOI : https://doi.org/10.1186/s12912-024-02232-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Self-directed learning
• Traditional learning model
• Airway management
• Nurses’ competencies
• Airway-related incidents

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]

Coastal cities must adapt faster to climate change

A new study investigating the status of climate change adaptation in coastal cities worldwide discovers progress and shortcomings.

Coastal cities play a key role in the global economy and have important functions for society at large. At the same time, they are severely affected by the impact of climate change. That is why their role in global climate adaptation is crucial. To find out how coastal cities are adapting, an international team led by Professor Matthias Garschagen, a geographer at Ludwig-Maximilians-Universität München (LMU), has now analyzed the current state of adaptation.

Based on studies of 199 cities across 54 countries, the researchers investigated whether and how cities take certain risk factors into account in their adaptation efforts. Climate factors like rising sea levels, storms, flooding and heat were among the key parameters considered. Other aspects were also taken into account in the analysis, such as the exposure and vulnerability of the population, the infrastructure and the ecosystems in the respective region.

Climate measures are mostly inadequate

Most of the measures taken to adapt to climate change relate primarily to sea level rise, flooding and, to a lesser extent, storm surges, cyclones and erosion. Technical and institutional measures such as large-scale levees or urban planning innovations are more common in wealthier regions like North America and Europe. In less prosperous regions such as in many parts of Africa and Asia, behavior-related measures are the dominant type, with affected households and companies being largely left to their own devices.

Overall, the LMU researchers found that most adaptation measures are inadequate in their depth, scope and speed -- regardless of the region or its prosperity. The researchers also found little evidence of a sustainable reduction in risk as a result of the measures taken.

"Our findings reveal that there is plenty of work still to be done on all levels," explains Prof. Matthias Garschagen. "There has been little truly far-reaching change involving a fundamental rethink of risk management. Cities often attempt to optimize their disaster management on the basis of past experience without fundamentally questioning whether these approaches are still going to be viable in the future," says Garschagen.

Global research on climate change needs to be done in all regions of the world

The research also found that it is rare for adaptation planning to be based on quantifiable factors. Although cities do take future natural risks such as flooding and heat into account, they rarely consider socioeconomic factors such as future trends in societal vulnerability or spatial growth and exposure. "But those trends those are important," says Garschagen, "because the Lagos or Jakarta of today is not the same as it's going to be in 20 years' time. There are certainly big research aps and we need better scenarios and better modeling methods. Another important question is about when it makes more sense to abandon coastal protection measures and consider resettling the population instead."

Matthias Garschagen is therefore calling for a major increase in research activity in the Global South. Most of the research activity to date has been concentrated on cities in the Global North. "Global climate change research that covers all regions of the world would enable us to fight the climate crisis faster and more effectively," says Matthias Garschagen.

• Environmental Awareness
• Global Warming
• Environmental Issues
• Environmental Policies
• Ocean Policy
• Resource Shortage
• Land Management
• Climate change mitigation
• Global warming controversy
• Climate engineering
• Supervolcano
• Global climate model
• Temperature record of the past 1000 years
• IPCC Report on Climate Change - 2007

Story Source:

Materials provided by Ludwig-Maximilians-Universität München . Note: Content may be edited for style and length.

Journal Reference :

• Mia Wannewitz, Idowu Ajibade, Katharine J. Mach, Alexandre Magnan, Jan Petzold, Diana Reckien, Nicola Ulibarri, Armen Agopian, Vasiliki I. Chalastani, Tom Hawxwell, Lam T. M. Huynh, Christine J. Kirchhoff, Rebecca Miller, Justice Issah Musah-Surugu, Gabriela Nagle Alverio, Miriam Nielsen, Abraham Marshall Nunbogu, Brian Pentz, Andrea Reimuth, Giulia Scarpa, Nadia Seeteram, Ivan Villaverde Canosa, Jingyao Zhou, Matthias Garschagen. Progress and gaps in climate change adaptation in coastal cities across the globe . Nature Cities , 2024; DOI: 10.1038/s44284-024-00106-9

Cite This Page :

Explore More

• Six New Rogue Worlds: Star Birth Clues
• Ancient Climate Change and Fossilized Shells
• How Black Holes Produce Vast Intergalactic Jets
• Hidden Magmatism On the Moon
• Black Holes in High Res
• Your Favorite Film Genres and Your Brain
• Annual Breast Cancer Screening for Women Over 40
• Pregnancy After 'Permanent Sterilization'
• Climate: Coastal Cities Must Adapt Faster
• No Crisis in Cosmology, After All

Trending Topics

Strange & offbeat.

What Gartner's 2024 hype cycle forecast tells us about the future of AI (and other tech)

Technology often has a fairly predictable adoption cycle , going from innovators and early adopters to mainstream use, to the point where even those who are way behind the curve catch up and start using the technology.

But there's another cycle at play -- the hype cycle -- and this impacts everything from budgeting to forecasting to startup investments. Coined back in 1995 by research firm Gartner , every annual Hype Cycle report attempts to show whether a technology is on track for productive use, or is still in the smoke-and-mirrors phase of its life.

Also: Time for businesses to move past generative AI hype and find real value

Gartner defined five key phases in the cycle. 

Five phases of the hype cycle

The Innovation Trigger phase is all about building excitement. This is where a new technology like generative AI begins to show some serious promise, and where engineers, marketers, and investors can see the potential -- even though most of that potential is as yet unfulfilled and, in many cases, not even possible with current technology.

Then comes the Peak of Inflated Expectations. By this point, press coverage has been breathless and overwhelming, entrepreneurs have been pitching new startups, marketers have been adding allusions to the technology to everything they're pitching, and… enough, already!

AI is a good example of this. I mean, wow. Aren't you reaching a saturation point with all the over-the-top AI hype getting thrown around? I just got a 3D printer that was drenched in an AI washing effort . Although the tech in this printer was exactly the same as it's always been, the product came with "AI assisted" plastered all over the product casing, the website, and the promotional materials.

Next -- and I think this is the real innovation in Gartner's cycle -- comes the Trough of Disillusionment. Just as teenagers go through a phase where nothing's ever good enough, so too do tech products. After what seems like an unending promotion with little real uptake and deployment, the technology previously subjected to such lofty and exuberant fuss now appears to have wings made of wax. Expectations come crashing to the ground.

Although Gartner doesn't describe this, I've often seen how this phase is accompanied by ridicule. Anyone who -- post-peak -- recommends or discusses the so-called "failed" technology is considered a out of touch or a fanboi who hasn't accepted reality.

Also: 7 upgrades Apple Vision Pro needs to succeed in business

VR has been in this phase repeatedly, and -- I expect -- will go through it again. Take Apple's Vision Pro headset . It's wildly expensive, amazing to use, uncomfortable, and -- at least for now -- pretty much a novelty except for some specific vertical uses.

In fact, in Gartner's 2024 Hype Cycle for Emerging Technologies, the analyst firm places spatial computing at the early edge of the Innovation Trigger phase. But I'm not so sure. As someone who's been covering the technology's developments all year, I'd suggest that spatial computing -- at least as it pertains to the Vision Pro -- has landed in the Trough of Disillusionment. In a few years, when Apple introduces a cheaper and lighter headset, I'm sure the Vision product line will once again run the Hype Cycle curve, possibly with better results. 

Finally, some technologies crawl out of the Trough of Disillusionment and begin their climb up the Slope of Enlightenment and the Plateau of Productivity. These two phases refer to the time when a technology begins finding its footing, its specific value propositions are proven, and it enters some level of productive use, albeit without the associated hype dogging its every step.

Gartner's Hype Cycle for Emerging Technologies, 2024

Each year, Gartner issues a total of 25 different hype cycles. ZDNET has been covering their cycle for emerging technology since, well -- I found an article from 2009 . What makes this particular hype cycle about emerging technologies so compelling? It helps us predict what will be hot and what will not. It also helps businesses predict where to put their cash, where to assign staff to evaluate potential, and where it might be practical to innovate.

But you need to take the hype cycle with a grain of salt. Back in 2021, we wrote that Gartner predicted, "Artificial intelligence's impact on generating code, augmenting design and innovation is all 5- to 10-years away." That was wrong. Generative AI began making a substantial impact in just two years, at the very beginning of 2023.

Also: AI Engineering: The next frontier for research and technological advances

But that was then, and this is now. In 2024, Gartner has identified four major themes that are just starting to climb the big Innovation Trigger hill. These are: autonomous AI, developer productivity, total experience, and human-centric security. We'll break each of these themes down next.

Autonomous AI

The obvious first point of contact here is self-driving car technology. Beyond that, think of large action models (where AIs take action, not just spew information), machine customers (where machines buy stuff), humanoid working robots (every science fiction movie you've ever seen), autonomous agents , and reinforcement learning.

The big idea here is that AI systems will take on tasks that humans performed previously. This goes beyond generative AI writing essays for college students who just want to have fun. Instead, we're looking at machines that can perform physical tasks (cars and robots, for example), and machines that interact with the rest of the world (like printers that automatically order printer ink or cars that automatically schedule their own maintenance visits to the local dealer).

Also: 5 ways ChatGPT can help you write an essay

Obviously, there are quite a few obstacles before autonomous AI can achieve real productivity, not the least of which is that most of us are nervous about letting robots loose in the world. I mean, who hasn't seen Terminator?

But there are other issues, including regulatory concerns, areas where data is scarce and yet AIs need to make decisions, lack of trust, overall computational requirements (as well as battery power duration), and more.

Keep in mind that different projects may be at different points along the hype cycle. For example, Apple canceled its multi-billion dollar self-driving car project, while Alphabet's robo-taxi service actually doubled the number of riders over the last few months.

AI-augmented software development

While the hype over AI writing code is huge, even the leading players fail miserably --  as we've seen through  ZDNET's hands-on testing . The hype is incredible, and perfectly in line with the idea that AI-augmented software development is on the Innovation Trigger rocket flight.

And, to be fair, it is exciting. When I actually got ChatGPT to write a WordPress plugin for my wife's e-commerce business, I was astounded. Subsequently, I have used ChatGPT to help me write a ton of code. Overall, I estimate that it saved me weeks, if not a month or two, on my projects over the last year.

Also:  The best AI for coding in 2024 (and what not to use)

But here's the thing: The AI didn't write my code. The AI helped me write my code. Most of the hype around AI coding implies that the AIs will just generate the app you have in mind, as long as you can type " Write me an app that will make me a million dollars " into the prompt bar.

Those who rely too much on AI coding will take a deep dive into that Trough of Disillusionment. But those who use AI to help write carefully defined and tested snippets of code will find some very big benefits.

Empower with total experience

Every few years, there's another customer-centric buzzword that promises endless profits. Once upon a time, it was multichannel -- the idea that you meet the customer wherever they want you to be, whether that's on their phone, in their desktop browser, on social media, or even in a physical location.

Gartner's premise for "total experience" is that vendors will create super-salient shared experiences that "intertwine customer experience, employee experience, multi-experience, and user experience practices."

I know. It makes my head hurt, too.

Also:  Artificial intelligence, real anxiety: Why we can't stop worrying and love AI

It might make more sense if you look at the emerging technologies Gartner attributes to this trend: 6G , spatial computing, and digital twins of customers.

Nobody has fully defined 6G yet, but the best description was the one a telecommunications executive told me during a discussion of future technology: super-fast 5G with a lot of AI help. Specifically, think of this as collapsed latency, so it's possible to respond in real-time to whatever is happening. This will also aid self-driving cars.

Spatial computing is something we're getting to know in the Vision Pro and the Meta Quest 3 , but it will become far more constructive once it works in regular glasses, rather than headsets that weigh the same as a brick.

The digital twins of customers concept is creepy as heck. Basically, it describes a way companies can model consumer interests and behaviors so accurately that they can simulate customer interaction and affinity based on their established data history. All to better manipulate folks into buying! And yes, this same technology can be used to influence elections. Yikes.

Deliver human-centric security and privacy

The last major trend has to do with the need for across-the-board improved security. The concept behind "human-centric" is that individuals have to be part of the overall security footprint. That includes a focus on the user experience, finding behavioral insights, encouraging security behavior, and building trust through transparency.

Also: AI PCs bring new security protections and risks. Here's what users need to know

But Gartner sees a bunch of technological trends supporting this effort. They include AI TRISM (AI trust, risk, and security management), which approaches security from a trustworthy, secure, transparent, and ethical approach. Mesh architecture security environments are intended to make security scalable and modular. The idea of a digital immune system combines technologies and practices to build resilience by proactively identifying threats and responding to them.

AI comes into play here as well, across all the solution areas. One big push is into the idea of federated machine learning, where the learnings captured in one part of the enterprise network are federated (made available) to the entire network.

Are Gartner's predictions on the right track?

Every year, it looks like we're getting closer and closer to the world of Blade Runner . I found the idea of customer twins and spatial advertising particularly evocative of replicants and the customized marketing shown in the classic movie.

Gartner's predictions are just that: predictions. As the chart above shows, the research firm has identified more emerging trends beyond those I've discussed. These four trends, however, are the ones you should look out for this year, going into next year.

What do you think? Is Gartner on the right track? Are there other trends we should be looking at? Let us know in the comments below.

You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter , and follow me on Twitter/X at @DavidGewirtz , on Facebook at Facebook.com/DavidGewirtz , on Instagram at Instagram.com/DavidGewirtz , and on YouTube at YouTube.com/DavidGewirtzTV .

Artificial Intelligence

Chatgpt is (obviously) the most popular ai app - but the runners up may surprise you, ai hype, in dollars and sense, so long, point-and-click: how generative ai will redefine the user interface.