literature review on learning organizations

• Search Menu
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Acquisition
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Religion
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Lifestyle, Home, and Garden
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business Strategy
• Business History
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Systems
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Politics and Law
• Public Policy
• Public Administration
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Developmental and Physical Disabilities Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

22 Learning Organization and Organizational Performance

School of Business, Sungkyunkwan University

College of Education, University of Georgia

• Published: 08 January 2020
• Cite Icon Cite
• Permissions Icon Permissions

One of the strategic ways of continuing sustainable success is becoming a learning organization, an ideal environment for securing organizational performance. This chapter begins by reviewing the literature on a learning organization and organizational performance focusing on their research context, instruments, analytic methods, and empirical findings. Then, it discusses the importance of a learning organization in promoting organizational performance, especially an organization’s capacity of connecting it to its external environment and the existence of leadership that supports learning. This chapter further addresses the stronger relationship between a learning organization and non-financial performance together with the necessity of dimensionalizing organizational performance and developing corresponding measures. Lastly, this chapter suggests methodological recommendations for future studies.

Introduction

Becoming a learning organization assists an organization to overcome the challenges in a contemporary organizational environment because of the nature of a learning organization—a learning organization can be tailored with efforts, which enables an organization to reach a hoped-for state (Örtenblad 2001 ). Abundant studies have addressed areas where an organization should make efforts, and common recommendations are as follows: a learning organization has systems that encourage and share learning across an organization (Garvin 1993 ; Goh 1998 ; McGill, Slocum, and Lei 1992 ; Pedler, Burgoyne, and Boydell 1991 ; Senge 1990 ; Ulrich, Jick, and Von Glinow 1993 ; Watkins and Marsick 1993 ); in addition, a learning organization has a strong sense of its external environment as well as the ability to connect to its external environment (Garvin 1993 ; Goh 1998 ; McGill et al. 1992 ; Pedler et al. 1991 ; Watkins and Marsick 1993 ). Moreover, a learning organization has leaders who support learning (Goh 1998 ; McGill et al. 1992 ; Ulrich et al. 1993 ). Lastly, a learning organization emphasizes individual- and group-level learning in addition to system-level learning (Pedler et al. 1991 ; Senge 1990 ; Ulrich et al. 1993 ; Watkins and Marsick 1993 ) and a supportive atmosphere towards learning (Gephart, Marsick, Buren, and Spiro 1996 ). By taking these approaches, an organization is able to adapt to changes and transform itself in a timely manner (Watkins and Marsick 1993 ).

One advantage of becoming a learning organization can be securing sustainable success (Kim, Watkins, and Lu 2017 ). Indeed, abundant studies have identified the impact of a learning organization on diverse facets in organizations, such as interpersonal trust, organizational commitment, or work engagement (Watkins and Kim 2018 ). As these outcomes form a favorable condition for facilitating performance at the organizational level, such findings can be understood in line with what March ( 1991 ) argued—learning provides a stable environment for producing performance. Although it is fairly reasonable to assume the positive consequences of a learning organization in terms of expecting improved organizational performance, yet, there is little knowledge of the dynamics between a learning organization and organizational performance (Pedler and Burgoyne 2017 ; Watkins and Kim 2018 ).

This chapter reviews studies focusing on the learning organization and organizational performance and seeks to identify relationships that underlie them. An internet-based search was used to identify academic articles for inclusion in this review. “Learning organization” and “organizational performance” were used as major keywords in the search process.

Literature Review

This section reviews the literature on the learning organization and organizational performance by focusing on research contexts, instruments, analytic methods, and empirical findings.

Research Contexts and Instruments

Studies on the learning organization and organizational performance have been conducted under different cultural settings. They started from understanding organizations in the Americas (e.g., Davis and Daley 2008 ; Hernandez 2000 ; Watkins, Milton, and Kurz 2009 ; Yang, Watkins, and Marsick 2004 ), in Asia (e.g., Laeeque and Babar 2015 ; Noubar, Rose, Kumar, and Salleh 2011 ; Shieh 2011 ; Zhou, Hu, and Shi 2015 ), and in the Middle East (e.g., Bhaskar and Mishra 2017 ; Ngah, Tai, and Bontis 2016 ). These studies have focused on diverse organizational contexts from public sectors (e.g., Bhaskar and Mishra 2017 ; Ngah et al. 2016 ; Pokharel and Choi 2015 ) to private companies (e.g., Hernandez 2000 ; Shieh 2011 ) and even multinational organizations (Shieh, Wang, and Wang 2009 ).

Regarding instruments for measuring a learning organization, studies have operationalized the concept of a learning organization by using items based on Senge ( 1990 ) (Shieh 2011 ), adopting items from Garvin, Edmondson, and Gino ( 2008 ) and Watkins and Marsick’s (1997) Dimensions of a Learning Organization Questionnaire (DLOQ) (Zhou et al. 2015 ), or applying the DLOQ to measure a learning organization (e.g., Lien, Hung, Yang, and Li 2006 ; Ngah et al. 2016 ; Pokharel and Choi 2015 ; Yang et al. 2004 ; Yu and Chen 2015 ). The items based on Senge ( 1990 ) represent the following five fundamental components: personal mastery, mental models, shared vision, team learning, and system thinking. The items of Garvin et al. (2008) measure the degree to which the learning environment, the learning process, and leadership reinforce learning. Watkins and Marsick’s DLOQ is a questionnaire that includes items capturing the following seven dimensions: continuous learning opportunities, dialogue and inquiry, team learning, systems to capture and share learning, empowerment, connectivity to the environment, and strategic leadership for learning. Also, the DLOQ consists of perceptual organizational performance items.

Most studies examined organizational performance as an outcome variable. These studies have focused on organizational financial performance as well as non-financial performance, such as innovation or satisfaction. In order to measure organizational performance, these studies have adopted objective measures, such as sales turnover or return on assets (Ngah et al. 2016 ; Zhou et al. 2015 ), subjective measures, such as perceptual financial performance and innovation capability (Shipton, Zhou, and Mooi 2013 ; Zhou et al. 2015 ), and used a combination of several indicators to measure organizational performance (Kontoghiorghes, Awbre, and Feurig 2005 ; Shieh 2011 ), or the DLOQ (e.g., Jain and Moreno 2015 ; Watkins et al. 2009 ; Yang et al. 2004 ) depending on their research purpose and context.

Analytic Methods and Empirical Findings

Studies have hypothesized that a learning organization influences the organizational performance. In order to test such a hypothesized relationship, many studies applied correlation or regression analysis, and a few studies performed structural equation modeling (Kim et al. 2017 ; Ngah et al. 2016 ; Pokharel and Choi 2015 ).

Studies which applied correlation analysis have found a significant correlation between the two (e.g., Davis and Daley 2008 ; Shieh 2011 ; Watkins et al. 2009 ; Yang et al. 2004 ). Correlation analysis produces an association coefficient called correlation that describes the degree to which two variables are related. For different types of data, there are different types of correlation, among which the Pearson’s correlation for two continuous variables is the most popular one. A correlation ranges from −1.0 (perfect negative relationship) to 1.0 (perfect positive relationship). Studies that adopted the DLOQ showed correlations ranging from 0.10 to 0.63 for financial performance and 0.22 to 0.73 for knowledge performance (e.g., Davis and Daley 2008 ; Hernandez 2000 ; Watkins et al. 2009 ; Yang et al. 2004 ; Zhang, Zhang, and Yang 2004 ). More specifically, these studies have shown that an organization’s ability to connect to its external environment and the presence of leaders who support learning tend to produce a higher correlation. Shieh ( 2011 ) used items based on Senge ( 1990 ) and Chien ( 2004 ) to measure a learning organization and organizational performance, respectively. This study also reported that a learning organization is highly correlated to organizational performance by exhibiting correlations higher than 0.80.

As correlation does not imply causation, studies using regression analysis have provided evidence that a learning organization is able to predict organizational performance. Regression analysis is a powerful statistical method to examine the causal effects between two or more variables of interest. In other words, it examines the influence of one or more independent variables (or causes, here, learning organization) on a dependent variable (or an effect, here, organizational performance). More specifically, it helps to explain the variation in organizational performance given the change in learning organizations, or to predict the organizational performance in the future by changing learning organizations. Applying this regression analysis, studies have investigated how much of the variability observed in organizational performance can be explained by the seven learning organization dimensions of the DLOQ (Bhaskar and Mishra 2017 ; Laeeque and Babar 2015 ; Noubar et al. 2011 ; Rose, Salleh, and Kumar 2006 ). Empirical findings of these studies include the following: Rose et al. (2006) showed that the team learning, system connection, and leadership dimensions explain 53 percent of the variance in financial performance; the dialogue and inquiry, embedded systems, and leadership dimensions explain 59 percent of variance in knowledge performance in the MSC in Malaysia ( N = 208). Kumar and Idris ( 2006 ) demonstrated that 41 percent of the variance in knowledge performance is accounted for by team learning, system connection, and leadership dimensions in educational institutions in Malaysia ( N = 235). Yu and Chen ( 2015 ) reported that the continuous learning, system connection, and leadership dimensions explain 41 percent of the variance in universities and college libraries in Taiwan, China ( N = 478). Noubar et al. (2011) revealed that the seven learning organization dimensions jointly explain 27 and 34 percent of the variance in financial and knowledge performance, respectively, when analyzing organizations in Malaysia ( N = 218). Bhaskar and Mishra ( 2017 ) found that the continuous learning, dialogue and inquiry, system connection, and leadership dimensions explain 27 percent of the variance in financial performance; the system connection and leadership dimensions explain 59 percent of the variance in knowledge performance in public sector organizations in India ( N = 204).

Recently, structural equation models are becoming more and more important in the research of learning organizations. For example, studies suggested the mediating role of a learning organization in enhancing organizational performance and also the paths among the seven dimensions of the DLOQ and performance outcomes (Ngah et al. 2016 ; Pokharel and Choi 2015 ). By examining organizations in public sectors in UAE ( N = 255), Ngah et al. (2016) not only proved that a knowledge management capability on infrastructure (i.e., structure, culture, and information and communication technology) and process (i.e., from knowledge acquisition to dissemination and protection) of an organization has an effect in differentiating organizational performance, but also found that a learning organization fully mediates the relationship between the two. Pokharel and Choi ( 2015 ) stressed the importance of an organization’s ability in connecting to its external environment: this allows the organization to mediate the effect of individuals’ continuous learning and empowerment on organizational performance based on the analysis of public sector organizations in the US ( N = 331).

Thus far, this section has reviewed the literature on the learning organization and organizational performance. Researchers have conducted studies on the relationship between these two under diverse cultural and organizational settings and with different measures. By providing empirical evidence derived from correlation, regression, and structural equation modeling, these studies have supported the view that a learning organization not only correlates to but also predicts organizational performance.

Discussion and Suggestions

This section discusses the empirical findings of these studies and suggests future research directions. First, it stresses the importance of organizational capacity in connecting the organization to the environment and the role of leadership. Then, it demonstrates the implications of the findings focusing on non-financial performance followed by ideas on quantitative analytic methods for future studies.

Connectivity to the Environment and Leadership

The learning organization literature suggests that enhanced organizational performance is closely related both to an organization’s ability to connect to its external environment and to leadership (Bhaskar and Mishra 2017 ; Laeeque and Babar 2015 ; Noubar et al. 2011 ; Rose et al. 2006 ), which are the common themes that have emerged across different approaches to understanding the learning organization.

For an organization, having the capacity to align structures or strategies with its environment allows the organization to respond to changes in its environment proactively (Brown and Duguid 2001 ). A capacity that manages tensions within structures, processes, and cultures brings innovations (Tushman and O’Reilly III 1996). In this vein, a learning organization, especially its role in connecting the organization to its external environment, creates an ideal environment for the organization to enjoy improved organizational performance.

A leader also serves a critical role in creating a learning organization and expecting enhanced organizational performance (Bhaskar and Mishra 2017 ; Laeeque and Babar 2015 ; Noubar et al. 2011 ; Rose et al. 2006 ). In fact, the presence of a strategic leader for learning achieved the highest rank among the seven dimensions when analyzing published DLOQ studies (Watkins and Kim 2018 ). A recent study sought to understand the linkage between a learning organization and traditional leadership theories. Milić, Grubić-Nešić, Kuzmanović, and Delić ( 2017 ) demonstrated that the aspects of self-awareness, balanced processing, and internalized moral perspective of authentic leadership influence perceived learning organization at the organizational level either directly or indirectly through affective commitment in the manufacturing and service companies in the Republic of Serbia ( N = 502). According to them, a feeling of attachment to an organization encourages employees to be proactive in performing their tasks; the increased willingness to perform tasks beyond the assigned ones facilitates a learning organization.

Although contextual factors must be considered, the literature suggests some directions to identify how an organization can develop ideal leadership or promote adequate leader behaviors that will be a key to the success of an organization. For example, a leader is able to use learning strategically for business by exercising transformational leadership (Sahaya 2012 ). More specifically, Sahaya ( 2012 ) suggested that the individualized consideration perspective of transformational leadership, which focuses on individual talents and needs, positively influences a learning organization, and played a role in differentiating organizational performance when measuring return on assets in firms in Thailand ( N = 400).

These findings suggest that an ideal type of leadership or leader behaviors can be closer to that of a mentor or coach, especially when expecting improved organizational performance. A leader is able to exhibit individualized consideration “by listening attentively and paying close attention to their followers’ needs for achievement and growth by acting as mentors or coaches while encouraging them to take on increasingly more responsibilities in order to develop their full potential” (Avolio, Zhu, Koh, and Bhatia 2004 : 954). Recently, Kim and Watkins ( 2018 ) analyzed the six items measuring the leadership dimension of the DLOQ and found that each item produces a different coefficient and significance in terms of financial and non-financial performance. In other words, different leader behaviors are required depending on organizational contexts and their desired outcomes (e.g., whether an organization is in need of creation and enhancement of products or service, concerned about changes in its environment, or expects financial health). In spite of the differences, there is one item that is able to predict both financial and non-financial performance: “Leaders mentor and coach those they lead.”

Coaching and mentoring “leads to fostering of shared values, teamwork and an increase in morale and motivation across [an] organization resulting in greater productivity, achievement of excellent service” (Adeyemi 2011 : 370). As perceived organizational support possibly moderates the positive relationship between a learning organization and organizational performance (Siddique 2018 ), putting in place organizational support emphasizing the development of coaching and mentoring skills for a leader is, thus, suggested (Kim and Watkins 2018 ).

Non-Financial Performance

Table 22.1 shows the results of a meta-analysis of the seven dimensions of the DLOQ and organizational performance (Viechtbauer 2010 ). When analyzing the studies using the DLOQ that report Pearson’s correlation coefficients (Davis and Daley 2008 ; Hernandez 2000 ; Kim 2016 ; Watkins et al. 2009 ; Weldy and Gillis 2010 ; Wetherington and Daniels 2013 ; Yang et al. 2004 ; Zhang et al. 2004 ), ZCOR, a figure that better reflects its position in the collection of all coefficients, showed that the dimensions of empowerment, connectivity to the environment, and strategic leadership for learning show a higher correlation for both financial and knowledge performance. These results are consistent with the findings of the studies reviewed earlier.

Note : CL = Continuous Learning; DI = Dialogue and Inquiry; TL = Team Learning; ES = Embedded Systems; EP = Empowered People; SC = System Connection; SL = Strategic Leadership; k (number of studies) = 8; N = 4,320; ZCOR = Fisher’s z transformation of correlation; The analysis was conducted by using the statistical software R, the Metafor package (Viechtbauer 2010 ).

The table shows that the correlations between the seven dimensions and knowledge performance are slightly higher than financial performance. These results further support the findings of the regression studies—a learning organization explains more variance in knowledge performance than financial performance (Laeeque and Babar 2015 ; Noubar et al. 2011 ; Rose et al. 2006 ). These results indicate that a learning organization can be more closely associated with non-financial performance.

Thus, there is a need for identifying dimensions of non-financial performance derived from intangible assets and facilitated by learning (Watkins and Kim 2018 ). These assets fall into one of the following four components: external relationship, people, internal structure, and property (Bontis 2001 ; Marr and Adams 2004 ). Although the knowledge performance items of the DLOQ capture the intangible assets considerably, a potential dimension can be related to adaptive performance that includes factors like responding to changes, seizing new opportunities, and handling unexpected situations, which assists an organization in positioning itself better in its environment (Kontoghiorghes et al. 2005 ; Kim 2016 ). Dimensionalizing and measuring organizational performance is a complex task. Nonetheless, identifying performance dimensions, developing solid corresponding measures, and validating them broaden the intellectual horizon of a learning organization (Watkins and Kim 2018 ).

Unveiling mechanisms between financial and non-financial intangible performance in tandem with a learning organization can be another avenue for future research. Studies have shown that a learning organization impacts both intangible and tangible organizational performance. Intangible performance leads to tangible performance from a long-term perspective (Kaplan and Norton 1992 ; March 1991 ). Recently, studies consistently showed a learning organization invisibly promotes intangible performance, which is eventually realized as increased tangible performance, a critical outcome for the continuous survival of an organization (Kim 2016 ; Kim et al. 2017 ). More empirical studies are recommended to verify such findings (Watkins and Kim 2018 ).

Quantitative Analytic Methods

Applying diverse quantitative methods allows researchers to address research questions in a more robust and precise manner, but it can be seen that the literature on organizational performance has mostly used a correlation and regression analysis with a few exceptions. Thus, future research is encouraged to use diverse analytic methods. Some methodological suggestions and recommendations in conducting learning organization studies are as follows.

To identify dimensions and develop measures, exploratory factor analysis (EFA) can be helpful to uncover the underlying latent structure of non-financial performance. Confirmatory factor analysis (CFA) can be followed to confirm the hypothesized factor structure identified by EFA and to test whether the data fit a hypothesized measurement model, which is based on theory or previous empirical research. Thus, EFA and CFA are strongly suggested for the development of new measures and evaluation of the psychometric properties of new and existing measures (Harrington 2009 ).

As a special type of CFA, item response theory (IRT) can be used for test items with categorical outcomes or responses, such as binary or polychromous, by applying transformations (mainly logistic or probit) (Baker and Kim 2004 ; Embretson and Reise 2013 ). Similar to CFA, IRT can be used to model responses to test items along with the latent traits that determine how individuals or organizations react to these items (Foster, Min, and Zickar 2017 ). IRT would help to address many specific research questions, for example, to locate individuals’ or organizations’ latent levels of learning ability on an overall measure, to measure the true levels of non-financial performance in a learning organization, or to find any observed or latent factors which can explain the improvement or decrease of learning abilities or non-financial performance.

Regarding the dynamics between a learning organization and organizational performance or between financial and non-financial performance, structural equation modeling (SEM) would be appropriate. SEM can help researchers to uncover complex relationships among variables of interest. For example, SEM enables researchers to examine regression, mediating, and moderating effects simultaneously (Bowen and Guo 2012 ).

When considering the importance of interactions between individuals in enhancing organizational performance in a learning organization, social network analysis (SNA) would produce useful information to coaching or mentoring individuals as it is able to identify individuals and their expertise in a network and connections within/between groups/leaders by analyzing centrality or density within a personal network (Parise 2007 ).

When data are collected from more than one level or collected repeatedly through multiple time points/waves from the same participants, hierarchical linear modeling (HLM) is recommended (Raudenbush and Bryk 2002 ). HLM, also called multi-level modeling or mixed-effect modeling, decomposes the variation of outcome variables into different levels, such as within organization and between organizations. For nested organizations/units, for example, when individuals are nested within organizations, variations come from not only observations/measures but also hierarchical structures (e.g., individual, team, organization). HLM can help to understand how certain variables/constructs are observed/measured and interpreted at the different level of organizations (Hox 2002 ). For longitudinal data, HLM can help not only to understand the intra-individual or intra-organization trend of the change of performance but also to investigate the inter-individual or inter-organization differences (Fitzmaurice, Davidian, Verbeke, and Molenberghs 2008 ; Hedeker and Gibbons 2006 ; Singer and Willett 2003 ). In this regard, HLM allows researchers to infer their findings in organizational performance in a more precise manner.

Although more empirical evidence is needed, the literature reviewed in this chapter suggests that an organization that is flexible and copes with constant changes secures the success of the organization. Under different organizational contexts, studies have shown that a learning organization can play a significant role in enhancing organizational performance. The findings of these studies claim that a learning organization not only is positively associated with but also significantly predicts such performance.

For future studies on the learning organization and organizational performance, this chapter suggests ways of measuring them as follows. One way is adopting existing measures (see Moilanen 2001 for diagnostic instruments of a learning organization). For example, the DLOQ has items measuring a learning organization and perceptual organizational performance. Together with the analytic methods addressed earlier, this approach will provide a helpful source for understanding how a learning organization is connected to organizational performance. Another way is linking a learning organization with actual indicators, especially when interested in tangible performance. Such measures can be return on investment, return on equity, return on assets, earnings per share, and percentage of sales from new products (Davis and Daley 2008 ; Ellinger, Ellinger, Yang, and Howton 2002 ). Choosing a measure of a learning organization, identifying adequate and quantifiable performance indices according to the context where an organization is situated, and examining the correlation, causal, or other relationship between these two will generate useful information. Of course, a combination of perceptual and objective measures will bring more information in interpreting the linkage between a learning organization and organizational performance from a diverse perspective.

Last but not least, an organization should take a look at its ability to connect to its external environment and the leadership that supports learning as they have a greater potential in altering organizational outcomes. Identifying dimensions of and relationships between organizational performance, developing concrete measures, and applying sophisticated analytic methods will contribute to the advancement in knowledge about learning organizations.

Adeyemi, S.   2011 . “ Enhancing Organizational Performance through Effective Mentoring: Chapter 28. ” IFE PsychologiA: An International Journal 1: pp. 366–78.

Google Scholar

Avolio, B. , W. Zhu , W. Koh , and P. Bhatia . 2004 . “ Transformational Leadership and Organizational Commitment: Mediating Role of Psychological Empowerment and Moderating Role of Structural Distance. ” Journal of Organizational Behavior 25 (8): pp. 951–68.

Baker, F. B. , and S.-H. Kim . 2004 . Item Response Theory: Parameter Estimation Techniques . New York: CRC Press.

Google Preview

Bhaskar, A. U. , and B. Mishra . 2017 . “ Exploring Relationship between Learning Organizations Dimensions and Organizational Performance. ” International Journal of Emerging Markets 12 (3): pp. 593–609.

Bontis, N.   2001 . “ Managing Organizational Knowledge by Diagnosing Intellectual Capital: Framing and Advancing the State of the Field. ” International Journal of Technology Management 18 (5–8): pp. 433–62.

Bowen, N. , and S. Guo . 2012 . Structural Equation Modeling: Pocket Guides to Social Research Methods . New York: Oxford University Press.

Brown, J. S. , and P. Duguid . 2001 . “ Knowledge and Organization: A Social-Practice Perspective. ” Organization Science 12 (2): pp. 198–213.

Chien, M. H.   2004 . “ A Study to Improve Organizational Performance: A View from SHRM. ” Journal of American Academy of Business 4 (1/2): pp. 289–91.

Davis, D. , and B. J. Daley . 2008 . “ The Learning Organization and Its Dimensions as Key Factors in Firms’ Performance. ” Human Resource Development International 11 (1): pp. 51–66.

Ellinger, A. D. , A. E. Ellinger , B. Yang , and S. W. Howton . 2002 . “ The Relationship between the Learning Organization Concept and Firms’ Financial Performance: An Empirical Assessment. ” Human Resource Development Quarterly 13 (1): pp. 5–22.

Embretson, S. E. , and S. P. Reise . 2013 . Item Response Theory . Mahwah, NJ: Psychology Press.

Fitzmaurice, G. , M. Davidian , G. Verbeke , and G. Molenberghs (eds.). 2008 . Longitudinal Data Analysis . Boca Raton, FL: CRC Press.

Foster, G. C. , H. Min , and M. J. Zickar . 2017 . “ Review of Item Response Theory Practices in Organizational Research: Lessons Learned and Paths Forward. ” Organizational Research Methods 20 (3): pp. 465–86.

Garvin, D. A.   1993 . “ Building a Learning Organization. ” Harvard Business Review 71 (4): pp. 78–91.

Garvin, D. A. , A. C. Edmondson , and F. Gino . 2008 . “ Is Yours a Learning Organization? ” Harvard Business Review 86 (3): pp. 109–16.

Gephart, M. A. , V. J. Marsick , M. E. Buren , and M. S. Spiro . 1996 . “ Learning Organizations Come Alive. ” Training & Development 50 (12): pp. 34–46.

Goh, S. C.   1998 . “ Toward a Learning Organization: The Strategic Building Blocks. ” SAM Advanced Management Journal 63 (2): pp. 15–22.

Harrington, D.   2009 . Confirmatory Factor Analysis . New York: Oxford University Press.

Hedeker, D. , and R. D. Gibbons . 2006 . Longitudinal Data Analysis . Vol. 451. Hoboken, NJ: John Wiley.

Hernandez, M. 2000. “The Impact of the Dimensions of the Learning Organization on the Transfer of Tacit Knowledge Process and Performance Improvement within Private Manufacturing Firms in Colombia.” PhD thesesis, The University of Georgia. Retrieved from ProQuest (UMI: 9994101).

Hox, J. J.   2002 . Multilevel Analysis Techniques and Applications . Mahwah, NJ: Lawrence Erlbaum Associates.

Jain, A. K. , and A. Moreno . 2015 . “ Organizational Learning, Knowledge Management Practices and Firm’s Performance: An Empirical Study of a Heavy Engineering Firm in India. ” The Learning Organization 22 (1): pp. 14–39.

Kaplan, R. S. , and D. P. Norton . 1992 . “ The Balanced Scorecard: Measures that Drive Performance. ” Harvard Business Review 70 (1): pp. 71–9.

Kim, K. 2016. “The Impact of Learning Organizations on Knowledge Performance, Adaptive Performance, and Financial Performance.” PhD thesis, Athens, GA: University of Georgia.

Kim, K. , and K. E. Watkins . 2018. “Leader Behaviors and Intangible Performance.” Paper presented at the University Forum for Human Resource Development Conference 2018. Newcastle.

Kim, K. , K. E. Watkins , and Z. L. Lu . 2017 . “ The Impact of a Learning Organization on Performance: Focusing on Knowledge Performance and Financial Performance. ” European Journal of Training and Development 41 (1): pp. 177–93.

Kontoghiorghes, C. , S. M. Awbre , and P. L. Feurig . 2005 . “ Examining the Relationship between Learning Organization Characteristics and Change Adaptation, Innovation, and Organizational Performance. ” Human Resource Development Quarterly 16 (2): pp. 185–212.

Kumar, N. , and K. Idris . 2006 . “ An Examination of Educational Institutions’ Knowledge Performance: Analysis, Implications and Outlines for Future Research. ” The Learning Organization 13 (1): pp. 96–116.

Laeeque, S. H. , and S. F. Babar . 2015 . “ Learning Organization as a Strategy to Improve Performance of Pakistani Hospitals. ” Journal of Managerial Sciences 9 (2): pp. 256–66.

Lien, B. Y.-H. , R. Y.-Y. Hung , B. Yang , and M. Li . 2006 . “ Is the Learning Organization a Valid Concept in the Taiwanese Context? ” International Journal of Manpower 27 (2): pp. 189–203.

McGill, M. E. , J. W. Slocum , and D. Lei . 1992 . “ Management Practices in Learning Organizations. ” Organizational Dynamics 21 (1): pp. 5–17.

March, J. G.   1991 . “ Exploration and Exploitation in Organizational Learning. ” Organization Science 2 (1): pp. 71–87.

Marr, B. , and C. Adams . 2004 . “ The Balanced Scorecard and Intangible Assets: Similar Ideas, Unaligned Concepts. ” Measuring Business Excellence 8 (34): pp. 18–27.

Milić, B. , L. Grubić-Nešić , B. Kuzmanović , and M. Delić . 2017 . “ The Influence of Authentic Leadership on the Learning Organization at the Organizational Level: The Mediating Role of Employees’ Affective Commitment. ” Journal of East European Management Studies 22 (1): pp. 9–38.

Moilanen, R.   2001 . “ Diagnostic Tools for Learning Organizations. ” The Learning Organization 8 (1): pp. 6–20.

Ngah, R. , T. Tai , and N. Bontis . 2016 . “ Knowledge Management Capabilities and Organizational Performance in Roads and Transport Authority of Dubai: The Mediating Role of Learning Organization. ” Knowledge and Process Management 23 (3): pp. 184–193.

Noubar, H. , R. C. Rose , N. Kumar , and L. M. Salleh . 2011 . “ Learning Culture to Organizational Breakthroughs in Malaysian Companies. ” Economics and Management 16: pp. 852–8.

Örtenblad, A.   2001 . “ On Differences between Organizational Learning and Learning Organization. ” The Learning Organization 8 (3): pp. 125–33.

Parise, S.   2007 . “ Knowledge Management and Human Resource Development: An Application in Social Network Analysis Methods. ” Advances in Developing Human Resources 9 (3): pp. 359–83.

Pedler, M. , and J. Burgoyne . 2017 . “ Is the Learning Organization Still Alive? ” The Learning Organization 24 (2): pp. 119–26.

Pedler, M. , J. Burgoyne , and T. Boydell . 1991 . The Learning Company . Maidenhead: McGraw-Hill.

Pokharel, M. P. , and S. O. Choi . 2015 . “ Exploring the Relationships between the Learning Organization and Organizational Performance. ” Management Research Review 38 (2): pp. 126–48.

Raudenbush, S. W. , and A. S. Bryk . 2002 . Hierarchical Linear Models: Applications and Data Analysis Methods , vol. 1. Thousand Oaks, CA: Sage.

Rose, R. C. , A. Salleh , and N. Kumar . 2006 . “ Learning Culture and Performance Outcomes: Evidence from Malaysian Organizations. ” Management & Change 10 (2): pp. 131–52.

Sahaya, N.   2012 . “ A Learning Organization as a Mediator of Leadership Style and Firms’ Financial Performance. ” International Journal of Business and Management 7 (14): pp. 96–113.

Senge, P. M.   1990 . The Fifth Discipline: The Art and Practice of the Learning Organization . New York: Doubleday.

Shieh, C.-J.   2011 . “ Study on the Relations among the Customer Knowledge Management, Learning Organization, and Organizational Performance. ” The Service Industries Journal 31 (5): pp. 791–807.

Shieh, C.-J. , I.-M. Wang , and F.-J. Wang . 2009 . “ The Relationships among Cross-Cultural Management, Learning Organization, and Organizational Performance in Multinationals. ” Social Behavior and Personality: An International Journal 37 (1): pp. 15–30.

Shipton, H. , Q. Zhou , and E. Mooi . 2013 . “ Is There a Global Model of Learning Organizations? An Empirical, Cross-Nation Study. ” International Journal of Human Resource Management 24 (12): pp. 2278–98.

Siddique, M. C.   2018 . “ Learning Organization and Firm Performance: Making a Business Case for the Learning Organization Concept in the United Arab Emirates. ” International Journal of Emerging Markets 13 (4): pp. 689–708.

Singer, J. D. , and J. B. Willett . 2003 . Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence . New York: Oxford University Press.

Tushman, M. L. , and C. A. O’Reilly III . 1996 . “ Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change. ” California Management Review 38 (4): pp. 8–29.

Ulrich, D. , T. Jick , and M. A. Von Glinow . 1993 . “ High-Impact Learning: Building and Diffusing Learning Capability. ” Organizational Dynamics 22 (2): pp. 52–66.

Viechtbauer, W.   2010 . “ Conducting Meta-Analyses in R with the Metafor Package. ” Journal of Statistical Software 36 (3): pp. 1–48.

Watkins, K. E. , and K. Kim . 2018 . “ Current Status and Promising Directions for Research on the Learning Organization. ” Human Resource Development Quarterly 29 (1): pp. 15–29.

Watkins, K. E. , and V. J. Marsick . 1993 . Sculpting the Learning Organization: Lessons in the Art and Science of Systemic Change . San Francisco, CA: Jossey-Bass.

Watkins, K. E. , and V. J. Marsick . 1997 . Dimensions of the Learning Organization Questionnaire . Warwick, RI: Partners for the Learning Organization.

Watkins, K. E. , J. Milton , and D. Kurz . 2009 . “ Diagnosing the Learning Culture in Public Health Agencies. ” International Journal of Continuing Education and Lifelong Learning 2 (1): pp. 65–81.

Weldy, T. G. , and W. E. Gillis . 2010 . “ The Learning Organization : Variations at Different Organizational Levels.” The Learning Organization 17 (5): pp. 455–470.

Wetherington, J. M. , and K. M. Daniels . 2013 . “ The Relationship between Learning Organization Dimensions and Performance in the Nonprofit Sector. ” Journal for Nonprofit Management 16 (1): pp. 90–107.

Yang, B. , K. E. Watkins , and V. J. Marsick . 2004 . “ The Construct of the Learning Organization: Dimensions, Measurement, and Validation. ” Human Resource Development Quarterly 15 (1): pp. 31–55.

Yu, T. , and C.-C. Chen . 2015 . “ The Relationship of Learning Culture, Learning Method, and Organizational Performance in the University and College Libraries in Taiwan. ” Libri 65 (1): pp. 1–14.

Zhang, D. , Z. Zhang , and B. Yang . 2004 . “Learning Organization in Mainland China: Empirical Research on its Application to Chinese State-Owned Enterprises.” International Journal of Training and Development 8 (4): pp. 258–73.

Zhou, W. , H. Hu , and X. Shi . 2015 . “ Does Organizational Learning Lead to Higher Firm Performance? An Investigation of Chinese Listing Companies. ” The Learning Organization 22 (5): pp. 271–88.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Reference Manager
• Simple TEXT file

People also looked at

Review article, building organizational resilience through organizational learning: a systematic review.

• 1 Department of Technology and Safety, UiT the Arctic University of Norway, Tromsø, Norway
• 2 Department of Leadership and Organisation, Kristiania University College, Oslo, Norway

With organizational environments becoming increasingly complex and volatile, the concept of “organizational resilience” has become the “new normal”. Organizational resilience is a complex and multidimensional concept which builds on the myriad of capabilities that an organization develops during its lifecycle. As learning is an inherent and essential part of these developments, it has become a central theme in literature on organizational resilience. Although organizational resilience and organizational learning are inherently interrelated, little is known of the dynamics of effective learning that may enhance organizational resilience. This study explores how to achieve organizational learning that can serve to promote organizational resilience. Our aim is to contribute to a more comprehensive knowledge of the relation between organizational resilience and organizational learning. We present the results of a systematic literature review to assess how organizational learning may make organizations more resilient. As both organizational resilience and organizational learning are topics of practical importance, our study offers a specifically targeted investigation of this relation. We examine the relevant literature on organizational learning and resilience, identifying core themes and the connection between the two concepts. Further, we provide a detailed description of data collection and analysis. Data were analyzed thematically using the qualitative research software NVivo. Our review covered 41 empirical, 12 conceptual and 6 literature review articles, all indicating learning as mainly linked to adaptation capabilities. However, we find that learning is connected to all three stages of resilience that organizations need to develop resilience: anticipation, coping, and adaptation. Effective learning depends upon appropriate management of experiential learning, on a systemic approach to learning, on the organizational ability to unlearn, and on the existence of the context that facilitates organizational learning.

Introduction

With organizational environments becoming more and more complex and volatile, the concept of “organizational resilience” (OR) has become increasingly significant for practice and research. OR is here understood as the organization's “ability to anticipate potential threats, to cope effectively with adverse events, and to adapt to changing conditions” ( Duchek, 2020 , p. 220). Thus, anticipation, coping, and adaptation represent three stages of OR. Further, the literature indicates that OR is an essential organizational meta-capability for the success of modern organizations ( Parsons, 2010 ; Näswall et al., 2013 ; Britt et al., 2016 ; Suryaningtyas et al., 2019 ). OR has indeed become the “new normal” ( Linnenluecke, 2017 ) regarding organizational survival as well as recovery and successful re-emergence after disruptions. Understanding OR is therefore more important than ever ( Ruiz-Martin et al., 2018 ). However, OR is still an emerging field ( Ma et al., 2018 )—and a key question that remains unanswered is how to achieve it ( Boin and Lodge, 2016 ; Chen R. et al., 2021 ).

Research is explicit on the complexity and multidimensional nature of OR: it is associated with an organization's capabilities to learn, adapt, and self-organize ( Linnenluecke and Griffiths, 2010 ), where learning is an inherent and essential element ( Boin and van Eeten, 2013 ). This links OR to learning processes (see, e.g., Lengnick-Hall et al., 2011 ; Rodríguez-Sánchez and Vera Perea, 2015 ), where learning has become a common theme in resilience literature. Khan et al. (2019 , p. 18) argue, “[o]rganizational learning capability is positively related to building and sustaining organizational resilience capability.” While OR is defined at the organizational level, the inherent learning is organizational learning (OL), understood as an “[ongoing] social process of individuals participating in situated practices that reproduce and expand organizational knowledge structures and link multiple levels of OL” ( Popova-Nowak and Cseh, 2015 , p. 318). Furthermore, research has noted the similarities between OR and OL ( Sitkin, 1992 ; Linnenluecke and Griffiths, 2010 ), as both require routines, values, models, and capabilities essential for organizations facing uncertainty. OR has also been defined as an outcome of organizational learning ( Sitkin, 1992 ; Sutcliffe and Vogus, 2003 ), suggesting that organizational learning capability may be enhanced by OR ( Rodríguez-Sánchez et al., 2021 ). However, OR is also a process ( Boin et al., 2010 ) that facilitates OL and feeds organizational self-development over time ( Lombardi et al., 2021 ). This makes OL both an important precondition for OR which relies on past learning, and an outcome of it that fosters future learning ( Vogus and Sutcliffe, 2007 ). OR and OL may therefore reinforce each other.

Although OR and OL are inherently interrelated, our understanding of the dynamics of effective learning is limited ( Antonacopoulou and Sheaffer, 2014 ), and further study is needed of the relationship between organizational learning and resilience ( Mousa et al., 2017 , 2020 ; Rodríguez-Sánchez et al., 2021 ). Further research on learning connected to OR is needed to understand “the character of this learning and what specific resources give rise to it” ( Vogus and Sutcliffe, 2007 , p. 3421). Moreover, investigation is needed of what triggers learning and corresponding processes and exploring of the effective learning strategies that allow resilient organizations to avoid pathological learning cycles ( Vogus and Sutcliffe, 2007 ). Our aim with this study is therefore to contribute to more comprehensive knowledge on the relationship between OR and OL and to further explore the relationship between them by asking the research question: How to improve organizational learning to make organizations more resilient?

Theoretical Framework

OL assumes interaction of its multiple levels of analysis, including the individual, group, organizational, and inter-organizational levels ( Lundberg, 1995 ; Örtenblad, 2004 ; Popova-Nowak and Cseh, 2015 ). Being a social process, OL is embedded in everyday organizational practice when individuals acquire, produce, reproduce, and expand organizational knowledge ( Lave and Wenger, 1991 ; Gherardi et al., 1998 ; Gherardi and Nicolini, 2002 ; Gherardi, 2008 ; Chiva et al., 2014 ). This individual knowledge, either explicit or tacit ( Cook and Yanow, 1993 ), must become part of organizational repository that includes tools, routines, social networks and transactive memory systems ( Huber, 1991 , p. 89–90; Walsh and Ungson, 1991 ; Argote and Ingram, 2000 ; Argote, 2011 ). OL directly affects organizations facing turbulence ( Baker and Sinkula, 1999 ) and involves “the extraction of positive lessons from the negativity of life” ( Giustiniano et al., 2018 , p. 133) that are useful to the whole organization. OL will therefore directly affect how resilient organizational performance is ( Giustiniano et al., 2018 ).

Learning is emergent in nature ( Antonacopoulou and Sheaffer, 2014 ). As a continuous process, OL implies accomplishment of specific steps. However, what those steps are varies, though with certain overlaps, across the literature (see, e.g., Huber, 1991 ; Argyris and Schön, 1996 ; Crossan et al., 1999 ; Lawrence et al., 2005 ; Jones and Macpherson, 2006 ; Argote and Miron-Spektor, 2011 ; Argote et al., 2020 ). Further, OL may vary in complexity and outcomes. At the lower (single-loop) level, OL results in detection and correction of errors “without questioning or altering the underlying values of the system” ( Argyris and Schön, 1978 , p. 8). At the higher (double-loop) level of learning, “errors are corrected by changing the governing values and then the actions” ( Argyris, 2002 , p. 206). Triple-loop learning (deutero-learning) enables organizations to learn about their own learning processes ( Argyris and Schön, 1978 , 1996 ). OL may be exploratory—associated with “search, variation, risk taking, experimentation, play, flexibility, discovery and innovation” ( March, 1991 , p. 71), or exploitive—utilizing the “old certainties” ( March, 1991 , p. 71). The trade-off between the two is a key concern of studies of adaptive processes; organizations need to have an appropriate balance between these strategies ( Levinthal and March, 1993 ) to maintain “ambidexterity” ( Lavie et al., 2010 ). Importantly, OL is not necessarily always a conscious or intentional effort, neither does it imply behavioral change ( Hernes and Irgens, 2013 ) or always increase the learner's effectiveness (even potential effectiveness); finally, it does not always lead to true knowledge, as organizations “can incorrectly learn, and they can correctly learn that which is incorrect” ( Huber, 1991 , p. 89).

Organizations struggle to implement OL ( Lipshitz et al., 2002 ; Reich, 2007 ; Garvin et al., 2008 ; Taylor et al., 2010 ; Antonacopoulou and Sheaffer, 2014 ) due to a wide range of barriers (see, e.g., Schilling and Kluge, 2009 ). Productive OL is complex and relies on the interaction of various facets—cultural, psychological, policy, and contextual ( Lipshitz et al., 2002 ; see also Garvin, 1993 ). These interactions may produce differing configurations and will vary across organizations. Experience has a special role as a key prerequisite for OL, but experience is extremely diverse in nature ( Argote and Todorova, 2007 ; Argote, 2011 ; Argote and Miron-Spektor, 2011 ) and its relevance is only partial ( Weick and Sutcliffe, 2015 ). In order for experience to be a “good teacher” ( March, 2010 ) organizations must understand its nature and how different types of experience interplay ( Argote and Miron-Spektor, 2011 ). The relationship between the experience and learning processes and outcomes is moderated by context ( Argote, 2011 , p. 441). Effective OL relies on a suitable context ( Antonacopoulou and Chiva, 2007 , p. 289) that can be complex and multidimensional ( Argote and Miron-Spektor, 2011 ): inter alia , external organizational environments, organizational culture, strategy and structure, power relationship within the organization and inter-organizational processes and interactions. The contextual components through which learning occurs are active, whereas others that shape the active context are latent (see Argote and Miron-Spektor, 2011 for details).

Thus, OR is founded on learning processes (assessment, sense making, distilling lessons learned and integration of new understandings into existing practice) that are embedded in organizational routines ( Powley and Cameron, 2020 ) that penetrate all stages of OR ( Duchek, 2020 ). Achieving OR requires commitment and studying this commitment implies an enquiry into organizational learning, knowledge, and capability development ( Weick and Sutcliffe, 2015 , p. 108). Research has noted that different types of resilience associate with different learning strategies: adaptive resilience has been associated with single-loop learning ( Lombardi et al., 2021 , p. 2). In contrast, reactive resilience refers to the ability to view disruptions as sources of learning and growth at various organizational levels, where organizations must adopt new practices based on their experience, resulting in a double-loop learning. Resilience also entails a process of deutero-learning or “learning to learn” ( Andersen, 2016 ), thus requiring a completely new experimentation approach. OR is enhanced when organizations build routines that can facilitate OL. The major challenge for an organization that aims at enabling resilience is to establish the right learning routines (see, e.g., Kayes, 2015 ): that is to say, those that achieve effective/productive OL.

A systematic literature review (SLR) was chosen since it facilitates gathering of a wide range of relevant sources ( Crossan and Apaydin, 2010 ) and ensures clarity of inclusion and exclusion criteria ( Mackay and Zundel, 2017 )—important when, as with OR, intellectual coherence or a standard theoretical framework is lacking ( Liñán and Fayolle, 2015 ). Our review was outcomes-oriented, aiming to identify “central issues” ( Cooper, 1988 , p. 109); relevant literature was retrieved through an exhaustive review with selective citation, to consider all relevant publications for the research question. SLR involves two stages: a sampling and an analytical stage ( Figure 1 ).

Figure 1 . Methodological steps for literature review (source: the authors).

Conducting the Review

The sampling stage.

Our initial search criteria were broad to include as many relevant results as possible. To obtain an overview of the available literature, the following databases were used: Science Direct, Web of Knowledge (Search in the core collection) and Google Scholar (for the latest publications and gray literature). 1 As Google Scholar is a compilation of records from other databases ( Kugley et al., 2017 ) several articles had already been identified by Science Direct and Web of knowledge. All databases are frequently used by researchers of various disciplines ( Xiao and Watson, 2019 ). The literature search was performed by the first author, in the period March–May 2021, covering publications between 1900 and 2021. A diary was made to keep record of the results, with search dates, search strings and results.

Search strings were developed by applying the keywords “organizational resilience” and “organizational learning”. In Web of Knowledge, the Boolean operator (AND) was applied together with truncation symbol of the asterisk to include all forms of the words [TS = (resilien * AND organi * AND learning)]. The same keywords were applied in Science Direct; as both UK and US spelling variants are supported, there was no need for truncation symbol. Google Scholar offers limited search terms options, and the selection is not as transparent as Web of Knowledge and Science Direct. However, we performed a search in Google Scholar to broaden the number of publications and ensure the latest articles from all fields and disciplines. Only the three first pages were included. The initial search performed by the first author yielded 2,985 articles. Next, the same author went through the abstracts and keywords. In cases where abstracts were not sufficiently informative, the article was read through quickly. Duplicates were removed, which reduced the number of articles from 170 to 165.

The following criteria were used in the screening process resulting in 2,985 publications:

Inclusion Criteria

1. Empirical, conceptual, and theoretically oriented publications about organizational learning within organizational resilience

2. Publications written in English

3. Article type (applied only in Science Direct): Review articles, research articles, book chapters, conference abstracts and data articles

4. Subject areas (only in Science Direct): Social sciences, environmental science, business, management and accounting, engineering

Exclusion Criteria

1. Non-academic journals

2. Publications not issued in English or Norwegian

3. Article type (only in Science Direct): encyclopedias, book reviews, case reports, conference info, correspondence, discussions, editorials, errata; examinations, mini-reviews, news, practice guidelines, short communications, software publications, other

4. Subject areas (only in Science Direct): medicine and dentistry, psychology, agricultural and biological sciences, computer science, energy, neuro-science

Analytical Stage

The articles were randomly distributed to the three authors for a full reading, so that each article could be sorted as follows: (1) How organizational learning contributes to organizational resilience; (2) How OR contributes to organizational learning; (3) Uncertain. Articles placed in the latter category were discussed and given an additional full reading by the first author before a decision on category placement, or exclusion was made. As a result of this analytical screening process, 59 articles were included in the final review. After the phases of thematic analysis ( Braun and Clarke, 2006 ) the articles were coded in NVivo 20 (Release 1.5). A deductive coding scheme was developed, based partly on Boyatzis (1998) approach. Theory on organizational learning informed the following deductive coding categories: experience; practices; strategies; effective learning; mechanisms; knowledge; processes and context. The Duchek (2020) conceptualization of resilience informed the codes: anticipation; coping; adaptation. The coding process started deductively; we inductively created additional codes underway. The first round of coding was performed by the first author and then presented to the co-authors. The second round of coding was performed by the first and second authors, and themes were created when we found “something important in relation to the overall research question” ( Braun and Clarke, 2006 , p. 83). Data were aggregated into clustered codes ( Miles and Huberman, 1994 ). A memo of the coding process was kept in NVivo. Analysis of our findings is presented below.

Literature Review

The literature on organizational resilience has expanded massively in recent years, developing from being highly conceptual to containing increasingly more empirical contributions. Our 59-article review is presented in Table 1 : 41 empirical, 12 conceptual and 6 literature-review articles. The empirical articles use various types of data from different contexts; 19 use qualitative data, 13 apply quantitative data and six mixed methods; further, healthcare (13), universities (4), SMEs (5) and transport (4) are the dominant contexts. The articles were published in 45 different journals, in addition to two book volumes and two conference/symposium proceedings; and no single journal dominates. The highest number of contributions within one journal was three, for Reliability Engineering & System Safety . The journals are located within many different fields, with business (11) predominant, followed by safety (7), learning (4) and healthcare (5). Regarding geographical context for the empirical studies, all continents are represented, with Europe dominant (19), followed by Australasia (6) and the USA (5).

Table 1 . Overview of articles and stage of organizational resilience for their organizational learning focus (Source: the authors).

Our review shows that learning is connected to resilience through the capabilities that organizations must possess and develop. Scholten et al. (2019) pointed out that “learning is ongoing across all stages of a disruption” (p. 439); we found that the same can be said about learning in the resilience stages. Our review shows that learning can prepare organizations for future events. Eight articles explicitly address learning as part of the anticipation capability . Overall, we see that anticipation refers to proactive action ( Hardy, 2014 ; Duchek, 2020 ) where organizations detect “emerging problems” ( Anderson et al., 2020 , p. 2), “anticipate what could happen in an actual event” ( Hermelin et al., 2020 , p. 670) and then act on this. Adding experience to the organization's knowledge base is crucial, influencing how successful the organizational ability to anticipate further needs will be. Anticipation is dependent on the organizational ability to learn ( Hillmann et al., 2018 ), which “guides and supports” ( Ritz et al., 2015 , p. 1868) advancement to the next stage of the resilience process: coping. Moreover, learning developed during this stage will inevitably influence capabilities developed in the two other stages ( Anderson et al., 2020 ), and further improve organizational response. Nine articles explicitly address learning as part of capabilities to cope with uncertainty and sudden changes ( Al-Atwi et al., 2021 ). We noted a tendency to address the role of learning in coping as a chance for organizations to expand their cognitive and behavioral perspectives ( Tasic et al., 2020 ) and thus broaden the range of actions ( Duchek, 2020 ) to build resilience to offer “better future protection” ( Manfield and Newey, 2018 , p. 1161). However, our study shows that the literature on OR learning is overwhelmingly concerned with adaptation capabilities. Altogether 38 articles explicitly include learning as part of adaptation , which could be explained by the fact that “adaptation may be what truly defines resilience” ( Hardy, 2014 ). Learning is central to the adaptive capacity of an organization ( Orth and Schuldis, 2021 ) and adaptability is held to be closely related to organizational learning ( Bhaskara and Filimonau, 2021 ). Organizations must continuously absorb information and adapt to changes in the environment, in turn building on continuous learning ( Battisti et al., 2019 ). Similarly, Crick and Bentley (2020) state that the interaction between absorbing information and the environment that can lead to adaptation is driven by learning; while Dutra et al. (2015) argue that adaptation occurs through learning and transmission of knowledge. In the context of developing organizational resilience, adaptation means more than simply getting the organization back to normal—it also involves developing capabilities to change and learn ( Scholten et al., 2019 ; Duchek et al., 2020 ). As shown by Bragatto et al. (2021) , building resilience involves more than just adapting disaster management plans: it also entails understanding and managing people's behavioral norms and mental models to help them unlearn behaviors which might have led to failure in the first place.

Analysis of Findings

Our findings affirm and strengthen the link between Organizational Learning (OL) and Organizational Resilience (OR). Whereas, Pal et al. (2014) found that learning increased resilient performance, Nyman (2019) argued that learning is a “precondition for resilience”. Mousa et al. (2020) have showed the role that organizational learning plays in predicting OR, while others, like Bhaskara and Filimonau (2021) , highlight this relationship by arguing that limited organizational learning is a disadvantage for developing resilience: it is important to “aim at facilitating organizational learning” (p. 373). Our findings highlight the importance of identifying the determinants of OL in order to improve OR ( Bhaskara and Filimonau, 2021 ); further, that resilience can be learned and therefore deliberately built ( Manfield and Newey, 2018 ; Salanova, 2020 ). The identified main elements of OL for improving OR are appropriate management of experiential learning, a systemic approach to learning, the organizational ability to unlearn, and the existence of the context that facilitates organizational learning.

Learning From Experience

The importance of experiential learning has been heavily stressed in the OR literature ( Hecht et al., 2019 ; Bragatto et al., 2021 ; Habiyaremye, 2021 ). Chand and Loosemore (2016) point out that such experience can be acquired during real events, training exercises and drills. Several authors address the important role of training and exercises (e.g., Chand and Loosemore, 2016 ; Adini et al., 2017 ; Khan et al., 2017 ; Hecht et al., 2019 ; Hermelin et al., 2020 ; Tasic et al., 2020 ; Bhaskara and Filimonau, 2021 ), including the post-exercise debriefing session as learning-promoting activities that will enhance OR. Moreover, OR may be improved by experience and learning from accidents, with a focus on clear communication and common training to share experiences from “accidents, fatalities and good practices” ( Johnsen and Habrekke, 2009 , p. 8). Both positive and negative learning from own experiences is crucial for showing how to increase positive outcomes and avoid negative ones ( Anderson et al., 2020 ). Learning from failures is among the key capabilities of a resilient organization ( Herbane, 2019 ; Bhaskara and Filimonau, 2021 ); further, some (e.g., Madsen and Desai, 2010 , cited by Manfield and Newey, 2018 ) have argued that organizations can learn more from failure than success, particularly on the case of major failures. There are also important lessons to be learned from successes ( Hardy, 2014 ; Hermelin et al., 2020 ) and from reflecting on positive outcomes. Scholten et al. (2019 , p. 438) point out that organizations that do not “reflect on positive outcomes might inhibit organizations in seizing all the benefits of intentional experiential learning.”

However, past experiences may provide limited learning opportunities ( Bhaskara and Filimonau, 2021 ). Although experience can enable organizations to replay what has been previously learned, they may fail “to prepare […] for unforeseen and unpredicted events” ( Elliott and Macpherson, 2010 , p. 16). Established “best practices” may not be suitable for other crisis situations ( Elliott and Macpherson, 2010 , p. 16) and knowledge gained from one context cannot always be readily transferred to a different context: “coping with crisis cannot just be about deliberately acquiring a set of transmitted abilities, since to achieve competent practice depends on becoming better by doing” ( Elliott and Macpherson, 2010 , p. 6). The diverse nature of some events may also impede effective OL ( Bhaskara and Filimonau, 2021 ). Past experiences may be codified into standard practices with “step-by-step” guidelines and operating procedures. However, such “codified learning” is problematic, as “the actual practice evolves as those in charge or involved in circumstances make sense of the ambiguous information, confused circumstances and incomplete data with which they are faced” ( Elliott and Macpherson, 2010 , p. 10). Therefore, codified learning can “only ever be partially successful” ( Elliott and Macpherson, 2010 , p. 11).

Another finding is the importance of how experience is dealt with . To ensure a true learning experience, acquired knowledge should be applied to real situations ( Hillmann et al., 2018 ). Assuming that resilience is built through a combination of specific theoretical input and experiential learning, a combination will positively influence e.g., long-term learning and “thinking in complexity through imagining different futures” ( Hillmann et al., 2018 , p. 481). For example, extreme weather events have been found to “provide the best opportunities for experiential learning about how to improve hospital resilience to such events” and “embedding such experiences into hospital disaster management planning processes” ( Chand and Loosemore, 2016 , p. 885). Although crisis events have been subjected to extensive investigation, it is evident that organizations often fail to learn effectively, even when crises are regular events ( Bhaskara and Filimonau, 2021 ). One contributing factor for this failure may be the fragmented nature of our understanding, and the resulting piecemeal conceptualization of the learning process ( Elliott and Macpherson, 2010 ). For example, a disturbance may be familiar to the organization; but, due to bounded rationality, the need for new learning is not always identified, as organizations will often choose to fall back on old practices instead of developing new ones ( Manfield and Newey, 2018 ). This results in lowered reintegration and internationalization of learning and reduced organizational resilience that stops being a growth experience ( Manfield and Newey, 2018 ). Moreover, time lag and spatial distance may challenge the opportunity to learn from experience with a specific situation ( Anderson et al., 2020 ). Therefore, improved organizational learning must involve better understanding of the causes of accidents ( Johnsen and Habrekke, 2009 ), with a focus on triple-loop learning ( Bragatto et al., 2021 ). It is not sufficient merely to collect reports of problems: organizations need to ensure that the reports are studied, and corrective actions implemented ( Hardy, 2014 ). Another cause of unsuccessful OL is in confusing learning with identifying lessons ( Elliott and Macpherson, 2010 ): “organizations often generate new knowledge (lessons learned) but fail to translate this knowledge into new behaviors” ( Duchek, 2020 , p. 231). It is important to recognize that lessons have not been learned until they are successfully implemented ( Chand and Loosemore, 2016 ; Duchek, 2020 ).

Several authors highlight the value of organizations learning from the practices and experiences of other organizations ( Gressgard and Hansen, 2015 ; Johannesen et al., 2020 ; Bhaskara and Filimonau, 2021 ; Fasey et al., 2021 ; Friday et al., 2021 ; Habiyaremye, 2021 as central to OL ( Khan et al., 2017 ; Herbane, 2019 ). This may also spread further, to a whole network, as suppliers interact with each other, thereby also facilitating network resilience ( Chen K. D. et al., 2021 ). Effective learning involves critical reflection at several levels, with effective communication and information sharing among the involved actors throughout the system ( Johnsen and Habrekke, 2009 ; Dutra et al., 2015 ; Nicolletti et al., 2019 ). Such collaboration should “not only capture lessons learnt but also allow effective use and sharing of information across the multiple stakeholders involved in disaster planning” ( Chand and Loosemore, 2016 , p. 885). Our findings highlight the importance of a holistic approach to understanding this collective learning process among the many stakeholders involved in a disaster response ( Bragatto et al., 2021 ). Lack of appropriate collaboration with other relevant stakeholders inhibits OL by depriving organizations of valuable opportunities to learn from others ( Johnsen and Habrekke, 2009 ; Bhaskara and Filimonau, 2021 ). Moreover, learning from others may be inhibited by differing “resources, objectives and variations in learning experiences” between organizations ( Friday et al., 2021 , p. 262).

Importance of Continuity and Need for a System

Building resilience requires capturing and embodiment of learning into a capability ( Hillmann et al., 2018 ). This in turn implies learning from adversity and codifying this learning into resilience capabilities against specific threats, thereby offering better future protection ( Folke et al., 2004 , cited by Manfield and Newey, 2018 ). Findings also demonstrate the importance of having systems in place for organizational learning to happen , and remaining continuous. Such systems must incorporate a range of learning practices that will ensure better reflection of experienced crises and as an outcome a more effective learning ( Duchek et al., 2020 ). Organizations learn “in, from and for crisis” ( Elliott and Macpherson, 2010 , p. 3). To enhance OR, learning has to be ongoing ( Chand and Loosemore, 2016 ; Bragatto et al., 2021 ), running across “the continuum of situations” from everyday practice to action during critical events ( Hegde et al., 2020a , p. 75). Our findings emphasize that a resilient system continually learns, improves, and adjusts, even when stressed, and improves after a disturbance through adaptation (see also Hardy, 2014 ; Martinelli et al., 2018 ). In contrast to the results of other studies on learning from near misses , Azadegan et al. (2019) found that organizations “do learn significantly from such events” (p. 224). Further, when organizations experience near-misses, they consider long-term issues by “implementing procedural response strategies” ( Azadegan et al., 2019 , p. 221), such as formal protocols, policies, and procedures, in contrast to theoretical suggestions that often indicate consideration of flexible strategies. Such procedural strategies are in line with double-loop learning, whereas flexible strategies are more in line with single-loop learning ( Azadegan et al., 2019 ). Moreover, learning from accidents, or even more extreme events such as emergencies and catastrophes, needs to be integrated with learning from minor consequence events or even from the normal functioning of everyday activities ( Hollnagel, 2011 , cited by Patriarca et al., 2018 , p. 267). The literature reviewed highlights how resilient learning is “ambidextrous,” with a diversity of practices that organizations should explore and exploit ( Al-Atwi et al., 2021 ), balancing flexible and procedural strategies ( Azadegan et al., 2019 ).

Our findings also reveal emphasis on the knowledge base , and how knowledge is managed within the organization (see Elliott and Macpherson, 2010 ; Nicolletti et al., 2019 ; Anderson et al., 2020 ; Duchek, 2020 ; Duchek et al., 2020 ; Steen and Ferreira, 2020 ; Habiyaremye, 2021 ). Knowledge is generated through the stages of resilience—in other words, from the crisis event context that can create the need for change. Some authors hold that the organizational reaction to change is expressed by adaptation ( Naimoli and Saxena, 2018 ; Fridell et al., 2020 ) so organizations must develop their capacity for change, which is predicated on the capacity for continuous learning ( Morais-Storz and Nguyen, 2017 ). Adaptive learning is crucial to the ability to bounce back from adverse events that underlie crises ( Habiyaremye, 2021 ). Therefore, we hold that learning is a mechanism for change which is needed in developing organizational resilience in the face of new problems ( Anderson et al., 2020 ; Steen and Ferreira, 2020 ; Fasey et al., 2021 ). Knowledge, as the key antecedent of OR ( Duchek, 2020 ), is also fundamental for resilient system performance irrespective of the activity in focus ( Adini et al., 2017 ). It is, however, important to distinguish among sources of knowledge, as different sources are associated with different performance outcomes ( Battisti et al., 2019 ).

To develop resilience, knowledge must remain in the organization, as employees may come and go ( Dohaney et al., 2020 ). That being said, improved OL relies on the feedback process where individual lessons are shared collectively ( Chand and Loosemore, 2016 ). Gressgard and Hansen (2015) argue that, for learning to contribute to building resilience, diversity of opinions and perspectives is important . Further, that knowledge exchange between and across units in the organization increases the ability to learn from failure, as compared to knowledge exchange within units. This highlights the need to improve the feedback process ( Bragatto et al., 2021 ) and develop an appropriate system for knowledge-sharing from the individual to the organizational level ( Bhaskara and Filimonau, 2021 ), relying on various practices (see e.g., Khan et al., 2017 ; Martinelli et al., 2018 ; Hegde et al., 2020a ; Habiyaremye, 2021 ). This system should be based on trust and inclusion, to ensure efficient and appropriate communication ( Rangachari and Woods, 2020 ). Moreover, organizations should develop processes and structures to utilize knowledge and implement this knowledge into future responses ( Chand and Loosemore, 2016 ). In the absence of such systems, OR will remain “reactive (brittle) and restricted to the frontlines, with no way of advancing to team and organizational levels” ( Rangachari and Woods, 2020 , p. 6).

Dimensions of Learning Practices

Our findings also show that organizational learning is established through both formal and informal practices (see Gressgard and Hansen, 2015 ; Hecht et al., 2019 ; Hermelin et al., 2020 ; Orth and Schuldis, 2021 )—and that formal practices are particularly associated with learning from failure ( Hardy, 2014 ). Some studies find that formal practices ensure more thorough transfer of information, highlighting that disruption may undermine informal systems for knowledge exchange ( Orth and Schuldis, 2021 ). Yet, our findings underscore the important role of informal practices—indicating that earlier analyses have been overly focused on formal rules and policies, and that new insights might emerge through a fuller examination of how informal organizational rules, norms and practices work ( Bragatto et al., 2021 ). Finally, Chand and Loosemore (2016) note that informal organizational rules, norms, and practices may “undermine formal rules in determining... resilience” (p. 886).

With regard to the dimensions of learning, the literature reviewed for this study also focuses on investigating how lessons are learned and transferred among the various stakeholders ( Bragatto et al., 2021 ) by examining the specific learning mechanisms that lead to differing resilient performance effects over time ( Battisti et al., 2019 ). Taking as a starting point that learning is ongoing across all stages of a disruption [preparation (anticipation), response (coping) and recovery (adaptation)], Scholten et al. (2019) uncover six specific learning mechanisms and their nine antecedents for building supply-chain resilience. They place these mechanisms in two large categories associated with learning: intentional and unintentional. Intentional mechanisms are anticipative, situational, and vicarious learning. Anticipative learning takes place in anticipation of possible disruptions, aiming at knowledge transfer through formal training, education and collaboration; it results in the establishment of new routines or improvement of existing ones. Situational learning occurs during the coping stage, in the moment of disruption when organizations need to target the challenges that could have been anticipated but were not. Vicarious learning occurs during the adaption stage; it involves knowledge transfer based on the experiences and reflections of others. Unintentional learning mechanisms are processual, collaborative, and experiential learning. Processual learning occurs because of the proactive knowledge creation deduced from inherent organizational processes (e.g., changes in strategy, organizational growth, and operational refinement). Collaborative learning may occur during the response phase of disruption, when an immediate solution is needed, but procedures are lacking. Such instances may trigger collaboration and knowledge transfer across the actors involved. Experiential learning is associated with the recovery phase of disruption; it occurs through transfer of knowledge. Improved future performance relies heavily on rigorous and thorough learning from experience ( Ellis and Shpielberg, 2003 , cited by Scholten et al., 2019 )—as highlighted above. The trap of retrospective simplification of experience ( Christianson et al., 2009 , cited by Scholten et al., 2019 ) can be avoided by focusing on interpreting experience ( Levinthal and March, 1993 , cited by Scholten et al., 2019 ) instead of simplifying it. Finally, Scholten et al. (2019) highlight the largely overlooked value of unintentional learning.

The Role of Unlearning

OL is a cyclical process consisting of unlearning and learning , the “metamorphosis cycle” that is central to strategic resilience ( Starbuck, 1967 p. 113, cited by Morais-Storz and Nguyen, 2017 , p. 4). The deliberate process of unlearning can be approached as a stand-alone process ( Fiol and O'Connor, 2017 ; Grisold et al., 2020 ), but it is a constituent component of this cycle ( Tsang and Zahra, 2008 , cited by Morais-Storz and Nguyen, 2017 ). The importance of unlearning has received particular attention in the literature on OR ( Morais-Storz and Nguyen, 2017 ; Orth and Schuldis, 2021 ). Unlearning is associated with “a process of getting rid of certain things from an organization” ( Tsang and Zahra, 2008 , p. 1437), often triggered by crisis ( Fiol and O'Connor, 2017 ) that requires organizations to adopt new ways of thinking and abandon old mental models and processes ( Duchek, 2020 ). In a world of turbulence and uncertainty, organizations are expected to act proactively, before action is desperately needed, through their own continual transformation ( Morais-Storz and Nguyen, 2017 ). Organizations must be able to identify the early warning signs of when action is needed, as shown through a “web of symptoms” ( Baer et al., 2013 , p. 199; Morais-Storz and Nguyen, 2017 ). Ideally, new learning should be created before the need for change has become desperately obvious ( Morais-Storz and Nguyen, 2017 ).

Learning and unlearning are mutually supportive in creating knowledge and organizational learning ( Morais-Storz and Nguyen, 2017 ). The most important role of unlearning is to clear away obstacles created from misleading knowledge and obsolete routines, so as to pave the way for future learning, but unlearning can also aid the effective acquisition of new understandings ( Fiol and O'Connor, 2017 ; Grisold et al., 2020 ). For most organizations, learning is impossible without unlearning: it is in fact the precondition for new learning, enhancing the effectiveness of learning in a process of change. It has been proposed that the greater the capacity for unlearning capability, the stronger may the positive effect of OL on OR be ( Orth and Schuldis, 2021 ). The metamorphosis cycle is driven by these two processes, organizational unlearning capability exerting a positive moderating effect on the relationship between organizational learning and organizational resilience ( Orth and Schuldis, 2021 ).

Context for Learning

Our review findings underscore the importance of context, to share and “capture the relevant information and to create a social learning process” ( Prasad et al., 2015 , p. 454) where individuals are committed and motivated to learn, to achieve improved OL ( Gilson et al., 2020 ). The main function of this context, necessary within and between organizations, is to support OL ( Naimoli and Saxena, 2018 ). While organizations learn from their own activities as well as from other organizations activities, there is also a question of different contexts for learning different knowledge. Given the nature and demands of adversity, the context also needs to be active— “not necessarily fully controlled and sequential, but instead open to innovative ways of tackling open problems” ( Hermelin et al., 2020 , p. 670).

Reflecting on the complexity of context (see Argote and Miron-Spektor, 2011 ) our review has identified some contextual components that affect learning. Central here is the role of leadership. Attentive leadership and the wellbeing of employees is seen as “the core of learning and culture” ( Pal et al., 2014 , p. 418). “Listening, being respectful, allowing others to lead and creating spaces for learning from experience are important practices of leadership in complexity and for resilience” ( Belrhiti et al., 2018 ; Petrie and Swanson, 2018 , cited by Gilson et al., 2020 , p. 9). Associated with leadership are empowerment and role clarity that enable the extraction, distribution, and application of information “from failures made in various parts of the organizational system;” they are important to knowledge-exchange and are thus related to creating a context for learning from failure ( Gressgard and Hansen, 2015 , p. 173). Further, organizations need to be able to take in new information and reflect on experiences, in order to cope and adapt to situations of adversity ( Orth and Schuldis, 2021 ). Aspiring to learn and improve entails organizational desire to accept risk and failure, as both are inherent precursors of OL ( Fasey et al., 2021 ). Such exchange can be facilitated though enhanced work engagement and an open collaborative work climate ( Fasey et al., 2021 ). This in turn relies on leadership involvement and requires a more organic structure; where employees feel “responsible for the organization's development, they are more likely open to change” ( Duchek, 2020 , p. 237).

The findings indicate that organizational resistance to change has been noted as the main impediment to organizational transformation, and consequently to successful learning ( Hardy, 2014 ). Such resistance can be found in individuals and in organizations ( Donahue and Tuohy, 2006 , cited by Hardy, 2014 ). Within organizations, “ change fatigue” and lack of employee motivation may inhibit learning ( Manfield and Newey, 2018 , p. 1171). Motivation for learning is important ( Gilson et al., 2020 ), but so are other aspects like policy and administrative demands, often in combination with resource constraints ( Naimoli and Saxena, 2018 ) and the cost of studying reports and implementing actions ( Hardy, 2014 ). Learning from other organizations may be inhibited by “resources, objectives and variations in learning experiences” between organizations ( Friday et al., 2021 , p. 262).

Summary of the Analysis

In sum, our findings indicate that OL is essential to OR. However, the role of learning varies, depending on which stage of the resilience process is in focus. The frequent use of learning in relation to adaptation as opposed to anticipation and coping shows that learning is especially important in this resilience stage. However, as Table 1 shows, learning is also addressed in the two latter stages; and, as underlined by several authors, it is a central part of overall resilience. Resilience can be built by improving the effectiveness of learning. Our results indicate that experiential learning is central to how organizations gain and expand knowledge in order to improve their capabilities. Effective OL relies on a system to ensure its continuity, knowledge-transfer across organizational levels, with organizational processes that allow for formal as well as informal practices. Our review has also shown that unlearning is necessary to facilitate and adopt new and updated learning, thereby ensuring further growth toward OR. Finally, effective learning requires a supportive context.

Our review shows that OR is becoming an important goal for various types of organizations regarding crisis management, but most of all, improved performance in a world of high uncertainty. The dominance of qualitative data may be interpreted as a sign of this being a relatively young field of research. Further, it seems reasonable for empirical studies from high-risk industries like healthcare and transport dominate the field. Interestingly, however, also other fields, like tourism, food, retail, public administration and universities, also are represented as empirical fields. We interpret this as a sign of the growing interest in improving organizational performance under conditions of adversity in all branches and sectors because of the increased global threat picture. The representation of all continents as geographical contexts, and the high number of recent articles (46 out of 59 published the between 2017 and 2021) shows the growing interest in the connection between OR and OL as an emerging field of research worldwide.

Despite some variation in how explicit the studies examined here are in their use of terminology, our review clearly shows the fundamental role of OL in building OR. Some articles specify and highlight learning in connection to anticipation, coping or adaptation; others do not. Regardless, learning is still implicitly present and arguably crucial for improving performance and developing OR. Yet, the literature on resilience would stand to benefit from addressing learning more directl y, rather than as implicit, or in “broad terms only” ( Battisti et al., 2019 , p. 39). In this study, we have focused on the capabilities underlying the above mentioned stages of resilience—specifically on learning as a means for building them. Our findings show that adaptation is recognized as vital for resilience, but that goes for learning as well, as it facilitates the development of the other resilience stages and capabilities. Just as OL relies on multiple levels of interactions within and outside an organization so does OR. The frequent use of learning in relation to the adaptation stage, in comparison to the anticipation and coping stages, shows that learning is especially important in this stage of OR.

From our findings on how learning is addressed in the literature on resilience, we argue that the dynamic nature of learning in resilience is particularly evident in the conceptualizations of coping. Organizations learn “in, from and for crisis” ( Elliott and Macpherson, 2010 , p. 3) and cope by using past experience (both positive and negative) and knowledge to manage current situations. The interaction between coping and the two other stages of resilience can be said to be strongly driven by learning. In turn, this implies that the capabilities that belong to other stages will be strengthened simultaneously. We hold that organizations can build resilience by focusing on improving their ability to learn. We find it reasonable to suggest that it may be advantageous for organizations to focus on their learning processes in daily organizational life, not only during disturbances and crisis events, if they wish to strengthen resilience.

Our review indicates that learning deserves greater emphasis in relation to how organizations can develop resilience . It also highlights the importance of identifying the determinants of OL in order to build OR. By elaborating the various facets of OL in OR, the value of informal and unintentional learning processes, the need for a system, contextual factors, and the focus on unlearning, our findings and analysis contribute with deeper insights to this field of study also reflecting on the complexity of OL interactions stated in theory. OR is indeed enhanced by facilitating OL, but many aspects influence how effective that learning will be. In practice this implies that organizations may improve learning by first identifying where there is a need for changing their practices and routines.

This study has affirmed and further nuanced OL as intentional and unintentional processes highlighting of the overlooked value of unintentional learning in particular. Effective OL is a matter of transforming relevant knowledge into practice including the transformation of “unintentional learning into explicit learning” ( Scholten et al., 2019 , p. 439). However, unintentional learning might, in fact, require more from the organization in terms of flexibility and attentiveness, to be able to recognize the learning opportunities that can improve performance and create appropriate systems for knowledge transfer. These intentional and unintentional learning processes are closely linked to the discussion of formal and informal learning practices. Practices that focus heavily on formal rules and learning policies are criticized, and the value of analyzing informal organizational rules, norms and practices is highlighted. Recognition of the importance of unlearning is an aspect of the connection between OL and OR that was not included in our theoretical framework. This constitutes one of the most important contributions of our study. Unlearning in developing OR involves abandoning old mental constructs in favor of new, more relevant ones—which in turn implies that organizations must identify which of their current practices and routines obstruct growth, to pave the way for necessary changes. Our findings show that double-loop and triple-loop learning are especially crucial for developing OR. This deeper learning is necessary to avoid pitfalls that hinder effective learning. Further, improved OL depends on a better understanding of root causes of events, with consideration given to long-term issues as opposed to correcting errors. We also found that focusing on learning processes (triple-loop learning), by establishing processes and routines appropriate for learning specific, relevant lessons can foster the development of OR.

We argue that effective learning is facilitated through a learning system that captures the diverse nature of learning practices that are both flexible but also embedded in organizational routines relying on formal protocols, policies, and procedures. A main finding is that such a system is critical for developing OR because learning must be transformed into resilience capabilities. Moreover, a system for effective learning must facilitate communication and allow knowledge, experiences, opinions, and perspectives to be shared, both within and across organizations and stakeholders. We point out that collective inter-organizational learning is central to OR.

Limitations

One limitation of this article concerns the risk of failing to identify relevant contributions during the sampling stage and/or excluding some during the analytical screening process. Moreover, several important contributions have been published after May 2021. The amount of data in the 59 selected articles is huge and the scope of this article limited, so several interesting findings have had to be omitted. Thus, our selection of what to include constitutes another limitation. There is a further risk of missing something, or misinterpreting the findings, during the analytical cycles of the coding process. There exist various OR frameworks; we have chosen the one proposed by Duchek (2020) , but it might be that other frameworks would address OL differently. Learning is truly an inherent part of OR; and, as our focus has been on learning as part of resilience, we have not delved into the various frameworks for OR. We acknowledge the variations of terms and concepts employed to conceptualize resilience, such as monitoring and responding ( Adini et al., 2017 ; Anderson et al., 2020 ), but here we have emphasized what the terms and concept capture and express in terms of learning . We have not addressed the complexity of OL, which, however, should be clear from our data on aspects of unlearning and intended/unintended learning. Finally, we recognize the synergy between OR and OL ( Vogus and Sutcliffe, 2007 ; Lombardi et al., 2021 ; Rodríguez-Sánchez et al., 2021 ) the scope of this article has been limited to how OL influences OR; the reverse effect has remained unexplored.

To our knowledge this is the first review to focus solely on the relationship between organizational learning and resilience, a relationship that has been discussed and established by scholars from various fields. More work is needed on how organizations can improve their learning abilities, as learning is essential for organizations to evolve from one resilience stage to another. OR can indeed be learned, so effective learning can serve as a critical driver for building OR. The effectiveness of OL can be increased by a more comprehensive understanding of the link between experiences and improved performance, with more focus on the value of diversity. OR is dependent upon an appropriate system to ensure continuous, inclusive, purposeful OL, capable of facilitating intentional and unintentional learning, and supported by an active context that enables new knowledge to enter the picture. Effective OL toward OR also requires the ability to unlearn previous ways of doing things, to learn and engage in new and improved ways of response. Lastly, organizations do not exist in a vacuum. OL must involve collaboration between organizations, to ensure sharing and exchange of valuable knowledge and experience, to build OR.

This article has theoretical and practical implications. As regards theory, our study contributes with insights on why learning is so central to resilience, through all the stages of capability. Our findings shed light on how learning can be targeted more effectively and how it can facilitate resilience. Second, our work shows the role of unlearning in OR, a point that deserves more attention in further research. In terms of theory, this study offers further insights into aspects of learning from experience and how this should be managed to build resilient organizations. On a practical note, there is still a need for empirical verification of the effects of learning on OR. More understanding is also needed of how learning interacts over time with other multilevel processes that contribute to building OR. Our findings have made clear the importance of establishing a system where organizations can build on the experiences and knowledge of other organizations in building resilience.

Our review also reveals need for further research . Current understanding of the dynamics of effective learning is at a very early stage, so more investment in systematic research on learning in organizations and their link to resilience-building ( Naimoli and Saxena, 2018 ) is called for. Further, there is a need for better understanding the correlation, if any, between disastrous events, their driving hazards, and major consequences; how learning occurs in affected organizations, and how long this organizational learning lasts ( Bhaskara and Filimonau, 2021 , p. 366). A key gap involves the scant attention given to the processes of knowledge transfer ( Elliott and Macpherson, 2010 ). Also needed is a deeper understanding of how learning interacts over time with other multilevel processes that contribute to building OR ( Fasey et al., 2021 ). Since learning is central if organizations are to evolve from one resilience stage to another, this review reveals the need for more research on how organizations can improve their learning abilities in general. More research, preferably empirical, is needed on the role, and potential, of informal practices and unintentional learning processes to improve OL related to OR, and on the role and practices of unlearning. Our study has also revealed the need for more research on the link between OL and learning at the individual, group and interorganizational levels. Even if it may seem paradoxical to “organize” for informal and unintended processes, this links in with the need for continuity and a coherent learning system.

Author Contributions

LE and MS conceptualized the article and coded and analyzed the material. MS provided the analytical framework. LE performed the data sampling, organized the material, and performed the first round of coding. AG provided Table 1 and contributed with critical editing of the whole manuscript. All authors contributed in the screening process, analytical stage with writing and critical editing, and have approved the submitted text.

Conflict of Interest

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

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ “Grey literature stands for manifold document types produced on all levels of government, academics, business and industry in print and electronic formats that are protected by intellectual property rights, of sufficient quality to be collected and preserved by library holdings or institutional repositories, but not controlled by commercial publishers, i.e., where publishing is not the primary activity of the producing body” ( Schöpfel, 2010 ).

Adini, B., Cohen, O., Eide, A. W., Nilsson, S., Aharonson-Daniel, L., and Herrera, I. A. (2017). Striving to be resilient: what concepts, approaches and practices should be incorporated in resilience management guidelines? Technol. Forecast. Soc. Change 121, 39–49. doi: 10.1016/j.techfore.2017.01.020

CrossRef Full Text | Google Scholar

Al-Atwi, A. A., Amankwah-Amoah, J., and Khan, Z. (2021). Micro-foundations of organizational design and sustainability: the mediating role of learning ambidexterity. Int. Bus. Rev. 30, 11. doi: 10.1016/j.ibusrev.2019.101656

Andersen, E. (2016). Learning to learn. Harvard Buisness Review , 94. Available online at: https://hbsp.harvard.edu/product/R1603J-PDF-ENG (accessed December 12, 2021).

Anderson, J. E., Ross, A. J., Macrae, C., and Wiig, S. (2020). Defining adaptive capacity in healthcare: a new framework for researching resilient performance. Appl. Ergon. 87, 9. doi: 10.1016/j.apergo.2020.103111

PubMed Abstract | CrossRef Full Text | Google Scholar

Annarelli, A., Battistella, C., and Nonino, F. (2020). A Framework to evaluate the effects of organizational resilience on service quality. Sustainability 12, 958. doi: 10.3390/su12030958

Antonacopoulou, E., and Chiva, R. (2007). The social complexity of organizational learning: the dynamics of learning and organizing. Manage. Learn. 38, 277–295. doi: 10.1177/1350507607079029

Antonacopoulou, E. P., and Sheaffer, Z. (2014). Learning in crisis:rethinking the relationship between organizational learning and crisis management. J. Manage. Inquiry 23, 5–21. doi: 10.1177/1056492612472730

Argote, L. (2011). Organizational learning research: past, present and future. Manage. Learn. 42, 439–446. doi: 10.1177/1350507611408217

Argote, L., and Ingram, P. (2000). Knowledge transfer: a basis for competitive advantage in firms. Organ. Behav. Hum. Decis. Process. 82, 150–169. doi: 10.1006/obhd.2000.2893

Argote, L., Lee, S., and Park, J. (2020). Organizational learning processes and outcomes: major findings and future research directions. Manage. Sci. 67, 5399–5429. doi: 10.1287/mnsc.2020.3693

Argote, L., and Miron-Spektor, E. (2011). Organizational learning: from experience to knowledge. Organization Sci. 22, 1123–1137. doi: 10.1287/orsc.1100.0621

Argote, L., and Todorova, G. (2007). “Organizational learning: review and future directions,” in International Review of Industrial and Organizational Psychology , editors G. P. Hodgkinson and J. K. Ford (New York, Ny: John Wiley & Sons Ltd), 193–234.

Google Scholar

Argyris, C. (2002). Double-loop learning, teaching, and research. Acad. Manage. Learn. Educ. 1, 206–218. doi: 10.5465/amle.2002.8509400

Argyris, C., and Schön, D. A. (1978). Organizational Learning: A Theory of Action Perspective . Reading, MA: Addison-Wesley.

Argyris, C., and Schön, D. A. (1996). Organizational Learning II: Theory, Method and Practice. Reading, MA: Addison-Wesley.

Azadegan, A., Srinivasan, R., Blome, C., and Tajeddini, K. (2019). Learning from near-miss events: an organizational learning perspective on supply chain disruption response. Int. J. Prod. Econ. 216, 215–226. doi: 10.1016/j.ijpe.2019.04.021

Baer, M., Dirks, K. T., and Nickerson, J. A. (2013). Microfoundations of strategic problem formulation. Strategic Manage. J. 34, 197–214. doi: 10.1002/smj.2004

Baker, W. E., and Sinkula, J. M. (1999). The synergistic effect of market orientation and learning orientation on organizational performance. J. Acad. Mark. Sci. 27, 411. doi: 10.1177/0092070399274002

Battisti, M., Beynon, M., Pickernell, D., and Deakins, D. (2019). Surviving or thriving: the role of learning for the resilient performance of small firms. J. Bus. Res. 100, 38–50. doi: 10.1016/j.jbusres.2019.03.006

Belrhiti, Z., Nebot Geralt, A., and Marchal, B. (2018). Complex leadership in healthcare: a scoping review. Int. J. Health Policy Manage. 7, 1073–1084. doi: 10.15171/ijhpm.2018.75

Bhaskara, G. I., and Filimonau, V. (2021). The COVID-19 pandemic and organisational learning for disaster planning and management: a perspective of tourism businesses from a destination prone to consecutive disasters. J. Hosp. Tourism Manage. 46, 364–375. doi: 10.1016/j.jhtm.2021.01.011

Boin, A., Comfort, L. K., and Demchak, C. C. (2010). “The rise of resilience,” in Designing Resilience: Preparing for Extreme Events , editors L. K. Comfort, A. Boin, and C. C. Demchak (Pittsburgh, Pennsylvania: University of Pittsburgh Press).

Boin, A., and Lodge, M. (2016). Designing Resilient Institutions for transboundary crisis management: a time for public administration. Public Adm. 94, 289–298. doi: 10.1111/padm.12264

Boin, A., and van Eeten, M. J. G. (2013). The resilient organization. Public Manage. Rev. 15, 429–445. doi: 10.1080/14719037.2013.769856

Boyatzis, R. E. (1998). Transforming Qualitative Information: Thematic Analysis and Code Development . Thousand Oaks, CA: Sage Publications, Inc.

Bragatto, P., Vairo, T., Milazzo, M. F., and Fabiano, B. (2021). The impact of the COVID-19 pandemic on the safety management in Italian Seveso industries. J. Loss Prevent. Process Ind. 70, 104393. doi: 10.1016/j.jlp.2021.104393

Braun, V., and Clarke, V. (2006). Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101. doi: 10.1191/1478088706qp063oa

Britt, T. W., Shen, W., Sinclair, R. R., Grossman, M. R., and Klieger, D. M. (2016). How Much do we really know about employee resilience? Ind. Organ. Psychol. 9, 378–404. doi: 10.1017/iop.2015.107

Chand, A. M., and Loosemore, M. (2016). Hospital learning from extreme weather events: using causal loop diagrams. Build. Res. Inform. 44, 875–888. doi: 10.1080/09613218.2016.1097805

Chen, K. D., Li, Y. H., and Linderman, K. (2021). Supply network resilience learning: an exploratory data analytics study. Decision Sci. 20, 1–20. doi: 10.1111/deci.12513

Chen, R., Xie, Y., and Liu, Y. (2021). Defining, conceptualizing, and measuring organizational resilience: a multiple case study. Sustainability 13, 2517. doi: 10.3390/su13052517

Chiva, R., Ghauri, P., and Alegre, J. (2014). Organizational learning, innovation and internationalization: a complex system model. Br. J. Manage. 25, 687–705. doi: 10.1111/1467-8551.12026

Christianson, M. K., Farkas, M. T., Sutcliffe, K. M., and Weick, K. E. (2009). Learning through rare events: significant interruptions at the Baltimore & Ohio Railroad Museum. Organization Sci. 20, 846–860. doi: 10.1287/orsc.1080.0389

Cook, S. D. N., and Yanow, D. (1993). Culture and organizational learning. J. Manage. Inquiry 2, 373–390. doi: 10.1177/105649269324010

Cooper, H. M. (1988). Organizing knowledge syntheses: a taxonomy of literature reviews. Knowledge Soc. 1, 104. doi: 10.1007/BF03177550

Crick, R., and Bentley, J. (2020). Becoming a resilient organisation: integrating people and practice in infrastructure services. Int. J. Sustain. Eng. 13, 423–440. doi: 10.1080/19397038.2020.1750738

Crossan, M. M., and Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: a systematic review of the literature. J. Manage. Stud. 47, 1154–1191. doi: 10.1111/j.1467-6486.2009.00880.x

Crossan, M. M., Lane, H. W., and White, R. E. (1999). An organizational learning framework: from intuition to institution. Acad. Manage. Rev. 24, 522–537. doi: 10.2307/259140

Dohaney, J., de Roiste, M., Salmon, R. A., and Sutherland, K. (2020). Benefits, barriers, and incentives for improved resilience to disruption in university teaching. Int. J. Disaster Risk Reduct. 50, 9. doi: 10.1016/j.ijdrr.2020.101691

Donahue, A., and Tuohy, R. (2006). Lessons we don't learn: a study of the lessons of disasters, why we repeat them, and how we can learn them. Homeland Security Affairs 2, 4. Available online at: https://www.hsaj.org/articles/167 (accessed December 10, 2021).

Duchek, S. (2020). Organizational resilience: a capability-based conceptualization. Bus. Res. 13, 215–246. doi: 10.1007/s40685-019-0085-7

Duchek, S., Raetze, S., and Scheuch, I. (2020). The role of diversity in organizational resilience: a theoretical framework. Bus. Res. 13, 387–423. doi: 10.1007/s40685-019-0084-8

Dutra, L. X. C., Bustamante, R. H., Sporne, I., van Putten, I., Dichmont, C. M., Ligtermoet, E., et al. (2015). Organizational drivers that strengthen adaptive capacity in the coastal zone of Australia. Ocean Coast. Manag. 109, 64–76. doi: 10.1016/j.ocecoaman.2015.02.008

Ekstedt, M., and Odegard, S. (2015). Exploring gaps in cancer care using a systems safety perspective. Cogn. Technol. Work 17, 5–13. doi: 10.1007/s10111-014-0311-1

Elliott, D., and Macpherson, A. (2010). Policy and practice: recursive learning from crisis. Group Org. Manage. 35, 572–605. doi: 10.1177/1059601110383406

Ellis, S., and Shpielberg, N. (2003). Organizational learning mechanisms and managers' perceived uncertainty. Hum. Relat. 56, 1233–1254. doi: 10.1177/00187267035610004

Fannoun, S., and Kerins, J. (2019). Towards organisational learning enhancement: assessing software engineering practice. Learn. Org. 26, 44–59. doi: 10.1108/TLO-09-2018-0149

Fasey, K. J., Sarkar, M., Wagstaff, C. R. D., and Johnston, J. (2021). Defining and characterizing organizational resilience in elite sport. Psychol. Sport Exerc. 52, 12. doi: 10.1016/j.psychsport.2020.101834

Fiol, M., and O'Connor, E. (2017). Unlearning established organizational routines – Part I. Learn. Org. 24, 13–29. doi: 10.1108/TLO-09-2016-0056

Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., et al. (2004). Regime shifts, resilience, and biodiversity in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35, 557–581. doi: 10.1146/annurev.ecolsys.35.021103.105711

Friday, D., Savage, D. A., Melnyk, S. A., Harrison, N., Ryan, S., and Wechtler, H. (2021). A collaborative approach to maintaining optimal inventory and mitigating stockout risks during a pandemic: capabilities for enabling health-care supply chain resilience. J. Hum. Logistics Supply Chain Manage. 11, 24. doi: 10.1108/JHLSCM-07-2020-0061

Fridell, M., Edwin, S., von Schreeb, J., and Saulnier, D. D. (2020). Health system resilience: what are we talking about? A scoping review mapping characteristics and keywords. Int. J. Health Policy Manage. 9, 6–16. doi: 10.15171/ijhpm.2019.71

Garvin, D. A. (1993). Building a learning organization. Harv. Bus. Rev. 71, 78–91.

Garvin, D. A., Edmondson, A. C., and Gino, F. (2008). Is yours a learning organization? Harvard Bus. Rev. 86, 109–116, 134. Available online at: https://hbr.org/2008/03/is-yours-a-learning-organization (accessed December 5, 2021).

Gherardi, S. (2008). “Situated knowledge and situated action: What do practice-based studies promise?,” in The SAGE Handbook of New Approaches in Management and Organization , editors D. Barry and H. Hansen (Los Angeles, CA: Sage), 516–525.

Gherardi, S., and Nicolini, D. (2002). Learning in a constellation of interconnected practices: canon or dissonance? J. Manage. Stud. 39, 419–436. doi: 10.1111/1467-6486.t01-1-00298

Gherardi, S., Nicolini, D., and Odella, F. (1998). Toward a social understanding of how people learn in organizations:the notion of situated curriculum. Manage. Learn. 29, 273–297. doi: 10.1177/1350507698293002

Gilson, L., Ellokor, S., Lehmann, U., and Brady, L. (2020). Organizational change and everyday health system resilience: lessons from Cape Town, South Africa. Soc. Sci. Med. 266, 10. doi: 10.1016/j.socscimed.2020.113407

Giustiniano, L., Clegg, S. R., Cunha, M. P., and Rego, A. (2018). Elgar Introduction to Theories of Organizational Resilience . Cheltenham, UK: Edward Elgar Publishing. doi: 10.4337/9781786437044

Gressgard, L. J., and Hansen, K. (2015). Knowledge exchange and learning from failures in distributed environments: the role of contractor relationship management and work characteristics. Reliab. Eng. Syst. Saf. 133, 167–175. doi: 10.1016/j.ress.2014.09.010

Grisold, T., Klammer, A., and Kragulj, F. (2020). Two forms of organizational unlearning: insights from engaged scholarship research with change consultants. Manage. Learn. 51, 598–619. doi: 10.1177/1350507620916042

Habiyaremye, A. (2021). Co-operative learning and resilience to COVID-19 in a small-sized South African enterprise. Sustainability 13, 17. doi: 10.3390/su13041976

Hardy, T. L. (2014). “Resilience: a holistic safety approach,” in 2014 60th Annual Reliability and Maintainability Symposium (New York).

Hecht, A. A., Biehl, E., Barnett, D. J., and Neff, R. A. (2019). Urban food supply chain resilience for crises threatening food security: a qualitative study. J. Acad. Nutr. Diet. 119, 211–224. doi: 10.1016/j.jand.2018.09.001

Hegde, S., Hettinger, A. Z., Fairbanks, R. J., Wreathall, J., Krevat, S. A., and Bisantz, A. M. (2020a). Knowledge elicitation to understand resilience: a method and findings from a health care case study. J. Cogn. Eng. Decis. Mak. 14, 75–95. doi: 10.1177/1555343419877719

Hegde, S., Hettinger, A. Z., Fairbanks, R. J., Wreathall, J., Krevat, S. A., Jackson, C. D., et al. (2020b). Qualitative findings from a pilot stage implementation of a novel organizational learning tool toward operationalizing the Safety-II paradigm in health care. Appl. Ergon. 82, 8. doi: 10.1016/j.apergo.2019.102913

Herbane, B. (2019). Rethinking organizational resilience and strategic renewal in SMEs. Entrepreneurship Regional Dev. 31, 476–495. doi: 10.1080/08985626.2018.1541594

Hermelin, J., Bengtsson, K., Woltjer, R., Trnka, J., Thorstensson, M., Pettersson, J., et al. (2020). Operationalising resilience for disaster medicine practitioners: capability development through training, simulation and reflection. Cogn. Technol. Work 22, 667–683. doi: 10.1007/s10111-019-00587-y

Hernes, T., and Irgens, E. J. (2013). Keeping things mindfully on track: organizational learning under continuity. Manage. Learn. 44, 253–266. doi: 10.1177/1350507612445258

Hillmann, J., Duchek, S., Meyr, J., and Guenther, E. (2018). Educating future managers for developing resilient organizations: the role of scenario planning. J. Manage. Educ. 42, 461–495. doi: 10.1177/1052562918766350

Hillmann, J., and Guenther, E. (2021). Organizational resilience: a valuable construct for management research? Int. J. Manag. Rev. 23, 7–44. doi: 10.1111/ijmr.12239

Hollnagel, E. (2011). “Prologue: the scope of resilience engineering,” in Resilience Engineering in Practice: A Guidebook , editors E. Hollnagel, J. Pariès, D. Woods, and J. Wreathall (England: Surrey: Taylor & Francis Group), 30–41.

Huber, G. P. (1991). Organizational learning: the contributing processes and the literatures. Org. Sci. 2, 88–115. doi: 10.1287/orsc.2.1.88

Johannesen, D. T. S., Lindoe, P. H., and Wiig, S. (2020). Certification as support for resilience? Behind the curtains of a certification body: a qualitative study. BMC Health Serv. Res. 20, 14. doi: 10.1186/s12913-020-05608-5

Johnsen, S. O., and Habrekke, S. (2009). Can Organisational Learning Improve Safety and Resilience During Changes? Boca Raton: Crc Press-Taylor & Francis Group.

Johnsen, S. O., and Stene, T. (2014). Improving Human Resilience in Space and Distributed Environments by CRIOP. Boca Raton: CRC, FL Press/Taylor & Francis Group.

Jones, O., and Macpherson, A. (2006). Inter-organizational learning and strategic renewal in SMEs: extending the 4I framework. Long Range Plann. 39, 155–175. doi: 10.1016/j.lrp.2005.02.012

Kayes, D. C. (2015). Organizational Resilience: How Learning Sustains Organizations in Crisis, Disaster, and Breakdown . Oxford: Oxford University Press.

Khan, S. J., Deere, D., Leusch, F. D. L., Humpage, A., Jenkins, M., Cunliffe, D., et al. (2017). Lessons and guidance for the management of safe drinking water during extreme weather events. Environ. Sci. Water Res. Technol. 3, 262–277. doi: 10.1039/C6EW00165C

Khan, T. Z. A., Farooq, W., and Rasheed, H. (2019). Organizational resilience: a dynamic capability of complex systems. J. Manage. Res. 6, 1–26. doi: 10.29145//JMR/61/0601001

Klockner, K., and Meredith, P. (2020). Measuring resilience potentials: a pilot program using the resilience assessment grid. Safety 6, 17. doi: 10.3390/safety6040051

Kugley, S., Wade, A., Thomas, J., Mahood, Q., Jørgensen, A.-M. K., Hammerstrøm, K., et al. (2017). Searching for studies: a guide to information retrieval for Campbell systematic reviews. Campbell Syst. Rev. 13, 1–73. doi: 10.4073/cmg.2016.1

CrossRef Full Text

Lave, J., and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation . Cambridge: Cambridge University Press.

Lavie, D., Stettner, U., and Tushman, M. L. (2010). Exploration and exploitation within and across organizations. Acad. Manag. Ann. 4, 109–155. doi: 10.5465/19416521003691287

Lawrence, T. B., Mauws, M. K., Dyck, B., and Kleysen, R. F. (2005). The politics of organizational learning: integrating power into the 4I framework. Acad. Manage. Rev. 30, 180–191. doi: 10.5465/amr.2005.15281451

Lengnick-Hall, C. A., Beck, T. E., and Lengnick-Hall, M. L. (2011). Developing a capacity for organizational resilience through strategic human resource management. Hum. Resource Manage. Rev. 21, 243–255. doi: 10.1016/j.hrmr.2010.07.001

Levinthal, D. A., and March, J. G. (1993). The myopia of learning. Strategic Manage. J. 14, 95–112. doi: 10.1002/smj.4250141009

Liñán, F., and Fayolle, A. (2015). A systematic literature review on entrepreneurial intentions: citation, thematic analyses, and research agenda. Int. Entrepreneurship Manage. J. 11, 907–933. doi: 10.1007/s11365-015-0356-5

Linnenluecke, M., and Griffiths, A. (2010). Beyond adaptation: resilience for business in light of climate change and weather extremes. Bus. Soc. 49, 477–511. doi: 10.1177/0007650310368814

Linnenluecke, M. K. (2017). Resilience in business and management research: a review of influential publications and a research agenda. Int. J. Manag. Rev. 19, 4–30. doi: 10.1111/ijmr.12076

Lipshitz, R., Popper, M., and Friedman, V. J. (2002). A multifacet model of organizational learning. J. Appl. Behav. Sci. 38, 78–98. doi: 10.1177/0021886302381005

Lombardi, S., Pina e Cunha, M., and Giustiniano, L. (2021). Improvising resilience: the unfolding of resilient leadership in COVID-19 times. Int. J. Hosp. Manage. 95, 102904. doi: 10.1016/j.ijhm.2021.102904

Lundberg, C. C. (1995). Learning in and by organization: three conceptual issues. Int. J. Org. Anal. 3, 10–23. doi: 10.1108/eb028821

Ma, Z. Z., Xiao, L., and Yin, J. L. (2018). Toward a dynamic model of organizational resilience. Nankai Bus. Rev. Int. 9, 246–263. doi: 10.1108/NBRI-07-2017-0041

Mackay, D., and Zundel, M. (2017). Recovering the divide: a review of strategy and tactics in business and management. Int. J. Manag. Rev. 19, 175–194. doi: 10.1111/ijmr.12091

Madsen, P. M., and Desai, V. (2010). Failing to learn? The effects of failure and success on organizational learning in the global orbital launch vehicle industry. Acad. Manage. J. 53, 451–476. doi: 10.5465/amj.2010.51467631

Manfield, R. C., and Newey, L. R. (2018). Resilience as an entrepreneurial capability: integrating insights from a cross-disciplinary comparison. Int. J. Entrepreneurial Behav. Res. 24, 1155–1180. doi: 10.1108/IJEBR-11-2016-0368

March, J. G. (1991). Exploration and exploitation in organizational learning. Org. Sci. 2, 71–87. doi: 10.1287/orsc.2.1.71

March, J. G. (2010). The Ambiguities of Experience. Ithaca, NY: Cornell University Press.

Martinelli, E., Tagliazucchi, G., and Marchi, G. (2018). The resilient retail entrepreneur: dynamic capabilities for facing natural disasters. Int. J. Entrepreneurial Behav. Res. 24, 1222–1243. doi: 10.1108/IJEBR-11-2016-0386

Miles, M. B., and Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook. 2nd Edn. Thousand Oaks, CA: Sage.

Morais-Storz, M., and Nguyen, N. (2017). The role of unlearning in metamorphosis and strategic resilience. Learn. Org. 24, 93–106. doi: 10.1108/TLO-12-2016-0091

Mousa, M., Abdelgaffar, H. A., Chaouali, W., and Aboramadan, M. (2020). Organizational learning, organizational resilience and the mediating role of multi-stakeholder networks A study of Egyptian academics. J. Workplace Learn. 32, 161–181. doi: 10.1108/JWL-05-2019-0057

Mousa, M., Sai, A., and Salhin, G. (2017). An exploration for the motives behind enhancing senior banker's level of organizational resilience: a holistic case study. J. Intercultural Manage. 9, 141–163. doi: 10.1515/joim-2017-0025

Naimoli, J. F., and Saxena, S. (2018). Realizing their potential to become learning organizations to foster health system resilience: opportunities and challenges for health ministries in low- and middle-income countries. Health Policy Plan. 33, 1083–1095. doi: 10.1093/heapol/czy100

Näswall, K., Kuntz, J., Hodliffe, M., and Malinen, S. (2013). Employee Resilience Scale (EmpRes): Resilient Organisations Research Report 2013/06 ISSN 1178-7279 . Available online at: http://hdl.handle.net/10092/9469 (accessed December 12, 2021).

Nicolletti, M., Lutti, N., Souza, R., and Pagotto, L. (2019). Social and organizational learning in the adaptation to the process of climate change: the case of a Brazilian thermoplastic resins and petrochemical company. J. Clean. Prod. 226, 748–758. doi: 10.1016/j.jclepro.2019.04.058

Nyman, M. R. (2019). collective learning in organizations: opportunities and constraints. Case study of an avalanche blocking a railway line. Risk Hazards Crisis Public Policy 10, 332–359. doi: 10.1002/rhc3.12159

Örtenblad, A. (2004). The learning organization: towards an integrated model. Learn. Org. 11, 129–144. doi: 10.1108/09696470410521592

Orth, D., and Schuldis, P. M. (2021). Organizational learning and unlearning capabilities for resilience during COVID-19. Learn. Org . 28, 509–522. doi: 10.1108/TLO-07-2020-0130

Pal, R., Torstensson, H., and Mattila, H. (2014). Antecedents of organizational resilience in economic crises: an empirical study of Swedish textile and clothing SMEs. Int. J. Prod. Econ. 147, 410–428. doi: 10.1016/j.ijpe.2013.02.031

Parsons, D. (2010). Organisational resilience. Aust. J. Emerg. Manage. 25, 18–20. doi: 10.3316/ielapa.084557405183732

Patriarca, R., Di Gravio, G., Costantino, F., Falegnami, A., and Bilotta, F. (2018). An analytic framework to assess organizational resilience. Saf. Health Work 9, 265–276. doi: 10.1016/j.shaw.2017.10.005

Petrie, D. A., and Swanson, R. C. (2018). The mental demands of leadership in complex adaptive systems. Healthcare Manage. Forum 31, 206–213. doi: 10.1177/0840470418778051

Popova-Nowak, I. V., and Cseh, M. (2015). The meaning of organizational learning:a meta-paradigm perspective. Hum. Resource Dev. Rev. 14, 299–331. doi: 10.1177/1534484315596856

Powley, E. H., and Cameron, K. S. (2020). “Resilience and organizational culture: a competing values perspective,” in Research Handbook on Organizational Resilience , editors E. H Powley, B. B. Caza, and A. Caza (Cheltenham, UK: Edward Elgar), 261–274.

Prasad, S., Su, H. C., Altay, N., and Tata, J. (2015). Building disaster-resilient micro enterprises in the developing world. Disasters 39, 447–466. doi: 10.1111/disa.12117

Provan, D. J., Woods, D. D., Dekker, S. W. A., and Rae, A. J. (2020). Safety II professionals: how resilience engineering can transform safety practice. Reliab. Eng. Syst. Saf. 195, 106740. doi: 10.1016/j.ress.2019.106740

Rangachari, P., and Woods, J. L. (2020). Preserving organizational resilience, patient safety, and staff retention during COVID-19 requires a holistic consideration of the psychological safety of healthcare workers. Int. J. Environ. Res. Public Health 17, 4267. doi: 10.3390/ijerph17124267

Reich, B. H. (2007). Managing knowledge and learning in it projects: a conceptual framework and guidelines for practice. Proj. Manage. J. 38, 5–17. doi: 10.1177/875697280703800202

Ritz, F., Kleindienst, C., Brüngger, J., and Koch, J. (2015). Coping with unexpected safety-critical situations: a concept for resilient (simulator) team training for control room teams. Proc. Manufact. 3, 1865–1871. doi: 10.1016/j.promfg.2015.07.228

Rodríguez-Sánchez, A., Guinot, J., Chiva, R., and López-Cabrales, Á. (2021). How to emerge stronger: antecedents and consequences of organizational resilience. J. Manage. Org. 27, 442–459. doi: 10.1017/jmo.2019.5

Rodríguez-Sánchez, A. M., and Vera Perea, M. (2015). The secret of organisation success: a revision on organisational and team resilience. Int. J. Emerg. Serv. 4, 27–36. doi: 10.1108/IJES-09-2014-0018

Ruiz-Martin, C., López-Paredes, A., and Wainer, G. (2018). What we know and do not know about organizational resilience. Int. J. Prod. Manage. Eng. 6, 11–28. doi: 10.4995/ijpme.2018.7898

Salanova, M. (2020). How to survive COVID-19? Notes from organisational resilience. Int. J. Soc. Psychol. 35, 670–676. doi: 10.1080/02134748.2020.1795397

Schilling, J., and Kluge, A. (2009). Barriers to organizational learning: an integration of theory and research. Int. J. Manag. Rev. 11:337–360. doi: 10.1111/j.1468-2370.2008.00242.x

Scholten, K., Scott, P. S., and Fynes, B. (2019). Building routines for non-routine events: supply chain resilience learning mechanisms and their antecedents. Supply Chain Manage. Int. J. 24, 430–442. doi: 10.1108/SCM-05-2018-0186

Schöpfel, J. (2010). “Towards a Prague definition of grey literature,” in Twelfth International Conference on Grey Literature: Transparency in Grey Literature. Grey Tech Approaches to High Tech Issues. Prague, 6–7 December 2010 , 11–26.

Sengul, H., Marsan, D., and Gun, T. (2019). Survey assessment of organizational resiliency potential of a group of Seveso organizations in Turkey. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 233, 470–486. doi: 10.1177/1748006X18802655

Sitkin, S. B. (1992). Learning through failure: the strategy of small losses. Res. Org. Behav. 14, 231–266.

Starbuck, W. H. (1967). Organizational metamorphosis. Acad. Manage. Proc. 1, 113–132. doi: 10.5465/ambpp.1967.4980662

Steen, R., and Ferreira, P. (2020). Resilient flood-risk management at the municipal level through the lens of the functional resonance analysis model. Reliabil. Eng. Syst. Saf. 204, 107150. doi: 10.1016/j.ress.2020.107150

Suryaningtyas, D., Sudiro, A., Eka, T. A., and Dodi, I. W. (2019). Organizational resilience and organizational performance: examining the mediating roles of resilient leadership and organizational culture. Acad. Strat. Manage. J. 18, 1. Available online at: https://www.abacademies.org/journals/academy-of-strategic-management-journal-home.html (accessed December 12, 2021).

Sutcliffe, K. M., and Vogus, T. J. (2003). “Organizing for resilience,” in Positive Organizational Scholarship: Foundations of a New Discipline , editors K. S. Cameron, J. E. Dutton, and R. E. Quinn (San Francisco, CA: Berrett-Koehler), 94–110.

Tasic, J., Amir, S., Tan, J., and Khader, M. (2020). A multilevel framework to enhance organizational resilience. J. Risk Res. 23, 713–738. doi: 10.1080/13669877.2019.1617340

Taylor, G., Templeton, G., and Baker, L. (2010). Factors influencing the success of organizational learning implementation: a policy facet perspective. Int. J. Manag. Rev. 12, 353–364. doi: 10.1111/j.1468-2370.2009.00268.x

Tsang, E. W. K., and Zahra, S. A. (2008). Organizational unlearning. Hum. Relat. 61, 1435–1462. doi: 10.1177/0018726708095710

Ungar, M. (2018). Systemic resilience: principles and processes for a science of change in contexts of adversity. Ecol. Soc. 23, 17. doi: 10.5751/ES-10385-230434

van Trijp, J., Boersma, K., and Groenewegen, P. (2018). Resilience from the real world towards specific organisational resilience in emergency response organisations. Int. J. Emerg. Manage. 14, 303–321. doi: 10.1504/IJEM.2018.097358

Vogus, T. J., and Sutcliffe, K. M. (2007). “Organizational resilience: towards a theory and research agenda,” in IEEE International Conference on Systems, Man and Cybernetics , 3418–3422.

PubMed Abstract | Google Scholar

Walsh, J. P., and Ungson, G. R. (1991). Organizational memory. Acad. Manage. Rev. 16, 57–91. doi: 10.2307/258607

Weick, K. E., and Sutcliffe, K. M. (2015). Managing the Unexpected: Sustained Performance in a Complex World , 3rd Edn. New Jersey, NY: John Wiley & Sons Inc.

Wright, C., Suh, C. S., and Leggett, C. (2009). If at first you don't succeed: globalized production and organizational learning at the Hyundai Motor Company. Asia Pacific Bus. Rev. 15, 163–180. doi: 10.1080/13602380701698418

Xiao, Y., and Watson, M. (2019). Guidance on conducting a systematic literature review. J. Plan. Educ. Res. 39, 93–112. doi: 10.1177/0739456X17723971

Keywords: organizational resilience, organizational learning, organizational unlearning, organizational capabilities, experiential learning, crisis management

Citation: Evenseth LL, Sydnes M and Gausdal AH (2022) Building Organizational Resilience Through Organizational Learning: A Systematic Review. Front. Commun. 7:837386. doi: 10.3389/fcomm.2022.837386

Received: 16 December 2021; Accepted: 31 January 2022; Published: 25 February 2022.

Reviewed by:

Copyright © 2022 Evenseth, Sydnes and Gausdal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lise L. Evenseth, lise.l.evenseth@uit.no ; Maria Sydnes, maria.sydnes@uit.no

This article is part of the Research Topic

Emergency, Crisis, and Risk Management: Current Perspectives on the Development of Joint Risk Mitigation, Preparedness, and Response Efforts

Organizational unlearning as a process: What we know, what we don’t know, what we should know

• Open access
• Published: 23 April 2024

Cite this article

You have full access to this open access article

• Adrian Klammer   ORCID: orcid.org/0000-0001-9665-0419 1 ,
• Thomas Grisold 2 ,
• Nhien Nguyen 3 &
• Shih-wei Hsu 4  

Although the field of organizational unlearning has recently gained increased interest, its conceptual foundations and raison d’être are still debated. In this review, we aim to revisit various discourses and arguments to advance the understanding of organizational unlearning in management and organization studies. Using an integrative literature review approach with systematic elements, we examine the existing body of research on organizational unlearning. We review the literature from different perspectives, focusing on a process-based understanding in terms of why and how organizations intentionally discard knowledge. Based on our review, we develop an integrative framework that portrays organizational unlearning as a dynamically unfolding process over time. We propose implications and offer research directions that will allow future researchers to develop a more profound understanding of the concept.

Avoid common mistakes on your manuscript.

1 Introduction

Organizational unlearning implies that organizations intentionally and deliberately discard undesired, obsolete, or harmful knowledge—often to make room for the creation of new knowledge (Tsang and Zahra 2008 ). To this end, organizational unlearning can target different knowledge structures, such as systems, routines, basic assumptions, values, or norms. Moreover, it can occur in various contexts, such as innovation (Wang et al. 2013 ; Yang et al. 2014 ; Açıkgöz et al. 2021 ), mergers and acquisitions (Tsang 2008 ; Wang et al. 2017 ), organizational change (Grisold et al. 2020 ), and social care (Brook et al. 2016 ), among others.

Despite the considerable uptake of organizational unlearning in research, the concept has sparked controversy, primarily owing to its conceptual ambiguities (see Klein  1989 ; Martin de Holan 2011b ; Howells and Scholderer 2016 ; Tsang 2017a , b ; 1989 ); Klammer et al. 2019b ). Along these lines, it has been argued that the term organizational unlearning conveys the impression that knowledge can be eliminated from organizations, essentially insinuating that targeted knowledge structures can be objectified and selectively erased (Turc and Baumard 2007 ; Howells and Scholderer 2016 ; Grisold et al. 2017 ). The main objection to these claims is that a large share of organizational knowledge is embedded in mental models, practices, and routines, which cannot be removed or taken out in any literal sense (e.g., Cowan et al. 2000 ; Tsoukas and Vladimirou 2001 ).

In response to these claims, emerging arguments emphasize that organizational unlearning should be understood as a process (e.g., Fiol and O’Connor 2017a , b ; Grisold et al. 2017 ; Kluge and Gronau 2018 ; Peschl 2019 ; Burton et al. 2023 ). These arguments depart from the observation that organizational knowledge is deeply embedded in collective beliefs and routines. If some of these knowledge structures are to be unlearned, one has to focus on how they become less dominant over time . In other words, from a process-based perspective, organizational unlearning implies that organizational actors gradually reduce the influence of unwanted or harmful knowledge structures by blocking or preventing their enactment (Grisold et al. 2017 ; Kluge and Gronau 2018 ). As this process progresses, old knowledge becomes less likely to be used (and new knowledge, if any, becomes more likely to be used).

Such process-based views of unlearning evoke considerable interest in the field. They not only resonate with perspectives from other fields, such as psychology and cognitive sciences (e.g., Kluge and Gronau 2018 ; Peschl 2019 ; Haase et al. 2020 ), but also inform practical interventions to enable or support unlearning initiatives (Klammer et al. 2019a ; Grisold et al. 2020 ). However, we lack a systematic understanding of what we know about the process behind organizational unlearning. Some open questions include the following: what does this process imply? How does it evolve? Why and when does it succeed or fail?

Existing reviews of organizational unlearning (e.g., Tsang and Zahra 2008 ; Klammer and Gueldenberg 2019 ; Sharma and Lenka 2022a , b ) highlight various important aspects, but do not establish a process-based understanding of organizational unlearning. Hence, in this review, we pursue the following questions: what do we know about the process of organizational unlearning, and how can we synthesize existing perspectives? To answer these questions, we develop a multi-perspective and integrative view to explain how organizational unlearning evolves over time.

2 Review approach

We followed an integrative review approach, including systematic elements, to search for relevant literature. Due to the field’s fragmented understanding, we deem it necessary and suitable to bring different perspectives together to surface the nature of the concept, develop implications, and provide avenues for future research. This procedure is motivated by the observation that organizational unlearning is discussed within the broader realm of management and organization studies (MOS), but its conceptual assumptions and conversation topics remain within rather insulated communities in specific sub-fields, thereby fostering and reproducing different perspectives on the same concept.

We (the authors) ascribe ourselves as researchers in the broader field of MOS, although each of us has researched organizational unlearning from a different perspective, based on different scholarly communities. This enabled us to adopt different perspectives to examine the same phenomenon. We initially engaged in several rounds of discussion and sensemaking to establish our position and define the scope of our review (Cronin and George 2023 ). In the time between these discussions, each author conducted initial, non-systematic searches (Rojon et al. 2021 ) to bring in different perspectives. We then established our final position that organizational unlearning is a processual phenomenon warranting attention to the antecedents, outcomes, and dynamics of intentionally discarding undesired or outdated knowledge from organizations.

After establishing our position, we applied various systematic steps to build the foundation for our review (Tranfield et al. 2003 ). We searched for literature on organizational unlearning written in English from 1981 (Hedberg’s book chapter as the starting point) to February 2024. Using the keywords [organization* AND unlearn*], we conducted a title and abstract search in Web of Science, EBSCOhost (Business Source Premier), ProQuest (ABI/INFORM), and Elsevier (ScienceDirect) databases (n = 1104). Next, we merged all results from the databases into a list and, subsequently, deleted duplicate results (n = 759). In an initial review, we read all titles and abstracts and applied two specific criteria to exclude false positives. First, we removed literature from research fields that have no connection to the broader domain of MOS (e.g., clinical psychology). Second, we excluded studies that only serendipitously mentioned the term unlearning in the title or abstract (n = 246). Next, we screened and assessed the remaining full texts. At this stage, we identified literature that fell outside our scope. In doing so, we eliminated non-substantive works that use the term “unlearning” in the title or abstract, while not thoroughly addressing or discussing the phenomenon in the remainder of the paper (n = 88).

As an important additional step, we added an integrative dimension to maximize the comprehensiveness of our review. We conducted hand-searching, snow-balling, and citation-tracking to identify relevant literature that did not fit our search criteria and might have been missed (cf. Trullen et al. 2020 ). Additionally, we integrated literature from our respective communities to acquire different perspectives (cf. Cronin and George 2023 ). This approach allowed us to incorporate relevant literature beyond our initial, systematic search strings. In doing so, we illustrated that some works examine, at their core, intentional loss of knowledge in the context of MOS, without actually using the term unlearning (e.g., Polites and Karahanna 2012 ; Pentland et al. 2020 ), but are deemed useful to further the understanding of the phenomenon (n = 124) (Fig.  1 ).

Overview of the search process

We analyzed and synthesized the final sample using an Excel data extraction template to elicit both quantitative (e.g., authors, publication information) and qualitative (e.g., methodology, findings) information. In terms of the content, we identified relevant perspectives that previous researchers have used to empirically investigate and theorize about organizational unlearning, and which are relevant to examining organizational unlearning as a process.

3.1 Organizational unlearning as a process: Definitions and viewpoints

The concept’s raison d’être has been discussed from various perspectives. Starbuck (in: Nguyen 2017 ) explains the origins of unlearning as an organizational phenomenon in MOS. Hedberg and Starbuck observe that organizations find it difficult to adapt to crises and changing environments; they face failure, reluctance, or hesitancy to unlearn (e.g., Hedberg et al. 1976 ; Starbuck et al. 1978 ; Nystrom and Starbuck 1984 ; Starbuck 1996 ). While some assert that unlearning is subsumable under organizational learning (Huber 1991 ), or argue for its inclusion in the wider context of learning dynamics (Visser 2017 ), others recognize the merits of treating organizational unlearning as a distinct, isolated, and stand-alone phenomenon (Tsang 2017a , b ; Becker 2019 ).

While terms, such as knowledge, dominant logics, or routines are loosely used to describe what organizational unlearning entails, existing studies fall short of clearly defining the kinds of knowledge structures being investigated, respectively unlearned. We found that cognitive and behavioral knowledge structures are two of the most widely used perspectives for pinpointing the locus of the unlearning process (Akgün et al. 2007b ; Tsang and Zahra 2008 ). While the cognitive perspective describes how unlearning helps discard knowledge that has been collectively interpreted, the behavioral perspective refers to how routines, habits, or procedures are collectively abandoned (Easterby-Smith and Lyles 2011 ). The collective lens of shared beliefs and assumptions is thought to be a vital part of the unlearning process (Turc and Baumard 2007 ). Sinkula ( 2002 ) suggests that organizational unlearning starts with changing cognitive structures, mental models, dominant logics, and other core assumptions that guide behavior. In turn, organizations can destabilize and eliminate behaviors, such as routines, habits, or procedures (Martin de Holan and Phillips 2004b ; Fiol and O’Connor 2017a , b ).

Visser ( 2017 ) highlights the interplay of complex social processes as organizational unlearning necessitates individuals to let go of part of their identities as enacted practices are strongly connected to social identities (McKeown 2012 ). In addition, unlearning has also been explored from emotional (Pratt and Barnett 1997 ; Rushmer and Davies 2004 ) and normative perspectives (Yildiz and Fey 2010 ). Hence, organizational unlearning is a multi-faceted term yielding multiple associations regarding the dynamics of knowledge loss.

3.2 Organizational unlearning mechanisms and conceptualizations

Several studies aim to shed light on different mechanisms of unlearning explaining how organizations discard existing knowledge. Bowker ( 1997 ), for example, distinguishes between clearance and erasure of organizational knowledge. Similarly, unlearning has been described as the process by which organizational members gradually refrain from enacting existing routines over time by removing cues (Kluge and Gronau 2018 ). Organizations might unlearn through tailored interventions, such as inactivating specific knowledge structures or rivaling enforced enactment (Turc and Baumard 2007 ).

Several quantitative empirical studies investigate the mechanisms of organizational unlearning. For example, the “unlearning context,” introduced by Cegarra-Navarro and Sánchez-Polo ( 2008 ) includes sequential unlearning steps from the individual to the organizational level. This model has been widely used in other studies (e.g., Cegarra-Navarro et al. 2010 , 2011a , b , 2013 , 2014 , 2016 , 2021 ; Cepeda-Carrion et al. 2012a , b ; Cegarra-Navarro and Cepeda-Carrion 2013 ; Ortega-Gutiérrez et al. 2015 , 2022 ; Wensley and Cegarra-Navarro 2015 ; Cegarra-Navarro and Wensley 2019 ; Lyu et al. 2022 ). Akgün et al. ( 2006 , 2007a , b ) operationalize unlearning as changes in beliefs and routines, a conceptualization that has been used in several other studies (e.g., Wang et al. 2013 , 2017 ; Yang et al. 2014 ; Xi et al. 2020 ; Zhao and Wang 2020 ).

Qualitative empirical studies paint a more fine-grained picture of unlearning mechanisms in organizations. Mechanisms to facilitate organizational unlearning might vary, depending on the timing of their occurrence or the desired outcomes of the process (e.g., Grisold et al. 2020 ; Xu et al. 2023 ). Rezazade Mehrizi and Lashkarbolouki ( 2016 ) outline the cognitive and behavioral dynamics of organizational unlearning when discarding troubled business models including the stages of realizing, revitalizing, parallelizing, and marginalizing. Similarly, Tsang ( 2008 ) finds organizational unlearning mechanisms at different stages of knowledge transfer to acquisition joint ventures. Stage-driven process models are often found in practitioner articles that typically provide advice on how managers can help their organizations unlearn as they follow a sequence of steps (Reese 2017 ; Klammer et al. 2019a ; Govindarajan et al. 2020 , 2021 ).

Another way to unpack organizational unlearning mechanisms is to sketch its recursive nature. The key assumption here is that unlearning is a fragile and highly dynamic process wherein discarding and learning activities unfold interchangeably (Nygren et al. 2017 ), or sometimes occur simultaneously (Fiol and O’Connor 2017a , b ). Organizational unlearning cycles (Pratt and Barnett 1997 ; Cegarra-Navarro and Wensley 2019 ; Hamza-Orlinska et al. 2024 ) or spirals (Macdonald 2002 ; Grisold and Kaiser 2017 ) provide additional insights into the recursive nature of the process. Peschl ( 2019 ) argues that the exact process of unlearning cannot be defined; embracing an unknown future means to embark on an uncertain and emergent process.

Further, we identified studies that relate organizational unlearning to learning and relearning, often contextualized in sequential learning-unlearning-relearning steps (e.g., Azmi 2008 ; Rupcic 2019 ; Sharma and Lenka 2019 ; Zhao and Wang 2020 ). This idea stresses that unlearning occurs in relation to existing knowledge (prior learning) and relearning (new learning of knowledge). From this viewpoint, new learning cannot be acquired before established knowledge has been removed. Existing views on mechanisms and conceptualizations share the commonality that organizational unlearning is a process characterized by context-specific dynamics in terms of discarding and/or acquiring knowledge.

3.3 Levels of unlearning

We found different views regarding the levels as well as their interdependence and interplay during unlearning processes. Generally, unlearning is portrayed as an organizational phenomenon that helps describe learning, adaptation, and change, or how firms deal with crises (Nguyen 2017 ; Vu and Nguyen 2022 ). Researching organizational unlearning, however, also requires an understanding of individuals and groups, as organizations do not have cognitive capabilities per se (Hedberg 1981 ; Brooks et al. 2022 ).

For example, awareness and relinquishing capabilities are strongly connected to intentional knowledge loss of individuals (Becker 2008 , 2010 ). Individual unlearning can also be described as a transformative journey of discernment including receptivity, recognition, and grieving (Macdonald 2002 ). Further, individual unlearning in organizational contexts has been typologized into routine unlearning, wiping, and deep unlearning depending on the depth of the discarding process (Rushmer and Davies 2004 ; Hislop et al. 2014 ).

A conceptual attempt to explain the interplay between different levels suggests that individual unlearning first promotes group and, subsequently, organizational unlearning, or vice versa (Zhao et al. 2013 ). We identified two viewpoints on how unlearning transfers across levels: top-down and bottom-up. The idea of unlearning as a top-down activity refers to instances wherein organizational decision-makers introduce changes that require individuals to discard existing assumptions, mental models, behaviors, or routines (e.g., Nystrom and Starbuck 1984 ; Martin de Holan et al. 2004 ; Martin de Holan and Phillips 2004a ; Nguyen 2017 ; Grisold et al. 2020 ; Klammer 2021 ). On the other hand, unlearning as a bottom-up activity describes the effects of individuals’ decisions to discard existing knowledge structures of an organization (e.g., Becker 2008 , 2010 ; Hislop et al. 2014 ; Matsuo 2019a ). Additionally, we found studies that specifically deal with the individual level (Tanaka 2023 ; Yin 2023 ) or group levels (e.g., Akgün et al. 2006 , 2007a ; Klammer and Gueldenberg 2020 ; Açıkgöz et al. 2021 ). The process of organizational unlearning can differ significantly, depending on whether and how unlearning unfolds within or between different organizational levels and entities over time.

3.4 Timing of organizational unlearning

Existing research highlights how the process of unlearning depends on timing-related decisions. To ensure strategic resilience in a world of turbulence and uncertainty, organizations should take action before it is desperately needed, thus unlearning should be a proactive process (Morais-Storz and Nguyen 2017 ). Managers should be able to identify early warning signs of an inflection point, that is, a shift in the external environment causing change that alters the basic assumptions upon which a business is built (McGrath 2019 ; Sharma and Lenka 2024 ). An early warning system may help identify and unlearn basic assumptions that are no longer applicable (McGrath and Euchner 2020 ).

Numerous studies indicate, however, that this approach can be challenging. First, it is difficult to anticipate the exact timing of environmental change (Martignoni and Keil 2021 ) to initiate the process of organizational unlearning. Second, organizations might find it difficult to find and adopt new operating methods because they have become firmly dependent on past methods (Starbuck 2017 ; Snihur 2018 ) and might be stuck in competence traps due to inertia arising from prior success (Leonard-Barton 1992 ). Third, it is not easy to tell whether companies render an old belief obsolete (Nguyen 2017 ), because it can often only be known retrospectively if an organization’s belief has become obsolete and, therefore, should have been discarded (Martignoni and Keil 2021 ). Fourth, unlearning requires a collective decision-making process, challenged by specialized personnel, who see their careers as tied to existing strategies and their core beliefs (Starbuck 2017 ).

We found two conflicting paradigms regarding the timing of organizational unlearning: (i) the reactive paradigm, which suggests that unlearning can only take place after noticeable failures or major interruptions, and (ii) the proactive paradigm, which implies that unlearning should occur prior to inflection points. We observed that many empirical studies empirically investigate organizational unlearning from the perspective of the reactive paradigm. For example, organizations tend to introduce technical and organizational change only after the occurrence of catastrophic failures, as in the case of NASA during the Challenger disaster (Starbuck and Milliken 1988 ). Conversely, only very few studies investigate proactive unlearning approaches at the organizational level. For example, Burt and Nair ( 2020 ) investigate how an organization proactively discards deeply held assumptions about its business logic, and thus initiates strategic change. Hence, the point of initiating the purposeful discarding of knowledge seems vital to navigating unlearning processes in organizations.

3.5 Critical views of organizational unlearning

We also found that critical approaches shed light on the process of organizational unlearning. These approaches are considered “critical” because they fit in with what Fournier and Grey ( 2000 ; cf. Alakavuklar and Alamgir 2018 ) called “non-performative intent,” an important theme in critical management studies. In general, they highlight the importance of unlearning, but reject “the instrumental and performative use of unlearning in the sole service of attaining organizational goals” in the neoliberal system (Visser 2017 , p. 49). In this regard, these views differ from many other MOS approaches to organizational unlearning.

Although Contu et al. ( 2003 , p. 934) do not directly address the concept itself, they offer a useful starting point for the critical understanding of organizational unlearning in MOS and identify two central issues as learning can become “antithetical:” to learn is to disorganize and increase variety, but to organize is to reduce variety. That is, learning can be used as a tool to enhance organizational performance, but it can also have a wider impact beyond managerial concerns and may violate the common social good. These views have important implications for a critical understanding of the organizational unlearning process.

Brook et al. ( 2016 , p. 371) contend that there is a cultural tendency to see learning as an unquestionably “good thing,” which altogether is exacerbating rather than resolving the problems confronting business and societies (cf. Contu et al. 2003 ; Hsu 2013 ). In Brook et al.’s ( 2016 ) account, organizational unlearning is a necessity because it not only problematizes the self-evident, positive views of learning, but also reveals the political nature of learning; they applied the concept of organizational unlearning in the field of (critical) action learning and argue that unlearning is particularly relevant to address “wicked problems,” like global warming.

Drawing upon Foucault’s ( 1991 ) governmentality, Chokr ( 2009 , p. 61) perceives unlearning as a reflective, enduring capability for individuals “not to be governed” by “the illusory world of all the ideas, notions and, beliefs that hem, jostle, whirl, confuse and oppress them.” Ultimately, for Chokr ( 2009 , p. 49), unlearning should generate “well-trained minds and individuals capable of questioning, critical thinking, imagination, creativity and self-reflective deliberation as engaged citizens.” Pedler and Hsu ( 2014 ) apply this approach to MOS and suggest that power is an inseparable, unmanageable, and uncontrollable dimension of learning, and that unlearning implies an individual’s capability to recognize the inevitable power relations in the process of learning, and making ethical judgments over time. Hsu ( 2021 ) articulates three capabilities implied by an on-going attempt of unlearning in the field of management education: the capability to think differently, to approach knowledge autonomously, and to act as self-governed, self-reflective, self-engaged citizens.

Antonacopoulou ( 2009 , p. 424) views unlearning as an on-going practice of “asking different questions by extending the outcomes sought” which is “in sharp contrast to previous conceptualizations” to remove “old knowledge in favour of new knowledge.” Unlearning ought to trigger “difference” (Deleuze 1994 ). Hsu ( 2013 ) contends that unlearning, as a practice, bears liberating and emancipatory implications as it enables individuals to develop a capability to problematize institutionalized ideologies and actions; epistemologically, unlearning assists the rediscovery of what Foucault ( 1980 ) called “subjugated knowledge.” Such subjugated knowledge may include that wisdom has been marginalized within predominant theories and practices, for example, the wisdom of non-action (Hsu 2013 ). Drawing upon a feminist, de-colonial, and arts-based perspective, Krauss ( 2019 ) views unlearning as a collective practice that assists individuals in creating alternative forms of living while breaking with the promise of economic advancement and growth. Taken together, these views suggest that the process of organizational unlearning requires several skills and practices associated with the capability or possibility of individuals and collectives to question and discard knowledge.

3.6 Summary of key findings

The following table provides an overview of the key points of each perspective in the process of organizational unlearning (Table  1 ). Our findings form the foundation of the implications, the integrative framework as well as future research directions.

4 Implications

The current body of literature shares three common underlying assumptions about the concept:

Organizational unlearning is perceived as a process that is based on an organization’s intention to discard—often multiple and intertwined—existing organizational knowledge structures;

Organizational unlearning evolves through mechanisms that assume different shapes and forms, depend significantly on the context, and are mostly introduced reactively to ensure organizational survival during crises, facilitate organizational change and learning, and improve innovativeness; and

Organizational unlearning is regarded as a highly complex organizational phenomenon as it dynamically unfolds within and across multiple levels, such as groups or individuals.

Our review, however, also reveals that the concept of organizational unlearning is imbalanced and fragmented (cf. Martin de Holan and Phillips 2011 ; Klammer and Gueldenberg 2019 ) which has led to its contestation (cf. Klein 1989 ; Howells and Scholderer 2016 ; Tsang 2017a , b ), because our understanding of how unlearning unfolds in organizational settings over time is still vague.

Three issues stand out. First, studies use different underlying assumptions about the concept, each typically arising from and remaining within its own domain. Using different terminologies (e.g., intentional forgetting, unlearning) or using the same terminology to describe different underlying assumptions about unlearning (e.g., unlearning following a sequential, recursive, or dialectic logic) leads to discrepancies and hampers our understanding of the concept. This also pertains to the process of unlearning; for instance, do organizations try to overwrite established knowledge by enacting new knowledge, or is knowledge aimed to be erased? Second, existing literature tends to focus on specific aspects of organizational unlearning (e.g., levels, antecedents, outcomes) without setting studies in a wider context, thereby leading to fragmentation. This perpetuates existing conceptual issues regarding the process of unlearning. Third, and in contrast to the previous point, other studies disregard the clarification of underlying assumptions about organizational unlearning (e.g., problematization or clearly defining levels), fostering a lack of decipherability.

We find that literature lacks an encompassing perspective that synthesizes existing conceptualizations and empirical studies to clarify why unlearning occurs, what it entails, and how the process actually unfolds. We propose and visualize an integrative framework that considers the issues outlined above and incorporates various fragments and streams in the field of organizational unlearning. To build a framework that is applicable across all communities within MOS, we assert that viewing unlearning as a process and making the concept dynamic are key to bringing different perspectives together. In the following, we articulate and discuss four implications that help future studies navigate through the profound and dynamic nature of organizational unlearning.

4.1 Implication 1: Organizational unlearning involves multiple levels

Unlearning entails a profound interdependence and interplay between and within different levels of an organization. However, existing research reflects a distinction between levels, with studies typically focusing on the individual level (Hislop et al. 2014 ; Matsuo 2018 , 2019a , b ), the group level (Akgün et al. 2006 ; Lee and Sukoco 2011 ; Klammer and Gueldenberg 2020 ), or the organizational level (Yang et al. 2014 ; Snihur 2018 ). Whether initiated top-down or bottom-up (Klammer et al. 2019a ; Padan and Nguyen 2020 ; Grisold et al. 2020 ), unlearning cannot be perceived as an isolated phenomenon. It dynamically and sometimes even simultaneously affects all entities including individuals, groups as well as the entire organization. Literature highlights the vital role of individuals and groups in the process of unlearning (Zhao et al. 2013 ; Hislop et al. 2014 ; Kluge 2023 ); since these claims are conceptual, however, we know little about the dynamics that unfold across these levels.

We suggest that the unlearning process manifests at all organizational levels. It is crucial to stress that in order to understand unlearning at the collective level, one cannot aggregate and extrapolate individual-level cognitive processes (Grisold and Kaiser 2017 ). Rather, collective unlearning involves complex feedback mechanisms that either reinforce or diminish the influence of old knowledge on organizational practices, which, in turn, spills over to collective activities (e.g., Crossan et al. 1999 ).

4.2 Implication 2: Motives behind organizational unlearning need to be translated into interventions

Organizational unlearning is enabled by intentional interventions that specifically aim to support the process of discarding obsolete knowledge structures over time. Several empirical studies offer initial insights into the workings and dynamics of interventions as mechanisms of organizational unlearning.

Perhaps the most challenging and complex intervention is to reduce the influence of old knowledge over time. While explicit, codified knowledge, such as written rules and regulations can be discarded relatively easily, implicit knowledge structures, like assumptions, beliefs, values, or norms are unequally harder to be unlearned. For this intervention, it is important to eliminate retrieval cues that make individuals draw less from old knowledge or habits over time (Kluge and Gronau 2018 ). This also holds true when no new knowledge should be implemented; reducing the influence of old knowledge is key in discarding an organization’s obsolete cognitive and behavioral knowledge structures to free up space for future possibilities (Peschl 2019 ). Combining both approaches, appreciative inquiry, for example, can facilitate the discarding of old knowledge while simultaneously addressing the creation of new knowledge (Srithika and Bhattacharyya 2009 ). Additionally, the benefits of the “new” should be constantly reinforced through feedback and clear communication (Grisold et al. 2020 ).

4.3 Implication 3: Processes of organizational unlearning differ in form, antecedents, and outcomes

We suggest that antecedents can be based on reactive and proactive grounds, and that the (desired) outcomes of organizational unlearning can only be fully known once the process has been completed. Generally, scholars promote the understanding that organizational unlearning is a reactive phenomenon (Snihur 2018 ) typically triggered by problems (Hedberg 1981 ; Nystrom and Starbuck 1984 ) or different cues (Sinkula 2002 ). More recent studies show that organizational unlearning also entails a proactive dimension and is advantageous when executed proactively (Morais-Storz and Nguyen 2017 ). In terms of outcomes and consequences, unlearning is generally perceived as a positive phenomenon. It is regarded as a facilitator of organizational change (e.g., Johannessen and Hauan 1994 ; Turc and Baumard 2007 ; Martin de Holan 2011a ; Mull et al. 2023 ; van Oers et al. 2023 ; Hamza-Orlinska et al. 2024 ) and an enabler of innovation and innovative behavior (e.g., Becker 2008 ; Cepeda-Carrion et al. 2012a ; Leal-Rodríguez et al. 2015 ; Zhang et al. 2022 ; Zhao et al. 2022 ; Klammer et al. 2023 ).

Researchers have seldom questioned the positive value of organizational unlearning. However, as knowledge is intertwined throughout the organization and embedded in assumptions, world views, values, habits, routines, processes, etc., unlearning specific knowledge structures might lead to a decrease of value or functioning of other parts (Zahra et al. 2011 ). Therefore, it is difficult to judge the value of (to-be) discarded knowledge. Organizational unlearning prompts a clash between the past, present, and future and involves different elements, such as culture, assumptions, beliefs, structures, strategies, routines, or habits. Hence, and contrary to managerial expectations (Govindarajan et al. 2021 ), the outcome of organizational unlearning can only be fully understood once the process is complete.

4.4 Implication 4: Prevalent organizational contexts highly influence the unlearning process

Researchers need to acknowledge that organizational unlearning comes with different reasons, decisions, and strategies. Studying idiosyncratic features of a given organizational context contrasts with the prevalent focus in organizational unlearning research. Some studies provide in-depth insights about how unlearning unfolds in a specific organizational context (Martin de Holan and Phillips 2004b ; Rezazade Mehrizi and Lashkarbolouki 2016 ; Burt and Nair 2020 ). The contexts or situated features in which unlearning occurs, however, remain elusive as the main interest is often placed on abstract sequences or phases that characterize unlearning (e.g., Nygren et al. 2017 ; Cegarra-Navarro et al. 2021 ; Kim and Park 2022 ). This comes at the cost of understanding how organizational unlearning actually unfolds and what elements it entails.

Empirical studies that embrace the processes through which organizational phenomena unfold typically find that these processes are tied to the specific situated context of organizations (Langley et al. 2013 ). Based on this line of thinking, we argue that the elaboration of an empirically examined unlearning process should be tied to its organizational context and other prevailing situated features.

We summarize and visualize our implications in an integrative framework (Fig.  2 ) to highlight the characteristics of the organizational unlearning process. Unlearning in organizations depends on a variety of factors that can alter the course of the process. In the following, we propose future research avenues that can further our understanding of organizational unlearning.

Process-based framework of organizational unlearning

5 Future research directions

5.1 forging organizational unlearning research as process-based studies.

Discarded knowledge that has once been enacted in organizations is difficult to capture. Researchers have attempted to capture this process using cross-sectional surveys (e.g., Sheaffer and Mano-Negrin 2003 ). We believe that—although efforts to operationalize unlearning are immensely valuable—existing questionnaires fall short of capturing the full extent of the organizational unlearning process; not capturing the full extent of unlearning does not allow for explaining non-linear dynamics that underlie the process (e.g., actors may find it more difficult to unlearn initially, but it becomes significantly easier after knowledge has been used less often). We assert that researchers need to study the concept more profoundly and longitudinally by examining different antecedents, processes, interventions, outcomes, levels, knowledge structures, and so on, from a process-based perspective (Langley and Tsoukas 2017 ). This can be achieved through methods, such as ethnography or case study research, that capture discarded knowledge and allow for a deep observation of the organizational unlearning process.

New research methods for generating insightful data may contribute to a clearer understanding of the phenomenon. One of the issues in survey-based research, for example, is knowledge retrieval; asking subjects if they currently need to unlearn, or have unlearned knowledge recently, might trigger an association with old knowledge. Hence, the process of unlearning could be disturbed. Methods that track the development and paths of knowledge to make it more explicit are especially interesting (Kluge et al. 2019 ).

Turning to research methods in digital environments, for example, may allow researchers to generate fresh insights into organizational unlearning. The increasing availability of digital trace data, i.e., digital footprints that are automatically recorded whenever actors use information technology, such as ERP systems (Pentland et al. 2020 ) or online platforms (Lindberg et al. 2016 ), renders promising opportunities. Digital trace data are considered particularly useful by organizational researchers because they provide an unobtrusive and unbiased way of studying organizational work (e.g., Berente et al. 2019 ). Using digital trace data to study unlearning processes allows researchers to gain an accurate picture of the more and less frequently adopted actions, and how processes change over time (e.g., before and after an unlearning-related intervention). Therefore, using digital trace data could open entirely new avenues for investigating organizational unlearning. Researchers could conduct in-depth analyses to examine whether, and/or how, interventions yield desired outcomes, undesired routines vanish, or single actions disappear over time.

Process-based studies can also shed a more nuanced light on mechanisms, antecedents, or outcomes (Langley et al. 2013 ). Our findings on the timing of unlearning imply that organizations, although seldom investigated empirically, do not always wait until they have no other choice but to unlearn. This challenges the assumption that organizational unlearning is caused exclusively by endogenous or exogenous shocks and, in turn, raises questions about the antecedents and expected outcomes of the process. Diagnosing antecedents and outcomes seems to be a major challenge, often because we can only observe organizational unlearning retrospectively.

If organizations understand how knowledge abandonment can help them achieve specific goals, they can design a setup for the type of unlearning that matches their objectives. For example, for organizations that want to improve gradually and continuously, shallow unlearning would be a good option because it contributes to day-to-day adaptation without destroying operational stability. Organizations that want to challenge their deeply held beliefs or taken-for-granted assumptions might require a proactive and deep unlearning approach. Following this line of thinking, we suggest for future studies to focus on the dynamic nature of the concept to highlight the specific facets and interventions of organizational unlearning processes, and provide in-depth explanations of how organizations intentionally refrain from using old knowledge over time. Focusing on such dynamics might also provide fresh perspectives on the interdependence and interplay at different organizational levels. These insights are needed, from our point of view, to strengthen the conceptual understanding of the organizational unlearning phenomenon, and demarcate it from related concepts, such as organizational learning and change (Howells and Scholderer 2016 ).

5.2 Highlighting contextual features and the nature of the unlearning process

Putting increased focus on the context of an organization may shed light on how or why organizations detach from—or keep adhering to—old routines, assumptions, and beliefs. Foregrounding the idiosyncratic features of old knowledge and how they are tied to the context of an organization might inform the design of effective interventions in a given situation. As such, unlearning interventions have both explanatory and normative value for organizational unlearning research. From an explanatory perspective, focusing on the context of unlearning interventions enables researchers to outline why an unlearning process unfolds the way it does. Differences in the width and depth of unlearning interventions, paired with the desired outcomes of the process, may explain how organizations intentionally remove knowledge from points A to B in a specific context. From a normative perspective, the awareness of contextual features can guide organizations, policy-makers, and other stakeholders in initiating and guiding different unlearning processes.

This also corresponds with emerging claims that MOS researchers should increasingly engage in real-world problem solving (e.g., Hideg et al. 2020 ; Howard-Grenville 2021 ). For example, scholars in the field of MOS have increasingly focused on grand challenges, questioning how organizations can effectively address complex social and environmental threats (e.g., Ambos and Tatarinov 2022 ; Voegtlin et al. 2022 ; Sele et al. 2024 ). One underlying theme in this stream of research is that organizations need to replace their established logics and routines, which are often profit-oriented, with new and more conducive ones. The transition from old to new ways of doing things, however, rarely works smoothly. Several studies have found that organizations tend to fall back on old detrimental knowledge as they tackle grand challenges (e.g., Wright and Nyberg 2017 ; van Wijk et al. 2020 ). Focusing on contextual features and the in-situ nature of unlearning processes helps researchers understand the latent, sub-conscious facets of why knowledge abandonment might or might not unfold in a given situation.

5.3 Spotlighting power, power relations, and politics in unlearning processes

Critical perspectives of unlearning, informed by critical management studies, problematize the predominant managerial understanding of organizational unlearning, because they recognize that the process is highly power-laden. Such views differ from the vast majority of existing unlearning literature. While critical perspectives do not forsake the idea of unlearning and learning, they suggest that these processes may have far-reaching effects, for which organizations and managers purport to take responsibility. However, to date, critical views of unlearning have had little impact on mainstream MOS literature, but may enrich the aforementioned research possibilities.

First, future studies could focus on the power relations embedded in the process of organizational unlearning. For instance, managerial intervention in the unlearning process inevitably reflects different interests and may generate resistance because unlearning, like learning, is also a socially constructed entity with relations of power (Pedler and Hsu 2014 ). It is important to understand the different stakeholders and organizational politics involved in this process, including the beneficiaries and victims of organizational unlearning. Second, the critical views of unlearning may legitimize what Pedler and Hsu ( 2019 ) called an “alternative paradigm” of learning organizations. Future studies could explore how the unlearning process stimulates incompatible organizational purpose that collides with the prevailing one. Researchers may also explore different forms of wisdom and their relationship with organizational unlearning, and how unlearning helps inspire alternative organizational realities.

6 Practical implications

Organizational unlearning, particularly seen as a process that evolves over time, has significant practical implications for how organizations progress, innovate, and adapt to changing environments. By actively unlearning outdated or inefficient practices, organizations can adopt innovative methods and technologies more effectively (Di Maria et al. 2023 ). This process is crucial in rapidly changing industries where clinging to old ways can be a significant disadvantage. Unlearning, when understood as an on-going and persistent effort, helps to create a culture of agility and flexibility. Organizations become more adept at responding to market changes, customer needs, and emerging trends.

Furthermore, leaders and managers play a crucial role in initiating, modelling, and facilitating unlearning. This process calls for adaptable and self-aware leaders capable of challenging the status quo. It also requires them to be effective communicators in guiding their teams through unlearning processes. Organizational unlearning encourages a culture of critical thinking and open-mindedness, which is essential for strategic planning and problem-solving. To summarize, understanding organizational unlearning as an on-going effort requires deliberate strategies and a supportive organizational culture as it involves systematic approaches to identify what needs to be unlearned, mechanisms to facilitate the unlearning process, and the integration of new learning and knowledge into an organization's operations.

7 Conclusion

Our review of the existing literature in the broader context of MOS and its respective domains reveals a fragmented field of organizational unlearning, including studies based on different underlying assumptions about the concept. To bring different viewpoints together and highlight concerns about the phenomenon, we propose implications and possible future research directions that will help researchers navigate through the jungle of different understandings of unlearning. Table 2 presents exemplary research questions that can serve as starting points for future research. Organizational unlearning is best understood and researched as an intentionally initiated and dynamically unfolding process that aims to discard or reduce undesired knowledge structures over time.

Data availability

The authors do not generate new datasets in this literature review. All articles and works included in this literature review can be accessed through the databases mentioned in the text.

References marked with an asterisk indicate works analyzed in the literature review.

*Açıkgöz A, Demirkan I, Latham GP, Kuzey C (2021) The relationship between unlearning and innovation ambidexterity with the performance of new product development teams. Group Decis Negot 30:945–982

Article   Google Scholar  

*Akgün AE, Lynn GS, Byrne JC (2006) Antecedents and consequences of unlearning in new product development teams. J Prod Innov Manag 23:73–88

*Akgün AE, Byrne JC, Lynn GS, Keskin H (2007a) New product development in turbulent environments: impact of improvisation and unlearning on new product performance. J Eng Technol Manag 24:203–230

*Akgün AE, Byrne JC, Lynn GS, Keskin H (2007b) Organizational unlearning as changes in beliefs and routines in organizations. J Organ Change Manag 20:794–812

*Alakavuklar ON, Alamgir F (2018) Ethics of resistance in organisations: a conceptual proposal. J Bus Ethics 149:31–43

Ambos TC, Tatarinov K (2022) Building responsible innovation in international organizations through intrapreneurship. J Manag Stud 59:92–125

*Antonacopoulou EP (2009) Impact and scholarship: unlearning and practising to co-create actionable knowledge. Manag Learn 40:421–430

*Azmi FT (2008) Mapping the learn-unlearn-relearn model: imperatives for strategic management. Eur Bus Rev 20:240–259

*Becker KL (2008) Unlearning as a driver of sustainable change and innovation: three Australian case studies. Int J Technol Manag 42:89–106

*Becker KL (2010) Facilitating unlearning during implementation of new technology. J Organ Change Manag 23:251–268

*Becker KL (2019) Organizational unlearning: the challenges of a developing phenomenon. Learn Organ 26:534–541

Berente N, Seidel S, Safadi H (2019) Research commentary––data-driven computationally intensive theory development. Inf Syst Res 30:50–64

*Bowker GC (1997) Lest we remember: organizational forgetting and the production of knowledge. Account Manag Inf Technol 7:113–138

Google Scholar  

*Brook C, Pedler M, Abbott C, Burgoyne J (2016) On stopping doing those things that are not getting us to where we want to be: unlearning, wicked problems and critical action learning. Hum Relat 69:369–389

*Brooks J, Grugulis I, Cook H (2022) Unlearning and consent in the UK Fire and Rescue Service. Hum Relat 75:2300–2317

*Burt G, Nair AK (2020) Rigidities of imagination in scenario planning: strategic foresight through ‘unlearning.’ Technol Forecast Soc Change 153:119927

*Burton N, Vu MC, Hawkins M (2023) Secular discernment: a process of individual unlearning and collective relearning. Manag Learn 54:680–704

*Cegarra-Navarro JG, Cepeda-Carrion G (2013) Implementing telemedicine technologies through an unlearning context in a homecare setting. Behav Inf Technol 32:80–90

*Cegarra-Navarro JG, Sánchez-Polo MT (2008) Linking the individual forgetting context with customer capital from a seller’s perspective. J Oper Res Soc 59:1614–1623

*Cegarra-Navarro JG, Wensley AKP (2019) Promoting intentional unlearning through an unlearning cycle. J Organ Change Manag 32:67–79

*Cegarra-Navarro JG, Eldridge S, Martinez-Martinez A (2010) Managing environmental knowledge through unlearning in Spanish hospitality companies. J Environ Psychol 30:249–257

*Cegarra-Navarro JG, Cepeda-Carrion G, Eldridge S (2011a) Balancing technology and physician–patient knowledge through an unlearning context. Int J Inf Manag 31:420–427

*Cegarra-Navarro JG, Sánchez-Vidal ME, Cegarra-Leiva D (2011b) Balancing exploration and exploitation of knowledge through an unlearning context: an empirical investigation in SMEs. Manag Decis 49:1099–1119

*Cegarra-Navarro JG, Martinez-Martinez A, Ortega-Gutiérrez J, Leal-Rodríguez AL (2013) Environmental knowledge, unlearning, and performance in hospitality companies. Manag Decis 51:341–360

*Cegarra-Navarro JG, Eldridge S, Wensley AKP (2014) Counter-knowledge and realized absorptive capacity. Eur Manag J 32:165–176

*Cegarra-Navarro JG, Sánchez-Vidal ME, Cegarra-Leiva D (2016) Linking unlearning with work-life balance: an initial empirical investigation into SMEs. J Small Bus Manag 54:373–391

*Cegarra-Navarro JG, Wensley AKP, Batisic S, Evans M, Cubillas Para C (2021) Minimizing the effects of defensive routines on knowledge hiding though unlearning. J Bus Res 137:58–68

*Cepeda-Carrion G, Cegarra-Navarro JG, Jimenez-Jimenez D (2012a) The effect of absorptive capacity on innovativeness: context and information systems capability as catalysts. Brit J Manag 23:110–129

*Cepeda-Carrion G, Cegarra-Navarro JG, Martinez-Caro E (2012b) Improving the absorptive capacity through unlearning context: an empirical investigation in hospital-in-the-home units. Serv Ind J 32:1551–1570

*Chokr NN (2009) Unlearning or ‘How NOT To Be Governed?’ Societas, Charlottesville

*Contu A, Grey C, Örtenblad A (2003) Against learning. Hum Relat 56:931–952

Cowan R, David PA, Foray D (2000) The explicit economics of knowledge codification and tacitness. Ind Corp Change 9:211–253

Cronin MA, George E (2023) The why and how of the integrative review. Organ Res Methods 26:186–192

Crossan MM, Lane HW, White RE (1999) An organizational learning framework: from intuition to institution. Acad Manag Rev 24:522–537

*Deleuze G (1994) Difference and repetition. Athlone Press, London

Di Maria M, Walter D, Schoormann T, Knackstedt R (2023) Practical support for unlearning—a systematic review to organize the field. In: Aenestad M, Klein S, Tarafdar M (eds) Proceedings of the 31st European Conference on Information Systems

*Easterby-Smith M, Lyles MA (2011) In praise of organizational forgetting. J Manag Inq 20:311–316

*Fiol CM, O’Connor EJ (2017a) Unlearning established organizational routines—Part I. Learn Organ 24:13–29

*Fiol CM, O’Connor EJ (2017b) Unlearning established organizational routines—Part II. Learn Organ 24:82–92

*Foucault M (1980) Power/knowledge: selected interviews and other writings 1972–1977. Pantheon Books, New York

Foucault M (1991) Discipline and punish: the birth of the prison. Penguin Books, London

*Fournier V, Grey C (2000) At the critical moment: conditions and prospects from critical management studies. Hum Relat 53:7–32

Govindarajan V, Srivastava A, Grisold T, Klammer A (2020) COVID-imposed opportunity to selectively unlearn past practices. In: California Management Review Insight. https://cmr.berkeley.edu/2020/10/selective-unlearning . Accessed 2 March 2023

Govindarajan V, Srivastava A, Grisold T, Klammer A (2021) Resist old routines when returning to the office. In: Harvard Business Review. https://hbr.org/2021/05/resist-old-routines-when-returning-to-the-office . Accessed 3 March 2023

*Grisold T, Kaiser A (2017) Leaving behind what we are not: applying a systems thinking perspective to present unlearning as an enabler for finding the best version of the self. J Organ Transform Soc Change 14:39–55

*Grisold T, Klammer A, Kragulj F (2020) Two forms of organizational unlearning: insights from engaged scholarship research with change consultants. Manag Learn 51:598–619

*Grisold T, Kaiser A, Hafner J (2017) Unlearning before creating new knowledge: A cognitive process. In: Bui TX, Sprague Jr. R (eds) Proceedings of the 50th Hawaii International Conference on System Sciences, IEEE Computer Society Press, New York, pp 4614–4623

Haase J, Matthiesen J, Schüffler AS, Kluge A (2020) Retentivity beats prior knowledge as predictor for the acquisition and adaptation of new production processes. In: Bui TX, Sprague Jr. R (eds) Proceedings of the 53rd Hawaii International Conference on System Sciences, IEEE Computer Society Press, New York, pp 4796–4805

*Hamza-Orlinska A, Maj J, Shantz A, Vassilopoulou J (2024) Unlearning diversity management. J World Bus 59:101519

*Hedberg BLT (1981) How organizations learn and unlearn. In: Nystrom PC, Starbuck WH (eds) Handbook of organizational design: adapting organizations to their environments. Oxford University Press, Oxford, pp 3–27

*Hedberg BLT, Nystrom PC, Starbuck WH (1976) Camping on seesaws: prescriptions for a self-designing organization. Adm Sci Q 21:41–65

Hideg I, DeCelles KA, Tihanyi L (2020) From the editors: publishing practical and responsible research in AMJ. Acad Manag J 63:1681–1686

*Hislop D, Bosley S, Coombs CR, Holland J (2014) The process of individual unlearning: a neglected topic in an under-researched field. Manag Learn 45:540–560

Howard-Greenville J (2021) Grand challenges, Covid-19 and the future of organizational scholarship. J Manag Stud 58:254–258

*Howells J, Scholderer J (2016) Forget unlearning? How an empirically unwarranted concept from psychology was imported to flourish in management and organization studies. Manag Learn 47:443–463

*Hsu S-w (2013) Alternative learning organization. In: Örtenblad A (ed) Handbook of research on the learning organization: adaptation and context. Edward Elgar, Cheltenham, pp 358–371

*Hsu S-w (2021) Exploring an alternative: Foucault-Chokr’s unlearning approach to management education. Int J Manag Educ 19:100496

Huber GP (1991) Organizational learning: the contributing processes and the literatures. Organ Sci 2:88–115

*Johannessen JA, Hauan A (1994) Organizational unlearning. Create Innov Manag 3:43–53

*Kim EJ, Park S (2022) Unlearning in the workplace: antecedents and outcomes. Hum Resour Dev Q 33:273–296

*Klammer A (2021) Embracing organisational unlearning as a facilitator of business model innovation. Int J Innov Manag 25:2150061

*Klammer A, Gueldenberg S (2019) Unlearning and forgetting in organizations: a systematic review of literature. J Knowl Manag 23:860–888

*Klammer A, Gueldenberg S (2020) Honor the old, welcome the new: an account of unlearning and forgetting in NPD teams. Eur J Innov Manag 23:581–603

*Klammer A, Grisold T, Gueldenberg S (2019a) Introducing a “stop-doing” culture: How to free your organization from rigidity. Bus Horiz 62:451–458

*Klammer A, Grisold T, Nguyen N (2019b) Guest editorial: opportunities and interdisciplinary perspectives for organizational unlearning. Learn Organ 26:445–453

*Klammer A, Hora W, Kailer N (2023) Opposites attract: How incumbents learn and unlearn in coopetitive relationships with start-ups. Ind Mark Manag 112:85–97

*Klein JI (1989) Parenthetic learning in organizations: toward the unlearning of the unlearning model. J Manag Stud 26:291–308

*Kluge A (2023) Recent findings on organizational unlearning and intentional forgetting research (2019–2022). Front Psychol 14:1160173

*Kluge A, Gronau N (2018) Intentional forgetting in organizations: the importance of eliminating retrieval cues for implementing new routines. Front Psychol. https://doi.org/10.3389/fpsyg.2018.00051

*Kluge A, Schüffler AS, Thim C, Haase J, Gronau N (2019) Investigating unlearning and forgetting in organizations: research methods, designs and implications. Learn Organ 26:518–533

*Krauss A (2019) Unlearning institutional habits: an arts-based perspective on organizational unlearning. Learn Organ 26:485–499

Langley A, Tsoukas H (2017) The SAGE handbook of process organization studies. SAGE, Thousand Oaks

Langley A, Smallman C, Tsoukas H, Van de Ven AH (2013) Process studies of change in organization and management: unveiling temporality, activity, and flow. Acad Manag J 56:1–13

*Leal-Rodríguez AL, Eldridge S, Roldán JL, Leal-Millán AG, Ortega-Gutiérrez J (2015) Organizational unlearning, innovation outcomes, and performance: the moderating effect of firm size. J Bus Res 68:803–809

*Lee LTS, Sukoco BM (2011) Reflexivity, stress, and unlearning in the new product development team: the moderating effect of procedural justice. R&D Manag 41:410–423

Leonard-Barton D (1992) Core capabilities and core rigidities: a paradox in managing new product development. Strat Manag J 13:111–125

Lindberg A, Berente N, Gaskin J, Lyytinen K (2016) Coordinating interdependencies in online communities: a study of an open source software project. Inf Syst Res 27:751–772

*Lyu C, Zhang F, Ji J, Teo TSH, Wang T, Liu Z (2022) Competitive intensity and new product development outcomes: the roles of knowledge integration and organizational unlearning. J Bus Res 139:121–133

*Macdonald G (2002) Transformative unlearning: safety, discernment and communities of learning. Nurs Inq 9:170–178

*Martignoni D, Keil T (2021) It did not work? Unlearn and try again—unlearning success and failure beliefs in changing environments. Strat Manag J 42:1057–1082

*Martin de Holan P (2011a) Agency in voluntary organizational forgetting. J Manag Inq 20:317–322

*Martin de Holan P (2011b) Organizational forgetting, unlearning, and memory systems. J Manag Inq 20:302–304

*Martin de Holan P, Phillips N (2004a) Organizational forgetting as strategy. Strat Organ 2:423–433

*Martin de Holan P, Phillips N (2004b) Remembrance of things past? The dynamics of organizational forgetting. Manag Sci 50:1603–1613

*Martin de Holan P, Phillips N (2011) Organizational forgetting. In: Easterby-Smith M, Lyles MA (eds) Handbook of organizational learning and knowledge management. Wiley, Chichester, pp 433–451

*Martin de Holan P, Phillips N, Lawrence TB (2004) Managing organizational forgetting. MIT Sloan Manag Rev 45:45–51

*Matsuo M (2018) Goal orientation, critical reflection, and unlearning: an individual-level study. Hum Resour Dev Q 29:49–66

*Matsuo M (2019a) Critical reflection, unlearning, and engagement. Manag Learn 50:465–481

*Matsuo M (2019b) The unlearning of managerial skills: a qualitative study of executive officers. Eur Manag Rev 16:303–315

McGrath RG (2019) Seeing around corners: How to spot inflection points in business before they happen. Houghton Mifflin Harcourt, Boston

McGrath RG, Euchner J (2020) Seeing around corners: an interview with Rita McGrath. Res-Technol Manag 63:12–17

*McKeown I (2012) Teaching old dogs new tricks: Why unlearning matters in SMEs. Int J Entrep Innov 13:25–34

*Morais-Storz M, Nguyen N (2017) The role of unlearning in metamorphosis and strategic resilience. Learn Organ 24:93–106

*Mull M, Duffy C, Silberman D (2023) Forgetting to learn and learning to forget: the call for organizational unlearning. Eur J Train Dev 47:586–598

*Nguyen N (2017) The journey of organizational unlearning: a conversation with William H. Starbuck Learn Organ 24:58–66

*Nygren NV, Jokinen A, Nikula A (2017) Unlearning in managing wicked biodiversity problems. Landsc Urban Plan 167:473–482

*Nystrom PC, Starbuck WH (1984) To avoid organizational crises, unlearn. Organ Dyn 12:53–65

*Ortega-Gutiérrez J, Cegarra-Navarro JG, Cepeda-Carrion G, Leal-Rodríguez AL (2015) Linking unlearning with quality of health services through knowledge corridors. J Bus Res 68:815–822

*Ortega-Gutiérrez J, Cepeda-Carrión I, Alves H (2022) The role of absorptive capacity and organizational unlearning in the link between social media and service dominant orientation. J Knowl Manag 26:920–942

*Padan T, Nguyen N (2020) Responsible management unlearning. In: Laasch O, Suddaby R, Freeman RE, Jamali D (eds) Research handbook of responsible management. Edward Elgar, Cheltenham, pp 501–515

*Pedler M, Hsu S-w (2014) Unlearning, critical action learning and wicked problems. Action Learn Res Pract 11:296–310

*Pedler M, Hsu S-w (2019) Regenerating the learning organization: towards an alternative paradigm. Learn Organ 26:97–112

*Pentland B, Liu P, Kremser W, Hærem T (2020) The dynamics of drift in digitized processes. MIS Q 44:19–47

*Peschl MF (2019) Unlearning towards an uncertain future: on the back end of future-driven unlearning. Learn Organ 26:454–469

*Polites GL, Karahanna E (2012) Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Q 36:21–42

*Pratt MG, Barnett CK (1997) Emotions and unlearning in Amway recruiting techniques: promoting change through ‘safe’ ambivalence. Manag Learn 28:65–88

*Reese S (2017) Putting organizational unlearning into practice: a few steps for the practitioner. Learn Organ 24:67–69

*Rezazade Mehrizi MH, Lashkarbolouki M (2016) Unlearning troubled business models: from realization to marginalization. Long Range Plan 49:298–323

Rojon C, Okupe A, McDowall A (2021) Utilization and development of systematic reviews in management research: what do we know and where do we go from here? Int J Manag Rev 23:191–223

*Rupcic N (2019) Learning-forgetting-unlearning-relearning—the learning organization’s learning dynamics. Learn Organ 26:542–548

*Rushmer R, Davies HTO (2004) Unlearning in health care. BMJ Qual Saf 13:ii10–ii15

Sele K, Mahringer CA, Danner-Schröder A, Grisold T, Renzl B (2024) We are all pattern makers! How a flat ontology connects organizational routines and grand challenges. Strat Organ. https://doi.org/10.1177/14761270231215685

*Sharma S, Lenka U (2019) Exploring linkages between unlearning and relearning in organizations. Learn Organ 26:500–517

*Sharma S, Lenka U (2022a) Counterintuitive, yet essential: taking stock of organizational unlearning research through a scientometric analysis (1976–2019). Knowl Manag Res Pract 20:152–174

*Sharma S, Lenka U (2022b) On the shoulders of giants: uncovering key themes of organizational unlearning research in mainstream management journals. Rev Manag Sci 16:1599–1695

*Sharma S, Lenka U (2024) How does transformational leadership impact organizational unlearning: insights from persistence theories. J Organ Change Manag 37:150–172

*Sheaffer Z, Mano-Negrin R (2003) Executives’ orientations as indicators of crisis management policies and practices. J Manag Stud 40:573–606

*Sinkula JM (2002) Market-based success, organizational routines, and unlearning. J Bus Ind Mark 17:253–269

*Snihur Y (2018) Responding to business model innovation: organizational unlearning and firm failure. Learn Organ 25:190–198

*Srithika TM, Bhattacharyya S (2009) Facilitating organizational unlearning using appreciative inquiry as an intervention. Vikalpa 34:67–78

*Starbuck WH (1996) Unlearning ineffective or obsolete technologies. Int J Technol Manag 11:725–737

*Starbuck WH (2017) Organizational learning and unlearning. Learn Organ 24:30–38

Starbuck WH, Milliken FJ (1988) Challenger: fine-tuning the odds until something breaks. J Manag Stud 25:319–340

*Starbuck WH, Greve A, Hedberg BLT (1978) Responding to Crises. J Bus Adm 9:111–137

*Tanaka S (2023) Effects of goal orientation and unlearning on individual exploration activities. J Workplace Learn 35:57–74

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Brit J Manag 14:207–222

Trullen J, Bos-Nehles A, Valverde M (2020) From intended to actual and beyond: a cross-disciplinary view of (human resource management) implementation. Int J Manag Rev 22:150–176

*Tsang EWK (2008) Transferring knowledge to acquisition joint ventures: an organizational unlearning perspective. Manag Learn 39:5–20

*Tsang EWK (2017a) How the concept of organizational unlearning contributes to studies of learning organizations: a personal reflection. Learn Organ 24:39–48

*Tsang EWK (2017b) Stop eulogizing, complicating or straitjacketing the concept of organizational unlearning, please. Learn Organ 24:78–81

*Tsang EWK, Zahra SA (2008) Organizational unlearning. Hum Relat 61:1435–1462

Tsoukas H, Vladimirou E (2001) What is organizational knowledge? J Manag Stud 38:973–993

*Turc E, Baumard P (2007) Can organizations really unlearn? In: McInerney CR, Day RE (eds) Rethinking knowledge management: from knowledge objects to knowledge processes. Springer, Berlin, pp 125–146

Chapter   Google Scholar  

*van Oers L, Feola G, Runhaar H, Moors E (2023) Unlearning in sustainability transitions: insight from two Dutch community-supported agriculture farms. Environ Innov Soc Transit 46:100693

van Wijk J, van Wijk J, Drost S, Stam W (2020) Challenges in building robust interventions in contexts of poverty: insights from an NGO-driven multi-stakeholder network in Ethiopia. Organ Stud 41:1391–1415

*Visser M (2017) Learning and unlearning: a conceptual note. Learn Organ 24:49–57

Voegtlin C, Scherer AG, Stahl GK, Hawn O (2022) Grand societal challenges and responsible innovation. J Manag Stud 59:1–28

*Vu MC, Nguyen LA (2022) Mindful unlearning in unprecedented times: implications for management and organizations. Manag Learn 53:797–817

*Wang X, Lu Y, Zhao Y, Gong S, Li B (2013) Organizational unlearning, organizational flexibility and innovation capability: an empirical study of SMEs in China. Int J Technol Manag 61:132–155

*Wang X, Xi Y, Xie J, Zhao Y (2017) Organizational unlearning and knowledge transfer in cross-border M&A: the roles of routine and knowledge compatibility. J Knowl Manag 21:1580–1595

*Wensley AKP, Cegarra-Navarro JG (2015) Overcoming knowledge loss through the utilization of an unlearning context. J Bus Res 68:1563–1569

Wright C, Nyberg D (2017) An inconvenient truth: how organizations translate climate change into business as usual. Acad Manag J 60:1633–1661

*Xi Y, Wang X, Zhu Y (2020) Organizational unlearning and knowledge transfer in cross-border M&As: the mediating role of knowledge integration from a routine-based view. J Knowl Manag 24:841–860

*Xu N, Wu X, Du J, Li S (2023) Organizational unlearning: a process of tension imposition, integration, and splitting. Eur Manag Rev. https://doi.org/10.1111/emre.12586

*Yang KP, Chou C, Chiu YJ (2014) How unlearning affects radical innovation: the dynamics of social capital and slack resources. Technol Forecast Soc Change 87:152–163

*Yildiz HE, Fey CF (2010) Compatibility and unlearning in knowledge transfer in mergers and acquisitions. Scand J Manag 26:448–456

*Yin J (2023) Effects of the paradox mindset on work engagement: the mediating role of seeking challenges and individual unlearning. Curr Psychol 42:2708–2718

*Zahra SA, Abdelgawad SG, Tsang EWK (2011) Emerging multinationals venturing into developed economies: Implications for learning, unlearning, and entrepreneurial capability. J Manag Inq 20:323–330

*Zhang F, Lyu C, Zhu L (2022) Organizational unlearning, knowledge generation strategies and radical innovation performance: evidence from a transitional economy. Eur J Mark 56:133–158

*Zhao Y, Wang X (2020) Organisational unlearning, relearning and strategic flexibility: from the perspective of updating routines and knowledge. Technol Anal Strat Manag 32:1251–1263

*Zhao Y, Lu Y, Wang X (2013) Organizational unlearning and organizational relearning: a dynamic process of knowledge management. J Knowl Manag 17:902–912

*Zhao Y, Wang Z, Feng T, Kong T, Zhang Q (2022) Organizational unlearning and inclusive innovation: the moderating role of green control ambidexterity. Sustain Dev 30:539–555

Download references

Open access funding provided by University of Liechtenstein. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and affiliations.

Liechtenstein Business School, University of Liechtenstein, Vaduz, Principality of Liechtenstein

Adrian Klammer

Institute of Information Management, University of St. Gallen, St. Gallen, Switzerland

Thomas Grisold

Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway

Nhien Nguyen

Center for General Education, National Yang Ming Chiao Tung University, Taipei, Taiwan

Shih-wei Hsu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Adrian Klammer .

Ethics declarations

Conflict of interest.

The authors have no relevant financial or non-financial interests to disclose.

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

Klammer, A., Grisold, T., Nguyen, N. et al. Organizational unlearning as a process: What we know, what we don’t know, what we should know. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00430-3

Download citation

Received : 12 October 2023

Accepted : 31 March 2024

Published : 23 April 2024

DOI : https://doi.org/10.1007/s11301-024-00430-3

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

• Organizational unlearning
• Unlearning process
• Knowledge loss
• Literature review
• Integrative literature review

JEL Classification

• Find a journal
• Publish with us
• Track your research

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
• Springer Nature - PMC COVID-19 Collection

Systematic Literature Review of E-Learning Capabilities to Enhance Organizational Learning

Michail n. giannakos.

1 Norwegian University of Science and Technology, Trondheim, Norway

Patrick Mikalef

Ilias o. pappas.

2 University of Agder, Kristiansand, Norway

E-learning systems are receiving ever increasing attention in academia, business and public administration. Major crises, like the pandemic, highlight the tremendous importance of the appropriate development of e-learning systems and its adoption and processes in organizations. Managers and employees who need efficient forms of training and learning flow within organizations do not have to gather in one place at the same time or to travel far away to attend courses. Contemporary affordances of e-learning systems allow users to perform different jobs or tasks for training courses according to their own scheduling, as well as to collaborate and share knowledge and experiences that result in rich learning flows within organizations. The purpose of this article is to provide a systematic review of empirical studies at the intersection of e-learning and organizational learning in order to summarize the current findings and guide future research. Forty-seven peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. This survey identifies five major directions of the research on the confluence of e-learning and organizational learning during the last decade. Future research should leverage big data produced from the platforms and investigate how the incorporation of advanced learning technologies (e.g., learning analytics, personalized learning) can help increase organizational value.

Introduction

E-learning covers the integration of information and communication technology (ICT) in environments with the main goal of fostering learning (Rosenberg and Foshay 2002 ). The term “e-learning” is often used as an umbrella term to portray several modes of digital learning environments (e.g., online, virtual learning environments, social learning technologies). Digitalization seems to challenge numerous business models in organizations and raises important questions about the meaning and practice of learning and development (Dignen and Burmeister 2020 ). Among other things, the digitalization of resources and processes enables flexible ways to foster learning across an organization’s different sections and personnel.

Learning has long been associated with formal or informal education and training. However organizational learning is much more than that. It can be defined as “a learning process within organizations that involves the interaction of individual and collective (group, organizational, and inter-organizational) levels of analysis and leads to achieving organizations’ goals” (Popova-Nowak and Cseh 2015 ) with a focus on the flow of knowledge across the different organizational levels (Oh 2019 ). Flow of knowledge or learning flow is the way in which new knowledge flows from the individual to the organizational level (i.e., feed forward) and vice versa (i.e., feedback) (Crossan et al. 1999 ; March 1991 ). Learning flow and the respective processes constitute the cornerstone of an organization’s learning activities (e.g., from physical training meetings to digital learning resources), they are directly connected to the psycho-social experiences of an organization’s members, and they eventually lead to organizational change (Crossan et al. 2011 ). The overall organizational learning is extremely important in an organization because it is associated with the process of creating value from an organizations’ intangible assets. Moreover, it combines notions from several different domains, such as organizational behavior, human resource management, artificial intelligence, and information technology (El Kadiri et al. 2016 ).

A growing body of literature lies at the intersection of e-learning and organizational learning. However, there is limited work on the qualities of e-learning and the potential of its qualities to enhance organizational learning (Popova-Nowak and Cseh 2015 ). Blockages and disruptions in the internal flow of knowledge is a major reason why organizational change initiatives often fail to produce their intended results (Dee and Leisyte 2017 ). In recent years, several models of organizational learning have been published (Berends and Lammers 2010 ; Oh 2019 ). However, detailed empirical studies indicate that learning does not always proceed smoothly in organizations; rather, the learning meets interruptions and breakdowns (Engeström et al. 2007 ).

Discontinuities and disruptions are common phenomena in organizational learning (Berends and Lammers 2010 ), and they stem from various causes. For example, organizational members’ low self-esteem, unsupportive technology and instructors (Garavan et al. 2019 ), and even crises like the Covid-19 pandemic can result in demotivated learners and overall unwanted consequences for their learning (Broadbent 2017 ). In a recent conceptual article, Popova-Nowak and Cseh ( 2015 ) emphasized that there is a limited use of multidisciplinary perspectives to investigate and explain the processes and importance of utilizing the available capabilities and resources and of creating contexts where learning is “attractive to individual agents so that they can be more engaged in exploring ways in which they can contribute through their learning to the ongoing renewal of organizational routines and practices” (Antonacopoulou and Chiva 2007 , p. 289).

Despite the importance of e-learning, the lack of systematic reviews in this area significantly hinders research on the highly promising value of e-learning capabilities for efficiently supporting organizational learning. This gap leaves practitioners and researchers in uncharted territories when faced with the task of implementing e-learning designs or deciding on their digital learning strategies to enhance the learning flow of their organizations. Hence, in order to derive meaningful theoretical and practical implications, as well as to identify important areas for future research, it is critical to understand how the core capabilities pertinent to e-learning possess the capacity to enhance organizational learning.

In this paper, we define e-learning enhanced organizational learning (eOL) as the utilization of digital technologies to enhance the process of improving actions through better knowledge and understanding in an organization. In recent years, a significant body of research has focused on the intersection of e-learning and organizational learning (e.g., Khandakar and Pangil 2019 ; Lin et al. 2019 ; Menolli et al. 2020 ; Turi et al. 2019 ; Xiang et al. 2020 ). However, there is a lack of systematic work that summarizes and conceptualizes the results in order to support organizations that want to move from being information-based enterprises to being knowledge-based ones (El Kadiri et al. 2016 ). In particular, recent technological advances have led to an increase in research that leverages e-learning capacities to support organizational learning, from virtual reality (VR) environments (Costello and McNaughton 2018 ; Muller Queiroz et al. 2018 ) to mobile computing applications (Renner et al. 2020 ) to adaptive learning and learning analytics (Zhang et al. 2019 ). These studies support different skills, consider different industries and organizations, and utilize various capacities while focusing on various learning objectives (Garavan et al. 2019 ). Our literature review aims to tease apart these particularities and to investigate how these elements have been utilized over the past decade in eOL research. Therefore, in this review we aim to answer the following research questions (RQs):

• RQ1: What is the status of research at the intersection of e-learning and organizational learning, seen through the lens of areas of implementation (e.g., industries, public sector), technologies used, and methodologies (e.g., types of data and data analysis techniques employed)?
• RQ2: How can e-learning be leveraged to enhance the process of improving actions through better knowledge and understanding in an organization?

Our motivation for this work is based on the emerging developments in the area of learning technologies that have created momentum for their adoption by organizations. This paper provides a review of research on e-learning capabilities to enhance organizational learning with the purpose of summarizing the findings and guiding future studies. This study can provide a springboard for other scholars and practitioners, especially in the area of knowledge-based enterprises, to examine e-learning approaches by taking into consideration the prior and ongoing research efforts. Therefore, in this paper we present a systematic literature review (SLR) (Kitchenham and Charters 2007 ) on the confluence of e-learning and organizational learning that uncovers initial findings on the value of e-learning to support organizational learning while also delineating several promising research streams.

The rest of this paper is organized as follows. In the next section, we present the related background work. The third section describes the methodology used for the literature review and how the studies were selected and analyzed. The fourth section presents the research findings derived from the data analysis based on the specific areas of focus. In the fifth section, we discuss the findings, the implications for practice and research, and the limitations of the selected methodological approach. In the final section, we summarize the conclusions from the study and make suggestions for future work.

Background and Related Work

E-learning systems.

E-learning systems provide solutions that deliver knowledge and information, facilitate learning, and increase performance by developing appropriate knowledge flow inside organizations (Menolli et al. 2020 ). Putting into practice and appropriately managing technological solutions, processes, and resources are necessary for the efficient utilization of e-learning in an organization (Alharthi et al. 2019 ). Examples of e-learning systems that have been widely adopted by various organizations are Canvas, Blackboard, and Moodle. Such systems provide innovative services for students, employees, managers, instructors, institutions, and other actors to support and enhance the learning processes and facilitate efficient knowledge flow (Garavan et al. 2019 ). Functionalities, such as creating modules to organize mini course information and learning materials or communication channels such as chat, forums, and video exchange, allow instructors and managers to develop appropriate training and knowledge exchange (Wang et al. 2011 ). Nowadays, the utilization of various e-learning capabilities is a commodity for supporting organizational and workplace learning. Such learning refers to training or knowledge development (also known in the literature as learning and development, HR development, and corporate training: Smith and Sadler-Smith 2006 ; Garavan et al. 2019 ) that takes place in the context of work.

Previous studies have focused on evaluating e-learning systems that utilize various models and frameworks. In particular, the development of maturity models, such as the e-learning capability maturity model (eLCMM), addresses technology-oriented concerns (Hammad et al. 2017 ) by overcoming the limitations of the domain-specific models (e.g., game-based learning: Serrano et al.  2012 ) or more generic lenses such as the e-learning maturity model (Marshall 2006 ). The aforementioned models are very relevant since they focus on assessing the organizational capabilities for sustainably developing, deploying, and maintaining e-learning. In particular, the eLCMM focuses on assessing the maturity of adopting e-learning systems and adds a feedback building block for improving learners’ experiences (Hammad et al. 2017 ). Our proposed literature review builds on the previously discussed models, lenses, and empirical studies, and it provides a review of research on e-learning capabilities with the aim of enhancing organizational learning in order to complement the findings of the established models and guide future studies.

E-learning systems can be categorized into different types, depending on their functionalities and affordances. One very popular e-learning type is the learning management system (LMS), which includes a virtual classroom and collaboration capabilities and allows the instructor to design and orchestrate a course or a module. An LMS can be either proprietary (e.g., Blackboard) or open source (e.g., Moodle). These two types differ in their features, costs, and the services they provide; for example, proprietary systems prioritize assessment tools for instructors, whereas open-source systems focus more on community development and engagement tools (Alharthi et al. 2019 ). In addition to LMS, e-learning systems can be categorized based on who controls the pace of learning; for example, an institutional learning environment (ILE) is provided by the organization and is usually used for instructor-led courses, while a personal learning environment (PLE) is proposed by the organization and is managed personally (i.e., learner-led courses). Many e-learning systems use a hybrid version of ILE and PLE that allows organizations to have either instructor-led or self-paced courses.

Besides the controlled e-learning systems, organizations have been using environments such as social media (Qi and Chau 2016 ), massive open online courses (MOOCs) (Weinhardt and Sitzmann 2018 ) and other web-based environments (Wang et al. 2011 ) to reinforce their organizational learning potential. These systems have been utilized through different types of technology (e.g., desktop applications, mobile) that leverage the various capabilities offered (e.g., social learning, VR, collaborative systems, smart and intelligent support) to reinforce the learning and knowledge flow potential of the organization. Although there is a growing body of research on e-learning systems for organizational learning due to the increasingly significant role of skills and expertise development in organizations, the role and alignment of the capabilities of the various e-learning systems with the expected competency development remains underexplored.

Organizational Learning

There is a large body of research on the utilization of technologies to improve the process and outcome dimensions of organizational learning (Crossan et al. 1999 ). Most studies have focused on the learning process and on the added value that new technologies can offer by replacing some of the face-to-face processes with virtual processes or by offering new, technology-mediated phases to the process (Menolli et al. 2020 ; Lau 2015 ) highlighted how VR capabilities can enhance organizational learning, describing the new challenges and frameworks needed in order to effectively utilize this potential. In the same vein, Zhang et al. ( 2017 ) described how VR influences reflective thinking and considered its indirect value to overall learning effectiveness. In general, contemporary research has investigated how novel technologies and approaches have been utilized to enhance organizational learning, and it has highlighted both the promises and the limitations of the use of different technologies within organizations.

In many organizations, alignment with the established infrastructure and routines, and adoption by employees are core elements for effective organizational learning (Wang et al. 2011 ). Strict policies, low digital competence, and operational challenges are some of the elements that hinder e-learning adoption by organizations (Garavan et al. 2019 ; Wang 2018 ) demonstrated the importance of organizational, managerial, and job support for utilizing individual and social learning in order to increase the adoption of organizational learning. Other studies have focused on the importance of communication through different social channels to develop understanding of new technology, to overcome the challenges employees face when engaging with new technology, and, thereby, to support organizational learning (Menolli et al. 2020 ). By considering the related work in the area of organizational learning, we identified a gap in aligning an organization’s learning needs with the capabilities offered by the various technologies. Thus, systematic work is needed to review e-learning capabilities and how these capabilities can efficiently support organizational learning.

E-learning Systems to Enhance Organizational Learning

When considering the interplay between e-learning systems and organizational learning, we observed that a major challenge for today’s organizations is to switch from being information-based enterprises to become knowledge-based enterprises (El Kadiri et al. 2016 ). Unidirectional learning flows, such as formal and informal training, are important but not sufficient to cover the needs that enterprises face (Manuti et al. 2015 ). To maintain enterprises’ competitiveness, enterprise staff have to operate in highly intense information and knowledge-oriented environments. Traditional learning approaches fail to substantiate learning flow on the basis of daily evidence and experience. Thus, novel, ubiquitous, and flexible learning mechanisms are needed, placing humans (e.g., employees, managers, civil servants) at the center of the information and learning flow and bridging traditional learning with experiential, social, and smart learning.

Organizations consider lack of skills and competences as being the major knowledge-related factors hampering innovation (El Kadiri et al. 2016 ). Thus, solutions need to be implemented that support informal, day-to-day, and work training (e.g., social learning, collaborative learning, VR/AR solutions) in order to develop individual staff competences and to upgrade the competence affordances at the organizational level. E-learning-enhanced organizational learning has been delivered primarily in the form of web-based learning (El Kadiri et al. 2016 ). More recently, the TEL tools portfolio has rapidly expanded to make more efficient joint use of novel learning concepts, methodologies, and technological enablers to achieve more direct, effective, and lasting learning impacts. Virtual learning environments, mobile-learning solutions, and AR/VR technologies and head-mounted displays have been employed so that trainees are empowered to follow their own training pace, learning topics, and assessment tests that fit their needs (Costello and McNaughton 2018 ; Mueller et al. 2011 ; Muller Queiroz et al. 2018 ). The expanding use of social networking tools has also brought attention to the contribution of social and collaborative learning (Hester et al. 2016 ; Wei and Ram 2016 ).

Contemporary learning systems supporting adaptive, personalized, and collaborative learning expand the tools available in eOL and contribute to the adoption, efficiency, and general prospects of the introduction of TEL in organizations (Cheng et al. 2011 ). In recent years, eOL has emphasized how enterprises share knowledge internally and externally, with particular attention being paid to systems that leverage collaborative learning and social learning functionalities (Qi and Chau 2016 ; Wang  2011 ). This is the essence of computer-supported collaborative learning (CSCL). The CSCL literature has developed a framework that combines individual learning, organizational learning, and collaborative learning, facilitated by establishing adequate learning flows and emerges effective learning in an enterprise learning (Goggins et al. 2013 ), in Fig.  1 .

Representation of the combination of enterprise learning and knowledge flows. (adapted from Goggins et al. 2013 )

Establishing efficient knowledge and learning flows is a primary target for future data-driven enterprises (El Kadiri et al. 2016 ). Given the involved knowledge, the human resources, and the skills required by enterprises, there is a clear need for continuous, flexible, and efficient learning. This can be met by contemporary learning systems and practices that provide high adoption, smooth usage, high satisfaction, and close alignment with the current practices of an enterprise. Because the required competences of an enterprise evolve, the development of competence models needs to be agile and to leverage state-of-the art technologies that align with the organization’s processes and models. Therefore, in this paper we provide a review of the eOL research in order to summarize the findings, identify the various capabilities of eOL, and guide the development of organizational learning in future enterprises as well as in future studies.

Methodology

To answer our research questions, we conducted an SLR, which is a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest. A SLR has the capacity to present a fair evaluation of a research topic by using a trustworthy, rigorous, and auditable methodology (Kitchenham and Charters 2007 ). The guidelines used (Kitchenham and Charters 2007 ) were derived from three existing guides adopted by medical researchers. Therefore, we adopted SLR guidelines that follow transparent and widely accepted procedures (especially in the area of software engineering and information systems, as well as in e-learning), minimize potential bias (researchers), and support reproducibility (Kitchenham and Charters 2007 ). Besides the minimization of bias and support for reproducibility, an SLR allows us to provide information about the impact of some phenomenon across a wide range of settings, contexts, and empirical methods. Another important advantage is that, if the selected studies give consistent results, SLRs can provide evidence that the phenomenon is robust and transferable (Kitchenham and Charters 2007 ).

Article Collection

Several procedures were followed to ensure a high-quality review of the literature of eOL. A comprehensive search of peer-reviewed articles was conducted in February 2019 (short papers, posters, dissertations, and reports were excluded), based on a relatively inclusive range of key terms: “organizational learning” & “elearning”, “organizational learning” & “e-learning”, “organisational learning” & “elearning”, and “organisational learning” & “e-learning”. Publications were selected from 2010 onwards, because we identified significant advances since 2010 (e.g., MOOCs, learning analytics, personalized learning) in the area of learning technologies. A wide variety of databases were searched, including SpringerLink, Wiley, ACM Digital Library, IEEE Xplore, Science Direct, SAGE, ERIC, AIS eLibrary, and Taylor & Francis. The selected databases were aligned with the SLR guidelines (Kitchenham and Charters 2007 ) and covered the major venues in IS and educational technology (e.g., a basket of eight IS journals, the top 20 journals in the Google Scholar IS subdiscipline, and the top 20 journals in the Google Scholar Educational Technology subdiscipline). The search process uncovered 2,347 peer-reviewed articles.

Inclusion and Exclusion Criteria

The selection phase determines the overall validity of the literature review, and thus it is important to define specific inclusion and exclusion criteria. As Dybå and Dingsøyr ( 2008 ) specified, the quality criteria should cover three main issues – namely, rigor, credibility, and relevance – that need to be considered when evaluating the quality of the selected studies. We applied eight quality criteria informed by the proposed Critical Appraisal Skills Programme (CASP) and related works (Dybå and Dingsøyr 2008 ). Table ​ Table1 1 presents these criteria.

Quality criteria

Therefore, studies were eligible for inclusion if they were focused on eOL. The aforementioned criteria were applied in stages 2 and 3 of the selection process (see Fig.  2 ), when we assessed the papers based on their titles and abstracts, and read the full papers. From March 2020, we performed an additional search (stage 4) following the same process for papers published after the initial search period (i.e., 2010–February 2019). The additional search returned seven papers. Figure ​ Figure2 2 summarizes the stages of the selection process.

Stages of the selection process

Each collected study was analyzed based on the following elements: study design (e.g., experiment, case study), area (e.g., IT, healthcare), technology (e.g., wiki, social media), population (e.g., managers, employees), sample size, unit of analysis (individual, firm), data collections (e.g., surveys, interviews), research method, data analysis, and the main research objective of the study. It is important to highlight that the articles were coded based on the reported information, that different authors reported information at different levels of granularity (e.g., an online system vs. the name of the system), and that in some cases the information was missing from the paper. Overall, we endeavored to code the articles as accurately and completely as possible.

The coding process was iterative with regular consensus meetings between the two researchers involved. The primary coder prepared the initial coding for a number of articles and both coders reviewed and agreed on the coding in order to reach the final codes presented in the Appendix . Disagreements between the coders and inexplicit aspects of the reviewed papers were discussed and resolved in regular consensus meetings. Although this process did not provide reliability indices (e.g., Cohen’s kappa), it did provide certain reliability in terms of consistency of the coding and what Krippendorff ( 2018 ) stated as the reliability of “the degree to which members of a designated community concur on the readings, interpretations, responses to, or uses of given texts or data”, which is considered acceptable research practice (McDonald et al. 2019 ).

In this section, we present the detailed results of the analysis of the 47 papers. Analysis of the studies was performed using non-statistical methods that considered the variables reported in the Appendix . This section is followed by an analysis and discussion of the categories.

Sample Size and Population Involved

The categories related to the sample of the articles and included the number of participants in each study (size), their position (e.g., managers, employees), and the area/topic covered by the study. The majority of the studies involved employees (29), with few studies involving managers (6), civil servants (2), learning specialists (2), clients, and researchers. Regarding the sample size, approximately half of the studies (20) were conducted with fewer than 100 participants; some (12) can be considered large-scale studies (more than 300 participants); and only a few (9) can be considered small scale (fewer than 20 participants). In relation to the area/topic of the study, most studies (11) were conducted in the context of the IT industry, but there was also good coverage of other important areas (i.e., healthcare, telecommunications, business, public sector). Interestingly, several studies either did not define the area or were implemented in a generic context (sector-agnostic studies, n = 10), and some studies were implemented in a multi-sector context (e.g., participants from different sections or companies, n = 4).

Research Methods

When assessing the status of research for an area, one of the most important aspects is the methodology used. By “method” in the Appendix , we refer to the distinction between quantitative, qualitative, and mixed methods research. In addition to the method, in our categorization protocol we also included “study design” to refer to the distinction between survey studies (i.e., those that gathered data by asking a group of participants), experiments (i.e., those that created situations to record beneficial data), and case studies (i.e., those that closely studied a group of individuals).

Based on this categorization, the Appendix shows that the majority of the papers were quantitative (34) and qualitative (7), with few studies (6) utilizing mixed methods. Regarding the study design, most of the studies were survey studies (26), 13 were case studies, and fewer were experiments (8). For most studies, the individual participant (40) was the unit of analysis, with few studies having the firm as the unit of analysis, and only one study using the training session as a unit of analysis. Regarding the measures used in the studies, most utilized surveys (39), with 11 using interviews, and only a few studies using field notes from focus groups (2) and log files from the systems (2). Only eight studies involved researchers using different measures to triangulate or extend their findings. Most articles used structural equation modeling (SEM) (17) to analyze their data, with 13 studies employing descriptive statistics, seven using content analysis, nine using regression analysis or analyses of variances/covariance, and one study using social network analysis (SNA).

Technologies

Concerning the technology used, most of the studies (17) did not study a specific system, referring instead in their investigation to a generic e-learning or technological solution. Several studies (9) named web-based learning environments, without describing the functionalities of the identified system. Other studies focused on online learning environments (4), collaborative learning systems (3), social learning systems (3), smart learning systems (2), podcasting (2), with the rest of the studies using a specific system (e.g., a wiki, mobile learning, e-portfolios, Second Life, web application).

Research Objectives

The research objectives of the studies could be separated into six main categories. The first category focuses on the intention of the employees to use the technology (9); the second focuses on the performance of the employees (8); the third focuses on the value/outcome for the organization (4); the fourth focuses on the actual usage of the system (7); the fifth focuses on employees’ satisfaction (4); and the sixth focuses on the ability of the proposed system to foster learning (9). In addition to these six categories, we also identified studies that focused on potential barriers for eOL in organizations (Stoffregen et al. 2016 ), the various benefits associated with the successful implementation of eOL (Liu et al. 2012 ), the feasibility of eOL (Kim et al. 2014 ; Mueller et al. 2011 ), and the alignment of the proposed innovation with the other processes and systems in the organization (Costello and McNaughton 2018 ).

E-learning Capabilities in Various Organizations and for Various Objectives

The technology used has an inherent role for both the organization and the expected eOL objective. E-learning systems are categorized based on their functionalities and affordances. Based on the information reported in the selected papers, we ranked them based on the different technologies and functionalities (e.g., collaborative, online, smart). To do so, we focused on the main elements described in the selected paper; for instance, a paper that described the system as wiki-based or indicated that the system was Second Life was ranked as such, rather than being added to collaborative systems or social learning respectively. We did this because we wanted to capture all the available information since it gave us additional insights (e.g., Second Life is both a social and a VR system).

To investigate the connection between the various technologies used to enhance organizational learning and their application in the various organizations, we utilized the coding (see Appendix ) and mapped the various e-learning technologies (or their affordances) with the research industries to which they applied (Fig.  3 ). There was occasionally a lack of detailed information about the capabilities of the e-learning systems applied (e.g., generic, or a web application, or an online system), which limited the insights. Figure ​ Figure3 3 provides a useful mapping of the confluence of e-learning technologies and their application in the various industries.

Association of the different e-learning technologies with the industries to which they are applied in the various studies. Note: The size of the circles depicts the frequency of studies, with the smallest circle representing one study and the largest representing six studies. The mapping is extracted from the data in the Appendix , which outlines the papers that belong in each of the circles

To investigate the connection between the various technologies used to enhance organizational learning and their intended objectives, we utilized the coding of the articles (see Appendix ) and mapped the various e-learning technologies (or their affordances) with the intended objectives, as reported in the various studies (Fig.  4 ). The results in Fig.  4 show the objectives that are central in eOL research (e.g., performance, fostering learning, adoption, and usage) as well as those objectives on which few studies have focused (e.g., alignment, feasibility, behavioral change). In addition, the results also indicate the limited utilization of the various e-learning capabilities (e.g., social, collaborative, smart) to achieve objectives connected with those capabilities (e.g., social learning and behavioral change, collaborative learning, and barriers).

Association of the different e-learning technologies with the objectives investigated in the various studies. Note: The size of the circles depicts the frequency of studies, with the smallest circle representing one study and the largest representing five studies. The mapping is extracted from the data in the Appendix , which outlines the papers that belong in each of the circles

5. Discussion

After reviewing the 47 identified articles in the area of eOL, we can observe that all the works acknowledge the importance of the affordances offered by different e-learning technologies (e.g., remote collaboration, anytime anywhere), the importance of the relationship between eOL and employees’ satisfaction and performance, and the benefits associated with organizational value and outcome. Most of the studies agree that eOL provides employees, managers, and even clients with opportunities to learn in a more differentiated manner, compared to formal and face-to-face learning. However, how the organization adopts and puts into practice these capabilities to leverage them and achieve its goals are complex and challenging procedures that seem to be underexplored.

Several studies (Lee et al. 2015a ; Muller Queiroz et al. 2018 ; Tsai et al. 2010 ) focused on the positive effect of perceived managerial support, perceived usefulness, perceived ease of use, and other technology acceptance model (TAM) constructs of the e-learning system in supporting all three levels of learning (i.e., individual, collaborative, and organizational). Another interesting dimension highlighted by many studies (Choi and Ko 2012 ; Khalili et al. 2012 ; Yanson and Johnson 2016 ) is the role of socialization in the adoption and usage of the e-learning systems that offer these capabilities. Building connections and creating a shared learning space in the e-learning system is challenging but also critical for the learners (Yanson and Johnson 2016 ). This is consistent with the expectancy-theoretical explanation of how social context impacts on employees’ motivation to participate in learning (Lee et al. 2015a ; Muller Queiroz et al. 2018 ).

The organizational learning literature suggests that e-learning may be more appropriate for the acquisition of certain types of knowledge than others (e.g., procedural vs. declarative, or hard-skills vs. soft-skills); however, there is no empirical evidence for this (Yanson and Johnson 2016 ). To advance eOL research, there is a need for a significant move to address complex, strategic skills by including learning and development professionals (Garavan et al. 2019 ) and by developing strategic relationships. Another important element is to utilize e-learning technology that addresses and integrates organizational, individual, and social perspectives in eOL (Wang  2011 ). This is also identified in our literature review since we found only limited specialized e-learning systems in domain areas that have traditionally benefited from such technology. For instance, although there were studies that utilized VR environments (Costello and McNaughton 2018 ; Muller Queiroz et al. 2018 ) and video-based learning systems (Wei et al. 2013 ; Wei and Ram 2016 ), there was limited focus in contemporary eOL research on how specific affordances of the various environments that are used in organizations (e.g., Carnetsoft, Outotec HSC, and Simscale for simulations of working environments; or Raptivity, YouTube, and FStoppers to gain specific skills and how-to knowledge) can benefit the intended goals or be integrated with the unique qualities of the organization (e.g., IT, healthcare).

For the design and the development of the eOL approach, the organization needs to consider the alignment of individual learning needs, organizational objectives, and the necessary resources (Wang  2011 ). To achieve this, it is advisable for organizations to define the expected objectives, catalogue the individual needs, and select technologies that have the capacity to support and enrich learners with self-directed and socially constructed learning practices in the organization (Wang  2011 ). This needs to be done by taking into consideration that on-demand eOL is gradually replacing the classic static eOL curricula and processes (Dignen and Burmeister 2020 ).

Another important dimension of eOL research is the lenses used to approach effectiveness. The selected papers approached effectiveness with various objectives, such as fostering learning, usage of the e-learning system, employees’ performance, and the added organizational value (see Appendix ). To measure these indices, various metrics (quantitative, qualitative, and mixed) have been applied. The qualitative dimensions emphasize employees’ satisfaction and system usage (e.g., Menolli et al. 2020 ; Turi et al. 2019 ), as well as managers’ perceived gained value and benefits (e.g., Lee et al. 2015b ; Xiang et al. 2020 ) and firms’ perceived effective utilization of eOL resources (López-Nicolás and Meroño-Cerdán 2011 ). The quantitative dimensions focus on usage, feasibility, and experience at different levels within an organization, based on interviews, focus groups, and observations (Costello and McNaughton 2018 ; Michalski 2014 ; Stoffregen et al. 2016 ). However, it is not always clear the how eOL effectiveness has been measured, nor the extent to which eOL is well aligned with and is strategically impactful on delivering the strategic agenda of the organization (Garavan et al. 2019 ).

Research on digital technologies is developing rapidly, and big data and business analytics have the potential to pave the way for organizations’ digital transformation and sustainable development (Mikalef et al. 2018 ; Pappas et al. 2018 ); however, our review finds surprisingly limited use of big data and analytics in eOL. Despite contemporary e-learning systems adopting data-driven mechanisms, as well as advances in learning analytics (Siemens and Long 2011 ), the results of our analysis indicate that learner-generated data in the context of eOL are used in only a few studies to extract very limited insights with respect to the effectiveness of eOL and the intended objectives of the respective study (Hung et al. 2015 ; Renner et al. 2020 ; Rober and Cooper 2011 ). Therefore, eOL research needs to focus on data-driven qualities that will allow future researchers to gain deeper insights into which capabilities need to be developed to monitor the effectiveness of the various practices and technologies, their alignment with other functions of the organization, and how eOL can be a strategic and impactful vehicle for materializing the strategic agenda of the organization.

Status of eOL Research

The current review suggests that, while the efficient implementation of eOL entails certain challenges, there is also a great potential for improving employees’ performance as well as overall organizational outcome and value. There are also opportunities for improving organizations’ learning flow, which might not be feasible with formal learning and training. In order to construct the main research dimensions of eOL research and to look more deeply at the research objectives of the studies (the information we coded as objectives in the Appendix ), we performed a content analysis and grouped the research objectives. This enabled us to summarize the contemporary research on eOL according to five major categories, each of which is describes further below. As the research objectives of the published work shows, the research on eOL conducted during the last decade has particularly focused on the following five directions.

Research has particularly focused on how easy the technology is to use, on how useful it is, or on how well aligned/integrated it is with other systems and processes within the organization. In addition, studies have used different learning technologies (e.g., smart, social, personalized) to enhance organizational learning in different contexts and according to different needs. However, most works have focused on affordances such as remote training and the development of static courses or modules to share information with learners. Although a few studies have utilized contemporary e-learning systems (see Appendix ), even in these studies there is a lack of alignment between the capabilities of those systems (e.g., open online course, adaptive support, social and collaborative learning) and the objectives and strategy of the organization (e.g., organizational value, fostering learning).

The reviewed work has emphasized how different factors contribute to different levels of organizational learning, and it has focused on practices that address individual, collaborative, and organizational learning within the structure of the organization. In particular, most of the reviewed studies recognize that organizational learning occurs at multiple levels: individual, team (or group), and organization. In other words, although each of the studies carried out an investigation within a given level (except for Garavan et al. 2019 ), there is a recognition and discussion of the different levels. Therefore, the results align with the 4I framework of organizational learning that recognizes how learning across the different levels is linked by social and psychological processes: intuiting, interpreting, integrating, and institutionalizing (the 4Is) (Crossan et al. 1999 ). However, most of the studies focused on the institutionalizing-intuiting link (i.e., top-down feedback); moreover, no studies focused on contemporary learning technologies and processes that strengthen the learning flow (e.g., self-regulated learning).

There is a considerable amount of predominantly qualitative studies that focus on potential barriers to eOL implementation as well as on the risks and requirements associated with the feasibility and successful implementation of eOL. In the same vein, research has emphasized the importance of alignment of eOL (both in processes and in technologies) within the organization. These critical aspects for effective eOL are sometimes the main objectives of the studies (see Appendix ). However, most of the elements relating to the effectiveness of eOL were measured with questionnaires and interviews with employees and managers, and very little work was conducted on how to leverage the digital technologies employed in eOL, big data, and analytics in order to monitor the effectiveness of eOL.

In most of the studies, the main objective was to increase employees’ adoption, satisfaction, and usage of the e-learning system. In addition, several studies focused on the e-learning system’s ability to improve employees’ performance, increase the knowledge flow in the organization, and foster learning. Most of the approaches were employee-centric, with a small amount of studies focusing on managers and the firm in general. However, employees were seen as static entities within the organization, with limited work investigating how eOL-based training exposes employees to new knowledge, broadens their skills repertoire, and has tremendous potential for fostering innovation (Lin and Sanders 2017 ).

A considerable number of studies utilized the firm (rather than the individual employee) as the unit of analysis. Such studies focused on how the implementation of eOL can increase employee performance, organizational value, and customer value. Although this is extremely helpful in furthering knowledge about eOL technologies and practices, a more granular investigation of the different e-learning systems and processes to address the various goals and strategies of the organization would enable researchers to extract practical insights on the design and implementation of eOL.

Research Agenda

By conducting an SLR and documenting the eOL research of the last decade, we have identified promising themes of research that have the potential to further eOL research and practice. To do so, we define a research agenda consisting of five thematic areas of research, as depicted in the research framework in Fig.  5 , and we provide some suggestions on how researchers could approach these challenges. In this visualization of the framework, on the left side we present the organizations as they were identified from our review (i.e., area/topic category in the Appendix ) and the multiple levels where organizational learning occurs (Costello and McNaughton 2018 ). On the right side, we summarize the objectives as they were identified from our review (i.e., the objectives category in the Appendix ). In the middle, we depict the orchestration that was conducted and how potential future research on eOL can improve the orchestration of the various elements and accelerate the achievement of the intended objectives. In particular, our proposed research agenda includes five research themes discussed in the following subsections.

E-learning capabilities to enhance organizational research agenda

Theme 1: Couple E-learning Capabilities With the Intended Goals

The majority of the eOL studies either investigated a generic e-learning system using the umbrella term “e-learning” or did not provide enough details about the functionalities of the system (in most cases, it was simply defined as an online or web system). This indicates the very limited focus of the eOL research on the various capabilities of e-learning systems. In other words, the literature has been very detailed on the organizational value and employees’ acceptance of the technology, but less detailed on the capabilities of this technology that needs to be put into place to achieve the intended goals and strategic agenda. However, the capabilities of the e-learning systems and their use are not one-size-fits-all, and the intended goals (to obtain certain skills and competences) and employees’ needs and backgrounds play a determining role in the selection of the e-learning system (Al-Fraihat et al. 2020 ).

Only in a very few studies (Mueller et al. 2011 ; Renner et al. 2020 ) were the capabilities of the e-learning solutions (e.g., mobile learning, VR) utilized, and the results were found to significantly contribute to the intended goals. The intended knowledge can be procedural, declarative, general competence (e.g., presentation, communication, or leadership skills) or else, and its particularities and the pedagogical needs of the intended knowledge (e.g., a need for summative/formative feedback or for social learning support) should guide the selection of the e-learning system and the respective capabilities. Therefore, future research needs to investigate how the various capabilities offered by contemporary learning systems (e.g., assessment mechanisms, social learning, collaborative learning, personalized learning) can be utilized to adequately reinforce the intended goals (e.g., to train personnel to use a new tool, to improve presentation skills).

Theme 2: Embrace the Particularities of the Various Industries

Organizational learning entails sharing knowledge and enabling opportunities for growth at the individual, group, team, and organizational levels. Contemporary e-learning systems provide the medium to substantiate the necessary knowledge flow within organizations and to support employees’ overall learning. From the selected studies, we can infer that eOL research is either conducted in an industry-agnostic context (either generic or it was not properly reported) or there is a focus on the IT industry (see Appendix ). However, when looking at the few studies that provide results from different industries (Garavan et al. 2019 ; Lee et al. 2014 ), companies indicate that there are different practices, processes, and expectations, and that employees have different needs and perceptions with regards to e-learning systems and eOL in general. Such particularities influence the perceived dimensions of a learning organization. Some industries noted that eOL promoted the development of their learning organizations, whereas others reported that eOL did not seem to contribute to their development as a learning organization (Yoo and Huang 2016 ). Therefore, it is important that the implementation of organizational learning embraces the particularities of the various industries and future research needs to identify how the industry-specific characteristics can inform the design and development of organizational learning in promoting an organization’s goals and agenda.

Theme 3: Utilize E-learning Capabilities to Implement Employee-centric Approaches

For efficient organizational learning to be implemented, the processes and technologies need to recognize that learning is linked by social and psychological processes (Crossan et al. 1999 ). This allows employees to develop learning in various forms (e.g., social, emotional, personalized) and to develop elements such as self-awareness, self-control, and interpersonal skills that are vital for the organization. Looking at the contemporary eOL research, we notice that the exploration of e-learning capabilities to nurture the aforementioned elements and support employee-centric approaches is very limited (e.g., personalized technologies, adaptive assessment). Therefore, future research needs to collect data to understand how e-learning capabilities can be utilized in relation to employees’ needs and perceptions in order to provide solutions (e.g., collaborative, social, adaptive) that are employee-centric and focused on development, and that have the potential to move away from standard one-size-fits-all e-learning solutions to personalized and customized systems and processes.

Theme 4: Employ Analytics-enabled eOL

There is a lot of emphasis on measuring, via various qualitative and quantitative metrics, the effectiveness of eOL implemented at different levels in organizations. However, most of these metrics come from surveys and interviews that capture employees’ and managers’ perceptions of various aspects of eOL (e.g., fostering of learning, organizational value, employees’ performance), and very few studies utilize analytics (Hung et al. 2015 ; Renner et al. 2020 ; Rober and Cooper 2011 ). Given how digital technologies, big data, and business analytics pave the way towards organizations’ digital transformation and sustainable development (Mikalef et al. 2018 ; Pappas et al. 2018 ), and considering the learning analytics affordances of contemporary e-learning systems (Siemens and Long 2011 ), future work needs to investigate how learner/employee-generated data can be employed to inform practice and devise more accurate and temporal effectiveness metrics when measuring the importance and impact of eOL.

Theme 5: Orchestrate the Employees’ Needs, Resources, and Objectives in eOL Implementation

While considerable effort has been directed towards the various building blocks of eOL implementation, such as resources (intangible, tangible, and human skills) and employees’ needs (e.g., vision, growth, skills development), little is known so far about the processes and structures necessary for orchestrating those elements in order to achieve an organization’s intended goals and to materialize its overall agenda. In other words, eOL research has been very detailed on some of the elements that constitute efficient eOL, but less so on the interplay of those elements and how they need to be put into place. Prior literature on strategic resource planning has shown that competence in orchestrating such elements is a prerequisite to successfully increasing business value (Wang et al. 2012 ). Therefore, future research should not only investigate each of these elements in silos, but also consider their interplay, since it is likely that organizations with similar resources will exert highly varied levels in each of these elements (e.g., analytics-enabled, e-learning capabilities) to successfully materialize their goals (e.g., increase value, improve the competence base of their employees, modernize their organization).

Implications

Several implications for eOL have been revealed in this literature review. First, most studies agree that employees’ or trainees’ experience is extremely important for the successful implementation of eOL. Thus, keeping them in the design and implementation cycle of eOL will increase eOL adoption and satisfaction as well as reduce the risks and barriers. Another important implication addressed by some studies relates to the capabilities of the e-learning technologies, with easy-to-use, useful, and social technologies resulting in more efficient eOL (e.g., higher adoption and performance). Thus, it is important for organizations to incorporate these functionalities in the platform and reinforce them with appropriate content and support. This should not only benefit learning outcomes, but also provide the networking opportunities for employees to broaden their personal networks, which are often lost when companies move from face-to-face formal training to e-learning-enabled organizational learning.

Limitations

This review has some limitations. First, we had to make some methodological decisions (e.g., selection of databases, the search query) that might lead to certain biases in the results. However, tried to avoid such biases by considering all the major databases and following the steps indicated by Kitchenham and Charters ( 2007 ). Second, the selection of empirical studies and coding of the papers might pose another possible bias. However, the focus was clearly on the empirical evidence, the terminology employed (“e-learning”) is an umbrella term that covers the majority of the work in the area, and the coding of papers was checked by two researchers. Third, some elements of the papers were not described accurately, leading to some missing information in the coding of the papers. However, the amount of missing information was very small and could not affect the results significantly. Finally, we acknowledge that the selected methodology (Kitchenham and Charters 2007 ) includes potential biases (e.g., false negatives and false positives), and that different, equally valid methods (e.g., Okoli and Schabram 2010 ) might have been used and have resulted in slightly different outcomes. Nevertheless, despite the limitations of the selected methodology, it is a well-accepted and widely used literature review method in both software engineering and information systems (Boell and Cecez-Kecmanovic 2014 ), providing certain assurance of the results.

Conclusions and Future Work

We have presented an SLR of 47 contributions in the field of eOL over the last decade. With respect to RQ1, we analyzed the papers from different perspectives, such as research methodology, technology, industries, employees, and intended outcomes in terms of organizational value, employees’ performance, usage, and behavioral change. The detailed landscape is depicted in the Appendix and Figs.  3 and ​ and4; 4 ; with the results indicating the limited utilization of the various e-learning capabilities (e.g., social, collaborative) to achieve objectives connected with those capabilities (e.g., social learning and behavioral change, collaborative learning and overcoming barriers).

With respect to RQ2, we categorized the main findings of the selected papers into five areas that reflect the status of eOL research, and we have discussed the challenges and opportunities emerging from the current review. In addition, we have synthesized the extracted challenges and opportunities and proposed a research agenda consisting of five elements that provide suggestions on how researchers could approach these challenges and exploit the opportunities. Such an agenda will strengthen how e-learning can be leveraged to enhance the process of improving actions through better knowledge and understanding in an organization.

A number of suggestions for further research have emerged from reviewing prior and ongoing work on eOL. One recommendation for future researchers is to clearly describe the eOL approach by providing detailed information about the technologies and materials used, as well as the organizations. This will allow meta-analyses to be conducted and it will also identify the potential effects of a firm’s size or area on the performance and other aspects relating to organizational value. Future work should also focus on collecting and triangulating different types of data from different sources (e.g., systems’ logs). The reviewed studies were conducted mainly by using survey data, and they made limited use of data coming from the platforms; thus, the interpretations and triangulation between the different types of collected data were limited.

Biographies

is a Professor of Interaction Design and Learning Technologies at the Department of Computer Science of NTNU, and Head of the Learner-Computer Interaction lab (https://lci.idi.ntnu.no/). His research focuses on the design and study of emerging technologies in online and hybrid education settings, and their connections to student and instructor experiences and practices. Giannakos has co-authored more than 150 manuscripts published in peer-reviewed journals and conferences (including Computers & Education, Computers in Human Behavior, IEEE TLT, Behaviour & Information Technology, BJET, ACM TOCE, CSCL, Interact, C&C, IDC to mention few) and has served as an evaluator for the EC and the US-NSF. He has served/serves in various organization committees (e.g., general chair, associate chair), program committees as well as editor and guest editor on highly recognized journals (e.g., BJET, Computers in Human Behavior, IEEE TOE, IEEE TLT, ACM TOCE). He has worked at several research projects funded by diverse sources like the EC, Microsoft Research, The Research Council of Norway (RCN), US-NSF, the German agency for international academic cooperation (DAAD) and Cheng Endowment; Giannakos is also a recipient of a Marie Curie/ERCIM fellowship, the Norwegian Young Research Talent award and he is one of the outstanding academic fellows of NTNU (2017-2021).

is an Associate Professor in Data Science and Information Systems at the Department of Computer Science. In the past, he has been a Marie Skłodowska-Curie post-doctoral research fellow working on the research project “Competitive Advantage for the Data-driven Enterprise” (CADENT). He received his B.Sc. in Informatics from the Ionian University, his M.Sc. in Business Informatics for Utrecht University, and his Ph.D. in IT Strategy from the Ionian University. His research interests focus the on strategic use of information systems and IT-business value in turbulent environments. He has published work in international conferences and peer-reviewed journals including the Journal of Business Research, British Journal of Management, Information and Management, Industrial Management & Data Systems, and Information Systems and e-Business Management.

Ilias O. Pappasis

an Associate Professor of Information Systems at the Department of Information Systems, University of Agder (UiA), Norway. His research and teaching activities include data science and digital transformation, social innovation and social change, user experience in different contexts,as well as digital marketing, e-services, and information technology adoption. He has published articles in peer reviewed journals and conferences including Journal of Business Research, European Journal of Marketing, Computers in Human Behavior, Information & Management, Psychology & Marketing, International Journal of Information Management, Journal of Systems and Software. Pappas has been a Guest Editor for the journals Information & Management, Technological Forecasting and Social Change, Information Systems Frontiers, Information Technology & People, and Information Systems and e-Business Management. Pappas is a recipient of ERCIM and Marie Skłodowska-Curiefellowships.

Survey = survey study; Exp. = experiment; CaseSt = case study; ND = non-defined; MGM = management; Telec. = telecommunication; Bsn = business; Univ. = university; Cons. = consulting; Public = public sector; Ent. = enterprise; Web = Web-based; KRS = knowledge repository system; OERs = open educational resources; SL = Second Life, Mg, = managers; Empl = employees; Stud = students; Res. = researchers; Learn. = learning specialists; Individ. = individual; Surv. = surveys; Int. = interviews; FG = focus groups; Log = log files; Obs. = observations; Reg. = regression analysis; Descr. = descriptive statistics; A-VA = analysis of variances/covariance; CA = content analysis; ItU = intention to use; Sat. = satisfaction; OV = organizational value; Per. = performance; Flearn = foster learning; Benef. = benefits; Align. = alignment; Feas. = feasibility; Barr. = barriers; Beh. = behavioral change

Open Access funding provided by NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital).

Publisher’s Note

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

Contributor Information

Michail N. Giannakos, Email: on.untn@gliahcim .

Patrick Mikalef, Email: [email protected] .

Ilias O. Pappas, Email: [email protected] .

• Al-Fraihat D, Joy M, Sinclair J. Evaluating E-learning systems success: An empirical study. Computers in Human Behavior. 2020; 102 :67–86. doi: 10.1016/j.chb.2019.08.004. [ CrossRef ] [ Google Scholar ]
• Alharthi AD, Spichkova M, Hamilton M. Sustainability requirements for eLearning systems: A systematic literature review and analysis. Requirements Engineering. 2019; 24 (4):523–543. doi: 10.1007/s00766-018-0299-9. [ CrossRef ] [ Google Scholar ]
• Alsabawy AY, Cater-Steel A, Soar J. IT infrastructure services as a requirement for e-learning system success. Computers & Education. 2013; 69 :431–451. doi: 10.1016/j.compedu.2013.07.035. [ CrossRef ] [ Google Scholar ]
• Antonacopoulou E, Chiva R. The social complexity of organizational learning: The dynamics of learning and organizing. Management Learning. 2007; 38 :277–295. doi: 10.1177/1350507607079029. [ CrossRef ] [ Google Scholar ]
• Berends H, Lammers I. Explaining discontinuity in organizational learning: A process analysis. Organization Studies. 2010; 31 (8):1045–1068. doi: 10.1177/0170840610376140. [ CrossRef ] [ Google Scholar ]
• Boell SK, Cecez-Kecmanovic D. A hermeneutic approach for conducting literature reviews and literature searches. Communications of the Association for Information Systems. 2014; 34 (1):12. [ Google Scholar ]
• Bologa R, Lupu AR. Organizational learning networks that can increase the productivity of IT consulting companies. A case study for ERP consultants. Expert Systems with Applications. 2014; 41 (1):126–136. doi: 10.1016/j.eswa.2013.07.016. [ CrossRef ] [ Google Scholar ]
• Broadbent J. Comparing online and blended learner’s self-regulated learning strategies and academic performance. The Internet and Higher Education. 2017; 33 :24–32. doi: 10.1016/j.iheduc.2017.01.004. [ CrossRef ] [ Google Scholar ]
• Cheng B, Wang M, Moormann J, Olaniran BA, Chen NS. The effects of organizational learning environment factors on e-learning acceptance. Computers & Education. 2012; 58 (3):885–899. doi: 10.1016/j.compedu.2011.10.014. [ CrossRef ] [ Google Scholar ]
• Cheng B, Wang M, Yang SJ, Peng J. Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support. Computers & Education. 2011; 57 (1):1317–1333. doi: 10.1016/j.compedu.2011.01.018. [ CrossRef ] [ Google Scholar ]
• Choi S, Ko I. Leveraging electronic collaboration to promote interorganizational learning. International Journal of Information Management. 2012; 32 (6):550–559. doi: 10.1016/j.ijinfomgt.2012.03.002. [ CrossRef ] [ Google Scholar ]
• Costello JT, McNaughton RB. Integrating a dynamic capabilities framework into workplace e-learning process evaluations. Knowledge and Process Management. 2018; 25 (2):108–125. doi: 10.1002/kpm.1565. [ CrossRef ] [ Google Scholar ]
• Crossan MM, Lane HW, White RE. An organizational learning framework: From intuition to institution. Academy of Management Review. 1999; 24 :522–537. doi: 10.2307/259140. [ CrossRef ] [ Google Scholar ]
• Crossan MM, Maurer CC, White RE. Reflections on the 2009 AMR decade award: Do we have a theory of organizational learning? Academy of Management Review. 2011; 36 (3):446–460. doi: 10.5465/amr.2010.0544. [ CrossRef ] [ Google Scholar ]
• Dee, J., & Leisyte, L. (2017). Knowledge sharing and organizational change in higher education. The Learning Organization, 24 (5), 355–365. 10.1108/TLO-04-2017-0034
• Dignen B, Burmeister T. Three pillars of organization and leadership in disruptive times. Cham: Springer; 2020. Learning and development in the organizations of the future; pp. 207–232. [ Google Scholar ]
• Dybå T, Dingsøyr T. Empirical studies of agile software development: A systematic review. Information and Software Technology. 2008; 50 (9–10):833–859. doi: 10.1016/j.infsof.2008.01.006. [ CrossRef ] [ Google Scholar ]
• El Kadiri S, Grabot B, Thoben KD, Hribernik K, Emmanouilidis C, Von Cieminski G, Kiritsis D. Current trends on ICT technologies for enterprise information systems. Computers in Industry. 2016; 79 :14–33. doi: 10.1016/j.compind.2015.06.008. [ CrossRef ] [ Google Scholar ]
• Engeström Y, Kerosuo H, Kajamaa A. Beyond discontinuity: Expansive organizational learning remembered. Management Learning. 2007; 38 (3):319–336. doi: 10.1177/1350507607079032. [ CrossRef ] [ Google Scholar ]
• Gal E, Nachmias R. Online learning and performance support in organizational environments using performance support platforms. Performance Improvement. 2011; 50 (8):25–32. doi: 10.1002/pfi.20238. [ CrossRef ] [ Google Scholar ]
• Garavan TN, Heneghan S, O’Brien F, Gubbins C, Lai Y, Carbery R, Grant K. L&D professionals in organisations: much ambition, unfilled promise. European Journal of Training and Development. 2019; 44 (1):1–86. [ Google Scholar ]
• Goggins SP, Jahnke I, Wulf V. Computer-supported collaborative learning at the workplace. New York: Springer; 2013. [ Google Scholar ]
• Hammad, R., Odeh, M., & Khan, Z. (2017). ELCMM: An e-learning capability maturity model. In Proceedings of the 15th International Conference (e-Society 2017) (pp. 169–178).
• Hester AJ, Hutchins HM, Burke-Smalley LA. Web 2.0 and transfer: Trainers’ use of technology to support employees’ learning transfer on the job. Performance Improvement Quarterly. 2016; 29 (3):231–255. doi: 10.1002/piq.21225. [ CrossRef ] [ Google Scholar ]
• Hung YH, Lin CF, Chang RI. Developing a dynamic inference expert system to support individual learning at work. British Journal of Educational Technology. 2015; 46 (6):1378–1391. doi: 10.1111/bjet.12214. [ CrossRef ] [ Google Scholar ]
• Iris R, Vikas A. E-Learning technologies: A key to dynamic capabilities. Computers in Human Behavior. 2011; 27 (5):1868–1874. doi: 10.1016/j.chb.2011.04.010. [ CrossRef ] [ Google Scholar ]
• Jia H, Wang M, Ran W, Yang SJ, Liao J, Chiu DK. Design of a performance-oriented workplace e-learning system using ontology. Expert Systems with Applications. 2011; 38 (4):3372–3382. doi: 10.1016/j.eswa.2010.08.122. [ CrossRef ] [ Google Scholar ]
• Joo YJ, Lim KY, Park SY. Investigating the structural relationships among organisational support, learning flow, learners’ satisfaction and learning transfer in corporate e-learning. British Journal of Educational Technology. 2011; 42 (6):973–984. doi: 10.1111/j.1467-8535.2010.01116.x. [ CrossRef ] [ Google Scholar ]
• Kaschig, A., Maier, R., Sandow, A., Lazoi, M., Barnes, S. A., Bimrose, J., … Schmidt, A. (2010). Knowledge maturing activities and practices fostering organisational learning: results of an empirical study. In European Conference on Technology Enhanced Learning (pp. 151–166). Berlin: Springer.
• Khalili A, Auer S, Tarasowa D, Ermilov I. International Conference on Knowledge Engineering and Knowledge Management. Berlin: Springer; 2012. SlideWiki: Elicitation and sharing of corporate knowledge using presentations; pp. 302–316. [ Google Scholar ]
• Khandakar MSA, Pangil F. Relationship between human resource management practices and informal workplace learning. Journal of Workplace Learning. 2019; 31 (8):551–576. doi: 10.1108/JWL-04-2019-0049. [ CrossRef ] [ Google Scholar ]
• Kim MK, Kim SM, Bilir MK. Investigation of the dimensions of workplace learning environments (WLEs): Development of the WLE measure. Performance Improvement Quarterly. 2014; 27 (2):35–57. doi: 10.1002/piq.21170. [ CrossRef ] [ Google Scholar ]
• Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01, 2007 . https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=35909B1B280E2032BF116BDC9DCB71EA? .
• Krippendorff, K. (2018). Content analysis: an introduction to its methodology. Thousand Oaks: Sage Publications.
• Lai HJ. Examining civil servants’ decisions to use Web 2.0 tools for learning, based on the decomposed theory of planned behavior. Interactive Learning Environments. 2017; 25 (3):295–305. doi: 10.1080/10494820.2015.1121879. [ CrossRef ] [ Google Scholar ]
• Lau K. Organizational learning goes virtual? A study of employees’ learning achievement in stereoscopic 3D virtual reality. The Learning Organization. 2015; 22 (5):289–303. doi: 10.1108/TLO-11-2014-0063. [ CrossRef ] [ Google Scholar ]
• Lee J, Choi M, Lee H. Factors affecting smart learning adoption in workplaces: Comparing large enterprises and SMEs. Information Technology and Management. 2015; 16 (4):291–302. doi: 10.1007/s10799-014-0201-5. [ CrossRef ] [ Google Scholar ]
• Lee J, Kim DW, Zo H. Conjoint analysis on preferences of HRD managers and employees for effective implementation of m-learning: The case of South Korea. Telematics and Informatics. 2015; 32 (4):940–948. doi: 10.1016/j.tele.2015.04.010. [ CrossRef ] [ Google Scholar ]
• Lee J, Zo H, Lee H. Smart learning adoption in employees and HRD managers. British Journal of Educational Technology. 2014; 45 (6):1082–1096. doi: 10.1111/bjet.12210. [ CrossRef ] [ Google Scholar ]
• Lin CH, Sanders K. HRM and innovation: A multi-level organizational learning perspective. Human Resource Management Journal. 2017; 27 (2):300–317. doi: 10.1111/1748-8583.12127. [ CrossRef ] [ Google Scholar ]
• Lin CY, Huang CK, Zhang H. Enhancing employee job satisfaction via e-learning: The mediating role of an organizational learning culture. International Journal of Human–Computer Interaction. 2019; 35 (7):584–595. doi: 10.1080/10447318.2018.1480694. [ CrossRef ] [ Google Scholar ]
• Liu YC, Huang YA, Lin C. Organizational factors’ effects on the success of e-learning systems and organizational benefits: An empirical study in Taiwan. The International Review of Research in Open and Distributed Learning. 2012; 13 (4):130–151. doi: 10.19173/irrodl.v13i4.1203. [ CrossRef ] [ Google Scholar ]
• López-Nicolás C, Meroño-Cerdán ÁL. Strategic knowledge management, innovation and performance. International Journal of Information Management. 2011; 31 (6):502–509. doi: 10.1016/j.ijinfomgt.2011.02.003. [ CrossRef ] [ Google Scholar ]
• Manuti A, Pastore S, Scardigno AF, Giancaspro ML, Morciano D. Formal and informal learning in the workplace: A research review. International Journal of Training and Development. 2015; 19 (1):1–17. doi: 10.1111/ijtd.12044. [ CrossRef ] [ Google Scholar ]
• March JG. Exploration and exploitation in organizational learning. Organization Science. 1991; 2 (1):71–87. doi: 10.1287/orsc.2.1.71. [ CrossRef ] [ Google Scholar ]
• Marshall S. New Zealand Tertiary Institution E-learning Capability: Informing and Guiding eLearning Architectural Change and Development. Report to the ministry of education. NZ: Victoria University of Wellington; 2006. [ Google Scholar ]
• McDonald, N., Schoenebeck, S., & Forte, A. (2019). Reliability and inter-rater reliability in qualitative research: Norms and guidelines for CSCW and HCI practice. In Proceedings of the ACM on Human–Computer Interaction, 3(CSCW) (pp. 1–23).
• Menolli, A., Tirone, H., Reinehr, S., & Malucelli, A. (2020). Identifying organisational learning needs: An approach to the semi-automatic creation of course structures for software companies. Behaviour & Information Technology, 39 (11), 1140–1155.
• Michalski MP. Symbolic meanings and e-learning in the workplace: The case of an intranet-based training tool. Management Learning. 2014; 45 (2):145–166. doi: 10.1177/1350507612468419. [ CrossRef ] [ Google Scholar ]
• Mikalef P, Pappas IO, Krogstie J, Giannakos M. Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management. 2018; 16 (3):547–578. doi: 10.1007/s10257-017-0362-y. [ CrossRef ] [ Google Scholar ]
• Mitić S, Nikolić M, Jankov J, Vukonjanski J, Terek E. The impact of information technologies on communication satisfaction and organizational learning in companies in Serbia. Computers in Human Behavior. 2017; 76 :87–101. doi: 10.1016/j.chb.2017.07.012. [ CrossRef ] [ Google Scholar ]
• Mueller J, Hutter K, Fueller J, Matzler K. Virtual worlds as knowledge management platform—A practice-perspective. Information Systems Journal. 2011; 21 (6):479–501. doi: 10.1111/j.1365-2575.2010.00366.x. [ CrossRef ] [ Google Scholar ]
• Muller Queiroz, A. C., Nascimento, M., Tori, A., Alejandro, R. Brashear, Veloso, T., de Melo, V., de Souza Meirelles, F., & da Silva Leme, M. I. (2018). Immersive virtual environments in corporate education and training. In AMCIS. https://aisel.aisnet.org/amcis2018/Education/Presentations/12/ .
• Navimipour NJ, Zareie B. A model for assessing the impact of e-learning systems on employees’ satisfaction. Computers in Human Behavior. 2015; 53 :475–485. doi: 10.1016/j.chb.2015.07.026. [ CrossRef ] [ Google Scholar ]
• Oh SY. Effects of organizational learning on performance: The moderating roles of trust in leaders and organizational justice. Journal of Knowledge Management. 2019; 23 :313–331. doi: 10.1108/JKM-02-2018-0087. [ CrossRef ] [ Google Scholar ]
• Okoli, C., & Schabram, K. (2010). A guide to conducting a systematic literature review of information systems research. Sprouts: Working Papers on Information Systems, 10 (26), 1–46.
• Pappas IO, Mikalef P, Giannakos MN, Krogstie J, Lekakos G. Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management. 2018; 16 :479–491. doi: 10.1007/s10257-018-0377-z. [ CrossRef ] [ Google Scholar ]
• Popova-Nowak IV, Cseh M. The meaning of organizational learning: A meta-paradigm perspective. Human Resource Development Review. 2015; 14 (3):299–331. doi: 10.1177/1534484315596856. [ CrossRef ] [ Google Scholar ]
• Qi, C., & Chau, P. Y. (2016). An empirical study of the effect of enterprise social media usage on organizational learning. In Pacific Asia Conference on Information Systems (PACIS'16). Proceedings , Paper 330. http://aisel.aisnet.org/pacis2016/330 .
• Renner B, Wesiak G, Pammer-Schindler V, Prilla M, Müller L, Morosini D, Cress U. Computer-supported reflective learning: How apps can foster reflection at work. Behaviour & Information Technology. 2020; 39 (2):167–187. doi: 10.1080/0144929X.2019.1595726. [ CrossRef ] [ Google Scholar ]
• Rober, M. B., & Cooper, L. P. (2011, January). Capturing knowledge via an” Intrapedia”: A case study. In 2011 44th Hawaii International Conference on System Sciences (pp. 1–10). New York: IEEE.
• Rosenberg MJ, Foshay R. E-learning: Strategies for delivering knowledge in the digital age. Performance Improvement. 2002; 41 (5):50–51. doi: 10.1002/pfi.4140410512. [ CrossRef ] [ Google Scholar ]
• Serrano, Á., Marchiori, E. J., del Blanco, Á., Torrente, J., & Fernández-Manjón, B. (2012). A framework to improve evaluation in educational games. The IEEE Global Engineering Education Conference (pp. 1–8). Marrakesh, Morocco.
• Siadaty, M., Jovanović, J., Gašević, D., Jeremić, Z., & Holocher-Ertl, T. (2010). Leveraging semantic technologies for harmonization of individual and organizational learning. In European Conference on Technology Enhanced Learning (pp. 340–356). Berlin: Springer.
• Siemens G, Long P. Penetrating the fog: Analytics in learning and education. EDUCAUSE Review. 2011; 46 (5):30. [ Google Scholar ]
• Škerlavaj M, Dimovski V, Mrvar A, Pahor M. Intra-organizational learning networks within knowledge-intensive learning environments. Interactive Learning Environments. 2010; 18 (1):39–63. doi: 10.1080/10494820802190374. [ CrossRef ] [ Google Scholar ]
• Smith PJ, Sadler-Smith E. Learning in organizations: Complexities and diversities. London: Routledge; 2006. [ Google Scholar ]
• Stoffregen JD, Pawlowski JM, Ras E, Tobias E, Šćepanović S, Fitzpatrick D, Friedrich H. Barriers to open e-learning in public administrations: A comparative case study of the European countries Luxembourg, Germany, Montenegro and Ireland. Technological Forecasting and Social Change. 2016; 111 :198–208. doi: 10.1016/j.techfore.2016.06.030. [ CrossRef ] [ Google Scholar ]
• Subramaniam, R., & Nakkeeran, S. (2019). Impact of corporate e-learning systems in enhancing the team performance in virtual software teams. In Smart Technologies and Innovation for a Sustainable Future (pp. 195–204). Berlin: Springer.
• Tsai CH, Zhu DS, Ho BCT, Wu DD. The effect of reducing risk and improving personal motivation on the adoption of knowledge repository system. Technological Forecasting and Social Change. 2010; 77 (6):840–856. doi: 10.1016/j.techfore.2010.01.011. [ CrossRef ] [ Google Scholar ]
• Turi JA, Sorooshian S, Javed Y. Impact of the cognitive learning factors on sustainable organizational development. Heliyon. 2019; 5 (9):e02398. doi: 10.1016/j.heliyon.2019.e02398. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang, M. (2011). Integrating organizational, social, and individual perspectives in Web 2.0-based workplace e-learning. Information Systems Frontiers, 13 (2), 191–205.
• Wang, M. (2018). Effects of individual and social learning support on employees’ acceptance of performance-oriented e-learning. In E-Learning in the Workplace (pp. 141–159). Springer. 10.1007/978-3-319-64532-2_13.
• Wang M, Ran W, Liao J, Yang SJ. A performance-oriented approach to e-learning in the workplace. Journal of Educational Technology & Society. 2010; 13 (4):167–179. [ Google Scholar ]
• Wang M, Vogel D, Ran W. Creating a performance-oriented e-learning environment: A design science approach. Information & Management. 2011; 48 (7):260–269. doi: 10.1016/j.im.2011.06.003. [ CrossRef ] [ Google Scholar ]
• Wang N, Liang H, Zhong W, Xue Y, Xiao J. Resource structuring or capability building? An empirical study of the business value of information technology. Journal of Management Information Systems. 2012; 29 (2):325–367. doi: 10.2753/MIS0742-1222290211. [ CrossRef ] [ Google Scholar ]
• Wang S, Wang H. Organizational schemata of e-portfolios for fostering higher-order thinking. Information Systems Frontiers. 2012; 14 (2):395–407. doi: 10.1007/s10796-010-9262-0. [ CrossRef ] [ Google Scholar ]
• Wei K, Ram J. Perceived usefulness of podcasting in organizational learning: The role of information characteristics. Computers in Human Behavior. 2016; 64 :859–870. doi: 10.1016/j.chb.2016.08.003. [ CrossRef ] [ Google Scholar ]
• Wei, K., Sun, H., & Li, H. (2013). On the driving forces of diffusion of podcasting in organizational settings: A case study and propositions. In PACIS 2013. Proceedings , 217. http://aisel.aisnet.org/pacis2013/217 .
• Weinhardt, J. M., & Sitzmann, T. (2018). Revolutionizing training and education? Three questions regarding massive open online courses (MOOCs). Human Resource Management Review, 29 (2), 218–225.
• Xiang, Q., Zhang, J., & Liu, H. (2020). Organisational improvisation as a path to new opportunity identification for incumbent firms: An organisational learning view. Innovation, 22 (4), 422–446. 10.1080/14479338.2020.1713001.
• Yanson R, Johnson RD. An empirical examination of e-learning design: The role of trainee socialization and complexity in short term training. Computers & Education. 2016; 101 :43–54. doi: 10.1016/j.compedu.2016.05.010. [ CrossRef ] [ Google Scholar ]
• Yoo SJ, Huang WD. Can e-learning system enhance learning culture in the workplace? A comparison among companies in South Korea. British Journal of Educational Technology. 2016; 47 (4):575–591. doi: 10.1111/bjet.12240. [ CrossRef ] [ Google Scholar ]
• Zhang X, Jiang S, Ordóñez de Pablos P, Lytras MD, Sun Y. How virtual reality affects perceived learning effectiveness: A task–technology fit perspective. Behaviour & Information Technology. 2017; 36 (5):548–556. doi: 10.1080/0144929X.2016.1268647. [ CrossRef ] [ Google Scholar ]
• Zhang X, Meng Y, de Pablos PO, Sun Y. Learning analytics in collaborative learning supported by Slack: From the perspective of engagement. Computers in Human Behavior. 2019; 92 :625–633. doi: 10.1016/j.chb.2017.08.012. [ CrossRef ] [ Google Scholar ]

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Organizational learning in government sector organizations: Literature review

PPG undertakes pure and applied research, policy evaluation and consultancy for government bodies, international organizations and major corporations active in the fields of policy evaluation, public management, budgeting and audit, and egovernment, survey or focus group research, public opinion, and the design of election systems.

Related Papers

Jason Ferdinand

Summary Although there are a number of overlaps between the literatures described above, there are also distinct traditions, as summarised in Table 1. Naturally the primary driver of the most scholars in each area is to advance the area, and they have therefore been less interested in the potential relevance and relationship with dynamic capabilities. However, by juxtaposing the traditions we hope to achieve three main purposes. First, to show that there are ideas in each of the other fields which may contribute to the richness of our understanding of dynamic capabilities; second to identify common themes, issues and gaps which run across all areas of literature; and third to enable us to test out the applicability of different perspectives on dynamic capabilities in the light of specific case studies. We tackle the latter issue in the next part of the paper, starting with a brief description of our methodology, and we then return towards the end of the paper to reconsider the first...

Journal of Knowledge Management

Luisa Fernanda Manrique Molina

Implementation Science

Lindsay Pennington

Social science & medicine (1982)

Ewan Ferlie

Andrew Schuster

International Research Society on Public Management Panel H8

Wulandari's thesis

Puspita Wulandari

This study is about organizational learning in a public service organization in Indonesia. This study focuses on examination of learning processes in that organization to obtain a rounded understanding of organizational learning within organization. An examination of learning processes enables this study to identify the influence on learning of internal and external organizational environments or context. For this reason, this study borrows from Bandura's social cognitive theory to inform the analysis.

Jean Hartley

Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online&#39;s data policy on reuse of materials please consult the policies page. This paper is a systematic review of the literature on organizational learning and knowledge with relevance to public service organizations. Organizational learning and knowledge are important to public sector organizations, which share complex external challenges with private organizations, but have different drivers and goals for knowledge. The evidence shows that the concepts of organizational learning and knowledge are under-researched in relation to the public sector and, importantly, this raises wider questions about the extent to which context is taken into consideration in terms of learning and knowledge more generally across all sectors. A dynamic model of organizational learning within and across organizational boundaries is develo...

International Journal of Management Reviews

Erin Withers , Lyndsay Rashman , Jean Hartley

Robyn Keast

Ngonidzashe Chipato

RELATED PAPERS

Susan Trenholm

International Journal of Public Administration

Nancy Beauregard

Management Learning

Elena Antonacopoulou

Hendrik Snyders

Rethinking Knowledge Management

Mark Aakhus

Dr Jean Ledger

THE PASSION FOR LEARNING AND KNOWING

Hannele Kerosuo

Luis E Sánchez , Ross Mitchell

André Sobczak

International Journal of …

Mary Hardie

Rod Sheaff , David Pilgrim

Don Scott-Kemmis

International Journal of Contemporary Management

Malgorzata Rozkwitalska-Welenc

Bernardo Peixoto

Public Administration

Lyndsay Rashman

Anders Örtenblad

American Journal of Industrial and Business Management

Said Al Hasan

Kevin Morrell

Mohamed Buheji

Rajiv Maher

Breno Nunes

madhu ranjan kumar

British Journal of Management

Robin S . Snell

Vera Ndrecaj

Nicolas Dechy

Abhipsa Sahu

Patrick S . W . Fong

Slovenian Entreprenurship Observatory

Karin Širec

Arvind Upadhyay

Nonette Febre

Sociology Compass

Catherine Casey

Massimo Tomassini

Journal of Manufacturing Technology Management

Nicholas Theodorakopoulos

Catalysis Today

Francesco Dalena

The International Journal of Entrepreneurship and Innovation

monique lortie

Knowledge Management International Conference (KMICe2004) – Penang, Malaysia

Dr. Graeme C M Smith , Graham Orange

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• Open access
• Published: 23 April 2024

Designing feedback processes in the workplace-based learning of undergraduate health professions education: a scoping review

• Javiera Fuentes-Cimma 1 , 2 ,
• Dominique Sluijsmans 3 ,
• Arnoldo Riquelme 4 ,
• Ignacio Villagran   ORCID: orcid.org/0000-0003-3130-8326 1 ,
• Lorena Isbej   ORCID: orcid.org/0000-0002-4272-8484 2 , 5 ,
• María Teresa Olivares-Labbe 6 &
• Sylvia Heeneman 7  

BMC Medical Education volume  24 , Article number:  440 ( 2024 ) Cite this article

Metrics details

Feedback processes are crucial for learning, guiding improvement, and enhancing performance. In workplace-based learning settings, diverse teaching and assessment activities are advocated to be designed and implemented, generating feedback that students use, with proper guidance, to close the gap between current and desired performance levels. Since productive feedback processes rely on observed information regarding a student's performance, it is imperative to establish structured feedback activities within undergraduate workplace-based learning settings. However, these settings are characterized by their unpredictable nature, which can either promote learning or present challenges in offering structured learning opportunities for students. This scoping review maps literature on how feedback processes are organised in undergraduate clinical workplace-based learning settings, providing insight into the design and use of feedback.

A scoping review was conducted. Studies were identified from seven databases and ten relevant journals in medical education. The screening process was performed independently in duplicate with the support of the StArt program. Data were organized in a data chart and analyzed using thematic analysis. The feedback loop with a sociocultural perspective was used as a theoretical framework.

The search yielded 4,877 papers, and 61 were included in the review. Two themes were identified in the qualitative analysis: (1) The organization of the feedback processes in workplace-based learning settings, and (2) Sociocultural factors influencing the organization of feedback processes. The literature describes multiple teaching and assessment activities that generate feedback information. Most papers described experiences and perceptions of diverse teaching and assessment feedback activities. Few studies described how feedback processes improve performance. Sociocultural factors such as establishing a feedback culture, enabling stable and trustworthy relationships, and enhancing student feedback agency are crucial for productive feedback processes.

Conclusions

This review identified concrete ideas regarding how feedback could be organized within the clinical workplace to promote feedback processes. The feedback encounter should be organized to allow follow-up of the feedback, i.e., working on required learning and performance goals at the next occasion. The educational programs should design feedback processes by appropriately planning subsequent tasks and activities. More insight is needed in designing a full-loop feedback process, in which specific attention is needed in effective feedforward practices.

Peer Review reports

The design of effective feedback processes in higher education has been important for educators and researchers and has prompted numerous publications discussing potential mechanisms, theoretical frameworks, and best practice examples over the past few decades. Initially, research on feedback primarily focused more on teachers and feedback delivery, and students were depicted as passive feedback recipients [ 1 , 2 , 3 ]. The feedback conversation has recently evolved to a more dynamic emphasis on interaction, sense-making, outcomes in actions, and engagement with learners [ 2 ]. This shift aligns with utilizing the feedback process as a form of social interaction or dialogue to enhance performance [ 4 ]. Henderson et al. (2019) defined feedback processes as "where the learner makes sense of performance-relevant information to promote their learning." (p. 17). When a student grasps the information concerning their performance in connection to the desired learning outcome and subsequently takes suitable action, a feedback loop is closed so the process can be regarded as successful [ 5 , 6 ].

Hattie and Timperley (2007) proposed a comprehensive perspective on feedback, the so-called feedback loop, to answer three key questions: “Where am I going? “How am I going?” and “Where to next?” [ 7 ]. Each question represents a key dimension of the feedback loop. The first is the feed-up, which consists of setting learning goals and sharing clear objectives of learners' performance expectations. While the concept of the feed-up might not be consistently included in the literature, it is considered to be related to principles of effective feedback and goal setting within educational contexts [ 7 , 8 ]. Goal setting allows students to focus on tasks and learning, and teachers to have clear intended learning outcomes to enable the design of aligned activities and tasks in which feedback processes can be embedded [ 9 ]. Teachers can improve the feed-up dimension by proposing clear, challenging, but achievable goals [ 7 ]. The second dimension of the feedback loop focuses on feedback and aims to answer the second question by obtaining information about students' current performance. Different teaching and assessment activities can be used to obtain feedback information, and it can be provided by a teacher or tutor, a peer, oneself, a patient, or another coworker. The last dimension of the feedback loop is the feedforward, which is specifically associated with using feedback to improve performance or change behaviors [ 10 ]. Feedforward is crucial in closing the loop because it refers to those specific actions students must take to reduce the gap between current and desired performance [ 7 ].

From a sociocultural perspective, feedback processes involve a social practice consisting of intricate relationships within a learning context [ 11 ]. The main feature of this approach is that students learn from feedback only when the feedback encounter includes generating, making sense of, and acting upon the information given [ 11 ]. In the context of workplace-based learning (WBL), actionable feedback plays a crucial role in enabling learners to leverage specific feedback to enhance their performance, skills, and conceptual understandings. The WBL environment provides students with a valuable opportunity to gain hands-on experience in authentic clinical settings, in which students work more independently on real-world tasks, allowing them to develop and exhibit their competencies [ 3 ]. However, WBL settings are characterized by their unpredictable nature, which can either promote self-directed learning or present challenges in offering structured learning opportunities for students [ 12 ]. Consequently, designing purposive feedback opportunities within WBL settings is a significant challenge for clinical teachers and faculty.

In undergraduate clinical education, feedback opportunities are often constrained due to the emphasis on clinical work and the absence of dedicated time for teaching [ 13 ]. Students are expected to perform autonomously under supervision, ideally achieved by giving them space to practice progressively and providing continuous instances of constructive feedback [ 14 ]. However, the hierarchy often present in clinical settings places undergraduate students in a dependent position, below residents and specialists [ 15 ]. Undergraduate or junior students may have different approaches to receiving and using feedback. If their priority is meeting the minimum standards given pass-fail consequences and acting merely as feedback recipients, other incentives may be needed to engage with the feedback processes because they will need more learning support [ 16 , 17 ]. Adequate supervision and feedback have been recognized as vital educational support in encouraging students to adopt a constructive learning approach [ 18 ]. Given that productive feedback processes rely on observed information regarding a student's performance, it is imperative to establish structured teaching and learning feedback activities within undergraduate WBL settings.

Despite the extensive research on feedback, a significant proportion of published studies involve residents or postgraduate students [ 19 , 20 ]. Recent reviews focusing on feedback interventions within medical education have clearly distinguished between undergraduate medical students and residents or fellows [ 21 ]. To gain a comprehensive understanding of initiatives related to actionable feedback in the WBL environment for undergraduate health professions, a scoping review of the existing literature could provide insight into how feedback processes are designed in that context. Accordingly, the present scoping review aims to answer the following research question: How are the feedback processes designed in the undergraduate health professions' workplace-based learning environments?

A scoping review was conducted using the five-step methodological framework proposed by Arksey and O'Malley (2005) [ 22 ], intertwined with the PRISMA checklist extension for scoping reviews to provide reporting guidance for this specific type of knowledge synthesis [ 23 ]. Scoping reviews allow us to study the literature without restricting the methodological quality of the studies found, systematically and comprehensively map the literature, and identify gaps [ 24 ]. Furthermore, a scoping review was used because this topic is not suitable for a systematic review due to the varied approaches described and the large difference in the methodologies used [ 21 ].

Search strategy

With the collaboration of a medical librarian, the authors used the research question to guide the search strategy. An initial meeting was held to define keywords and search resources. The proposed search strategy was reviewed by the research team, and then the study selection was conducted in two steps:

An online database search included Medline/PubMed, Web of Science, CINAHL, Cochrane Library, Embase, ERIC, and PsycINFO.

A directed search of ten relevant journals in the health sciences education field (Academic Medicine, Medical Education, Advances in Health Sciences Education, Medical Teacher, Teaching and Learning in Medicine, Journal of Surgical Education, BMC Medical Education, Medical Education Online, Perspectives on Medical Education and The Clinical Teacher) was performed.

The research team conducted a pilot or initial search before the full search to identify if the topic was susceptible to a scoping review. The full search was conducted in November 2022. One team member (MO) identified the papers in the databases. JF searched in the selected journals. Authors included studies written in English due to feasibility issues, with no time span limitation. After eliminating duplicates, two research team members (JF and IV) independently reviewed all the titles and abstracts using the exclusion and inclusion criteria described in Table  2 and with the support of the screening application StArT [ 25 ]. A third team member (AR) reviewed the titles and abstracts when the first two disagreed. The reviewer team met again at a midpoint and final stage to discuss the challenges related to study selection. Articles included for full-text review were exported to Mendeley. JF independently screened all full-text papers, and AR verified 10% for inclusion. The authors did not analyze study quality or risk of bias during study selection, which is consistent with conducting a scoping review.

The analysis of the results incorporated a descriptive summary and a thematic analysis, which was carried out to clarify and give consistency to the results' reporting [ 22 , 24 , 26 ]. Quantitative data were analyzed to report the characteristics of the studies, populations, settings, methods, and outcomes. Qualitative data were labeled, coded, and categorized into themes by three team members (JF, SH, and DS). The feedback loop framework with a sociocultural perspective was used as the theoretical framework to analyze the results.

The keywords used for the search strategies were as follows:

Clinical clerkship; feedback; formative feedback; health professions; undergraduate medical education; workplace.

Definitions of the keywords used for the present review are available in Appendix 1 .

As an example, we included the search strategy that we used in the Medline/PubMed database when conducting the full search:

("Formative Feedback"[Mesh] OR feedback) AND ("Workplace"[Mesh] OR workplace OR "Clinical Clerkship"[Mesh] OR clerkship) AND (("Education, Medical, Undergraduate"[Mesh] OR undergraduate health profession*) OR (learner* medical education)).

Inclusion and exclusion criteria

The following inclusion and exclusion criteria were used (Table  1 ):

Data extraction

The research group developed a data-charting form to organize the information obtained from the studies. The process was iterative, as the data chart was continuously reviewed and improved as necessary. In addition, following Levac et al.'s recommendation (2010), the three members involved in the charting process (JF, LI, and IV) independently reviewed the first five selected studies to determine whether the data extraction was consistent with the objectives of this scoping review and to ensure consistency. Then, the team met using web-conferencing software (Zoom; CA, USA) to review the results and adjust any details in the chart. The same three members extracted data independently from all the selected studies, considering two members reviewing each paper [ 26 ]. A third team member was consulted if any conflict occurred when extracting data. The data chart identified demographic patterns and facilitated the data synthesis. To organize data, we used a shared Excel spreadsheet, considering the following headings: title, author(s), year of publication, journal/source, country/origin, aim of the study, research question (if any), population/sample size, participants, discipline, setting, methodology, study design, data collection, data analysis, intervention, outcomes, outcomes measure, key findings, and relation of findings to research question.

Additionally, all the included papers were uploaded to AtlasTi v19 to facilitate the qualitative analysis. Three team members (JF, SH, and DS) independently coded the first six papers to create a list of codes to ensure consistency and rigor. The group met several times to discuss and refine the list of codes. Then, one member of the team (JF) used the code list to code all the rest of the papers. Once all papers were coded, the team organized codes into descriptive themes aligned with the research question.

Preliminary results were shared with a number of stakeholders (six clinical teachers, ten students, six medical educators) to elicit their opinions as an opportunity to build on the evidence and offer a greater level of meaning, content expertise, and perspective to the preliminary findings [ 26 ]. No quality appraisal of the studies is considered for this scoping review, which aligns with the frameworks for guiding scoping reviews [ 27 ].

The datasets analyzed during the current study are available from the corresponding author upon request.

A database search resulted in 3,597 papers, and the directed search of the most relevant journals in the health sciences education field yielded 2,096 titles. An example of the results of one database is available in Appendix 2 . Of the titles obtained, 816 duplicates were eliminated, and the team reviewed the titles and abstracts of 4,877 papers. Of these, 120 were selected for full-text review. Finally, 61 papers were included in this scoping review (Fig.  1 ), as listed in Table  2 .

PRISMA flow diagram for included studies, incorporating records identified through the database and direct searching

The selected studies were published between 1986 and 2022, and seventy-five percent (46) were published during the last decade. Of all the articles included in this review, 13% (8) were literature reviews: one integrative review [ 28 ] and four scoping reviews [ 29 , 30 , 31 , 32 ]. Finally, fifty-three (87%) original or empirical papers were included (i.e., studies that answered a research question or achieved a research purpose through qualitative or quantitative methodologies) [ 15 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ].

Table 2 summarizes the papers included in the present scoping review, and Table  3 describes the characteristics of the included studies.

The thematic analysis resulted in two themes: (1) the organization of feedback processes in WBL settings, and (2) sociocultural factors influencing the organization of feedback processes. Table 4 gives a summary of the themes and subthemes.

Organization of feedback processes in WBL settings.

Setting learning goals (i.e., feed-up dimension).

Feedback that focuses on students' learning needs and is based on known performance standards enhances student response and setting learning goals [ 30 ]. Discussing goals and agreements before starting clinical practice enhances students' feedback-seeking behavior [ 39 ] and responsiveness to feedback [ 83 ]. Farrell et al. (2017) found that teacher-learner co-constructed learning goals enhance feedback interactions and help establish educational alliances, improving the learning experience [ 50 ]. However, Kiger (2020) found that sharing individualized learning plans with teachers aligned feedback with learning goals but did not improve students' perceived use of feedback [ 64 ]

Two papers of this set pointed out the importance of goal-oriented feedback, a dynamic process that depends on discussion of goal setting between teachers and students [ 50 ] and influences how individuals experience, approach, and respond to upcoming learning activities [ 34 ]. Goal-oriented feedback should be embedded in the learning experience of the clinical workplace, as it can enhance students' engagement in safe feedback dialogues [ 50 ]. Ideally, each feedback encounter in the WBL context should conclude, in addition to setting a plan of action to achieve the desired goal, with a reflection on the next goal [ 50 ].

Feedback strategies within the WBL environment. (i.e., feedback dimension)

In undergraduate WBL environments, there are several tasks and feedback opportunities organized in the undergraduate clinical workplace that can enable feedback processes:

Questions from clinical teachers to students are a feedback strategy [ 74 ]. There are different types of questions that the teacher can use, either to clarify concepts, to reach the correct answer, or to facilitate self-correction [ 74 ]. Usually, questions can be used in conjunction with other communication strategies, such as pauses, which enable self-correction by the student [ 74 ]. Students can also ask questions to obtain feedback on their performance [ 54 ]. However, question-and-answer as a feedback strategy usually provides information on either correct or incorrect answers and fewer suggestions for improvement, rendering it less constructive as a feedback strategy [ 82 ].

Direct observation of performance by default is needed to be able to provide information to be used as input in the feedback process [ 33 , 46 , 49 , 86 ]. In the process of observation, teachers can include clarification of objectives (i.e., feed-up dimension) and suggestions for an action plan (i.e., feedforward) [ 50 ]. Accordingly, Schopper et al. (2016) showed that students valued being observed while interviewing patients, as they received feedback that helped them become more efficient and effective as interviewers and communicators [ 33 ]. Moreover, it is widely described that direct observation improves feedback credibility [ 33 , 40 , 84 ]. Ideally, observation should be deliberate [ 33 , 83 ], informal or spontaneous [ 33 ], conducted by a (clinical) expert [ 46 , 86 ], provided immediately after the observation, and clinical teacher if possible, should schedule or be alert on follow-up observations to promote closing the gap between current and desired performance [ 46 ].

Workplace-based assessments (WBAs), by definition, entail direct observation of performance during authentic task demonstration [ 39 , 46 , 56 , 87 ]. WBAs can significantly impact behavioral change in medical students [ 55 ]. Organizing and designing formative WBAs and embedding these in a feedback dialogue is essential for effective learning [ 31 ].

Summative organization of WBAs is a well described barrier for feedback uptake in the clinical workplace [ 35 , 46 ]. If feedback is perceived as summative, or organized as a pass-fail decision, students may be less inclined to use the feedback for future learning [ 52 ]. According to Schopper et al. (2016), using a scale within a WBA makes students shift their focus during the clinical interaction and see it as an assessment with consequences [ 33 ]. Harrison et al. (2016) pointed out that an environment that only contains assessments with a summative purpose will not lead to a culture of learning and improving performance [ 56 ]. The recommendation is to separate the formative and summative WBAs, as feedback in summative instances is often not recognized as a learning opportunity or an instance to seek feedback [ 54 ]. In terms of the design, an organizational format is needed to clarify to students how formative assessments can promote learning from feedback [ 56 ]. Harrison et al. (2016) identified that enabling students to have more control over their assessments, designing authentic assessments, and facilitating long-term mentoring could improve receptivity to formative assessment feedback [ 56 ].

Multiple WBA instruments and systems are reported in the literature. Sox et al. (2014) used a detailed evaluation form to help students improve their clinical case presentation skills. They found that feedback on oral presentations provided by supervisors using a detailed evaluation form improved clerkship students’ oral presentation skills [ 78 ]. Daelmans et al. (2006) suggested that a formal in-training assessment programme composed by 19 assessments that provided structured feedback, could promote observation and verbal feedback opportunities through frequent assessments [ 43 ]. However, in this setting, limited student-staff interactions still hindered feedback follow-up [ 43 ]. Designing frequent WBA improves feedback credibility [ 28 ]. Long et al. (2021) emphasized that students' responsiveness to assessment feedback hinges on its perceived credibility, underlining the importance of credibility for students to effectively engage and improve their performance [ 31 ].

The mini-CEX is one of the most widely described WBA instruments in the literature. Students perceive that the mini-CEX allows them to be observed and encourages the development of interviewing skills [ 33 ]. The mini-CEX can provide feedback that improves students' clinical skills [ 58 , 60 ], as it incorporates a structure for discussing the student's strengths and weaknesses and the design of a written action plan [ 39 , 80 ]. When mini-CEXs are incorporated as part of a system of WBA, such as programmatic assessment, students feel confident in seeking feedback after observation, and being systematic allows for follow-up [ 39 ]. Students suggested separating grading from observation and using the mini-CEX in more informal situations [ 33 ].

Clinical encounter cards allow students to receive weekly feedback and make them request more feedback as the clerkship progresses [ 65 ]. Moreover, encounter cards stimulate that feedback is given by supervisors, and students are more satisfied with the feedback process [ 72 ]. With encounter card feedback, students are responsible for asking a supervisor for feedback before a clinical encounter, and supervisors give students written and verbal comments about their performance after the encounter [ 42 , 72 ]. Encounter cards enhance the use of feedback and add approximately one minute to the length of the clinical encounter, so they are well accepted by students and supervisors [ 72 ]. Bennett (2006) identified that Instant Feedback Cards (IFC) facilitated mid-rotation feedback [ 38 ]. Feedback encounter card comments must be discussed between students and supervisors; otherwise, students may perceive it as impersonal, static, formulaic, and incomplete [ 59 ].

Self-assessments can change students' feedback orientation, transforming them into coproducers of learning [ 68 ]. Self-assessments promote the feedback process [ 68 ]. Some articles emphasize the importance of organizing self-assessments before receiving feedback from supervisors, for example, discussing their appraisal with the supervisor [ 46 , 52 ]. In designing a feedback encounter, starting with a self-assessment as feed-up, discussing with the supervisor, and identifying areas for improvement is recommended, as part of the feedback dialogue [ 68 ].

Peer feedback as an organized activity allows students to develop strategies to observe and give feedback to other peers [ 61 ]. Students can act as the feedback provider or receiver, fostering understanding of critical comments and promoting evaluative judgment for their clinical practice [ 61 ]. Within clerkships, enabling the sharing of feedback information among peers allows for a better understanding and acceptance of feedback [ 52 ]. However, students can find it challenging to take on the peer assessor/feedback provider role, as they prefer to avoid social conflicts [ 28 , 61 ]. Moreover, it has been described that they do not trust the judgment of their peers because they are not experts, although they know the procedures, tasks, and steps well and empathize with their peer status in the learning process [ 61 ].

Bedside-teaching encounters (BTEs) provide timely feedback and are an opportunity for verbal feedback during performance [ 74 ]. Rizan et al. (2014) explored timely feedback delivered within BTEs and determined that it promotes interaction that constructively enhances learner development through various corrective strategies (e.g., question and answers, pauses, etc.). However, if the feedback given during the BTEs was general, unspecific, or open-ended, it could go unnoticed [ 74 ]. Torre et al. (2005) investigated which integrated feedback activities and clinical tasks occurred on clerkship rotations and assessed students' perceived quality in each teaching encounter [ 81 ]. The feedback activities reported were feedback on written clinical history, physical examination, differential diagnosis, oral case presentation, a daily progress note, and bedside feedback. Students considered all these feedback activities high-quality learning opportunities, but they were more likely to receive feedback when teaching was at the bedside than at other teaching locations [ 81 ].

Case presentations are an opportunity for feedback within WBL contexts [ 67 , 73 ]. However, both students and supervisors struggled to identify them as feedback moments, and they often dismissed questions and clarifications around case presentations as feedback [ 73 ]. Joshi (2017) identified case presentations as a way for students to ask for informal or spontaneous supervisor feedback [ 63 ].

Organization of follow-up feedback and action plans (i.e., feedforward dimension).

Feedback that generates use and response from students is characterized by two-way communication and embedded in a dialogue [ 30 ]. Feedback must be future-focused [ 29 ], and a feedback encounter should be followed by planning the next observation [ 46 , 87 ]. Follow-up feedback could be organized as a future self-assessment, reflective practice by the student, and/or a discussion with the supervisor or coach [ 68 ]. The literature describes that a lack of student interaction with teachers makes follow-up difficult [ 43 ]. According to Haffling et al. (2011), follow-up feedback sessions improve students' satisfaction with feedback compared to students who do not have follow-up sessions. In addition, these same authors reported that a second follow-up session allows verification of improved performances or confirmation that the skill was acquired [ 55 ].

Although feedback encounter forms are a recognized way of obtaining information about performance (i.e., feedback dimension), the literature does not provide many clear examples of how they may impact the feedforward phase. For example, Joshi et al. (2016) consider a feedback form with four fields (i.e., what did you do well, advise the student on what could be done to improve performance, indicate the level of proficiency, and personal details of the tutor). In this case, the supervisor highlighted what the student could improve but not how, which is the missing phase of the co-constructed action plan [ 63 ]. Whichever WBA instrument is used in clerkships to provide feedback, it should include a "next steps" box [ 44 ], and it is recommended to organize a long-term use of the WBA instrument so that those involved get used to it and improve interaction and feedback uptake [ 55 ]. RIME-based feedback (Reporting, Interpreting, Managing, Educating) is considered an interesting example, as it is perceived as helpful to students in knowing what they need to improve in their performance [ 44 ]. Hochberg (2017) implemented formative mid-clerkship assessments to enhance face-to-face feedback conversations and co-create an improvement plan [ 59 ]. Apps for structuring and storing feedback improve the amount of verbal and written feedback. In the study of Joshi et al. (2016), a reasonable proportion of students (64%) perceived that these app tools help them improve their performance during rotations [ 63 ].

Several studies indicate that an action plan as part of the follow-up feedback is essential for performance improvement and learning [ 46 , 55 , 60 ]. An action plan corresponds to an agreed-upon strategy for improving, confirming, or correcting performance. Bing-You et al. (2017) determined that only 12% of the articles included in their scoping review incorporated an action plan for learners [ 32 ]. Holmboe et al. (2004) reported that only 11% of the feedback sessions following a mini-CEX included an action plan [ 60 ]. Suhoyo et al. (2017) also reported that only 55% of mini-CEX encounters contained an action plan [ 80 ]. Other authors reported that action plans are not commonly offered during feedback encounters [ 77 ]. Sokol-Hessner et al. (2010) implemented feedback card comments with a space to provide written feedback and a specific action plan. In their results, 96% contained positive comments, and only 5% contained constructive comments [ 77 ]. In summary, although the recommendation is to include a “next step” box in the feedback instruments, evidence shows these items are not often used for constructive comments or action plans.

Sociocultural factors influencing the organization of feedback processes.

Multiple sociocultural factors influence interaction in feedback encounters, promoting or hampering the productivity of the feedback processes.

Clinical learning culture

Context impacts feedback processes [ 30 , 82 ], and there are barriers to incorporating actionable feedback in the clinical learning context. The clinical learning culture is partly determined by the clinical context, which can be unpredictable [ 29 , 46 , 68 ], as the available patients determine learning opportunities. Supervisors are occupied by a high workload, which results in limited time or priority for teaching [ 35 , 46 , 48 , 55 , 68 , 83 ], hindering students’ feedback-seeking behavior [ 54 ], and creating a challenge for the balance between patient care and student mentoring [ 35 ].

Clinical workplace culture does not always purposefully prioritize instances for feedback processes [ 83 , 84 ]. This often leads to limited direct observation [ 55 , 68 ] and the provision of poorly informed feedback. It is also evident that this affects trust between clinical teachers and students [ 52 ]. Supervisors consider feedback a low priority in clinical contexts [ 35 ] due to low compensation and lack of protected time [ 83 ]. In particular, lack of time appears to be the most significant and well-known barrier to frequent observation and workplace feedback [ 35 , 43 , 48 , 62 , 67 , 83 ].

The clinical environment is hierarchical [ 68 , 80 ] and can make students not consider themselves part of the team and feel like a burden to their supervisor [ 68 ]. This hierarchical learning environment can lead to unidirectional feedback, limit dialogue during feedback processes, and hinder the seeking, uptake, and use of feedback [ 67 , 68 ]. In a learning culture where feedback is not supported, learners are less likely to want to seek it and feel motivated and engaged in their learning [ 83 ]. Furthermore, it has been identified that clinical supervisors lack the motivation to teach [ 48 ] and the intention to observe or reobserve performance [ 86 ].

In summary, the clinical context and WBL culture do not fully use the potential of a feedback process aimed at closing learning gaps. However, concrete actions shown in the literature can be taken to improve the effectiveness of feedback by organizing the learning context. For example, McGinness et al. (2022) identified that students felt more receptive to feedback when working in a safe, nonjudgmental environment [ 67 ]. Moreover, supervisors and trainees identified the learning culture as key to establishing an open feedback dialogue [ 73 ]. Students who perceive culture as supportive and formative can feel more comfortable performing tasks and more willing to receive feedback [ 73 ].

Relationships

There is a consensus in the literature that trusting and long-term relationships improve the chances of actionable feedback. However, relationships between supervisors and students in the clinical workplace are often brief and not organized as more longitudinally [ 68 , 83 ], leaving little time to establish a trustful relationship [ 68 ]. Supervisors change continuously, resulting in short interactions that limit the creation of lasting relationships over time [ 50 , 68 , 83 ]. In some contexts, it is common for a student to have several supervisors who have their own standards in the observation of performance [ 46 , 56 , 68 , 83 ]. A lack of stable relationships results in students having little engagement in feedback [ 68 ]. Furthermore, in case of summative assessment programmes, the dual role of supervisors (i.e., assessing and giving feedback) makes feedback interactions perceived as summative and can complicate the relationship [ 83 ].

Repeatedly, the articles considered in this review describe that long-term and stable relationships enable the development of trust and respect [ 35 , 62 ] and foster feedback-seeking behavior [ 35 , 67 ] and feedback-giver behavior [ 39 ]. Moreover, constructive and positive relationships enhance students´ use of and response to feedback [ 30 ]. For example, Longitudinal Integrated Clerkships (LICs) promote stable relationships, thus enhancing the impact of feedback [ 83 ]. In a long-term trusting relationship, feedback can be straightforward and credible [ 87 ], there are more opportunities for student observation, and the likelihood of follow-up and actionable feedback improves [ 83 ]. Johnson et al. (2020) pointed out that within a clinical teacher-student relationship, the focus must be on establishing psychological safety; thus, the feedback conversations might be transformed [ 62 ].

Stable relationships enhance feedback dialogues, which offer an opportunity to co-construct learning and propose and negotiate aspects of the design of learning strategies [ 62 ].

Students as active agents in the feedback processes

The feedback response learners generate depends on the type of feedback information they receive, how credible the source of feedback information is, the relationship between the receiver and the giver, and the relevance of the information delivered [ 49 ]. Garino (2020) noted that students who are most successful in using feedback are those who do not take criticism personally, who understand what they need to improve and know they can do so, who value and feel meaning in criticism, are not surprised to receive it, and who are motivated to seek new feedback and use effective learning strategies [ 52 ]. Successful users of feedback ask others for help, are intentional about their learning, know what resources to use and when to use them, listen to and understand a message, value advice, and use effective learning strategies. They regulate their emotions, find meaning in the message, and are willing to change [ 52 ].

Student self-efficacy influences the understanding and use of feedback in the clinical workplace. McGinness et al. (2022) described various positive examples of self-efficacy regarding feedback processes: planning feedback meetings with teachers, fostering good relationships with the clinical team, demonstrating interest in assigned tasks, persisting in seeking feedback despite the patient workload, and taking advantage of opportunities for feedback, e.g., case presentations [ 67 ].

When students are encouraged to seek feedback aligned with their own learning objectives, they promote feedback information specific to what they want to learn and improve and enhance the use of feedback [ 53 ]. McGinness et al. (2022) identified that the perceived relevance of feedback information influenced the use of feedback because students were more likely to ask for feedback if they perceived that the information was useful to them. For example, if students feel part of the clinical team and participate in patient care, they are more likely to seek feedback [ 17 ].

Learning-oriented students aim to seek feedback to achieve clinical competence at the expected level [ 75 ]; they focus on improving their knowledge and skills and on professional development [ 17 ]. Performance-oriented students aim not to fail and to avoid negative feedback [ 17 , 75 ].

For effective feedback processes, including feed-up, feedback, and feedforward, the student must be feedback-oriented, i.e., active, seeking, listening to, interpreting, and acting on feedback [ 68 ]. The literature shows that feedback-oriented students are coproducers of learning [ 68 ] and are more involved in the feedback process [ 51 ]. Additionally, students who are metacognitively aware of their learning process are more likely to use feedback to reduce gaps in learning and performance [ 52 ]. For this, students must recognize feedback when it occurs and understand it when they receive it. Thus, it is important to organize training and promote feedback literacy so that students understand what feedback is, act on it, and improve the quality of feedback and their learning plans [ 68 ].

Table 5 summarizes those feedback tasks, activities, and key features of organizational aspects that enable each phase of the feedback loop based on the literature review.

The present scoping review identified 61 papers that mapped the literature on feedback processes in the WBL environments of undergraduate health professions. This review explored how feedback processes are organized in these learning contexts using the feedback loop framework. Given the specific characteristics of feedback processes in undergraduate clinical learning, three main findings were identified on how feedback processes are being conducted in the clinical environment and how these processes could be organized to support feedback processes.

First, the literature lacks a balance between the three dimensions of the feedback loop. In this regard, most of the articles in this review focused on reporting experiences or strategies for delivering feedback information (i.e., feedback dimension). Credible and objective feedback information is based on direct observation [ 46 ] and occurs within an interaction or a dialogue [ 62 , 88 ]. However, only having credible and objective information does not ensure that it will be considered, understood, used, and put into practice by the student [ 89 ].

Feedback-supporting actions aligned with goals and priorities facilitate effective feedback processes [ 89 ] because goal-oriented feedback focuses on students' learning needs [ 7 ]. In contrast, this review showed that only a minority of the studies highlighted the importance of aligning learning objectives and feedback (i.e., the feed-up dimension). To overcome this, supervisors and students must establish goals and agreements before starting clinical practice, as it allows students to measure themselves on a defined basis [ 90 , 91 ] and enhances students' feedback-seeking behavior [ 39 , 92 ] and responsiveness to feedback [ 83 ]. In addition, learning goals should be shared, and co-constructed, through a dialogue [ 50 , 88 , 90 , 92 ]. In fact, relationship-based feedback models emphasize setting shared goals and plans as part of the feedback process [ 68 ].

Many of the studies acknowledge the importance of establishing an action plan and promoting the use of feedback (i.e., feedforward). However, there is yet limited insight on how to best implement strategies that support the use of action plans, improve performance and close learning gaps. In this regard, it is described that delivering feedback without perceiving changes, results in no effect or impact on learning [ 88 ]. To determine if a feedback loop is closed, observing a change in the student's response is necessary. In other words, feedback does not work without repeating the same task [ 68 ], so teachers need to observe subsequent tasks to notice changes [ 88 ]. While feedforward is fundamental to long-term performance, it is shown that more research is needed to determine effective actions to be implemented in the WBL environment to close feedback loops.

Second, there is a need for more knowledge about designing feedback activities in the WBL environment that will generate constructive feedback for learning. WBA is the most frequently reported feedback activity in clinical workplace contexts [ 39 , 46 , 56 , 87 ]. Despite the efforts of some authors to use WBAs as a formative assessment and feedback opportunity, in several studies, a summative component of the WBA was presented as a barrier to actionable feedback [ 33 , 56 ]. Students suggest separating grading from observation and using, for example, the mini-CEX in informal situations [ 33 ]. Several authors also recommend disconnecting the summative components of WBAs to avoid generating emotions that can limit the uptake and use of feedback [ 28 , 93 ]. Other literature recommends purposefully designing a system of assessment using low-stakes data points for feedback and learning. Accordingly, programmatic assessment is a framework that combines both the learning and the decision-making function of assessment [ 94 , 95 ]. Programmatic assessment is a practical approach for implementing low-stakes as a continuum, giving opportunities to close the gap between current and desired performance and having the student as an active agent [ 96 ]. This approach enables the incorporation of low-stakes data points that target student learning [ 93 ] and provide performance-relevant information (i.e., meaningful feedback) based on direct observations during authentic professional activities [ 46 ]. Using low-stakes data points, learners make sense of information about their performance and use it to enhance the quality of their work or performance [ 96 , 97 , 98 ]. Implementing multiple instances of feedback is more effective than providing it once because it promotes closing feedback loops by giving the student opportunities to understand the feedback, make changes, and see if those changes were effective [ 89 ].

Third, the support provided by the teacher is fundamental and should be built into a reliable and long-term relationship, where the teacher must take the role of coach rather than assessor, and students should develop feedback agency and be active in seeking and using feedback to improve performance. Although it is recognized that institutional efforts over the past decades have focused on training teachers to deliver feedback, clinical supervisors' lack of teaching skills is still identified as a barrier to workplace feedback [ 99 ]. In particular, research indicates that clinical teachers lack the skills to transform the information obtained from an observation into constructive feedback [ 100 ]. Students are more likely to use feedback if they consider it credible and constructive [ 93 ] and based on stable relationships [ 93 , 99 , 101 ]. In trusting relationships, feedback can be straightforward and credible, and the likelihood of follow-up and actionable feedback improves [ 83 , 88 ]. Coaching strategies can be enhanced by teachers building an educational alliance that allows for trustworthy relationships or having supervisors with an exclusive coaching role [ 14 , 93 , 102 ].

Last, from a sociocultural perspective, individuals are the main actors in the learning process. Therefore, feedback impacts learning only if students engage and interact with it [ 11 ]. Thus, feedback design and student agency appear to be the main features of effective feedback processes. Accordingly, the present review identified that feedback design is a key feature for effective learning in complex environments such as WBL. Feedback in the workplace must ideally be organized and implemented to align learning outcomes, learning activities, and assessments, allowing learners to learn, practice, and close feedback loops [ 88 ]. To guide students toward performances that reflect long-term learning, an intensive formative learning phase is needed, in which multiple feedback processes are included that shape students´ further learning [ 103 ]. This design would promote student uptake of feedback for subsequent performance [ 1 ].

Strengths and limitations

The strengths of this study are (1) the use of an established framework, the Arksey and O'Malley's framework [ 22 ]. We included the step of socializing the results with stakeholders, which allowed the team to better understand the results from another perspective and offer a realistic look. (2) Using the feedback loop as a theoretical framework strengthened the results and gave a more thorough explanation of the literature regarding feedback processes in the WBL context. (3) our team was diverse and included researchers from different disciplines as well as a librarian.

The present scoping review has several limitations. Although we adhered to the recommended protocols and methodologies, some relevant papers may have been omitted. The research team decided to select original studies and reviews of the literature for the present scoping review. This caused some articles, such as guidelines, perspectives, and narrative papers, to be excluded from the current study.

One of the inclusion criteria was a focus on undergraduate students. However, some papers that incorporated undergraduate and postgraduate participants were included, as these supported the results of this review. Most articles involved medical students. Although the authors did not limit the search to medicine, maybe some articles involving students from other health disciplines needed to be included, considering the search in other databases or journals.

The results give insight in how feedback could be organized within the clinical workplace to promote feedback processes. On a small scale, i.e., in the feedback encounter between a supervisor and a learner, feedback should be organized to allow for follow-up feedback, thus working on required learning and performance goals. On a larger level, i.e., in the clerkship programme or a placement rotation, feedback should be organized through appropriate planning of subsequent tasks and activities.

More insight is needed in designing a closed loop feedback process, in which specific attention is needed in effective feedforward practices. The feedback that stimulates further action and learning requires a safe and trustful work and learning environment. Understanding the relationship between an individual and his or her environment is a challenge for determining the impact of feedback and must be further investigated within clinical WBL environments. Aligning the dimensions of feed-up, feedback and feedforward includes careful attention to teachers’ and students’ feedback literacy to assure that students can act on feedback in a constructive way. In this line, how to develop students' feedback agency within these learning environments needs further research.

Boud D, Molloy E. Rethinking models of feedback for learning: The challenge of design. Assess Eval High Educ. 2013;38:698–712.

Article   Google Scholar  

Henderson M, Ajjawi R, Boud D, Molloy E. Identifying feedback that has impact. In: The Impact of Feedback in Higher Education. Springer International Publishing: Cham; 2019. p. 15–34.

Chapter   Google Scholar  

Winstone N, Carless D. Designing effective feedback processes in higher education: A learning-focused approach. 1st ed. New York: Routledge; 2020.

Google Scholar  

Ajjawi R, Boud D. Assessment & Evaluation in Higher Education Researching feedback dialogue: an interactional analysis approach. 2015. https://doi.org/10.1080/02602938.2015.1102863 .

Carless D. Feedback loops and the longer-term: towards feedback spirals. Assess Eval High Educ. 2019;44:705–14.

Sadler DR. Formative assessment and the design of instructional systems. Instr Sci. 1989;18:119–44.

Hattie J, Timperley H. The Power of Feedback The Meaning of Feedback. Rev Educ Res. 2007;77:81–112.

Zarrinabadi N, Rezazadeh M. Why only feedback? Including feed up and feed forward improves nonlinguistic aspects of L2 writing. Language Teaching Research. 2023;27(3):575–92.

Fisher D, Frey N. Feed up, back, forward. Educ Leadersh. 2009;67:20–5.

Reimann A, Sadler I, Sambell K. What’s in a word? Practices associated with ‘feedforward’ in higher education. Assessment evaluation in higher education. 2019;44:1279–90.

Esterhazy R. Re-conceptualizing Feedback Through a Sociocultural Lens. In: Henderson M, Ajjawi R, Boud D, Molloy E, editors. The Impact of Feedback in Higher Education. Cham: Palgrave Macmillan; 2019. https://doi.org/10.1007/978-3-030-25112-3_5 .

Bransen D, Govaerts MJB, Sluijsmans DMA, Driessen EW. Beyond the self: The role of co-regulation in medical students’ self-regulated learning. Med Educ. 2020;54:234–41.

Ramani S, Könings KD, Ginsburg S, Van Der Vleuten CP. Feedback Redefined: Principles and Practice. J Gen Intern Med. 2019;34:744–53.

Atkinson A, Watling CJ, Brand PL. Feedback and coaching. Eur J Pediatr. 2022;181(2):441–6.

Suhoyo Y, Schonrock-Adema J, Emilia O, Kuks JBM, Cohen-Schotanus JA. Clinical workplace learning: perceived learning value of individual and group feedback in a collectivistic culture. BMC Med Educ. 2018;18:79.

Bowen L, Marshall M, Murdoch-Eaton D. Medical Student Perceptions of Feedback and Feedback Behaviors Within the Context of the “Educational Alliance.” Acad Med. 2017;92:1303–12.

Bok HGJ, Teunissen PW, Spruijt A, Fokkema JPI, van Beukelen P, Jaarsma DADC, et al. Clarifying students’ feedback-seeking behaviour in clinical clerkships. Med Educ. 2013;47:282–91.

Al-Kadri HM, Al-Kadi MT, Van Der Vleuten CPM. Workplace-based assessment and students’ approaches to learning: A qualitative inquiry. Med Teach. 2013;35(SUPPL):1.

Dennis AA, Foy MJ, Monrouxe LV, Rees CE. Exploring trainer and trainee emotional talk in narratives about workplace-based feedback processes. Adv Health Sci Educ. 2018;23:75–93.

Watling C, LaDonna KA, Lingard L, Voyer S, Hatala R. ‘Sometimes the work just needs to be done’: socio-cultural influences on direct observation in medical training. Med Educ. 2016;50:1054–64.

Bing-You R, Hayes V, Varaklis K, Trowbridge R, Kemp H, McKelvy D. Feedback for Learners in Medical Education: What is Known? A Scoping Review Academic Medicine. 2017;92:1346–54.

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med. 2018;169:467–73.

Colquhoun HL, Levac D, O’brien KK, Straus S, Tricco AC, Perrier L, et al. Scoping reviews: time for clarity in definition methods and reporting. J Clin Epidemiol. 2014;67:1291–4.

StArt - State of Art through Systematic Review. 2013.

Levac D, Colquhoun H, O’Brien KK. Scoping studies: Advancing the methodology. Implementation Science. 2010;5:1–9.

Peters MDJ, BPharm CMGHK, Parker PMD, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13:141–6.

Bing-You R, Varaklis K, Hayes V, Trowbridge R, Kemp H, McKelvy D, et al. The Feedback Tango: An Integrative Review and Analysis of the Content of the Teacher-Learner Feedback Exchange. Acad Med. 2018;93:657–63.

Ossenberg C, Henderson A, Mitchell M. What attributes guide best practice for effective feedback? A scoping review. Adv Health Sci Educ. 2019;24:383–401.

Spooner M, Duane C, Uygur J, Smyth E, Marron B, Murphy PJ, et al. Self -regulatory learning theory as a lens on how undergraduate and postgraduate learners respond to feedback: A BEME scoping review: BEME Guide No. 66. Med Teach. 2022;44:3–18.

Long S, Rodriguez C, St-Onge C, Tellier PP, Torabi N, Young M. Factors affecting perceived credibility of assessment in medical education: A scoping review. Adv Health Sci Educ. 2022;27:229–62.

Bing-You R, Hayes V, Varaklis K, Trowbridge R, Kemp H, McKelvy D. Feedback for Learners in Medical Education: What is Known? A Scoping Review: Lippincott Williams and Wilkins; 2017.

Schopper H, Rosenbaum M, Axelson R. “I wish someone watched me interview:” medical student insight into observation and feedback as a method for teaching communication skills during the clinical years. BMC Med Educ. 2016;16:286.

Crommelinck M, Anseel F. Understanding and encouraging feedback-seeking behaviour: a literature review. Med Educ. 2013;47:232–41.

Adamson E, Kinga L, Foy L, McLeodb M, Traynor J, Watson W, et al. Feedback in clinical practice: Enhancing the students’ experience through action research. Nurse Educ Pract. 2018;31:48–53.

Al-Mously N, Nabil NM, Al-Babtain SA, et al. Undergraduate medical students’ perceptions on the quality of feedback received during clinical rotations. Med Teach. 2014;36(Supplement 1):S17-23.

Bates J, Konkin J, Suddards C, Dobson S, Pratt D. Student perceptions of assessment and feedback in longitudinal integrated clerkships. Med Educ. 2013;47:362–74.

Bennett AJ, Goldenhar LM, Stanford K. Utilization of a Formative Evaluation Card in a Psychiatry Clerkship. Acad Psychiatry. 2006;30:319–24.

Bok HG, Jaarsma DA, Spruijt A, Van Beukelen P, Van Der Vleuten CP, Teunissen PW, et al. Feedback-giving behaviour in performance evaluations during clinical clerkships. Med Teach. 2016;38:88–95.

Bok HG, Teunissen PW, Spruijt A, Fokkema JP, van Beukelen P, Jaarsma DA, et al. Clarifying students’ feedback-seeking behaviour in clinical clerkships. Med Educ. 2013;47:282–91.

Calleja P, Harvey T, Fox A, Carmichael M, et al. Feedback and clinical practice improvement: A tool to assist workplace supervisors and students. Nurse Educ Pract. 2016;17:167–73.

Carey EG, Wu C, Hur ES, Hasday SJ, Rosculet NP, Kemp MT, et al. Evaluation of Feedback Systems for the Third-Year Surgical Clerkship. J Surg Educ. 2017;74:787–93.

Daelmans HE, Overmeer RM, Van der Hem-Stokroos HH, Scherpbier AJ, Stehouwer CD, van der Vleuten CP. In-training assessment: qualitative study of effects on supervision and feedback in an undergraduate clinical rotation. Medical education. 2006;40(1):51–8.

DeWitt D, Carline J, Paauw D, Pangaro L. Pilot study of a ’RIME’-based tool for giving feedback in a multi-specialty longitudinal clerkship. Med Educ. 2008;42:1205–9.

Dolan BM, O’Brien CL, Green MM. Including Entrustment Language in an Assessment Form May Improve Constructive Feedback for Student Clinical Skills. Med Sci Educ. 2017;27:461–4.

Duijn CC, Welink LS, Mandoki M, Ten Cate OT, Kremer WD, Bok HG. Am I ready for it? Students’ perceptions of meaningful feedback on entrustable professional activities. Perspectives on medical education. 2017;6:256–64.

Elnicki DM, Zalenski D. Integrating medical students’ goals, self-assessment and preceptor feedback in an ambulatory clerkship. Teach Learn Med. 2013;25:285–91.

Embo MP, Driessen EW, Valcke M, Van der Vleuten CP. Assessment and feedback to facilitate self-directed learning in clinical practice of Midwifery students. Medical teacher. 2010;32(7):e263-9.

Eva KW, Armson H, Holmboe E, Lockyer J, Loney E, Mann K, et al. Factors influencing responsiveness to feedback: On the interplay between fear, confidence, and reasoning processes. Adv Health Sci Educ. 2012;17:15–26.

Farrell L, Bourgeois-Law G, Ajjawi R, Regehr G. An autoethnographic exploration of the use of goal oriented feedback to enhance brief clinical teaching encounters. Adv Health Sci Educ. 2017;22:91–104.

Fernando N, Cleland J, McKenzie H, Cassar K. Identifying the factors that determine feedback given to undergraduate medical students following formative mini-CEX assessments. Med Educ. 2008;42:89–95.

Garino A. Ready, willing and able: a model to explain successful use of feedback. Adv Health Sci Educ. 2020;25:337–61.

Garner MS, Gusberg RJ, Kim AW. The positive effect of immediate feedback on medical student education during the surgical clerkship. J Surg Educ. 2014;71:391–7.

Bing-You R, Hayes V, Palka T, Ford M, Trowbridge R. The Art (and Artifice) of Seeking Feedback: Clerkship Students’ Approaches to Asking for Feedback. Acad Med. 2018;93:1218–26.

Haffling AC, Beckman A, Edgren G. Structured feedback to undergraduate medical students: 3 years’ experience of an assessment tool. Medical teacher. 2011;33(7):e349-57.

Harrison CJ, Könings KD, Dannefer EF, Schuwirth LWTT, Wass V, van der Vleuten CPMM. Factors influencing students’ receptivity to formative feedback emerging from different assessment cultures. Perspect Med Educ. 2016;5:276–84.

Harrison CJ, Könings KD, Schuwirth LW, Wass V, Van der Vleuten CP, Konings KD, et al. Changing the culture of assessment: the dominance of the summative assessment paradigm. BMC medical education. 2017;17:1–4.

Harvey P, Radomski N, O’Connor D. Written feedback and continuity of learning in a geographically distributed medical education program. Medical teacher. 2013;35(12):1009–13.

Hochberg M, Berman R, Ogilvie J, Yingling S, Lee S, Pusic M, et al. Midclerkship feedback in the surgical clerkship: the “Professionalism, Reporting, Interpreting, Managing, Educating, and Procedural Skills” application utilizing learner self-assessment. Am J Surg. 2017;213:212–6.

Holmboe ES, Yepes M, Williams F, Huot SJ. Feedback and the mini clinical evaluation exercise. Journal of general internal medicine. 2004;19(5):558–61.

Tai JHM, Canny BJ, Haines TP, Molloy EK. The role of peer-assisted learning in building evaluative judgement: opportunities in clinical medical education. Adv Health Sci Educ. 2016;21:659–76.

Johnson CE, Keating JL, Molloy EK. Psychological safety in feedback: What does it look like and how can educators work with learners to foster it? Med Educ. 2020;54:559–70.

Joshi A, Generalla J, Thompson B, Haidet P. Facilitating the Feedback Process on a Clinical Clerkship Using a Smartphone Application. Acad Psychiatry. 2017;41:651–5.

Kiger ME, Riley C, Stolfi A, Morrison S, Burke A, Lockspeiser T. Use of Individualized Learning Plans to Facilitate Feedback Among Medical Students. Teach Learn Med. 2020;32:399–409.

Kogan J, Shea J. Implementing feedback cards in core clerkships. Med Educ. 2008;42:1071–9.

Lefroy J, Walters B, Molyneux A, Smithson S. Can learning from workplace feedback be enhanced by reflective writing? A realist evaluation in UK undergraduate medical education. Educ Prim Care. 2021;32:326–35.

McGinness HT, Caldwell PHY, Gunasekera H, Scott KM. ‘Every Human Interaction Requires a Bit of Give and Take’: Medical Students’ Approaches to Pursuing Feedback in the Clinical Setting. Teach Learn Med. 2022. https://doi.org/10.1080/10401334.2022.2084401 .

Noble C, Billett S, Armit L, Collier L, Hilder J, Sly C, et al. ``It’s yours to take{’’}: generating learner feedback literacy in the workplace. Adv Health Sci Educ. 2020;25:55–74.

Ogburn T, Espey E. The R-I-M-E method for evaluation of medical students on an obstetrics and gynecology clerkship. Am J Obstet Gynecol. 2003;189:666–9.

Po O, Reznik M, Greenberg L. Improving a medical student feedback with a clinical encounter card. Ambul Pediatr. 2007;7:449–52.

Parkes J, Abercrombie S, McCarty T, Parkes J, Abercrombie S, McCarty T. Feedback sandwiches affect perceptions but not performance. Adv Health Sci Educ. 2013;18:397–407.

Paukert JL, Richards ML, Olney C. An encounter card system for increasing feedback to students. Am J Surg. 2002;183:300–4.

Rassos J, Melvin LJ, Panisko D, Kulasegaram K, Kuper A. Unearthing Faculty and Trainee Perspectives of Feedback in Internal Medicine: the Oral Case Presentation as a Model. J Gen Intern Med. 2019;34:2107–13.

Rizan C, Elsey C, Lemon T, Grant A, Monrouxe L. Feedback in action within bedside teaching encounters: a video ethnographic study. Med Educ. 2014;48:902–20.

Robertson AC, Fowler LC. Medical student perceptions of learner-initiated feedback using a mobile web application. Journal of medical education and curricular development. 2017;4:2382120517746384.

Scheidt PC, Lazoritz S, Ebbeling WL, Figelman AR, Moessner HF, Singer JE. Evaluation of system providing feedback to students on videotaped patient encounters. J Med Educ. 1986;61(7):585–90.

Sokol-Hessner L, Shea J, Kogan J. The open-ended comment space for action plans on core clerkship students’ encounter cards: what gets written? Acad Med. 2010;85:S110–4.

Sox CM, Dell M, Phillipi CA, Cabral HJ, Vargas G, Lewin LO. Feedback on oral presentations during pediatric clerkships: a randomized controlled trial. Pediatrics. 2014;134:965–71.

Spickard A, Gigante J, Stein G, Denny JC. Automatic capture of student notes to augment mentor feedback and student performance on patient write-ups. J Gen Intern Med. 2008;23:979–84.

Suhoyo Y, Van Hell EA, Kerdijk W, Emilia O, Schönrock-Adema J, Kuks JB, et al. nfluence of feedback characteristics on perceived learning value of feedback in clerkships: does culture matter? BMC medical education. 2017;17:1–7.

Torre DM, Simpson D, Sebastian JL, Elnicki DM. Learning/feedback activities and high-quality teaching: perceptions of third-year medical students during an inpatient rotation. Acad Med. 2005;80:950–4.

Urquhart LM, Ker JS, Rees CE. Exploring the influence of context on feedback at medical school: a video-ethnography study. Adv Health Sci Educ. 2018;23:159–86.

Watling C, Driessen E, van der Vleuten C, Lingard L. Learning culture and feedback: an international study of medical athletes and musicians. Med Educ. 2014;48:713–23.

Watling C, Driessen E, van der Vleuten C, Vanstone M, Lingard L. Beyond individualism: Professional culture and its influence on feedback. Med Educ. 2013;47:585–94.

Soemantri D, Dodds A, Mccoll G. Examining the nature of feedback within the Mini Clinical Evaluation Exercise (Mini-CEX): an analysis of 1427 Mini-CEX assessment forms. GMS J Med Educ. 2018;35:Doc47.

Van De Ridder JMMM, Stokking KM, McGaghie WC, ten Cate OTJ, van der Ridder JM, Stokking KM, et al. What is feedback in clinical education? Med Educ. 2008;42:189–97.

van de Ridder JMMM, McGaghie WC, Stokking KM, ten Cate OTJJ. Variables that affect the process and outcome of feedback, relevant for medical training: a meta-review. Med Educ. 2015;49:658–73.

Boud D. Feedback: ensuring that it leads to enhanced learning. Clin Teach. 2015. https://doi.org/10.1111/tct.12345 .

Brehaut J, Colquhoun H, Eva K, Carrol K, Sales A, Michie S, et al. Practice feedback interventions: 15 suggestions for optimizing effectiveness. Ann Intern Med. 2016;164:435–41.

Ende J. Feedback in clinical medical education. J Am Med Assoc. 1983;250:777–81.

Cantillon P, Sargeant J. Giving feedback in clinical settings. Br Med J. 2008;337(7681):1292–4.

Norcini J, Burch V. Workplace-based assessment as an educational tool: AMEE Guide No. 31. Med Teach. 2007;29:855–71.

Watling CJ, Ginsburg S. Assessment, feedback and the alchemy of learning. Med Educ. 2019;53:76–85.

van der Vleuten CPM, Schuwirth LWT, Driessen EW, Dijkstra J, Tigelaar D, Baartman LKJ, et al. A model for programmatic assessment fit for purpose. Med Teach. 2012;34:205–14.

Schuwirth LWT, der Vleuten CPM. Programmatic assessment: from assessment of learning to assessment for learning. Med Teach. 2011;33:478–85.

Schut S, Driessen E, van Tartwijk J, van der Vleuten C, Heeneman S. Stakes in the eye of the beholder: an international study of learners’ perceptions within programmatic assessment. Med Educ. 2018;52:654–63.

Henderson M, Boud D, Molloy E, Dawson P, Phillips M, Ryan T, Mahoney MP. Feedback for learning. Closing the assessment loop. Framework for effective learning. Canberra, Australia: Australian Government, Department for Education and Training; 2018.

Heeneman S, Pool AO, Schuwirth LWT, van der Vleuten CPM, Driessen EW, Oudkerk A, et al. The impact of programmatic assessment on student learning: theory versus practice. Med Educ. 2015;49:487–98.

Lefroy J, Watling C, Teunissen P, Brand P, Watling C. Guidelines: the do’s, don’ts and don’t knows of feedback for clinical education. Perspect Med Educ. 2015;4:284–99.

Ramani S, Krackov SK. Twelve tips for giving feedback effectively in the clinical environment. Med Teach. 2012;34:787–91.

Telio S, Ajjawi R, Regehr G. The, “Educational Alliance” as a Framework for Reconceptualizing Feedback in Medical Education. Acad Med. 2015;90:609–14.

Lockyer J, Armson H, Könings KD, Lee-Krueger RC, des Ordons AR, Ramani S, et al. In-the-Moment Feedback and Coaching: Improving R2C2 for a New Context. J Grad Med Educ. 2020;12:27–35.

Black P, Wiliam D. Developing the theory of formative assessment. Educ Assess Eval Account. 2009;21:5–31.

Download references

Author information

Authors and affiliations.

Department of Health Sciences, Faculty of Medicine, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago, Chile

Javiera Fuentes-Cimma & Ignacio Villagran

School of Health Professions Education, Maastricht University, Maastricht, Netherlands

Javiera Fuentes-Cimma & Lorena Isbej

Rotterdam University of Applied Sciences, Rotterdam, Netherlands

Dominique Sluijsmans

Centre for Medical and Health Profession Education, Department of Gastroenterology, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

Arnoldo Riquelme

School of Dentistry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

Lorena Isbej

Sistema de Bibliotecas UC (SIBUC), Pontificia Universidad Católica de Chile, Santiago, Chile

María Teresa Olivares-Labbe

Department of Pathology, Faculty of Health, Medicine and Health Sciences, Maastricht University, Maastricht, Netherlands

Sylvia Heeneman

You can also search for this author in PubMed   Google Scholar

Contributions

J.F-C, D.S, and S.H. made substantial contributions to the conception and design of the work. M.O-L contributed to the identification of studies. J.F-C, I.V, A.R, and L.I. made substantial contributions to the screening, reliability, and data analysis. J.F-C. wrote th e main manuscript text. All authors reviewed the manuscript.

Corresponding author

Correspondence to Javiera Fuentes-Cimma .

Ethics declarations

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.

Supplementary Information

Supplementary material 1, supplementary material 2., 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/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Fuentes-Cimma, J., Sluijsmans, D., Riquelme, A. et al. Designing feedback processes in the workplace-based learning of undergraduate health professions education: a scoping review. BMC Med Educ 24 , 440 (2024). https://doi.org/10.1186/s12909-024-05439-6

Download citation

Received : 25 September 2023

Accepted : 17 April 2024

Published : 23 April 2024

DOI : https://doi.org/10.1186/s12909-024-05439-6

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

• Clinical clerkship
• Feedback processes
• Feedforward
• Formative feedback
• Health professions
• Undergraduate medical education
• Undergraduate healthcare education
• Workplace learning

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]