In Edwards grounded theory study, theoretical sampling led to the inclusion of the partners of women who had presented to the emergency department. ‘In one interview a woman spoke of being aware that the ED staff had not acknowledged her partner. This statement led me to ask other women during their interviews if they had similar experiences, and ultimately to interview the partners to gain their perspectives. The study originally intended to only focus on the women and the nursing staff who provided the care’ (p. 50).
Thus, theoretical sampling is used to focus and generate data to feed the iterative process of continual comparative analysis of the data. 6
Intermediate coding, identifying a core category, theoretical data saturation, constant comparative analysis, theoretical sensitivity and memoing occur in the next phase of the GT process. 6 Intermediate coding builds on the initial coding phase. Where initial coding fractures the data, intermediate coding begins to transform basic data into more abstract concepts allowing the theory to emerge from the data. During this analytic stage, a process of reviewing categories and identifying which ones, if any, can be subsumed beneath other categories occurs and the properties or dimension of the developed categories are refined. Properties refer to the characteristics that are common to all the concepts in the category and dimensions are the variations of a property. 37
At this stage, a core category starts to become evident as developed categories form around a core concept; relationships are identified between categories and the analysis is refined. Birks and Mills 6 affirm that diagramming can aid analysis in the intermediate coding phase. Grounded theorists interact closely with the data during this phase, continually reassessing meaning to ascertain ‘what is really going on’ in the data. 30 Theoretical saturation ensues when new data analysis does not provide additional material to existing theoretical categories, and the categories are sufficiently explained. 6
Birks and Mills 6 described advanced coding as the ‘techniques used to facilitate integration of the final grounded theory’ (p. 177). These authors promote storyline technique (described in the following section) and theoretical coding as strategies for advancing analysis and theoretical integration. Advanced coding is essential to produce a theory that is grounded in the data and has explanatory power. 6 During the advanced coding phase, concepts that reach the stage of categories will be abstract, representing stories of many, reduced into highly conceptual terms. The findings are presented as a set of interrelated concepts as opposed to presenting themes. 28 Explanatory statements detail the relationships between categories and the central core category. 28
Storyline is a tool that can be used for theoretical integration. Birks and Mills 6 define storyline as ‘a strategy for facilitating integration, construction, formulation, and presentation of research findings through the production of a coherent grounded theory’ (p. 180). Storyline technique is first proposed with limited attention in Basics of Qualitative Research by Strauss and Corbin 12 and further developed by Birks et al. 38 as a tool for theoretical integration. The storyline is the conceptualisation of the core category. 6 This procedure builds a story that connects the categories and produces a discursive set of theoretical propositions. 24 Birks and Mills 6 contend that storyline can be ‘used to produce a comprehensive rendering of your grounded theory’ (p. 118). Birks et al. 38 had earlier concluded, ‘storyline enhances the development, presentation and comprehension of the outcomes of grounded theory research’ (p. 405). Once the storyline is developed, the GT is finalised using theoretical codes that ‘provide a framework for enhancing the explanatory power of the storyline and its potential as theory’. 6 Thus, storyline is the explication of the theory.
Theoretical coding occurs as the final culminating stage towards achieving a GT. 39 , 40 The purpose of theoretical coding is to integrate the substantive theory. 41 Saldaña 40 states, ‘theoretical coding integrates and synthesises the categories derived from coding and analysis to now create a theory’ (p. 224). Initial coding fractures the data while theoretical codes ‘weave the fractured story back together again into an organized whole theory’. 18 Advanced coding that integrates extant theory adds further explanatory power to the findings. 6 The examples in Box 2 describe the use of storyline as a technique.
Writing the storyline.
Baldwin describes in her GT study how ‘the process of writing the storyline allowed in-depth descriptions of the categories, and discussion of how the categories of (i) , (ii) and (iii) fit together to form the final theory: ’ (pp. 125–126). ‘The use of storyline as part of the finalisation of the theory from the data ensured that the final theory was grounded in the data’ (p. 201). In Chamberlain-Salaun GT study, writing the storyline enabled the identification of ‘gaps in the developing theory and to clarify categories and concepts. To address the gaps the researcher iteratively returned to the data and to the field and refine the storyline. Once the storyline was developed raw data was incorporated to support the story in much the same way as dialogue is included in a storybook or novel’. |
As presented in Figure 1 , theoretical sensitivity encompasses the entire research process. Glaser and Strauss 5 initially described the term theoretical sensitivity in The Discovery of Grounded Theory. Theoretical sensitivity is the ability to know when you identify a data segment that is important to your theory. While Strauss and Corbin 12 describe theoretical sensitivity as the insight into what is meaningful and of significance in the data for theory development, Birks and Mills 6 define theoretical sensitivity as ‘the ability to recognise and extract from the data elements that have relevance for the emerging theory’ (p. 181). Conducting GT research requires a balance between keeping an open mind and the ability to identify elements of theoretical significance during data generation and/or collection and data analysis. 6
Several analytic tools and techniques can be used to enhance theoretical sensitivity and increase the grounded theorist’s sensitivity to theoretical constructs in the data. 28 Birks and Mills 6 state, ‘as a grounded theorist becomes immersed in the data, their level of theoretical sensitivity to analytic possibilities will increase’ (p. 12). Developing sensitivity as a grounded theorist and the application of theoretical sensitivity throughout the research process allows the analytical focus to be directed towards theory development and ultimately result in an integrated and abstract GT. 6 The example in Box 3 highlights how analytic tools are employed to increase theoretical sensitivity.
Theoretical sensitivity.
Hoare et al. described how the lead author ‘ in pursuit of heightened theoretical sensitivity in a grounded theory study of information use by nurses working in general practice in New Zealand’. The article described the analytic tools the researcher used ‘to increase theoretical sensitivity’ which included ‘reading the literature, open coding, category building, reflecting in memos followed by doubling back on data collection once further lines of inquiry are opened up’. The article offers ‘an example of how analytical tools are employed to theoretically sample emerging concepts’ (pp. 240–241). |
The meticulous application of essential GT methods refines the analysis resulting in the generation of an integrated, comprehensive GT that explains a process relating to a particular phenomenon. 6 The results of a GT study are communicated as a set of concepts, related to each other in an interrelated whole, and expressed in the production of a substantive theory. 5 , 7 , 16 A substantive theory is a theoretical interpretation or explanation of a studied phenomenon 6 , 17 Thus, the hallmark of grounded theory is the generation of theory ‘abstracted from, or grounded in, data generated and collected by the researcher’. 6 However, to ensure quality in research requires the application of rigour throughout the research process.
The quality of a grounded theory can be related to three distinct areas underpinned by (1) the researcher’s expertise, knowledge and research skills; (2) methodological congruence with the research question; and (3) procedural precision in the use of methods. 6 Methodological congruence is substantiated when the philosophical position of the researcher is congruent with the research question and the methodological approach selected. 6 Data collection or generation and analytical conceptualisation need to be rigorous throughout the research process to secure excellence in the final grounded theory. 44
Procedural precision requires careful attention to maintaining a detailed audit trail, data management strategies and demonstrable procedural logic recorded using memos. 6 Organisation and management of research data, memos and literature can be assisted using software programs such as NVivo. An audit trail of decision-making, changes in the direction of the research and the rationale for decisions made are essential to ensure rigour in the final grounded theory. 6
This article offers a framework to assist novice researchers visualise the iterative processes that underpin a GT study. The fundamental process and methods used to generate an integrated grounded theory have been described. Novice researchers can adapt the framework presented to inform and guide the design of a GT study. This framework provides a useful guide to visualise the interplay between the methods and processes inherent in conducting GT. Research conducted ethically and with meticulous attention to process will ensure quality research outcomes that have relevance at the practice level.
Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
Grounded theory is a useful approach when you want to develop a new theory based on real-world data Instead of starting with a pre-existing theory, grounded theory lets the data guide the development of your theory.
Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967.
It is important to note that grounded theory is an inductive approach where a theory is developed from collected real-world data rather than trying to prove or disprove a hypothesis like in a deductive scientific approach
You gather information, look for patterns, and use those patterns to develop an explanation.
It is a way to understand why people do things and how those actions create patterns. Imagine you’re trying to figure out why your friends love a certain video game.
Instead of asking an adult, you observe your friends while they’re playing, listen to them talk about it, and maybe even play a little yourself. By studying their actions and words, you’re using grounded theory to build an understanding of their behavior.
This qualitative method of research focuses on real-life experiences and observations, letting theories emerge naturally from the data collected, like piecing together a puzzle without knowing the final image.
Grounded theory research is useful for beginning researchers, particularly graduate students, because it offers a clear and flexible framework for conducting a study on a new topic.
Grounded theory works best when existing theories are either insufficient or nonexistent for the topic at hand.
Since grounded theory is a continuously evolving process, researchers collect and analyze data until theoretical saturation is reached or no new insights can be gained.
The final product of a grounded theory (GT) study is an integrated and comprehensive grounded theory that explains a process or scheme associated with a phenomenon.
The quality of a GT study is judged on whether it produces this middle-range theory
Middle-range theories are sort of like explanations that focus on a specific part of society or a particular event. They don’t try to explain everything in the world. Instead, they zero in on things happening in certain groups, cultures, or situations.
Think of it like this: a grand theory is like trying to understand all of weather at once, but a middle-range theory is like focusing on how hurricanes form.
This terminology reflects the iterative, inductive, and comparative nature of grounded theory, which distinguishes it from other research approaches.
Barney Glaser and Anselm Strauss first introduced grounded theory in 1967 in their book, The Discovery of Grounded Theory .
Their aim was to create a research method that prioritized real-world data to understand social behavior.
However, their approaches diverged over time, leading to two distinct versions: Glaserian and Straussian grounded theory.
The different versions of grounded theory diverge in their approaches to coding , theory construction, and the use of literature.
All versions of grounded theory share the goal of generating a middle-range theory that explains a social process or phenomenon.
They also emphasize the importance of theoretical sampling , constant comparative analysis , and theoretical saturation in developing a robust theory
Glaserian grounded theory emphasizes the emergence of theory from data and discourages the use of pre-existing literature.
Glaser believed that adopting a specific philosophical or disciplinary perspective reduces the broader potential of grounded theory.
For Glaser, prior understandings should be based on the general problem area and reading very wide to alert or sensitize one to a wide range of possibilities.
It prioritizes parsimony , scope , and modifiability in the resulting theory
Strauss and Corbin (1990) focused on developing the analytic techniques and providing guidance to novice researchers.
Straussian grounded theory utilizes a more structured approach to coding and analysis and acknowledges the role of the literature in shaping research.
It acknowledges the role of deduction and validation in addition to induction.
Strauss and Corbin also emphasize the use of unstructured interview questions to encourage participants to speak freely
Critics of this approach believe it produced a rigidity never intended for grounded theory.
This version, primarily associated with Charmaz, recognizes that knowledge is situated, partial, provisional, and socially constructed. It emphasizes abstract and conceptual understandings rather than explanations.
Kathy Charmaz expanded on original versions of GT, emphasizing the researcher’s role in interpreting findings
Constructivist grounded theory acknowledges the researcher’s influence on the research process and the co-creation of knowledge with participants
Developed by Clarke, this version builds upon Straussian and Constructivist grounded theory and incorporates postmodern , poststructuralist , and posthumanist perspectives.
Situational analysis incorporates postmodern perspectives and considers the role of nonhuman actors
It introduces the method of mapping to analyze complex situations and emphasizes both human and nonhuman elements .
Grounded theory can be conducted by individual researchers or research teams. If working in a team, it’s important to communicate regularly and ensure everyone is using the same coding system.
Grounded theory research is typically an iterative process. This means that researchers may move back and forth between these steps as they collect and analyze data.
Instead of doing everything in order, you repeat the steps over and over.
This cycle keeps going, which is why grounded theory is called a circular process.
Continue to gather and analyze data until no new insights or properties related to your categories emerge. This saturation point signals that the theory is comprehensive and well-substantiated by the data.
Theoretical sampling, collecting sufficient and rich data, and theoretical saturation help the grounded theorist to avoid a lack of “groundedness,” incomplete findings, and “premature closure.
Begin by considering the phenomenon you want to study and assess the current knowledge surrounding it.
However, refrain from detailing the specific aspects you seek to uncover about the phenomenon to prevent pre-existing assumptions from skewing the research.
Initially, select participants who are readily available ( convenience sampling ) or those recommended by existing participants ( snowball sampling ).
As the analysis progresses, transition to theoretical sampling , involving the deliberate selection of participants and data sources to refine your emerging theory.
This method is used to refine and develop a grounded theory. The researcher uses theoretical sampling to choose new participants or data sources based on the emerging findings of their study.
This could mean recruiting participants who can shed light on gaps in your understanding uncovered during the initial data analysis.
Theoretical sampling guides further data collection by identifying participants or data sources that can provide insights into gaps in the emerging theory
The goal is to gather data that will help to further develop and refine the emerging categories and theoretical concepts.
Theoretical sampling starts early in a GT study and generally requires the researcher to make amendments to their ethics approvals to accommodate new participant groups.
The researcher might use interviews, focus groups, observations, or a combination of methods to collect qualitative data.
Open coding is the first stage of coding in grounded theory, where you carefully examine and label segments of your data to identify initial concepts and ideas.
This process involves scrutinizing the data and creating codes grounded in the data itself.
The initial codes stay close to the data, aiming to capture and summarize critically and analytically what is happening in the data
To begin open coding, read through your data, such as interview transcripts, to gain a comprehensive understanding of what is being conveyed.
As you encounter segments of data that represent a distinct idea, concept, or action, you assign a code to that segment. These codes act as descriptive labels summarizing the meaning of the data segment.
For instance, if you were analyzing interview data about experiences with a new medication, a segment of data might describe a participant’s difficulty sleeping after taking the medication. This segment could be labeled with the code “trouble sleeping”
Open coding is a crucial step in grounded theory because it allows you to break down the data into manageable units and begin to see patterns and themes emerge.
As you continue coding, you constantly compare different segments of data to refine your understanding of existing codes and identify new ones.
For instance, excerpts describing difficulties with sleep might be grouped under the code “trouble sleeping”.
This iterative process of comparing data and refining codes helps ensure the codes accurately reflect the data.
Open coding is about staying close to the data, using in vivo terms or gerunds to maintain a sense of action and process
During open coding, it’s crucial to engage in memo writing. Memos serve as your “notes to self”, allowing you to reflect on the coding process, note emerging patterns, and ask analytical questions about the data.
Document your thoughts, questions, and insights in memos throughout the research process.
These memos serve multiple purposes: tracing your thought process, promoting reflexivity (self-reflection), facilitating collaboration if working in a team, and supporting theory development.
Early memos tend to be shorter and less conceptual, often serving as “preparatory” notes. Later memos become more analytical and conceptual as the research progresses.
Axial coding is the process of identifying connections between codes, grouping them together into categories to reveal relationships within the data.
Axial coding seeks to find the axes that connect various codes together.
For example, in research on school bullying, focused codes such as “Doubting oneself, getting low self-confidence, starting to agree with bullies” and “Getting lower self-confidence; blaming oneself” could be grouped together into a broader category representing the impact of bullying on self-perception.
Similarly, codes such as “Being left by friends” and “Avoiding school; feeling lonely and isolated” could be grouped into a category related to the social consequences of bullying.
These categories then become part of the emerging grounded theory, explaining the multifaceted aspects of the phenomenon.
Qualitative data analysis software often represents these categories as nested codes, visually demonstrating the hierarchy and interconnectedness of the concepts.
This hierarchical structure helps researchers organize their data, identify patterns, and develop a more nuanced understanding of the relationships between different aspects of the phenomenon being studied.
This process of axial coding is crucial for moving beyond descriptive accounts of the data towards a more theoretically rich and explanatory grounded theory.
During selective coding , the final development stage of grounded theory analysis, a researcher focuses on developing a detailed and integrated theory by selecting a core category and connecting it to other categories developed during earlier coding stages.
The core category is the central concept that links together the various categories and subcategories identified in the data and forms the foundation of the emergent grounded theory.
This core category will encapsulate the main theme of your grounded theory, that encompasses and elucidates the overarching process or phenomenon under investigation.
This phase involves a concentrated effort to refine and integrate categories, ensuring they align with the core category and contribute to the overall explanatory power of the theory.
The theory should comprehensively describe the process or scheme related to the phenomenon being studied.
For example, in a study on school bullying, if the core category is “victimization journey,” the researcher would selectively code data related to different stages of this journey, the factors contributing to each stage, and the consequences of experiencing these stages.
This might involve analyzing how victims initially attribute blame, their coping mechanisms, and the long-term impact of bullying on their self-perception.
Selective coding focuses on developing and saturating this core category, leading to a cohesive and integrated theory.
Through selective coding, researchers aim to achieve theoretical saturation, meaning no new properties or insights emerge from further data analysis.
This signifies that the core category and its related categories are well-defined, and the connections between them are thoroughly explored.
This rigorous process strengthens the trustworthiness of the findings by ensuring the theory is comprehensive and grounded in a rich dataset.
It’s important to note that while a grounded theory seeks to provide a comprehensive explanation, it remains grounded in the data.
The theory’s scope is limited to the specific phenomenon and context studied, and the researcher acknowledges that new data or perspectives might lead to modifications or refinements of the theory
Theoretical coding is a process in grounded theory where researchers use advanced abstractions, often from existing theories, to explain the relationships found in their data.
Theoretical coding often occurs later in the research process and involves using existing theories to explain the connections between codes and categories.
This process helps to strengthen the explanatory power of the grounded theory. Theoretical coding should not be confused with simply describing the data; instead, it aims to explain the phenomenon being studied, distinguishing grounded theory from purely descriptive research.
Using the developed codes, categories, and core category, create a model illustrating the process or phenomenon.
Present your findings in a clear and accessible manner, ensuring the theory is rooted in the data and explains the relationships between the identified concepts and categories.
The end product of this process is a well-defined, integrated grounded theory that explains a process or scheme related to the phenomenon studied.
Grounded Theory Review : This is an international journal that publishes articles on grounded theory.
3035 Accesses
7 Citations
Grounded theory (GT) is a common qualitative methodology in health professions education research used to explore the “how”, “what”, and “why” of social processes. With GT researchers aim to understand how study participants interpret reality related to the process in question. However, they risk misapplying the term to studies that do not actually use GT methodology. We outline key features that characterize GT research, namely iterative data collection and analysis, constant comparison, and theoretical sampling. Constructivist GT is a particular form of GT that explicitly recognizes the researcher’s role in knowledge creation throughout the analytic process. Data may be collected through interviews, field observations, video analysis, document review, or a combination of these methods. The analytic process involves several flexible coding phases that move from concrete initial coding to higher level focused codes and finally to axial coding with the goal of a conceptual understanding that is situated in the study context.
This is a preview of subscription content, log in via an institution to check access.
Subscribe and save.
Tax calculation will be finalised at checkout
Purchases are for personal use only
Institutional subscriptions
Harris I. What does “The discovery of grounded theory” have to say to medical education? Adv Health Sci Educ Theory Pract. 2003;8(1):49–61.
Article Google Scholar
Watling CJ, Lingard L. Grounded theory in medical education research: AMEE Guide No. 70. Med Teach. 2012;34(10):850–61.
Kennedy TJT, Lingard LA. Making sense of grounded theory in medical education. Med Educ. 2006;40(2):101–8.
Lingard L. When I say…grounded theory. Med Educ. 2014;48(8):748–9.
Glaser BG, Strauss AL. The discovery of grounded theory: strategies for qualitative research. Chicago: Aldine; 1967.
Google Scholar
Charmaz K. Constructing grounded theory. 2nd ed. London: SAGE Publications Ltd; 2014.
Higginbottom G, Lauridsen EI. The roots and development of constructivist grounded theory. Nurse Res. 2014;21(5):8–13.
Mills J, Bonner A, Francis K. The development of constructivist grounded theory. Int J Qual Methods. 2006;5(1):25–35.
Morse JM, Stern PN, Corbin J, Bowers B. Developing grounded theory: the second generation. New York: Taylor & Francis; 2009.
Thomas G, James D. Reinventing grounded theory: some questions about theory, ground and discovery. Br Educ Res J. 2006;32(6):767–95.
Timonen V, Foley G, Conlon C. Challenges when using grounded theory: a pragmatic introduction to doing gt research. Int J Qual Methods. 2018;17:1–10.
Lingard L, Garwood K, Schryer CF, Spafford MM. A certain art of uncertainty: case presentation and the development of professional identity. Soc Sci Med. 2003;56(3):603–16.
Article CAS Google Scholar
Kennedy TJT, Regehr G, Baker GR, Lingard LA. ‘It‘s a cultural expectation...’ The pressure on medical trainees to work independently in clinical practice. Med Educ. 2009;43(7):645–53.
Kennedy TJT, Regehr G, Baker GR, Lingard L. Point-of-care assessment of medical trainee competence for independent clinical work. Acad Med. 2008;83(10 Suppl):S89–92.
Kennedy TJT, Lingard L, Baker GR, Kitchen L, Regehr G. Clinical oversight: conceptualizing the relationship between supervision and safety. J Gen Intern Med Off J Soc Res Educ Prim Care Intern Med. 2007;22(8):1080–5.
Watling C, Driessen E, Van Der Vleuten CPM, Vanstone M, Lingard L. Beyond individualism: professional culture and its influence on feedback. Med Educ. 2013;47(6):585–94.
Watling C, Driessen E, Van Der Vleuten CPM, Lingard L. Learning culture and feedback: an international study of medical athletes and musicians. Med Educ. 2014;48(7):713–23.
Olmos-Vega FM, Dolmans DHJM, Vargas-Castro N, Stalmeijer RE. Dealing with the tension: how residents seek autonomy and participation in the workplace. Med Educ. Wiley/Blackwell (10.1111). 2017;51(7):699–707.
Olmos-Vega FM, Dolmans DH, Guzmán-Quintero C, Stalmeijer RE, Teunissen PW. Unravelling residents’ and supervisors’ workplace interactions: an intersubjectivity study. Med Educ. Wiley/Blackwell (10.1111). 2018;52(7):725–35.
Walton J, Chute E, Ball L. Negotiating the role of the professional nurse: the pedagogy of simulation: a grounded theory study. J Prof Nurs. 2011;27(5):299–310.
Watts PI, Ivankova N, Moss JA. Faculty evaluation of undergraduate nursing simulation: a grounded theory model. Clin Simul Nurs Elsevier Inc. 2017;13(12):616–23.
Tripathy S, Miller KH, Berkenbosch JW, McKinley TF, Boland KA, Brown SA, et al. When the Mannequin Dies, creation and exploration of a theoretical framework using a mixed methods approach. Simul Healthc. 2016;11(3):149–56.
McBride ME, Schinasi DA, Moga MA, Tripathy S, Calhoun A. Death of a simulated pediatric patient: toward a more robust theoretical framework. Simul Healthc. 2017;12(6):393–401.
Starks H, Trinidad SB. Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qual Health Res. 2007;17(10):1372–80.
Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80. Med Teach. 2013;35(8):e1365–79.
Varpio L, Ajjawi R, Monrouxe LV, O’Brien BC, Rees CE. Shedding the cobra effect: problematising thematic emergence, triangulation, saturation and member checking. Med Educ. 2017;51(1):40–50.
Charmaz K. The power and potential of grounded theory. Med Soc Online. 2012;6(3):1–15.
Bowen GA. Grounded theory and sensitizing concepts. Int J Qual Methods. 2nd ed. 2006;5(3):12–23.
Kelle U. “Emergence” vs. “Forcing” of empirical data? A crucial problem of “Grounded Theory” reconsidered. Forum Qual Soc Res. 2005;6(2):27. http://nbn-resolving.de/urn:nbn:de:0114-fqs0502275 .
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.
Suddaby R. From the editors: what grounded theory is not. Acad Manag J. 2006;49(4):633–42.
Urquhart C, Fernandez W. Using grounded theory method in information systems: the researcher as blank slate and other myths. J Inf Technol. Nature Publishing Group. 2013;28(3):224–36.
Download references
Authors and affiliations.
Feinberg School of Medicine, Department of Pediatrics, Division of Emergency Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Northwestern University, Chicago, IL, USA
Walter J. Eppich
Department of Anesthesiology, Faculty of Medicine, Pontificia Universidad Javeriana, Bogotá, Colombia
Francisco M. Olmos-Vega
Anesthesiology Department, Hospital Universitario San Ignacio, Bogotá, Colombia
Departments of Clinical Neurological Sciences and Oncology, Office of Postgraduate Medical Education, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
Christopher J. Watling
You can also search for this author in PubMed Google Scholar
Correspondence to Walter J. Eppich .
Editors and affiliations.
Monash Institute for Health and Clinical Education, Monash University, Clayton, VIC, Australia
Debra Nestel
Emergency Medicine, Kaiser Permanente, Los Angeles Medical Center, Los Angeles, CA, USA
Department of Surgery, University of Maryland, Baltimore, Baltimore, MD, USA
Kevin Kunkler
Department of Psychology, Old Dominion University, Norfolk, VA, USA
Mark W. Scerbo
Department of Pediatrics, University of Louisville School of Medicine, Louisville, KY, USA
Aaron W. Calhoun
Reprints and permissions
© 2019 Springer Nature Switzerland AG
Eppich, W.J., Olmos-Vega, F.M., Watling, C.J. (2019). Grounded Theory Methodology: Key Principles. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_18
DOI : https://doi.org/10.1007/978-3-030-26837-4_18
Published : 14 November 2019
Publisher Name : Springer, Cham
Print ISBN : 978-3-030-26836-7
Online ISBN : 978-3-030-26837-4
eBook Packages : Biomedical and Life Sciences Biomedical and Life Sciences (R0)
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
Policies and ethics
Home » Grounded Theory – Methods, Examples and Guide
Table of Contents
Definition:
Grounded Theory is a qualitative research methodology that aims to generate theories based on data that are grounded in the empirical reality of the research context. The method involves a systematic process of data collection, coding, categorization, and analysis to identify patterns and relationships in the data.
The ultimate goal is to develop a theory that explains the phenomenon being studied, which is based on the data collected and analyzed rather than on preconceived notions or hypotheses. The resulting theory should be able to explain the phenomenon in a way that is consistent with the data and also accounts for variations and discrepancies in the data. Grounded Theory is widely used in sociology, psychology, management, and other social sciences to study a wide range of phenomena, such as organizational behavior, social interaction, and health care.
Grounded Theory was first introduced by sociologists Barney Glaser and Anselm Strauss in the 1960s as a response to the limitations of traditional positivist approaches to social research. The approach was initially developed to study dying patients and their families in hospitals, but it was soon applied to other areas of sociology and beyond.
Glaser and Strauss published their seminal book “The Discovery of Grounded Theory” in 1967, in which they presented their approach to developing theory from empirical data. They argued that existing social theories often did not account for the complexity and diversity of social phenomena, and that the development of theory should be grounded in empirical data.
Since then, Grounded Theory has become a widely used methodology in the social sciences, and has been applied to a wide range of topics, including healthcare, education, business, and psychology. The approach has also evolved over time, with variations such as constructivist grounded theory and feminist grounded theory being developed to address specific criticisms and limitations of the original approach.
There are two main types of Grounded Theory: Classic Grounded Theory and Constructivist Grounded Theory.
This approach is based on the work of Glaser and Strauss, and emphasizes the discovery of a theory that is grounded in data. The focus is on generating a theory that explains the phenomenon being studied, without being influenced by preconceived notions or existing theories. The process involves a continuous cycle of data collection, coding, and analysis, with the aim of developing categories and subcategories that are grounded in the data. The categories and subcategories are then compared and synthesized to generate a theory that explains the phenomenon.
This approach is based on the work of Charmaz, and emphasizes the role of the researcher in the process of theory development. The focus is on understanding how individuals construct meaning and interpret their experiences, rather than on discovering an objective truth. The process involves a reflexive and iterative approach to data collection, coding, and analysis, with the aim of developing categories that are grounded in the data and the researcher’s interpretations of the data. The categories are then compared and synthesized to generate a theory that accounts for the multiple perspectives and interpretations of the phenomenon being studied.
Here are some general guidelines for conducting a Grounded Theory study:
Grounded Theory Data Collection Methods are as follows:
Grounded Theory Data Analysis Methods are as follows:
Here are some of the key applications of Grounded Theory:
Examples of Grounded Theory in different case studies are as follows:
A Grounded Theory Research Example Would be:
Research question : What is the experience of first-generation college students in navigating the college admission process?
Data collection : The researcher conducted interviews with first-generation college students who had recently gone through the college admission process. The interviews were audio-recorded and transcribed verbatim.
Data analysis: The researcher used a constant comparative method to analyze the data. This involved coding the data, comparing codes, and constantly revising the codes to identify common themes and patterns. The researcher also used memoing, which involved writing notes and reflections on the data and analysis.
Findings : Through the analysis of the data, the researcher identified several themes related to the experience of first-generation college students in navigating the college admission process, such as feeling overwhelmed by the complexity of the process, lacking knowledge about the process, and facing financial barriers.
Theory development: Based on the findings, the researcher developed a theory about the experience of first-generation college students in navigating the college admission process. The theory suggested that first-generation college students faced unique challenges in the college admission process due to their lack of knowledge and resources, and that these challenges could be addressed through targeted support programs and resources.
In summary, grounded theory research involves collecting data, analyzing it through constant comparison and memoing, and developing a theory grounded in the data. The resulting theory can help to explain the phenomenon being studied and guide future research and interventions.
The purpose of Grounded Theory is to develop a theoretical framework that explains a social phenomenon, process, or interaction. This theoretical framework is developed through a rigorous process of data collection, coding, and analysis, and is grounded in the data.
Grounded Theory aims to uncover the social processes and patterns that underlie social phenomena, and to develop a theoretical framework that explains these processes and patterns. It is a flexible method that can be used to explore a wide range of research questions and settings, and is particularly well-suited to exploring complex social phenomena that have not been well-studied.
The ultimate goal of Grounded Theory is to generate a theoretical framework that is grounded in the data, and that can be used to explain and predict social phenomena. This theoretical framework can then be used to inform policy and practice, and to guide future research in the field.
Following are some situations in which Grounded Theory may be particularly useful:
Grounded Theory is a qualitative research method that is characterized by several key features, including:
Advantages of Grounded Theory are as follows:
Limitations of Grounded Theory are as follows:
Researcher, Academic Writer, Web developer
Introduction, distinguishing features of grounded theory, the role and timing of the literature review.
Carley Turner, Felicity Astin, Grounded theory: what makes a grounded theory study?, European Journal of Cardiovascular Nursing , Volume 20, Issue 3, March 2021, Pages 285–289, https://doi.org/10.1093/eurjcn/zvaa034
Grounded theory (GT) is both a research method and a research methodology. There are several different ways of doing GT which reflect the different viewpoints of the originators. For those who are new to this approach to conducting qualitative research, this can be confusing. In this article, we outline the key characteristics of GT and describe the role of the literature review in three common GT approaches, illustrated using exemplar studies.
Describing the key characteristics of a Grounded theory (GT) study.
Considering the role and timing of the literature review in different GT approaches.
Qualitative research is a cornerstone in cardiovascular research. It gives insights in why particular phenomena occur or what underlying mechanisms are. 1 Over the past 2 years, the European Journal of Cardiovascular Nursing published 20 qualitative studies. 2–21 These studies used methods such as content analysis, ethnography, or phenomenology. Grounded theory (GT) has been used to a lesser extent.
Grounded theory is both a methodology and a method used in qualitative research ( Table 1 ). It is a research approach used to gain an emic insight into a phenomenon. In simple terms, this means understanding the perspective, or point of view, of an ‘insider’, those who have experience of the phenomenon. 22 Grounded theory is a research approach that originated from the social sciences but has been used in education and health research. The focus of GT is to generate theory that is grounded in data and shaped by the views of participants, thereby moving beyond description and towards theoretical explanation of a process or phenomenon. 23
Grounded theory as a method and methodology
. | Methodology . | Method . |
---|---|---|
Framework of principles on which the methods are based. | Strategy for conducting the research. Methods outline how data will be collected, analysed, and interpreted. | |
GT application | Researcher openness, with an inductive approach to data. Theory can be generated based on data. | Concurrent data collection and analysis, use of codes and memos for data analysis. |
. | Methodology . | Method . |
---|---|---|
Framework of principles on which the methods are based. | Strategy for conducting the research. Methods outline how data will be collected, analysed, and interpreted. | |
GT application | Researcher openness, with an inductive approach to data. Theory can be generated based on data. | Concurrent data collection and analysis, use of codes and memos for data analysis. |
One of the key issues with using GT, particularly for novices, is understanding the different approaches that have evolved as each specific GT approach is slightly different.
The tradition of GT began with the seminal text about classic GT written by Glaser and Strauss, 24 but since then GT has evolved into several different types. The approach to GT chosen by the researcher depends upon an understanding of the epistemological underpinnings of the different approaches, the match with the topic under investigation and the researcher’s own stance. Whilst GT is frequently used in applied health research, very few studies detail which GT approach has been used, how and why. Sometimes published studies claim to use GT methodology but the approaches that form the foundation of GT are not reported. This may be due to the word limit in academic journals or because legitimate GT approaches have not been followed. Either way, there is a lack of clarity about the practical application of GT. The purpose of this article is to outline the distinguishing characteristics of GT and outline practical considerations for the novice researcher regarding the place of the literature review in GT.
There are several distinguishing features of GT, as outlined by Sbaraini et al. 25 The first is that GT research is conducted through an inductive process. This means that the researcher is developing theory rather than testing it and must therefore remain ‘open’ throughout the study. In essence, this means that the researcher has no preconceived ideas about the findings. Taking an inductive approach means that the focus of the research may evolve over time as the researchers understand what is important to their participants through the data collection and analysis process.
With regards to data analysis, the use of coding is initially used to break down data into smaller components and labelling them to capture the essence of the data. The codes are compared to one another to understand and explain any variation in the data before they are combined to form more abstract categories. Memos are used to record and develop the researcher’s analysis of the data, including the detail behind the comparisons made between categories. Memos can stimulate the researcher’s thinking, as well as capturing the researcher’s ideas during data collection and analysis.
A further feature for data analysis in a GT study is the simultaneous data analysis and sampling to facilitate theoretical sampling. This means that as data analysis progresses participants are purposefully selected who may have characteristics or experiences that have arisen as being of interest from data collection and analysis so far. Questions in the topic guide may also be modified to follow a specific line of inquiry, test ideas and interpretations, or fill gaps in the analysis to build an emerging substantive theory. This evolving and non-linear methodology is to allow the evolution of the study as indicated by data, rather than analysing at the end of data collection. This approach to data analysis supports the researcher to take an inductive approach as discussed above.
Theoretical sampling facilitates the construction of theory until theoretical saturation is reached. Theoretical saturation is when all the concepts that form the theory being developed are well understood and grounded in data. Finally, the results are expressed as a theory where a set of concepts are related to one another and provide a framework for making predictions. 26 These key features of GT are summarized in Table 2 .
Distinguishing features of a GT study (adapted from Sbaraini et al. 25 )
Distinguishing feature . | Description . |
---|---|
Openness | Grounded Theory is concerned with the development of theory rather than testing it. The researcher has no preconceived ideas about the findings, and the study evolves over time. |
Concurrent data collection and data analysis | Data analysis occurs at the same time as data collection. |
Coding | Data are broken down into smaller components and assigned a label to capture the essence of the data. |
Memos | Memos are a record of the researcher’s ideas and thoughts during data collection and analysis. Use of memos helps to develop the researcher’s analysis. |
Theoretical sampling | Purposeful selection of participants who may have characteristics or experiences that have arisen as being of interest from data collection and analysis. Theoretical sampling also includes modifications to the topic guide to allow the researcher to explore ideas arising from the interviews or fill gaps in the developing theory. |
Theoretical saturation | When all the concepts that form the theory are well understood and grounded in data. |
Theory generation | The results of the study are expressed as a substantive theory. The key aim of GT is to generate a substantive theory, in other words, a theory to explain specific population experiences of a phenomenon. |
Distinguishing feature . | Description . |
---|---|
Openness | Grounded Theory is concerned with the development of theory rather than testing it. The researcher has no preconceived ideas about the findings, and the study evolves over time. |
Concurrent data collection and data analysis | Data analysis occurs at the same time as data collection. |
Coding | Data are broken down into smaller components and assigned a label to capture the essence of the data. |
Memos | Memos are a record of the researcher’s ideas and thoughts during data collection and analysis. Use of memos helps to develop the researcher’s analysis. |
Theoretical sampling | Purposeful selection of participants who may have characteristics or experiences that have arisen as being of interest from data collection and analysis. Theoretical sampling also includes modifications to the topic guide to allow the researcher to explore ideas arising from the interviews or fill gaps in the developing theory. |
Theoretical saturation | When all the concepts that form the theory are well understood and grounded in data. |
Theory generation | The results of the study are expressed as a substantive theory. The key aim of GT is to generate a substantive theory, in other words, a theory to explain specific population experiences of a phenomenon. |
The identification of a gap in the published literature is typically a requirement of successful doctoral studies and grant applications. However, in GT research there are different views about the role and timing of the literature review.
For researchers using classic Glaserian GT, the recommended approach is that the published literature should not be reviewed until data collection, analysis and theory development has been completed. 24 The rationale for the delay of the literature review is to enable the researcher to remain ‘open’ to discover theory emerging from data and free from contamination by avoiding forcing data into pre-conceived concepts derived from other studies. Furthermore, because the researcher is ‘open’ to whichever direction the data takes they cannot know in advance which aspects of the literature will be relevant to their study. 27
In Glaserian GT, the emerging concepts and theory from data analysis inform the scope of the literature review which is conducted after theory development. 24 This approach to the literature review aligns with the rather positivist stance of Glaser in which the researcher aims to remain free of assumptions so that the theory that emerges from the data is not influenced by the researcher. Reviewing the literature prior to data analysis would risk theory being imposed on the data. Perhaps counterintuitively, Glaser does recommend reading literature in unrelated fields to understand as many theoretical codes as possible. 28 However, it is unclear how this is different to reading literature directly related to the topic and could potentially still lead to the contamination of the theory arising from data that delaying the literature review is intended to avoid. It is also problematic regarding the governance processes around research, whereby funders and ethics committees would expect at least an overview of the existing literature as part of the justification for the study.
A study by Bergman et al. 29 used a classic Glaserian GT approach to examine and identify the motive of power in myocardial infarction patients’ rehabilitation process. Whilst the key characteristics of GT were evident in the way the study was conducted, there was no discussion about how the literature review contributed to the final theory. This may have been due to the word limit but illustrates the challenges that novice researchers may have in understanding where the literature review fits in studies using GT approaches.
In Straussian GT, a more pragmatic approach to the literature view is adopted. Strauss and Corbin 30 recognized that the researcher has prior knowledge, including that of the literature, before starting their research. They did not recommend dissociation from the literature, but rather that the literature be used across the various stages of the research. Published literature could identify important areas that could contribute to theory development, support useful comparisons in the data and stimulate further questions during the analytical process. According to Strauss and Corbin, researchers should be mindful about how published work could influence theory development. Whilst visiting the literature prior to data collection was believed to enhance data analysis, it was not thought necessary to review all the literature beforehand, but rather revisit the literature at later stages in the research process. 30
A study published by Salminen-Tuomaala et al. 31 used a Straussian GT approach to explore factors that influenced the way patients coped with hospitalization for acute myocardial infarction. The authors described a reflexive process in which the researcher noted down their preconceived ideas about the topic as part of the data analysis process. The literature review was conducted after data analysis.
The most recent step in the evolution of GT is the move towards a constructivist epistemological stance advocated by Charmaz. 32 In simple terms, this means that the underlying approach reflects the belief that theories cannot be discovered but are instead constructed by the researcher and their interactions with the participants and data. As the researcher plays a central role in the construction of the GT, their background, personal views, and culture will influence this process and the way data are analysed. For this reason, it is important to be explicit about these preconceptions and aim to maintain an open mind through reflexivity. 32 Therefore, engaging in a preliminary literature review and using this information to compare and contrast with findings from the research undertaken is desirable, alongside completing a comprehensive literature review after data analysis with a specific aim to present the GT.
A study published by Odell et al. 33 used the modified GT approach recommended by Charmaz 32 to study patients’ experiences of restenosis after Percutaneous Coronary Intervention. The authors described the different GT approaches and key features of GT methodology which clearly informed the conduct of the study. However, there was no detail about how the literature review was used to shape the data analysis process and findings.
Despite the clear differences in the approach to the literature review in GT, there appears to be a lack of precise guidance for novice researchers regarding how in depth or exhaustive a preliminary literature review should be. This lack of guidance can lead to a variety of different approaches as evidenced in the GT studies we have cited as examples, which is a challenge for the novice researcher. This uncertainty is further compounded by the concurrent approach to data collection and analysis which allows for the research focus to evolve as the study progresses. The complexity of the research process and the role and timing of the literature review is summarized in Figure 1 .
Literature review in Grounded Theory.
Taking a pragmatic approach, researchers will need to familiarize themselves with the literature to receive funding and approval for their study. This preliminary literature review can be followed up after data analysis by a more comprehensive review of the literature to help support the theory that was developed from the data. The key is to ensure transparency in reporting how the literature review has been used to develop the theory. The preliminary literature review can be used to set the scene for the research as part of the introduction, and the more extensive literature review can then be used during the discussion section to compare the theory developed from the data with existing literature, as per Probyn et al. 34
Whilst this pragmatic approach aligns with Straussian GT and Charmaz’s constructivist GT, it is at odds with Glaserian GT. Therefore, if Glaserian GT is chosen, the researcher should be explicit about deviation and provide a rationale.
Word count for journal articles is often a limiting factor in how much detail is included in why certain methodologies are used. Submitting detail about the methodology and rationale behind it can be presented as online supplementary material, thereby allowing interested readers to access further information about how and why the research was executed.
The use of GT as a methodology and method can shed light on areas where little knowledge is already known, generating theory directly from data. The traditional format of a published article does not always reflect the iterative approach to the literature review and data collection and analysis in GT. This can generate tension between how the research is presented in relation to how it was conducted. However, one simple way to ensure clarity in reporting is to be transparent in how the literature review is used.
The authors received no financial support for the research, authorship or publication of this article.
Conflict of interest : The authors declare that there are no conflicts of interest.
Greenhalgh T , Taylor R. Papers that go beyond numbers (qualitative research) . Br Med J 1997 ; 315 : 740 – 743 .
Google Scholar
Wilson RE , Rush KL , Reid RC , et al. The symptom experience of early and late treatment seekers before an atrial fibrillation diagnosis . Eur J Cardiovasc Nurs 2021 ; 20 :231--242.
Lauck SB , Achtem L , Borregaard B , et al. Can you see frailty? An exploratory study of the use of a patient photograph in the transcatheter aortic valve implantation programme . Eur J Cardiovasc Nurs 2021 ; 20 :252--260.
Sundelin R , Bergsten C , Tornvall P , Lyngå, P. et al. Self-rated stress and experience in patients with Takotsubo syndrome: a mixed methods study . Eur J Cardiovasc Nurs 2020 ; doi: 10.1177/1474515120919387.
Janssen DJ , Ament SM , Boyne J , et al. Characteristics for a tool for timely identification of palliative needs in heart failure: the views of Dutch patients, their families and healthcare professionals . Eur J Cardiovasc Nurs 2020 ; doi: 10.1177/1474515120918962.
Steffen EM , Timotijevic L , Coyle A. A qualitative analysis of psychosocial needs and support impacts in families affected by young sudden cardiac death: the role of community and peer support . Eur J Cardiovasc Nurs 2020 ; doi: 10.1177/1474515120922347.
Molzahn AE , Sheilds L , Bruce A , Schick-Makaroff K , Antonio M , Clark AM. Life and priorities before death: a narrative inquiry of uncertainty and end of life in people with heart failure and their family members . Eur J Cardiovasc Nurs 2020 ; 19 : 629 – 637 .
Wistrand C , Nilsson U , Sundqvist A-S. Patient experience of preheated and room temperature skin disinfection prior to cardiac device implantation: a randomised controlled trial . Eur J Cardiovasc Nurs 2020 ; 19 : 529 – 536 .
Widell C , Andréen S , Albertsson P , Axelsson ÅB. Octogenarian preferences and expectations for acute coronary syndrome treatment . Eur J Cardiovasc Nurs 2020 ; 19 : 521 – 528 .
Ferguson C , George A , Villarosa AR , Kong AC , Bhole S , Ajwani S. Exploring nursing and allied health perspectives of quality oral care after stroke: a qualitative study . Eur J Cardiovasc Nurs 2020 ; 19 : 505 – 512 .
Sutantri S , Cuthill F , Holloway A. ‘ A bridge to normal’: a qualitative study of Indonesian women’s attendance in a phase two cardiac rehabilitation programme . Eur J Cardiovasc Nurs 2019 ; 18 , doi: 10.1177/1474515119864208.
Liu X-L , Willis K , Fulbrook P , Wu C-J(J) , Shi Y , Johnson M. Factors influencing self-management priority setting and decision-making among Chinese patients with acute coronary syndrome and type 2 diabetes mellitus . Eur J Cardiovasc Nurs 2019 ; 18 : 700 – 710 .
Wingham J , Frost J , Britten N , Greaves C , Abraham C , Warren FC , Jolly K , Miles J , Paul K , Doherty PJ , Singh S , Davies R , Noonan M , Dalal H , Taylor RS. Caregiver outcomes of the REACH-HF multicentre randomized controlled trial of home-based rehabilitation for heart failure with reduced ejection fraction . Eur J Cardiovasc Nurs 2019 ; 18 : 611 – 620 .
Olsson K , Näslund U , Nilsson J , Hörnsten Å. Hope and despair: patients’ experiences of being ineligible for transcatheter aortic valve implantation . Eur J Cardiovasc Nurs 2019 ; 18 : 593 – 600 .
Heery S , Gibson I , Dunne D , Flaherty G. The role of public health nurses in risk factor modification within a high-risk cardiovascular disease population in Ireland – a qualitative analysis . Eur J Cardiovasc Nurs 2019 ; 18 : 584 – 592 .
Brännström M , Fischer Grönlund C , Zingmark K , Söderberg A. Meeting in a ‘free-zone’: clinical ethical support in integrated heart-failure and palliative care . Eur J Cardiovasc Nurs 2019 ; 18 : 577 – 583 .
Haydon G , van der Riet P , Inder K. Long-term survivors of cardiac arrest: a narrative inquiry . Eur J Cardiovasc Nurs 2019 ; 18 : 458 – 464 .
Freysdóttir GR , Björnsdóttir K , Svavarsdóttir MH. Nurses’ Use of Monitors in Patient Surveillance: An Ethnographic Study on a Coronary Care Unit . London, England : SAGE Publications ; 2019 . p 272 – 279 .
Google Preview
Pokorney SD , Bloom D , Granger CB , Thomas KL , Al-Khatib SM , Roettig ML , Anderson J , Heflin MT , Granger BB. Exploring patient–provider decision-making for use of anticoagulation for stroke prevention in atrial fibrillation: results of the INFORM-AF study . Eur J Cardiovasc Nurs 2019 ; 18 : 280 – 288 .
Instenes I , Fridlund B , Amofah HA , Ranhoff AH , Eide LS , Norekvål TM. ‘ I hope you get normal again’: an explorative study on how delirious octogenarian patients experience their interactions with healthcare professionals and relatives after aortic valve therapy . Eur J Cardiovasc Nurs 2019 ; 18 : 224 – 233 .
Palmar-Santos AM , Pedraz-Marcos A , Zarco-Colón J , Ramasco-Gutiérrez M , García-Perea E , Pulido-Fuentes M. The life and death construct in heart transplant patients . Eur J Cardiovasc Nurs 2019 ; 18 : 48 – 56 .
De Chesnay M , Banner D. Nursing Research Using Grounded Theory: Qualitative Designs and Methods . New York, NY : Springer Publishing Company ; 2015 .
Corbin J , Strauss A. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 3rd ed. California : SAGE ; 2007 .
Glaser BG , Strauss AL. The Discovery of Grounded Theory: Strategies for Qualitative Research . New York : Aldine ; 1967 .
Sbaraini A , Carter SM , Evans RW , Blinkhorn A. How to do a grounded theory study: a worked example of a study of dental practices . BMC Med Res Methodol 2011 ; 11 : 128 – 128 .
Charmaz K. ‘ Discovering’ chronic illness: using grounded theory . Soc Sci Med 1990 ; 30 : 1161 – 1172 .
Thornberg R , Dunne C. Literature review in grounded theory. In Bryant A , Charmaz K , eds. The SAGE Handbook of Current Developments in Grounded Theory . London : SAGE Publications ; 2019 .
Glaser BG. Doing Grounded Theory: Issues and Discussions . California : Sociology Press ; 1998 .
Bergman E , Berterö C. ‘ Grasp Life Again’. A qualitative study of the motive power in myocardial infarction patients . Eur J Cardiovasc Nurs 2003 ; 2 : 303 – 310 .
Strauss A , Corbin J. Basics of Qualitative Research: Grounded Theory Procedures and Techniques . 2nd ed. California : Sage ; 1998 .
Salminen-Tuomaala M , Åstedt-Kurki P , Rekiaro M , Paavilainen E. Coping—seeking lost control . Eur J Cardiovasc Nurs 2012 ; 11 : 289 – 296 .
Charmaz K. Constructing Grounded Theory . 2nd ed. Los Angeles : SAGE ; 2014 .
Odell A , Grip L , Hallberg LRM. Restenosis after percutaneous coronary intervention (PCI): experiences from the patients' perspective . Eur J Cardiovasc Nurs 2006 ; 5 : 150 – 157 .
Probyn J , Greenhalgh J , Holt J , Conway D , Astin F. Percutaneous coronary intervention patients’ and cardiologists’ experiences of the informed consent process in Northern England: a qualitative study . BMJ Open 2017 ; 7 : e015127 .
Month: | Total Views: |
---|---|
March 2021 | 36 |
April 2021 | 87 |
May 2021 | 275 |
June 2021 | 217 |
July 2021 | 177 |
August 2021 | 175 |
September 2021 | 167 |
October 2021 | 188 |
November 2021 | 196 |
December 2021 | 137 |
January 2022 | 190 |
February 2022 | 259 |
March 2022 | 345 |
April 2022 | 431 |
May 2022 | 420 |
June 2022 | 260 |
July 2022 | 304 |
August 2022 | 360 |
September 2022 | 385 |
October 2022 | 444 |
November 2022 | 527 |
December 2022 | 517 |
January 2023 | 514 |
February 2023 | 656 |
March 2023 | 777 |
April 2023 | 684 |
May 2023 | 663 |
June 2023 | 493 |
July 2023 | 505 |
August 2023 | 503 |
September 2023 | 698 |
October 2023 | 950 |
November 2023 | 832 |
December 2023 | 709 |
January 2024 | 813 |
February 2024 | 703 |
March 2024 | 1,038 |
April 2024 | 1,139 |
May 2024 | 964 |
June 2024 | 696 |
July 2024 | 712 |
August 2024 | 690 |
September 2024 | 603 |
Citing articles via.
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
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.
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.
Email citation, add to collections.
Your saved search, create a file for external citation management software, your rss feed.
Affiliation.
Case and grounded theory are two methods of qualitative research. Both methods have their roots in sociology and are focused on understanding, explaining, and/or predicting human behavior. They are ideal methods for nursing research, as they are useful for exploring human responses to health problems. The theoretical underpinnings, methodologies, strategies for data collection, requirements for trustworthiness, and examples of research using case and grounded theory are described.
PubMed Disclaimer
Full text sources.
NCBI Literature Resources
MeSH PMC Bookshelf Disclaimer
The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.
Theory development.
Grounded theory proposes that careful observation of the social world can lead to the construction of theory (Rice & Ezzy, 1999). It is iterative and evolving, aiming to construct new theory from collected data that accounts for those data. It is also known as the “grounded theory method”, although the terms have become interchangeable (Bryant & Charmaz, 2007).
Grounded theory characteristics include:
Notably, data collection is cyclical and reflective. This is different from the more linear processes occurring in other methodologies.
Theoretical sampling is a key aspect of the sampling stage of grounded theory. Recruitment continues until the sample finally represents all aspects that make up the theory the data represent (Starks & Brown Trinidad, 2007). Participants are recruited based on their different experiences of a phenomenon.
Researchers collect participant data using these methods:
Focus groups and interviews are typically being more practical in health research than observation (Starks & Brown Trinidad, 2007).
After the initial phase of data collection, researchers repeat the following cycle of steps:
Researchers’ developing understanding of the concepts, categories and relationships informs their actions at each step. These elements result in a theoretical framework explaining the data.
This cycle reflects two crucial components of grounded theory:
Attree, M. (2001). Patients' and relatives' experiences and perspectives of 'Good' and 'Not so Good' quality care . J Adv Nurs , 33(4), 456-466. doi: 10.1046/j.1365-2648.2001.01689.x
Lingard, L., Reznick, R., Espin, S., Regehr, G., & DeVito, I. (2002). Team communications in the operating room: talk patterns, sites of tension, and implications for novices . Acad Med , 77(3), 232-237. doi: 10.1097/00001888-200203000-00013
Pettersson, S., Ekstrom, M. P., & Berg, C. M. (2013). Practices of weight regulation among elite athletes in combat sports: a matter of mental advantage? J Athl Train , 48(1), 99-108. doi: 10.4085/1062-6050-48.1.04
Bryant, A., & Charmaz, K. (2007). The SAGE handbook of grounded theory : SAGE Publications Ltd.
Charmaz, K. (2017). An introduction to grounded theory : SAGE Publications Ltd.
Lingard, L., Albert, M., & Levinson, W. (2008). Grounded theory, mixed methods, and action research . BMJ , 337, a567. doi: 10.1136/bmj.39602.690162.47
Rice, P. L., & Ezzy, D. (1999). Qualitative research methods: a health focus . South Melbourne, Australia: Oxford University Press.
Starks, H., & Brown Trinidad, S. (2007). Choose Your Method: A Comparison of Phenomenology, Discourse Analysis, and Grounded Theory . Qualitative Health Research , 17(10), 1372-1380. doi: 10.1177/1049732307307031
Most recent answer.
Get help with your research
Join ResearchGate to ask questions, get input, and advance your work.
Research design and major issues in developing dynamic theories by secondary analysis of qualitative data, the philosophical and methodological approaches used by sport and business management student researchers in zimbabwe., factors influencing knowledge sharing amongst higher education academics at a university in south africa, 3 . 1 phase 1 : background knowledge building, an overview of grounded theory design in educational research.
Participatory modeling for high complexity, multi‐system issues: challenges and recommendations for balancing qualitative understanding and quantitative questions, managing enterprise resource planning and multi-organisational enterprise governance:a new contingency framework for the enterprisation of operations, understanding responsible management: emerging themes and variations from european business school programs, mechanisms for emergent usage of adaptive information systems: a critical realist case of e-financial systems in south africa, 108 references, qualitative methods in research on teaching, explorations of the usefulness of case study evaluations, the case-study method in psychology and related disciplines, qualitative research and case study applications in education.
Towards coherent pluralism in management science, present positions and future prospects in management science, triangulation in research: two confusions, researching your own practice: the discipline of noticing, advancing rigorous methodologies: a review of “towards rigor in reviews of multivocal literatures… ”, related papers.
Showing 1 through 3 of 0 Related Papers
Click through the PLOS taxonomy to find articles in your field.
For more information about PLOS Subject Areas, click here .
Loading metrics
Open Access
Peer-reviewed
Research Article
Roles Conceptualization, Formal analysis, Methodology, Supervision, Validation, Writing – original draft
* E-mail: [email protected]
Affiliation School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
Roles Conceptualization, Methodology, Resources, Writing – review & editing
Roles Formal analysis, Methodology, Resources, Validation
Affiliation Ericsson China Academy, Ericsson (China) Communications Company Ltd, Chongqing, China
Open-source communities(OSCs) are gaining significant attention in the current business environment of information technology(IT). More and more IT companies and individuals are exploring how to achieve innovation through open-source collaboration, and value co-creation(VCC) in the OSCs has become a trend. Therefore, it is particularly important to examine the mechanism of OSCs under the background of VCC theory. This study proposes a conceptual framework of open-source value co-creation (OSVCC), which is characterized by openness, sharing, collaboration, and freedom, for understanding the internal mechanisms and contextual conditions in the relationship between OSCs participants. This study constructed a pairwise combined four-category classification model combining the perspectives of the commercialization level (low and high) and the maturity stage (developmental and mature) of the OSCs. Based on the model, this study selects and analyzes four presentive cases of OSCs using a multiple case study approach. Then, this study proposes a framework for OSVCC to identify the crucial factors that promote the successful implementation of innovation and value creation. The OSVCC framework encompasses three primary participants, effective VCC processes, and key open-source principles. This study offers valuable managerial implications for enterprises that plan to participate in OSCs.
Citation: Luo Y, Jin Y, Ji Y (2024) Explore an open-source value co-creation framework: A multiple case study. PLoS ONE 19(9): e0310516. https://doi.org/10.1371/journal.pone.0310516
Editor: Silvia Escribano Cubas, University of Alicante Faculty of Health Sciences: Universitat d’Alacant Facultat de Ciencies de la Salut, SPAIN
Received: November 15, 2023; Accepted: September 3, 2024; Published: September 20, 2024
Copyright: © 2024 Luo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data are publicly available at the following Open Science Framework link: https://osf.io/qy7wr/ .
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
The open-source concept, representing the spirit of new capitalism, is a recent economic phenomenon in information technology(IT) that has emerged in the 21st century [ 1 ]. The flourishing growth of open-source communities(OSCs) has had a significant impact on the development of the digital economy, such as Linux and Android significantly contribute to the IT infrastructure of the digital economy. Consequently, the value creation of OSCs has attracted the research interest of scholars in the management field. Lin and Maruping [ 2 ] considered the sharing of inward knowledge and the reuse of outward knowledge to be a mechanism for creating value in open-source software. Shaikh and Levina [ 3 ] suggested that the active participation of software users in healthy OSCs can lead to long-term value creation. Mouakhar and Tellier [ 4 ] argued that open-source software is a global phenomenon that creates value, and it is essential for the world to collaborate in order to protect the values and principles of open-source software.
The value of open-source is undoubtedly realized through the co-creation of community participants. Value co-creation(VCC) theory is a consumer-centered theory in which suppliers and consumers co-create value [ 5 ]. It emphasizes that value is co-created through the interaction and cooperation among suppliers, consumers, partners, as well as other stakeholders [ 6 ]. However, we find that OSCs are rarely examined under the framework of VCC. There is no supplier-customer relationship in the OSCs, which may not apply VCC theory. OSCs are the unique phenomenon to the IT industry, and the existing VCC framework could not cover such segmented areas. In order to address the above problem of adaptation, Yang and YS [ 7 ] introduced the concept of Open-Source Value Co-Creation(OSVCC) which is a VCC system based on common goals and values and following the rules of OSCs, and emphasized that OSVCC is distinct from customer experience [ 8 ] and service-dominant(S-D) logic [ 9 ]. OSVCC, as a "beyond software" paradigm, prompts the IT companies to shift from product or service focus to engagement focus [ 10 ]. Given that OSVCC serves as a critical foundation for the digital economy, it is imperative to develop a conceptual framework for OSVCC.
To bridge the aforementioned theoretical gap, we begin with a pairwise combined four-category classification model along the dimensions of maturity and commercialization. Building on this model, we select four typical cases and detail our case analysis, and then propose a conceptual framework of OSVCC. We discover the relationship between the participants of OSVCC is no longer a supplier-customer relationship, but a triangle relationship, and the decision of either party may affect the stability of the triangle relationship. This framework stresses the importance of open-source collaboration and organization learning in OSCs. Our findings bridge the theoretical gap, provide valuable insights for understanding the VCC theory in OSCs, and guide enterprises and individuals involved in OSCs. Finally, we discuss both theoretical and managerial implications.
In this section, we review the relevant theories that will be used in this paper. Firstly, we compare the differences between OSVCC and other VCC research perspectives and review the past research of VCC framework. Then, we introduce the business ecosystem theory and open innovation theory related to OSCs which will be the theoretical basis for the construction of the classification model in the next section.
After more around 20 years of development, VCC theory has formed two main research perspectives [ 11 ]. One research perspective of the VCC theory is based on customer experience, and it mainly focuses on the creation of personalized customer experience value through the interaction between customers and suppliers [ 8 , 12 ]. However, there are only participants and contributors, and no clear customer-supplier distinction in the OSCs. Another one is based on S-D logic [ 9 , 13 ], including service logic, service science, and service ecosystem [ 14 ]. The S-D logic emphasizes that service exchange is the core of value creation, and value is determined and co-created by customers [ 15 , 16 ]. The S-D logic is an economic model proposed based on the macro perspective of economic development and evolution mode [ 17 ] and rarely guides individuals and enterprises from the micro perspective of enterprise strategic management [ 18 ].
Therefore, it is difficult for the two mainstream research perspectives of VCC to be directly applied to the research of OSCs, but a few scholars have made some attempts. Battistella, et al . [ 19 ]emphasized that VCC in OSCs relies heavily on the contributions of user innovators, and the sustainability of enterprise participants depends on the reputation of the firm and the improvement of working knowledge. Nagle [ 20 ] elaborates on the idea of gaining a competitive advantage by participating in the VCC of open-source software. It will not only bring multiple benefits and competitive advantages to individuals or organizations but also can promote social responsibility and citizenship, bringing more social value to an individual or organization [ 20 , 21 ]. Based on reviewing nearly two decades of articles on open-source software and value creation, Yang and YS [ 7 ] proposed OSVCC which is a VCC ecosystem characterized by openness, sharing, collaboration, and freedom, with open-source code as the carrier and multiple participants collaborative innovation. In OSVCC, open-source participants have the opportunity to actively engage in innovation, designing, programming, as well as decision-making regarding software development [ 7 ]. Compared to customer experience and S-D logic, OSVCC is more specific, focuses on VCC in OSCs, and emphasizes contributors co-create value through collaboration in OSCs(see Table 1 ).
https://doi.org/10.1371/journal.pone.0310516.t001
Scholars have shown sustained interest in researching VCC models for the past 20 years, resulting in some research findings. Prahalad and Ramaswamy [ 12 ] proposed the DART model, which categorized the structure of VCC into four subdimensions: dialogue, access, risk, and transparency. After conducting in-depth interviews with executives from 18 large organizations, Payne, et al . [ 15 ] proposed a VCC process framework that divided the process into three parts: customer VCC, supplier VCC, and encounters. Since then, scholars have sequentially refined the framework for the process of VCC in knowledge-intensive industries [ 22 ] and the mechanism for ecosystems of innovation [ 23 ]. However, the existing VCC framework does not target the OSCs. So, it is not suitable for those enterprises and individuals who wish to participate in the OSCs. Therefore, it is essential to construct an OSVCC framework, which has significant theoretical and practical implications for understanding the mechanism of value creation within OSCs. The framework could guide programmers who want to participate in OSCs and aid IT enterprises that plan to join OSCs in formulating open-source strategies.
The concept of a business ecosystem extends the idea of "ecology" in biology to the field of business. It highlights the symbiotic coexistence of enterprises and stakeholders, as well as the ability of enterprises to lead and cultivate the ecosystem. Moore [ 24 ] initially proposed the concept of the business ecosystem and defined the business ecosystem as an "economic union based on organizational interaction". Later, in order to further clarify the internal structural characteristics and evolution mechanism of the business ecosystem, Moore [ 25 ]pointed out that the business ecosystem is a dynamic structural system composed of organizations or groups with certain interests. After that, many scholars have devoted themselves to the study of business ecosystems. Iansiti and Levien [ 26 ] proposed the concept of niche to illustrate the structural characteristics of the business ecosystem. From the perspective of enterprise network, Den Hartigh, et al . [ 27 ] argued that a business ecosystem is a network of interdependent suppliers and customers around a certain core technology. Senyo, et al . [ 28 ]introduced that the digital business ecosystem is an extension of the business ecosystem, and the core enterprises could create value by building the digital business ecosystem. OSCs are a typical digital business ecosystem, which are online communities around core technology and follow the law of the business ecosystem. Therefore, it is appropriate to analyze OSCs from the perspective of business ecosystem theory. Business ecosystem theory provides a dynamic perspective that helps enterprises position themselves in an open-source environment, as well as establish collaborative relationships with other members of the ecosystem.
For a long time, enterprises have firmly believed that internal research and development capabilities are valuable strategic assets, emphasizing that successful innovation requires strong control. However, Chesbrough [ 29 ]held a different view and proposed the theory of open innovation through research on high-tech companies, advocating for companies to intentionally harness both internal and external innovation. Baldwin and Von Hippel [ 30 ]explored the paradigm shift in innovation patterns by comparing three innovation paradigms: single-user or company innovation, open collaborative innovation, and producer innovation, emphasizing the importance of new business models that support user innovation and open collaborative innovation. Subsequently, Chesbrough and Bogers [ 31 ]expanded the concept of open innovation and proposed that open innovation can be realized through different types of profit and non-profit mechanisms, which is a distributed innovation process that crosses organizational boundaries and effectively manages knowledge flows. OSCs are distributed innovation paradigms formed through open-source collaboration, and OSCs’ participants achieve innovation through knowledge flow across organizational boundaries. Therefore, open-source communities can be analyzed in the context of open innovation theory. Open innovation theory encourages IT enterprises to accelerate innovation processes through the sharing of code, knowledge, and best practices. By participating in OSCs, enterprises could absorb external innovation resources, and at the same time enhance their influence and competitiveness in the commercial market by contributing their own innovation achievements.
This section describes the combined use of the business ecosystem theory and the open innovation theory to develop a pairwise combined four-category classification model of OSCs. Subsequently, we outline the research method and selection of appropriate cases, according to the classification model.
According to the research questions, this paper mainly draws on the business ecosystem theory and open innovation theory to build up a classification model of OSCs. This classification model is to clarify the situational differences and conditions of OSCs, divides all OSCs into four categories, and provides a theoretical basis for case analysis.
Moore [ 24 ] introduced the concept of the business ecosystem and categorized its development into four stages: birth, expansion, leadership, and self-renewal. OSCs are ecosystems in which multiple participants collaborate to create and share value through innovative collaboration. This ecosystem also experiences a gradual process of development and maturity. Based on the research of open source software development process, Petrinja, et al . [ 32 ] proposed the open-source maturity model (OMM) and divided it into three maturity levels: basic, intermediate, and advanced. Kuwata, et al . [ 33 ] also categorized the development of OSCs into five stages: initial, managed, defined, quantitatively managed, and optimized. As technology advances and market needs change, the participants in OSCs continuously adapt their strategies to the changing ecosystem environment. In the early stages, the participants could focus more on the sharing of technology and the rapid iteration of code [ 34 ]. As ecosystems mature, they could shift to a greater focus on collaboration and standardization to ensure compatibility and interoperability between different components. With the increasing influence, participants could focus more on business model innovation and explore how to make money by providing value-added services, customizing solutions, or building specific platforms [ 19 ]. At the same time, they will pay close attention to user feedback and market trends, so as to timely adjust products and services to meet user needs. Ultimately, as the ecosystem stabilizes, participants are likely to place more emphasis on maintaining and optimizing the existing system, while continuing to explore new technological areas to keep the ecosystem dynamic and competitive [ 2 ]. This study introduces a two-stage model for OSCs: the developmental stage and the mature stage. The developmental stage aligns with Moore [ 24 ] proposed concept of birth and expansion, while the mature stage requires stability and the production of reliable open-source software products.
West and Lakhani [ 35 ] were the first to introduce the idea that the research paradigm of open innovation applies not only to enterprises but also to the study of OSCs. Morgan and Finnegan [ 36 ] argued that open-source software represents the most developed form of open innovation, and highlighted the benefits of engaging in OSCs, such as reducing costs through inward participation and creating value through outward participation. Chesbrough [ 37 ] believes that to obtain innovation profits, enterprises not only need technological innovation but also need appropriate business models to commercialize the technology. Enterprises can generate value by commercializing technology, and make strategic choices regarding open-source technologies [ 38 ]. Chesbrough and Schwartz [ 39 ]highlights the importance of co-development partnerships in business models, which are mutually beneficial working relationships between two or more parties aimed at creating and providing new products, technologies, or services. This partnership could allow OSCs to be commercialized without waiting for the technology to mature. The enterprise that dominates the community influences the commercialization of the community, leading to varying degrees of commercialization. When the dominant enterprise chooses to start commercialization, it usually needs to consider its own business situation[ 40 ] and the co-development relationships between participants in the community[ 41 ]. Therefore, we classify OSCs into two grades in the commercialization dimension: low and high.
According to the above analysis, OSCs can be classified into two dimensions: maturity and commercialization. In the developmental phase, OSCs have constructed a collaborative ecosystem and continue to attract a variety of participants. In the mature phase, OSCs have established stable cooperation among participants, gained influence in specific areas, and even become leaders in segment technology areas. The degree of commercialization is usually determined by the founder of the OSC. Founders who choose low commercialization often have alternative sources of revenue and strong financial resources that prioritize the long-term benefits of their open-source strategy. On the other hand, founders who choose to be highly commercial often focus solely on open-source projects or are enticed by significant market profits from open-source commercialization. We divide all OSCs into four categories through the two dimensions of maturity and commercialization. The resulting classification model for the OSCs is presented in Fig 1 .
https://doi.org/10.1371/journal.pone.0310516.g001
This study aims to investigate a framework for OSVCC to identify the crucial factors in OSCs that promote the successful implementation of innovation and value creation. The case study method is an effective approach for constructing and validating theories [ 42 ] and does not rely on previous empirical evidence or existing literature. It is suitable for addressing the “how” problems and exploring new phenomena that have emerged in practice. Therefore, the research objective aligned well with the case study method. In contrast to the use of a single case study, the multiple case study method allows for a more comprehensive understanding of the mechanism of how a phenomenon occurs relative to the cases studied. Additionally, it aids in expanding the existing theoretical system and constructing a new theoretical framework [ 43 ].
The conclusions drawn from a multiple case study are more general and persuasive, facilitating the extraction of stable and universal propositions from OSCs. Moreover, the multiple case study method effectively reveals the relationships between elements and also provides a deep understanding of the mechanisms of OSCs. Therefore, this study employs multiple case study methods to enhance the generality and validity of the conclusions and identify the crucial factors of OSVCC.
This study adopted the theoretical sampling method for case selection, which involved selecting and collecting the raw data according to the research objectives. This study primarily investigated the operational mechanism through the dimensions of maturity and commercialization of OSCs to develop a conceptual framework. Four OSCs were selected on the basis of the research questions and analytical method, using the following screening criteria:
Based on the above criteria, the four OSCs selected were TiDB, OpenHarmony, OpenAnolis, and PaddlePaddle. Table 2 presents the background information for each project.
https://doi.org/10.1371/journal.pone.0310516.t002
The data were collected from multiple sources, ensuring the reliability and validity of each case study through mutual verification [ 43 ]. This paper primarily collected data from online sources and interviews. The online data collection process occurred between September 2021 and September 2023 and comprised internal and external data. The internal data encompasses content from the community’s official website, official WeChat account, GitHub, Gitee, and corporate annual reports. The external data includes third-party research reports, news, and literature databases. The authors thoroughly reviewed and compared these data, and continuously replenished during the analysis. Additionally, cross-verification was conducted using multiple data sources to ensure the reliability, authenticity, and validity of the research. The data sources for each case study are displayed in Table 3 .
https://doi.org/10.1371/journal.pone.0310516.t003
The interviews were conducted via video conference during March 2024, and the interview data were used for the theoretical saturation. Using a semi-structured protocol, the interviews were conducted with a total of 14 participants and aimed at collecting the opinions of the respondents on the draft framework. The interviewees came from different organizations with different roles in the OSCs, and each interview involved at least two authors. Each interview lasted between 30 and 40 minutes, and the iFLYREC software was used to convert speech to text. After the interviews, the two authors cross-checked the audio recordings and texts to ensure the accuracy and credibility of the information. The interview information is displayed in Table 4 .
https://doi.org/10.1371/journal.pone.0310516.t004
This paper recruited multi-level coding to analyze the online data, including internal and external data of the case. The coding process was independently completed by one author, and the other author was responsible for review. Then, the two authors discussed and finalized the coding. The data analysis process adopted the grounded theory method, which decomposes and conceptualizes the data, and then forms a certain logical relationship diagram[ 44 ]. It was mainly divided into the following three steps [ 45 ]: In the first step, open coding was used to classify the data by source in order to identify categories and sub-categories. The second step is axial coding, which is the process of conceptualization of the core elements. The third step is selective coding to conceptualize the core elements of the OSCs.
In order to determine the type of OSC belongs, this study systematically screened a vast number of primary materials, and identified the relevant information, using a four-step process as follows:
In order to minimize bias resulting from subjective factors, it is essential to utilize objective data for clear and unambiguous measurements of the key concepts during the process of analyzing the cases. To assess the OSCs’ maturity, this study focuses on the number of stars, PR, and forks acquired by each case on the GitHub and Gitee platforms. To confirm the level of commercialization, this study examines the number of users, commercial cases, and business partnerships, and focuses on considering the business cases implemented in critical business systems. These measures ultimately affect the determination of the maturity stage and the commercialization of the cases.
This section provides a comprehensive analysis of each case, based on the four categories of OSCs in the classification model: high commercial maturity(HCM), high commercial development(HCD), low commercial development (LCD), and low commercial maturity (LCM). Following the case study analyses, the results show that three types of participants can be identified as the primary participants in the OSVCC.
TiDB, an open-source relational database, was independently designed and developed by PingCAP in 2015. It was announced as being open-source on the day of its release and has been adopted by over 2,000 companies in various industries as a production environment. Currently, the TiDB project has garnered over 34,000 stars on GitHub and has attracted more than 2,100 open-source contributors. It has established itself as a top-tier database project in the global IT field. TiKV, the storage engine of TiDB, graduated from the Cloud Native Computing Foundation (CNCF) in September 2020. It was the second Chinese original open-source project to graduate from CNCF.
After 8 years of rapid development, the TiDB community has become highly active, with frequent updates of both the English community on GitHub and the Chinese community on its official website. Currently, it has achieved an impressive milestone with 34,000 stars and 96,000 PR, which is remarkable for a database OSC. Therefore, the TiDB can be considered as a mature stage of OSC. In terms of commercialization, TiDB has also experienced rapid growth, attracting over 2,000 corporate users from important industries in sectors such as finance, the internet, and government. These users include well-known financial institutions, such as Ping An Insurance (the largest insurance company in China) and the Bank of China (one of the largest banks in China), renowned internet companies such as Meituan (one of the most popular APPs in China) and Zhihu (the largest Chinese knowledge-sharing community in the world), as well as significant state-owned corporations, such as China Telecom and State Grid. Consequently, this study identifies the TiDB community as a highly commercial and mature OSC.
The TiDB community consists of a wide variety of participants, who are mainly divided into developers and users. PingCAP, as the primary developer and contributor to TiDB, has assumed the role of the community leader. It has achieved this leadership position by actively contributing code, sponsoring various community activities, and influencing the technical direction. The users of TiDB primarily consist of enterprise users, encompassing both free and paid users. Paid users can be further categorized into subscription-based users and on-demand cloud users, depending on their terms of payment. The TiDB community encompasses both enterprise developers and individual developers, although the number of enterprise developers is relatively small. Nevertheless, individual developers play crucial roles within the community, with some having made continual contributions for nearly 8 years. To facilitate the users’ engagement, the TiDB community has established a user organization called the TiDB User Group (TUG), which actively encourages users to provide feedback. Relationships between the participants of the TiDB community. The relationship between the participants of the TiDB community is shown in Fig 2 .
https://doi.org/10.1371/journal.pone.0310516.g002
OpenHarmony is an open-source operating system that was donated by Huawei to the OpenAtom Foundation in 2020. It primarily targets consumer and industry markets for terminal devices and aims to provide a unified operating system for various smart devices. Right now, the Harmony OS, which is based on OpenHarmony, has become the third-largest operating system for smart devices in the world. OpenHarmony holds the top position in the Gitee Activity Index(GAI) and has 51 co-building organizations and over 5000 contributors. It is highly sought after as one of the most popular OSC in China.
The OpenHarmony community was established in September 2020 and has experienced rapid growth. The number of contributors increased from 1,000 in 2021 to 5,000 in 2022. Thus far, it has achieved excellent results on Gitee, with 22,000 stars and 55,000 forks. However, there is still a significant gap compared with mature smart operation system communities, such as Android, because only a handful of handset manufacturers have developed operating systems based on OpenHarmony. Therefore, this study considers that the OpenHarmony project is still in the developmental stages of OSC. Despite being established only 2 years ago, the OpenHarmony community has already achieved significant milestones in terms of commercialization. On the one hand, Huawei, as a significant player in the global consumer electronics market, saw a surge of 180 million new terminal devices using Harmony OS in 2022. These users have become the cornerstone of the OpenHarmony ecosystem. On the other hand, enterprise developers have issued multiple commercial operating systems derived from the OpenHarmony system, which have been implemented across a wide range of smart devices and industry solutions. Therefore, this study identifies that the OpenHarmony community is a highly commercial and developmental OSC.
The OpenHarmony community comprises the OpenAtom Foundation, the developers, and the users. The OpenAtom Foundation, as a nonprofit public welfare organization, has assumed the responsibility for the daily management and organization of the community’s activities. Huawei, as one of the founders of the OpenAtom Foundation and the primary contributor to the OpenHarmony project, plays a prominent role as a corporate developer within the community. Huawei actively contributes code, sponsors community activities, and engages in the governance of the community, thus taking a leading position in the OpenHarmony community. Apart from Huawei, the community of enterprise developers comprises 51 companies, including Kaihong, Hoperun, Pateo. Collectively, these enterprise developers have launched more than 100 commercial products, including software and hardware, built on the OpenHarmony platform. Additionally, some individual developers and free users are actively participating in the community’s co-construction. The relationship between the participants of the OpenHarmony community is shown in Fig 3 .
https://doi.org/10.1371/journal.pone.0310516.g003
The OpenAnolis community is an upstream Linux distribution community that was initiated by Alibaba in October 2020. Its main objective is to develop an open-source operating system called Anolis OS. Anolis OS supports various computing architectures, optimizes for cloud scenarios, and is compatible with the software ecosystem of CentOS.
The goal of Anolis OS is to provide developers and operations personnel with a stable, high-performance, secure, reliable, and open-source operating system, whose main users are the original CentOS users. Currently, OpenAnolis has garnered only 300 stars on Gitee and is in the donation period of the OpenAtom Foundation. Thus, this study posits that OpenAnolis is in the developmental stage of OSC. In terms of commercialization, Anolis OS was already widely used within the Alibaba Group before it became open-source software. The OpenAnolis 2022 whitepaper revealed that the majority of commercial cases are derived from the in-house applications of Alibaba Group, with fewer instances of external commercial utilization. Consequently, this study classifies the OpenAnolis community as a low commercial and developmental OSC.
The OpenAnolis community was established 2 years ago and is pursuing a distinct pathway compared with other Linux communities. In contrast to the conventional dual-helix model followed by operating systems providers and chip manufacturers, the OpenAnolis community actively incorporates public cloud service providers, such as Alibaba Cloud, China Telecom Cloud, and China Mobile Cloud, forming a collaborative model with chip manufacturers, referred to as the "Iron Triangle". Alibaba, as the primary contributor and user of Anolis OS, has assumed a leading role. Enterprise developers, including chip manufacturers, public cloud providers, and system integrators, cooperate to contribute to the community. The chip manufacturers primarily focus on adapting Anolis OS, while the public cloud providers serve as promotional channels. Additionally, a small number of individual developers participate in the collaborative development of the OpenAnolis community. The relationship between the participants of the OpenAnolis community is shown in Fig 4 .
https://doi.org/10.1371/journal.pone.0310516.g004
PaddlePaddle, launched by Baidu in 2015, was the first open-source deep-learning platform in China. PaddlePaddle integrates core frameworks, basic model libraries, end-to-end development kits, and a wide range of tool components. It enables developers to efficiently implement artificial intelligence (AI) ideas, develop new AI applications, and support various industries in achieving industrial intelligence upgrades.
The PaddlePaddle community has been established for 8 years and currently has 3600 followers on GitHub, with over 80,000 stars. PaddlePaddle is the most active Chinese OSC on GitHub and is one of the most widely used AI frameworks in China. Therefore, this study asserts that PaddlePaddle is a mature OSC. However, in terms of commercialization, the majority of PaddlePaddle’s users are utilizing the free community version, and there is a limited number of paid users. Despite Baidu’s efforts to expand its commercial development suite, the large-scale commercial AI system in China continues to be predominantly deployed on other open-source frameworks, such as TensorFlow and PyTorch. Consequently, this study classifies the PaddlePaddle community as a low commercial and mature OSC.
The PaddlePaddle community comprises many developers and users. Baidu, as the initiator and primary contributor of the community, has assumed a leading role. Additionally, Baidu is the largest commercial user of PaddlePaddle. PaddlePaddle community encompasses a significant number of individual developers, primarily consisting of enthusiasts, university students, and researchers. These individual developers are also the users of the software. While contributing to the community, individual enthusiasts also have access to the latest version of the software at no cost. Moreover, a small number of enterprise developers are in the community, representing various industries, who utilize PaddlePaddle to enhance their respective companies’ productivity. The relationship between the participants of the PaddlePaddle community is shown in Fig 5 .
https://doi.org/10.1371/journal.pone.0310516.g005
The realization of VCC varies depending on the differences in the participant’s relationships. Hence, it is essential to consider the variations between the participants. According to the analysis of the aforementioned four cases, this study posits that the following three roles are the primary participants in OSCs:
In the TiDB case, the DP, PingCAP, has profited from a high degree of commercialization, so it continues to provide sufficient support for the community and actively participates in OSVCC. Mature communities could provide rapid response and powerful supporting facilities for EUs, so EUs actively participate in OSVCC; however, higher maturity suppresses the space in which enterprises can participate and inhibits their willingness to develop, so the number of EDs in the community is small. IUs could get more effective support in mature communities, so they are willing to actively participate in contributions; at the same time, the participation of IDs in well-known open-source projects brings them prestige and job opportunities, so IPs actively participate in OSVCC.
In the OpenHarmony case, although the DP, Huawei, has benefited from commercialization, the community is still in the developmental stage and needs to attract more participants to make positive contributions. The community is not mature yet, but it has certain commercialization prospects, which means market opportunities, so EDs and EUs actively participate in OSVCC. Higher commercialization prospects could attract IDs, but the community is still in the developmental stage and the supporting facilities are not ready, resulting in fewer IDs and IUs joining in OSVCC.
In the case of OpenAnolis, the open-source software has already been used internally for several years, and the DP, Alibaba, could contribute accumulated experience to the community to accelerate OSVCC collaboration. Since the community is still in its infancy and the commercial prospects are unclear, EUs are usually not actively involved in OSVCC. However, considering the huge internal use demand of DP, EDs join in OSVCC collaboration under the influence of DP. In this case, IDs and IUs are mainly participating in the community out of interest, and a small quantity.
In the case of PaddlePaddle, the software has widely and fully utilized the DP’s internal and external resources, but there are shortcomings in commercial promotion. EDs prefer to use the mainstream AI framework in large-scale commercial use scenarios and try to participate in OSVCC in small-scale scenarios. Due to personal interest, the popularity of the community, and the complete supporting facilities, IPs and IUs actively participate in OSVCC collaboration. The comparison of participants in the four cases is shown in Table 5 .
https://doi.org/10.1371/journal.pone.0310516.t005
This section describes the main components of the new conceptual framework based on the principles of VCC and reveals the OSVCC mechanism. This new framework is an extension of the classic VCC framework.
As previously described, this study identifies that all four selected cases consist of three key types of participants: DP, EPs, and IPs. This deviates from the typical scenario of VCC that involves only suppliers and customers. Consequently, the framework of OSVCC needs to consider the three participants and their interrelationships, resulting in a more intricate architecture. The classic VCC framework proposed by Payne, et al . [ 15 ] comprises three parts: the customers’ VCC process, the suppliers’ VCC process, and the encounter process. Therefore, a framework comprising six processes, based on the participants and their relationships, is presented in Table 6 .
https://doi.org/10.1371/journal.pone.0310516.t006
This paper provides an overview of the framework structure and explains the components in detail, as shown in Fig 6 . In this framework, the participants in the OSVCC are more specific and explicit compared with the classical VCC framework. The DP typically initiates open-source projects, but the DP neither owns the final product nor has an employer-employee relationship with the other participants [ 49 ]. DP serves as the primary driving force behind the OSCs’ development and reaps the most significant social and commercial benefits. EPs have a complementary role in the open-source community, and their relationship with the DP is delicate [ 41 ]. EPs primarily comprise the upstream and downstream enterprises of the DP’s value chain. IPs frequently serve as the most active participants in the framework, deriving great satisfaction from contributions [ 50 ]. IPs join the community on the basis of their interests or career development. These three primary participants and the interactions between them constitute the crucial factors of OSVCC. Participants collaborate and work interdependently, while maintaining their independence to create value, guided by the principles of openness, sharing, collaboration, and freedom.
https://doi.org/10.1371/journal.pone.0310516.g006
Theoretical saturation means that the collected data will no longer generate new categories or theoretical discoveries [ 51 ]. This paper used a semi-structured interview and 14 participants were interviewed for their opinions on the framework of OSVCC. The two authors continued to analyze the interview data in a multilevel coding method. The results of the analysis showed that the concepts and categories obtained have been included in the existing categories, and no new concepts and categories have been generated. Therefore, this paper believes that the conceptual framework of OSVCC has a good theoretical saturation and can reflect the nature of the phenomenon.
5.2.1 the dp’s osvcc process..
The DP is usually an IT company with specific strengths, and its OSVCC process primarily involves breaking down organizational boundaries and engaging extensively with the OSCs. Payne, et al . [ 15 ] believe that supplier achieves VCC by designing and delivering relationship experiences and promoting organizational learning. Hence, in the context of OSVCC, the VCC process of the DP encompasses four stages:
The DP’s OSVCC process involves formulating open-source strategies, establishing open organizational structures, and attracting both EPs and IPs to join the OSVCC in order to collectively build an ecosystem. The DP also facilitates organizational learning through interactions with the other participants. Collaboration with other participants will bring new knowledge, resources, and opportunities for innovation to the DP, and helps the DP have a better understanding of the market’s demands and co-creating value. The DP’s learning enables the DP to continuously enhance its capabilities and adaptability, leading to improved VCC and business performance.
The EPs typically assign employees to engage in OSCs, contributing to the development, maintenance, and promotion of open-source projects, while also providing feedback to the OSCs regarding the use of open-source software. The EPs play dual roles as both supplier and customer in the OSVCC framework, compared with the classic framework proposed by Payne, et al . [ 15 ]. The EPs actively engage in designing and delivering relationship experiences, continuously contribute to the OSCs through their cognitive and behavioral efforts, and facilitate organizational learning.
The organizational learning of EPs is similar to that of the DP, which enhances the exchange of knowledge with the external world by opening up organizational boundaries. The difference lies in the proactive and strategic learning of the DP, compared with the passive and responsive organizational learning of EPs. EPs, as upstream and downstream companies in the value chain, strategically comply with the DP and choose to participate in specific open-source projects. Organizational learning enables the EPs to enhance their technological and innovative capabilities, as well as to promote VCC with other participants.
The IPs are normally independent developers, including enthusiasts and technical experts. The IPs contribute to innovation and diversity, offer their labor and time voluntarily, and foster a culture of learning in the OSCs. Payne, et al . [ 15 ] suggest that the customer’s value co-creating process is characterized by its dynamic, interactive, and nonlinear nature, and often occurs unconsciously. In the OSVCC framework, the IPs’ engagement in the relationship experience encompasses emotional, cognitive, and behavioral elements:
The IPs are highly proactive in their pursuit of learning and personal development, and not only improve their own skills but also enhance their professional prospects. The IPs engage in continuous learning while solving problems and completing tasks, actively seeking new knowledge and skills to enhance their contributions and value. The IPs actively participate in various activities and discussions, including conferences, seminars, and workshops, to foster face-to-face communication with other participants. The active involvement of IPs contributes vibrancy and creativity to OSCs.
Encounter processes are collaborative practices involving the exchange of resources and engagement in activities between suppliers and customers, including communication, use, and service [ 15 ]. Encounter processes become more complex in the OSVCC framework because it involves three participants. This study’s primary objective was to propose the framework for OSVCC, and thus it does not delve into an in-depth discussion of the encounter processes between the participants, although it is important to acknowledge their presence. The authors argue that the encounter processes between the participants should align with the principles of OSVCC, which include openness, sharing, collaboration, and freedom [ 7 ]. Participants should design and implement encounter processes according to these principles. Furthermore, there is a game of interest among the participants during the encounter processes [ 41 ].
The open-source concept has emerged as an important software development model in the IT industry, and the VCC has been widely embraced as a marketing strategy. However, there is a scarcity of research regarding VCC by OSCs. From a theoretical perspective, this research fills the gap in the existing literature. On the one hand, we systematically constructed the research framework of OSVCC and found the triangular relationship. This framework inherits the widely adopted VCC process model and emphasizes a view of the collaboration of the participants. On the other hand, we creatively combined the business ecosystem theory and the open innovation theory to build a four-quadrant classification model of OSCs. This classification model enables us to examine the existing OSCs from two dimensions: maturity and commercialization.
From a practical perspective, this research provides four managerial implications for the enterprise. Firstly, the complexity of the OSVCC process challenges traditional VCC management practices. The relationship between the participants in VCC is no longer the supplier-customer relationship, but the triangular relationship. The enterprise’s decision may affect the other two participants and ultimately affect the stability of the triangular relationship. Secondly, the framework shows that the enterprises choose different focuses in OSVCC. If an enterprise is the founder of the OSC, the enterprise should focus on capturing opportunities, formulating plans, implementing, and evaluating. If an enterprise is an OSC user or developer, it should focus on following the domination enterprise, looking for opportunities and taking actions based on its own cognition. Thirdly, this research stresses the importance of organizational learning. No matter what kind of role, an enterprise needs to break through organizational boundaries to learn and exchange knowledge when participating in OSVCC. Finally, the implementation issues of the OSVCC should be of concern. The participants need to understand the process of OSVCC and take the initiative to design and deliver relevant relational experiences. Our research also stresses the importance of organizational learning and knowledge management by managing and designing the encounter process from a long-term perspective.
This study has several limitations that warrant further research and exploration. Firstly, this study employs a static approach, which could not analyze the dynamic development of the cases. It is important to note that a successful OSC typically undergoes a multi-year development process and encounters fluctuations due to internal and external changes. Hence, future research could explore the potential of integrating the framework proposed in this paper to examine the interrelationships among participants, considering the dynamic development perspective. Secondly, this study only examined four Chinese OSCs, but almost all well-known open-source projects are born in the United States. So, the research conclusions may not be globally representative. The authors would welcome the application of the OSVCC framework to OSC studies in other countries. Thirdly, limited by various factors, this paper does not discuss the three encounter processes in the OSVCC in detail. Future research could address a more thorough analysis of the intricate encounter processes among participants by game theory.
We wish to thank Ericsson China Academy for supporting, and appreciate three anonymous reviewers for their insights and helpful comments on previous versions of this article.
IMAGES
VIDEO
COMMENTS
raphy, more interviews in grounded theory) and extent of data collection (e.g., only interviews in phenomenology, multiple forms in case study research to provide the in-depth case picture). At the data analysis stage, the differences are most pronounced. Not only is the distinction one of specificity of the analysis phase (e.g., grounded the-
This article tackles how to adapt grounded theory by blending it with case study techniques. Grounded theory is commended for enabling qualitative researchers to avoid priori assumptions and intensely explore social phenomena leading to enhanced theorization and deepened contextualized understanding. However, it is criticized for generating enormous data that is difficult to manage ...
While studies using grounded theory in management research are becoming more popular, these are often mixed with the case study approach, or they provide contradictory guidelines on how to use it. The aim of this paper is to provide a clear guide for researchers who wish to use grounded theory in exploratory studies in management research.
recommend combining case studies and grounded theory when the researcher aim is to develop theoretical models grounded on the data. To promote a constructivist perspective, researchers can draw predominantly from case study design (Merriam, 1998; Stake, 2006; Yin, 2003) and constructivist grounded theory approach to data analysis (Charmaz, 2006).
Figure 1. Research design framework: summary of the interplay between the essential grounded theory methods and processes. Grounded theory research involves the meticulous application of specific methods and processes. Methods are 'systematic modes, procedures or tools used for collection and analysis of data'. 25 While GT studies can ...
Glaser and Strauss are recognised as the founders of grounded theory. Strauss was conversant in symbolic interactionism and Glaser in descriptive statistics. 8-10 Glaser and Strauss originally worked together in a study examining the experience of terminally ill patients who had differing knowledge of their health status. Some of these suspected they were dying and tried to confirm or ...
Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967. Data shapes the theory: Instead of trying to prove an existing theory, you let the data guide your findings.
In 1967, sociologists Barney Glaser and Anselm Strauss published their seminal book "The discovery of grounded theory: Strategies for qualitative research" (Glaser and Strauss 1967), which lays the foundation for one of the most prominent and influential qualitative research methodologies in the social sciences and beyond.With their focus on theory development, they dissociate themselves ...
Since being developed as a research methodology in the 1960s, grounded theory (GT) has grown in popularity. In spite of its prevalence, considerable confusion surrounds GT, particularly in respect of the essential methods that characterize this approach to research. Misinformation is evident in the literature around issues such as the various ...
Grounded theory (GT) is a common qualitative methodology in health professions education research used to explore the "how", "what", and "why" of social processes. With GT researchers aim to understand how study participants interpret reality related to the process in question. However, they risk misapplying the term to studies that ...
n this chapter, we begin our detailed exploration of narrative research, phenomenology, grounded theory, ethnography, and case studies. For each approach, I pose a definition, briefly trace its history, explore types of stud-ies, introduce procedures involved in conducting a study, and indicate poten-tial challenges in using the approach.
Such experience produces rich and complex qualitative data for analysis, conducive to counselling and psychotherapy research. Furthermore, grounded theory (GT) can be adopted to produce a theory from qualitative data, fitting well with case study research that explores complex experiences regarding social, psychological and phenomenological ...
Examples of Grounded Theory in different case studies are as follows: Glaser and Strauss (1965): This study, which is considered one of the foundational works of Grounded Theory, explored the experiences of dying patients in a hospital. The researchers used Grounded Theory to develop a theoretical framework that explained the social processes ...
Grounded theory (GT) is both a research method and a research methodology. There are several different ways of doing GT which reflect the different viewpoints of the originators. For those who are new to this approach to conducting qualitative research, this can be confusing. In this article, we outline the key characteristics of GT and ...
Abstract. Case and grounded theory are two methods of qualitative research. Both methods have their roots in sociology and are focused on understanding, explaining, and/or predicting human behavior. They are ideal methods for nursing research, as they are useful for exploring human responses to health problems.
Definition. Grounded theory proposes that careful observation of the social world can lead to the construction of theory (Rice & Ezzy, 1999). It is iterative and evolving, aiming to construct new theory from collected data that accounts for those data. It is also known as the "grounded theory method", although the terms have become ...
Case study and grounded theory are two of the most popular qualitative research approaches. As more intellectuals have interests in researching social phenomena, the application of case study and grounded theory are growing rapidly. For example most of the medical and psychology research tend to apply case studies while
Classical grounded theory (GT) is arguably one of the most rigorous qualitative research methods, focusing on the development of theory from data grounded in participants' voices. As such, classical GT is an ideal methodological approach for conducting POR due to its rigor, patient-oriented focus, and generation of an empirical model focused ...
The paper will focus on the use of case study and grounded theory as possible methodologies for systems researchers to consider for future research projects. Both methodologies have been ...
Another key thing to thing about when combining case study and grounded theory development is what kind of case you think you have. Robert Yin provides a list of 5 motivations for a case study ...
The paper will focus on the use of case study and grounded theory as possible methodologies for systems researchers to consider for future research projects. Both methodologies have been successfully used by the authors to gain cultural change in organisations striving to become learning organisations.
Most commonly researchers (n = 62) reported using case study along with grounded theory in either their design or their analysis of the data. Methodological approaches used in other studies included action research, critical theory, discourse analysis, ethnography, evaluation study, hermeneutics, life history, mixed methods, narrative research ...
Based on the model, this study selects and analyzes four presentive cases of OSCs using a multiple case study approach. Then, this study proposes a framework for OSVCC to identify the crucial factors that promote the successful implementation of innovation and value creation. ... Strauss A.; Corbin J. Grounded theory methodology: An overview ...
Grounded theory (GT) is a widely applied research method that is spelled out in several books including the foundational work by Glaser and Strauss (1967); the current editions of pathbreaking works by Charmaz (2014), Clarke (2005), and Corbin and Strauss (2015); and the comprehensive outline by Bryant (2017).In these and other contributions, the GT method takes a number of different forms ...