case study grounded theory

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Grounded theory research: A design framework for novice researchers

Ylona chun tie.

1 Nursing and Midwifery, College of Healthcare Sciences, James Cook University, Townsville, QLD, Australia

Melanie Birks

Karen francis.

2 College of Health and Medicine, University of Tasmania, Australia, Hobart, TAS, Australia

Background:

Grounded theory is a well-known methodology employed in many research studies. Qualitative and quantitative data generation techniques can be used in a grounded theory study. Grounded theory sets out to discover or construct theory from data, systematically obtained and analysed using comparative analysis. While grounded theory is inherently flexible, it is a complex methodology. Thus, novice researchers strive to understand the discourse and the practical application of grounded theory concepts and processes.

The aim of this article is to provide a contemporary research framework suitable to inform a grounded theory study.

This article provides an overview of grounded theory illustrated through a graphic representation of the processes and methods employed in conducting research using this methodology. The framework is presented as a diagrammatic representation of a research design and acts as a visual guide for the novice grounded theory researcher.

Discussion:

As grounded theory is not a linear process, the framework illustrates the interplay between the essential grounded theory methods and iterative and comparative actions involved. Each of the essential methods and processes that underpin grounded theory are defined in this article.

Conclusion:

Rather than an engagement in philosophical discussion or a debate of the different genres that can be used in grounded theory, this article illustrates how a framework for a research study design can be used to guide and inform the novice nurse researcher undertaking a study using grounded theory. Research findings and recommendations can contribute to policy or knowledge development, service provision and can reform thinking to initiate change in the substantive area of inquiry.

Introduction

The aim of all research is to advance, refine and expand a body of knowledge, establish facts and/or reach new conclusions using systematic inquiry and disciplined methods. 1 The research design is the plan or strategy researchers use to answer the research question, which is underpinned by philosophy, methodology and methods. 2 Birks 3 defines philosophy as ‘a view of the world encompassing the questions and mechanisms for finding answers that inform that view’ (p. 18). Researchers reflect their philosophical beliefs and interpretations of the world prior to commencing research. Methodology is the research design that shapes the selection of, and use of, particular data generation and analysis methods to answer the research question. 4 While a distinction between positivist research and interpretivist research occurs at the paradigm level, each methodology has explicit criteria for the collection, analysis and interpretation of data. 2 Grounded theory (GT) is a structured, yet flexible methodology. This methodology is appropriate when little is known about a phenomenon; the aim being to produce or construct an explanatory theory that uncovers a process inherent to the substantive area of inquiry. 5 – 7 One of the defining characteristics of GT is that it aims to generate theory that is grounded in the data. The following section provides an overview of GT – the history, main genres and essential methods and processes employed in the conduct of a GT study. This summary provides a foundation for a framework to demonstrate the interplay between the methods and processes inherent in a GT study as presented in the sections that follow.

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 disconfirm their suspicions. Others tried to understand by interpreting treatment by care providers and family members. Glaser and Strauss examined how the patients dealt with the knowledge they were dying and the reactions of healthcare staff caring for these patients. Throughout this collaboration, Glaser and Strauss questioned the appropriateness of using a scientific method of verification for this study. During this investigation, they developed the constant comparative method, a key element of grounded theory, while generating a theory of dying first described in Awareness of Dying (1965). The constant comparative method is deemed an original way of organising and analysing qualitative data.

Glaser and Strauss subsequently went on to write The Discovery of Grounded Theory: Strategies for Qualitative Research (1967). This seminal work explained how theory could be generated from data inductively. This process challenged the traditional method of testing or refining theory through deductive testing. Grounded theory provided an outlook that questioned the view of the time that quantitative methodology is the only valid, unbiased way to determine truths about the world. 11 Glaser and Strauss 5 challenged the belief that qualitative research lacked rigour and detailed the method of comparative analysis that enables the generation of theory. After publishing The Discovery of Grounded Theory , Strauss and Glaser went on to write independently, expressing divergent viewpoints in the application of grounded theory methods.

Glaser produced his book Theoretical Sensitivity (1978) and Strauss went on to publish Qualitative Analysis for Social Scientists (1987). Strauss and Corbin’s 12 publication Basics of Qualitative Research: Grounded Theory Procedures and Techniques resulted in a rebuttal by Glaser 13 over their application of grounded theory methods. However, philosophical perspectives have changed since Glaser’s positivist version and Strauss and Corbin’s post-positivism stance. 14 Grounded theory has since seen the emergence of additional philosophical perspectives that have influenced a change in methodological development over time. 15

Subsequent generations of grounded theorists have positioned themselves along a philosophical continuum, from Strauss and Corbin’s 12 theoretical perspective of symbolic interactionism, through to Charmaz’s 16 constructivist perspective. However, understanding how to position oneself philosophically can challenge novice researchers. Birks and Mills 6 provide a contemporary understanding of GT in their book Grounded theory: A Practical Guide. These Australian researchers have written in a way that appeals to the novice researcher. It is the contemporary writing, the way Birks and Mills present a non-partisan approach to GT that support the novice researcher to understand the philosophical and methodological concepts integral in conducting research. The development of GT is important to understand prior to selecting an approach that aligns with the researcher’s philosophical position and the purpose of the research study. As the research progresses, seminal texts are referred back to time and again as understanding of concepts increases, much like the iterative processes inherent in the conduct of a GT study.

Genres: traditional, evolved and constructivist grounded theory

Grounded theory has several distinct methodological genres: traditional GT associated with Glaser; evolved GT associated with Strauss, Corbin and Clarke; and constructivist GT associated with Charmaz. 6 , 17 Each variant is an extension and development of the original GT by Glaser and Strauss. The first of these genres is known as traditional or classic GT. Glaser 18 acknowledged that the goal of traditional GT is to generate a conceptual theory that accounts for a pattern of behaviour that is relevant and problematic for those involved. The second genre, evolved GT, is founded on symbolic interactionism and stems from work associated with Strauss, Corbin and Clarke. Symbolic interactionism is a sociological perspective that relies on the symbolic meaning people ascribe to the processes of social interaction. Symbolic interactionism addresses the subjective meaning people place on objects, behaviours or events based on what they believe is true. 19 , 20 Constructivist GT, the third genre developed and explicated by Charmaz, a symbolic interactionist, has its roots in constructivism. 8 , 16 Constructivist GT’s methodological underpinnings focus on how participants’ construct meaning in relation to the area of inquiry. 16 A constructivist co-constructs experience and meanings with participants. 21 While there are commonalities across all genres of GT, there are factors that distinguish differences between the approaches including the philosophical position of the researcher; the use of literature; and the approach to coding, analysis and theory development. Following on from Glaser and Strauss, several versions of GT have ensued.

Grounded theory represents both a method of inquiry and a resultant product of that inquiry. 7 , 22 Glaser and Holton 23 define GT as ‘a set of integrated conceptual hypotheses systematically generated to produce an inductive theory about a substantive area’ (p. 43). Strauss and Corbin 24 define GT as ‘theory that was derived from data, systematically gathered and analysed through the research process’ (p. 12). The researcher ‘begins with an area of study and allows the theory to emerge from the data’ (p. 12). Charmaz 16 defines GT as ‘a method of conducting qualitative research that focuses on creating conceptual frameworks or theories through building inductive analysis from the data’ (p. 187). However, Birks and Mills 6 refer to GT as a process by which theory is generated from the analysis of data. Theory is not discovered; rather, theory is constructed by the researcher who views the world through their own particular lens.

Research process

Before commencing any research study, the researcher must have a solid understanding of the research process. A well-developed outline of the study and an understanding of the important considerations in designing and undertaking a GT study are essential if the goals of the research are to be achieved. While it is important to have an understanding of how a methodology has developed, in order to move forward with research, a novice can align with a grounded theorist and follow an approach to GT. Using a framework to inform a research design can be a useful modus operandi.

The following section provides insight into the process of undertaking a GT research study. Figure 1 is a framework that summarises the interplay and movement between methods and processes that underpin the generation of a GT. As can be seen from this framework, and as detailed in the discussion that follows, the process of doing a GT research study is not linear, rather it is iterative and recursive.

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 commence with a variety of sampling techniques, many commence with purposive sampling, followed by concurrent data generation and/or collection and data analysis, through various stages of coding, undertaken in conjunction with constant comparative analysis, theoretical sampling and memoing. Theoretical sampling is employed until theoretical saturation is reached. These methods and processes create an unfolding, iterative system of actions and interactions inherent in GT. 6 , 16 The methods interconnect and inform the recurrent elements in the research process as shown by the directional flow of the arrows and the encompassing brackets in Figure 1 . The framework denotes the process is both iterative and dynamic and is not one directional. Grounded theory methods are discussed in the following section.

Purposive sampling

As presented in Figure 1 , initial purposive sampling directs the collection and/or generation of data. Researchers purposively select participants and/or data sources that can answer the research question. 5 , 7 , 16 , 21 Concurrent data generation and/or data collection and analysis is fundamental to GT research design. 6 The researcher collects, codes and analyses this initial data before further data collection/generation is undertaken. Purposeful sampling provides the initial data that the researcher analyses. As will be discussed, theoretical sampling then commences from the codes and categories developed from the first data set. Theoretical sampling is used to identify and follow clues from the analysis, fill gaps, clarify uncertainties, check hunches and test interpretations as the study progresses.

Constant comparative analysis

Constant comparative analysis is an analytical process used in GT for coding and category development. This process commences with the first data generated or collected and pervades the research process as presented in Figure 1 . Incidents are identified in the data and coded. 6 The initial stage of analysis compares incident to incident in each code. Initial codes are then compared to other codes. Codes are then collapsed into categories. This process means the researcher will compare incidents in a category with previous incidents, in both the same and different categories. 5 Future codes are compared and categories are compared with other categories. New data is then compared with data obtained earlier during the analysis phases. This iterative process involves inductive and deductive thinking. 16 Inductive, deductive and abductive reasoning can also be used in data analysis. 26

Constant comparative analysis generates increasingly more abstract concepts and theories through inductive processes. 16 In addition, abduction, defined as ‘a form of reasoning that begins with an examination of the data and the formation of a number of hypotheses that are then proved or disproved during the process of analysis … aids inductive conceptualization’. 6 Theoretical sampling coupled with constant comparative analysis raises the conceptual levels of data analysis and directs ongoing data collection or generation. 6

The constant comparative technique is used to find consistencies and differences, with the aim of continually refining concepts and theoretically relevant categories. This continual comparative iterative process that encompasses GT research sets it apart from a purely descriptive analysis. 8

Memo writing is an analytic process considered essential ‘in ensuring quality in grounded theory’. 6 Stern 27 offers the analogy that if data are the building blocks of the developing theory, then memos are the ‘mortar’ (p. 119). Memos are the storehouse of ideas generated and documented through interacting with data. 28 Thus, memos are reflective interpretive pieces that build a historic audit trail to document ideas, events and the thought processes inherent in the research process and developing thinking of the analyst. 6 Memos provide detailed records of the researchers’ thoughts, feelings and intuitive contemplations. 6

Lempert 29 considers memo writing crucial as memos prompt researchers to analyse and code data and develop codes into categories early in the coding process. Memos detail why and how decisions made related to sampling, coding, collapsing of codes, making of new codes, separating codes, producing a category and identifying relationships abstracted to a higher level of analysis. 6 Thus, memos are informal analytic notes about the data and the theoretical connections between categories. 23 Memoing is an ongoing activity that builds intellectual assets, fosters analytic momentum and informs the GT findings. 6 , 10

Generating/collecting data

A hallmark of GT is concurrent data generation/collection and analysis. In GT, researchers may utilise both qualitative and quantitative data as espoused by Glaser’s dictum; ‘all is data’. 30 While interviews are a common method of generating data, data sources can include focus groups, questionnaires, surveys, transcripts, letters, government reports, documents, grey literature, music, artefacts, videos, blogs and memos. 9 Elicited data are produced by participants in response to, or directed by, the researcher whereas extant data includes data that is already available such as documents and published literature. 6 , 31 While this is one interpretation of how elicited data are generated, other approaches to grounded theory recognise the agency of participants in the co-construction of data with the researcher. The relationship the researcher has with the data, how it is generated and collected, will determine the value it contributes to the development of the final GT. 6 The significance of this relationship extends into data analysis conducted by the researcher through the various stages of coding.

Coding is an analytical process used to identify concepts, similarities and conceptual reoccurrences in data. Coding is the pivotal link between collecting or generating data and developing a theory that explains the data. Charmaz 10 posits,

codes rely on interaction between researchers and their data. Codes consist of short labels that we construct as we interact with the data. Something kinaesthetic occurs when we are coding; we are mentally and physically active in the process. (p. 5)

In GT, coding can be categorised into iterative phases. Traditional, evolved and constructivist GT genres use different terminology to explain each coding phase ( Table 1 ).

Comparison of coding terminology in traditional, evolved and constructivist grounded theory.

Adapted from Birks and Mills. 6

Coding terminology in evolved GT refers to open (a procedure for developing categories of information), axial (an advanced procedure for interconnecting the categories) and selective coding (procedure for building a storyline from core codes that connects the categories), producing a discursive set of theoretical propositions. 6 , 12 , 32 Constructivist grounded theorists refer to initial, focused and theoretical coding. 9 Birks and Mills 6 use the terms initial, intermediate and advanced coding that link to low, medium and high-level conceptual analysis and development. The coding terms devised by Birks and Mills 6 were used for Figure 1 ; however, these can be altered to reflect the coding terminology used in the respective GT genres selected by the researcher.

Initial coding

Initial coding of data is the preliminary step in GT data analysis. 6 , 9 The purpose of initial coding is to start the process of fracturing the data to compare incident to incident and to look for similarities and differences in beginning patterns in the data. In initial coding, the researcher inductively generates as many codes as possible from early data. 16 Important words or groups of words are identified and labelled. In GT, codes identify social and psychological processes and actions as opposed to themes. Charmaz 16 emphasises keeping codes as similar to the data as possible and advocates embedding actions in the codes in an iterative coding process. Saldaña 33 agrees that codes that denote action, which he calls process codes, can be used interchangeably with gerunds (verbs ending in ing ). In vivo codes are often verbatim quotes from the participants’ words and are often used as the labels to capture the participant’s words as representative of a broader concept or process in the data. 6 Table 1 reflects variation in the terminology of codes used by grounded theorists.

Initial coding categorises and assigns meaning to the data, comparing incident-to-incident, labelling beginning patterns and beginning to look for comparisons between the codes. During initial coding, it is important to ask ‘what is this data a study of’. 18 What does the data assume, ‘suggest’ or ‘pronounce’ and ‘from whose point of view’ does this data come, whom does it represent or whose thoughts are they?. 16 What collectively might it represent? The process of documenting reactions, emotions and related actions enables researchers to explore, challenge and intensify their sensitivity to the data. 34 Early coding assists the researcher to identify the direction for further data gathering. After initial analysis, theoretical sampling is employed to direct collection of additional data that will inform the ‘developing theory’. 9 Initial coding advances into intermediate coding once categories begin to develop.

Theoretical sampling

The purpose of theoretical sampling is to allow the researcher to follow leads in the data by sampling new participants or material that provides relevant information. As depicted in Figure 1 , theoretical sampling is central to GT design, aids the evolving theory 5 , 7 , 16 and ensures the final developed theory is grounded in the data. 9 Theoretical sampling in GT is for the development of a theoretical category, as opposed to sampling for population representation. 10 Novice researchers need to acknowledge this difference if they are to achieve congruence within the methodology. Birks and Mills 6 define theoretical sampling as ‘the process of identifying and pursuing clues that arise during analysis in a grounded theory study’ (p. 68). During this process, additional information is sought to saturate categories under development. The analysis identifies relationships, highlights gaps in the existing data set and may reveal insight into what is not yet known. The exemplars in Box 1 highlight how theoretical sampling led to the inclusion of further data.

Examples of theoretical sampling.

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

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

Advanced coding

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.

Theoretical sensitivity

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.

The grounded theory

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.

Quality and rigour

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.

Grounded Theory In Qualitative Research: A Practical Guide

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.

What Is Grounded Theory?

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.
• No guessing games:  You don’t start with assumptions or try to confirm your own biases.
• Data collection and analysis happen together:  You analyze information as you gather it, which helps you decide what data to collect next.

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.

When should you use grounded theory? 

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.

What is the final product of a GT study?

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.

Here are a few examples of what middle-range theories might try to explain:

• How people deal with feeling anxious in social situations.
• How people act and interact at work.
• How teachers handle students who are misbehaving in class.

Core Components of Grounded Theory

This terminology reflects the iterative, inductive, and comparative nature of grounded theory, which distinguishes it from other research approaches.

• Theoretical Sampling: The researcher uses theoretical sampling to choose new participants or data sources based on the emerging findings of their study. The goal is to gather data that will help to further develop and refine the emerging categories and theoretical concepts.
• Theoretical Sensitivity:  Researchers need to be aware of their preconceptions going into a study and understand how those preconceptions could influence the research. However, it is not possible to completely separate a researcher’s history and experience from the construction of a theory.
• Coding: Coding is the process of analyzing  qualitative data  (usually text) by assigning labels (codes) to chunks of data that capture their essence or meaning. It allows you to condense, organize and interpret your data.
• Core Category:  The core category encapsulates and explains the grounded theory as a whole. Researchers identify a core category to focus on during the later stages of their research.
• Memos: Researchers use memos to record their thoughts and ideas about the data, explore relationships between codes and categories, and document the development of the emerging grounded theory. Memos support the development of theory by tracking emerging themes and patterns.
• Theoretical Saturation:  This term refers to the point in a grounded theory study when collecting additional data does not yield any new theoretical insights. The researcher continues the process of collecting and analyzing data until theoretical saturation is reached.
• Constant Comparative Analysis:  This method involves the systematic comparison of data points, codes, and categories as they emerge from the research process. Researchers use constant comparison to identify patterns and connections in their data.

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

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

Straussian Grounded 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.

Constructivist 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

Situational Analysis

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 .

• Discover New Insights:  Grounded theory lets you uncover new theories based on what your data reveals, not just on pre-existing ideas.
• Data-Driven Results:  Your conclusions are firmly rooted in the data you’ve gathered, ensuring they reflect reality. This close relationship between data and findings is a key factor in establishing trustworthiness.
• Avoids Bias:  Because gathering data and analyzing it are closely intertwined, researchers are truly observing what emerges from data, and are less likely to let their preconceptions color the findings.
• Streamlined data gathering and analysis:  Analyzing and collecting data go hand in hand. Data is collected, analyzed, and as you gain insight from analysis, you continue gathering more data.
• Synthesize Findings : By applying grounded theory to a qualitative metasynthesis , researchers can move beyond a simple aggregation of findings and generate a higher-level understanding of the phenomena being studied.

Limitations

• Time-Consuming:  Analyzing qualitative data can be like searching for a needle in a haystack; it requires careful examination and can be quite time-consuming, especially without software assistance6.
• Potential for Bias:  Despite safeguards, researchers may unintentionally influence their analysis due to personal experiences.
• Data Quality:  The success of grounded theory hinges on complete and accurate data; poor quality can lead to faulty conclusions.

Practical Steps

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.

1. Planning and Philosophical Considerations

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.

• Discern a personal philosophical position.  Before beginning a research study, it is important to consider your philosophical stance and how you view the world, including the nature of reality and the relationship between the researcher and the participant. This will inform the methodological choices made throughout the study.
• Investigate methodological possibilities.  Explore different research methods that align with both the philosophical stance and research goals of the study.
• Plan the study.  Determine the research question, how to collect data, and from whom to collect data.
• Conduct a literature review.  The literature review is an ongoing process throughout the study. It is important to avoid duplicating existing research and to consider previous studies, concepts, and interpretations that relate to the emerging codes and categories in the developing grounded theory.

2. Recruit participants using theoretical sampling

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.

3. Collect Data

The researcher might use interviews, focus groups, observations, or a combination of methods to collect qualitative data.

• Observations : Watching and recording phenomena as they occur. Can be participant (researcher actively involved) or non-participant (researcher tries not to influence behaviors), and covert (participants unaware) or overt (participants aware).
• Interviews : One-on-one conversations to understand participants’ experiences. Can be structured (predetermined questions), informal (casual conversations), or semi-structured (flexible structure to explore emerging issues).
• Focus groups : Dynamic discussions with 4-10 participants sharing characteristics, moderated by the researcher using a topic guide.
• Ethnography : Studying a group’s behaviors and social interactions in their environment through observations, field notes, and interviews. Researchers immerse themselves in the community or organization for an in-depth understanding.

4. Begin open coding as soon as data collection starts

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

5. Reflect on thoughts and contradictions by writing grounded theory memos during analysis

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.

Memo Writing

• Reflexivity and Recognizing Assumptions:  Researchers should acknowledge the influence of their own experiences and assumptions on the research process. Articulating these assumptions, perhaps through memos, can enhance the transparency and trustworthiness of the study.
• Write memos throughout the research process.  Memo writing should occur throughout the entire research process, beginning with initial coding.67 Memos help make sense of the data and transition between coding phases.8
• Ask analytic questions in early memos.  Memos should include questions, reflections, and notes to explore in subsequent data collection and analysis.8
• Refine memos throughout the process.  Early memos will be shorter and less conceptual, but will become longer and more developed in later stages of the research process.7 Later memos should begin to develop provisional categories.

6. Group codes into categories using axial coding

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.

7. Define the core category using selective coding

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.

Continue collecting data and analyzing until you reach theoretical saturation

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

• Constant Comparative Analysis:  This method involves the systematic comparison of data points, codes, and categories as they emerge from the research process. Researchers use constant comparison to identify patterns and connections in their data. There are different methods for comparing excerpts from interviews, for example, a researcher can compare excerpts from the same person, or excerpts from different people. This process is ongoing and iterative, and it continues until the researcher has developed a comprehensive and well-supported grounded theory.
• Continue until reaching theoretical saturation : Continue to gather and analyze data until no new insights or properties related to your categories. This saturation point signals that the theory is comprehensive and well-substantiated by the data.

8. Theoretical coding and model development

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.

Here is some advice for novice researchers on how to apply theoretical coding:

• Begin with data analysis:  Don’t start with a pre-determined theory. Instead, allow the theory to emerge from your data through careful analysis and coding.
• Use existing theories as a guide:  While the theory should primarily emerge from your data, you can use existing theories from any discipline to help explain the connections you are seeing between your categories. This demonstrates how your research builds on established knowledge.
• Use Glaser’s coding families:  Consider applying Glaser’s (1978) coding families in the later stages of analysis as a simple way to begin theoretical coding. Remember that your analysis should guide which theoretical codes are most appropriate.
• Keep it simple:  Theoretical coding doesn’t need to be overly complex.   Focus on finding an existing theory that effectively explains the relationships you have identified in your data.
• Be transparent:  Clearly articulate the existing theory you are using and how it explains the connections between your categories.
• Theoretical coding is an iterative process : Remain open to revising your chosen theoretical codes as your analysis deepens and your grounded theory evolves.

9. Write your grounded theory

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.

• Develop a dissemination plan : Determine how to share the research findings with others.
• Evaluate and implement : Reflect on the research process and quality of findings, then share findings with relevant audiences in service of making a difference in the world

Reading List

Grounded Theory Review : This is an international journal that publishes articles on grounded theory.

• Birks, M., & Mills, J. (2015).  Grounded theory: A practical guide . Sage.
• Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13, 3-21.
• Charmaz, K. (2006). Constructing Grounded Theory: A practical guide through Qualitative Analysis. Thousand Oaks, California: Sage.
• Clarke, A. E. (2003). Situational analyses: Grounded theory mapping after the postmodern turn .  Symbolic interaction ,  26 (4), 553-576.
• Glaser, B. G. (1978).  Theoretical sensitivity . University of California.
• Glaser, B. G. (2005).  The grounded theory perspective III: Theoretical coding . Sociology Press.
• Glaser, B. G., & Holton, J. (2004, May). Remodeling grounded theory. In  Forum qualitative sozialforschung/forum: qualitative social research  (Vol. 5, No. 2).
• Charmaz, K. (2012). The power and potential of grounded theory.  Medical sociology online ,  6 (3), 2-15.
• Glaser, B., & Strauss, A. (1965). Awareness of dying. New Brunswick. NJ: Aldine. This was the first published grounded theory study
• Glaser, B., & Strauss, A. (2017).  Discovery of grounded theory: Strategies for qualitative research . Routledge.
• Pidgeon, N., & Henwood, K. (1997). Using grounded theory in psychological research. In N. Hayes (Ed.), Doing qualitative analysis in psychology Press/Erlbaum (UK) Taylor & Francis.

Grounded Theory Methodology: Key Principles

• First Online: 14 November 2019

Cite this chapter

• Walter J. Eppich 6 ,
• Francisco M. Olmos-Vega 7 , 8 &
• Christopher J. Watling 9  

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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.

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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

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Home » Grounded Theory – Methods, Examples and Guide

Grounded Theory – Methods, Examples and Guide

Table of Contents

Grounded Theory

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.

History of Grounded Theory

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.

Types of Grounded Theory

There are two main types of Grounded Theory: Classic Grounded Theory and Constructivist Grounded Theory.

Classic 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.

Constructivist Grounded Theory

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.

Grounded Theory Conducting Guide

Here are some general guidelines for conducting a Grounded Theory study:

• Choose a research question: Start by selecting a research question that is open-ended and focuses on a specific social phenomenon or problem.
• Select participants and collect data: Identify a diverse group of participants who have experienced the phenomenon being studied. Use a variety of data collection methods such as interviews, observations, and document analysis to collect rich and diverse data.
• Analyze the data: Begin the process of analyzing the data using constant comparison. This involves comparing the data to each other and to existing categories and codes, in order to identify patterns and relationships. Use open coding to identify concepts and categories, and then use axial coding to organize them into a theoretical framework.
• Generate categories and codes: Generate categories and codes that describe the phenomenon being studied. Make sure that they are grounded in the data and that they accurately reflect the experiences of the participants.
• Refine and develop the theory: Use theoretical sampling to identify new data sources that are relevant to the developing theory. Use memoing to reflect on insights and ideas that emerge during the analysis process. Continue to refine and develop the theory until it provides a comprehensive explanation of the phenomenon.
• Validate the theory: Finally, seek to validate the theory by testing it against new data and seeking feedback from peers and other researchers. This process helps to refine and improve the theory, and to ensure that it is grounded in the data.
• Write up and disseminate the findings: Once the theory is fully developed and validated, write up the findings and disseminate them through academic publications and presentations. Make sure to acknowledge the contributions of the participants and to provide a detailed account of the research methods used.

Data Collection Methods

Grounded Theory Data Collection Methods are as follows:

• Interviews : One of the most common data collection methods in Grounded Theory is the use of in-depth interviews. Interviews allow researchers to gather rich and detailed data about the experiences, perspectives, and attitudes of participants. Interviews can be conducted one-on-one or in a group setting.
• Observation : Observation is another data collection method used in Grounded Theory. Researchers may observe participants in their natural settings, such as in a workplace or community setting. This method can provide insights into the social interactions and behaviors of participants.
• Document analysis: Grounded Theory researchers also use document analysis as a data collection method. This involves analyzing existing documents such as reports, policies, or historical records that are relevant to the phenomenon being studied.
• Focus groups : Focus groups involve bringing together a group of participants to discuss a specific topic or issue. This method can provide insights into group dynamics and social interactions.
• Fieldwork : Fieldwork involves immersing oneself in the research setting and participating in the activities of the participants. This method can provide an in-depth understanding of the culture and social dynamics of the research setting.
• Multimedia data: Grounded Theory researchers may also use multimedia data such as photographs, videos, or audio recordings to capture the experiences and perspectives of participants.

Data Analysis Methods

Grounded Theory Data Analysis Methods are as follows:

• Open coding: Open coding is the process of identifying concepts and categories in the data. Researchers use open coding to assign codes to different pieces of data, and to identify similarities and differences between them.
• Axial coding: Axial coding is the process of organizing the codes into broader categories and subcategories. Researchers use axial coding to develop a theoretical framework that explains the phenomenon being studied.
• Constant comparison: Grounded Theory involves a process of constant comparison, in which data is compared to each other and to existing categories and codes in order to identify patterns and relationships.
• Theoretical sampling: Theoretical sampling involves selecting new data sources based on the emerging theory. Researchers use theoretical sampling to collect data that will help refine and validate the theory.
• Memoing : Memoing involves writing down reflections, insights, and ideas as the analysis progresses. This helps researchers to organize their thoughts and develop a deeper understanding of the data.
• Peer debriefing: Peer debriefing involves seeking feedback from peers and other researchers on the developing theory. This process helps to validate the theory and ensure that it is grounded in the data.
• Member checking: Member checking involves sharing the emerging theory with the participants in the study and seeking their feedback. This process helps to ensure that the theory accurately reflects the experiences and perspectives of the participants.
• Triangulation: Triangulation involves using multiple sources of data to validate the emerging theory. Researchers may use different data collection methods, different data sources, or different analysts to ensure that the theory is grounded in the data.

Applications of Grounded Theory

Here are some of the key applications of Grounded Theory:

• Social sciences : Grounded Theory is widely used in social science research, particularly in fields such as sociology, psychology, and anthropology. It can be used to explore a wide range of social phenomena, such as social interactions, power dynamics, and cultural practices.
• Healthcare : Grounded Theory can be used in healthcare research to explore patient experiences, healthcare practices, and healthcare systems. It can provide insights into the factors that influence healthcare outcomes, and can inform the development of interventions and policies.
• Education : Grounded Theory can be used in education research to explore teaching and learning processes, student experiences, and educational policies. It can provide insights into the factors that influence educational outcomes, and can inform the development of educational interventions and policies.
• Business : Grounded Theory can be used in business research to explore organizational processes, management practices, and consumer behavior. It can provide insights into the factors that influence business outcomes, and can inform the development of business strategies and policies.
• Technology : Grounded Theory can be used in technology research to explore user experiences, technology adoption, and technology design. It can provide insights into the factors that influence technology outcomes, and can inform the development of technology interventions and policies.

Examples of Grounded Theory

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 of dying, and that was grounded in the data.
• Charmaz (1983): This study explored the experiences of chronic illness among young adults. The researcher used Grounded Theory to develop a theoretical framework that explained how individuals with chronic illness managed their illness, and how their illness impacted their sense of self.
• Strauss and Corbin (1990): This study explored the experiences of individuals with chronic pain. The researchers used Grounded Theory to develop a theoretical framework that explained the different strategies that individuals used to manage their pain, and that was grounded in the data.
• Glaser and Strauss (1967): This study explored the experiences of individuals who were undergoing a process of becoming disabled. The researchers used Grounded Theory to develop a theoretical framework that explained the social processes of becoming disabled, and that was grounded in the data.
• Clarke (2005): This study explored the experiences of patients with cancer who were receiving chemotherapy. The researcher used Grounded Theory to develop a theoretical framework that explained the factors that influenced patient adherence to chemotherapy, and that was grounded in the data.

Grounded Theory Research Example

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.

Purpose of Grounded Theory

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.

When to use Grounded Theory

Following are some situations in which Grounded Theory may be particularly useful:

• Exploring new areas of research: Grounded Theory is particularly useful when exploring new areas of research that have not been well-studied. By collecting and analyzing data, researchers can develop a theoretical framework that explains the social processes and patterns underlying the phenomenon of interest.
• Studying complex social phenomena: Grounded Theory is well-suited to exploring complex social phenomena that involve multiple social processes and interactions. By using an iterative process of data collection and analysis, researchers can develop a theoretical framework that explains the complexity of the social phenomenon.
• Generating hypotheses: Grounded Theory can be used to generate hypotheses about social processes and interactions that can be tested in future research. By developing a theoretical framework that explains a social phenomenon, researchers can identify areas for further research and hypothesis testing.
• Informing policy and practice : Grounded Theory can provide insights into the factors that influence social phenomena, and can inform policy and practice in a variety of fields. By developing a theoretical framework that explains a social phenomenon, researchers can identify areas for intervention and policy development.

Characteristics of Grounded Theory

Grounded Theory is a qualitative research method that is characterized by several key features, including:

• Emergence : Grounded Theory emphasizes the emergence of theoretical categories and concepts from the data, rather than preconceived theoretical ideas. This means that the researcher does not start with a preconceived theory or hypothesis, but instead allows the theory to emerge from the data.
• Iteration : Grounded Theory is an iterative process that involves constant comparison of data and analysis, with each round of data collection and analysis refining the theoretical framework.
• Inductive : Grounded Theory is an inductive method of analysis, which means that it derives meaning from the data. The researcher starts with the raw data and systematically codes and categorizes it to identify patterns and themes, and to develop a theoretical framework that explains these patterns.
• Reflexive : Grounded Theory requires the researcher to be reflexive and self-aware throughout the research process. The researcher’s personal biases and assumptions must be acknowledged and addressed in the analysis process.
• Holistic : Grounded Theory takes a holistic approach to data analysis, looking at the entire data set rather than focusing on individual data points. This allows the researcher to identify patterns and themes that may not be apparent when looking at individual data points.
• Contextual : Grounded Theory emphasizes the importance of understanding the context in which social phenomena occur. This means that the researcher must consider the social, cultural, and historical factors that may influence the phenomenon of interest.

Advantages of Grounded Theory

Advantages of Grounded Theory are as follows:

• Flexibility : Grounded Theory is a flexible method that can be used to explore a wide range of research questions and settings. It is particularly well-suited to exploring complex social phenomena that have not been well-studied.
• Validity : Grounded Theory aims to develop a theoretical framework that is grounded in the data, which enhances the validity and reliability of the research findings. The iterative process of data collection and analysis also helps to ensure that the research findings are reliable and robust.
• Originality : Grounded Theory can generate new and original insights into social phenomena, as it is not constrained by preconceived theoretical ideas or hypotheses. This allows researchers to explore new areas of research and generate new theoretical frameworks.
• Real-world relevance: Grounded Theory can inform policy and practice, as it provides insights into the factors that influence social phenomena. The theoretical frameworks developed through Grounded Theory can be used to inform policy development and intervention strategies.
• Ethical : Grounded Theory is an ethical research method, as it allows participants to have a voice in the research process. Participants’ perspectives are central to the data collection and analysis process, which ensures that their views are taken into account.
• Replication : Grounded Theory is a replicable method of research, as the theoretical frameworks developed through Grounded Theory can be tested and validated in future research.

Limitations of Grounded Theory

Limitations of Grounded Theory are as follows:

• Time-consuming: Grounded Theory can be a time-consuming method, as the iterative process of data collection and analysis requires significant time and effort. This can make it difficult to conduct research in a timely and cost-effective manner.
• Subjectivity : Grounded Theory is a subjective method, as the researcher’s personal biases and assumptions can influence the data analysis process. This can lead to potential issues with reliability and validity of the research findings.
• Generalizability : Grounded Theory is a context-specific method, which means that the theoretical frameworks developed through Grounded Theory may not be generalizable to other contexts or populations. This can limit the applicability of the research findings.
• Lack of structure : Grounded Theory is an exploratory method, which means that it lacks the structure of other research methods, such as surveys or experiments. This can make it difficult to compare findings across different studies.
• Data overload: Grounded Theory can generate a large amount of data, which can be overwhelming for researchers. This can make it difficult to manage and analyze the data effectively.
• Difficulty in publication: Grounded Theory can be challenging to publish in some academic journals, as some reviewers and editors may view it as less rigorous than other research methods.

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Article Contents

Introduction, distinguishing features of grounded theory, the role and timing of the literature review.

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Grounded theory: what makes a grounded theory study?

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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

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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

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 )

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.

A solution: be clear on the approach taken to the literature review and why

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.

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Case and grounded theory as qualitative research methods

Affiliation.

• 1 School for Nursing, University of Massachusetts, Amherst, MA, USA.
• PMID: 15779692

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.

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Qualitative study design: Grounded theory

• Qualitative study design
• Phenomenology

Grounded theory

• Ethnography
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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:

• Data collection and analysis occurring simultaneously, with one informing the other.
• Data grouped into concepts, categories and themes.
• A data collection process influenced by the simultaneous development of those concepts, categories and themes.

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:

• Examination of documents
• Focus groups and interviews

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:

• The process of coding, sorting and organising data. This aims to increasingly move towards more abstract terms in order to develop a related theory for the data
• The principle of constant comparison. This refers to the process of noting issues of interest in data and comparing them to other examples to identify similarities and differences.
• Widely used across a wide range of disciplines (Bryant & Charmaz, 2007).  
• Facilitates theory construction and the construction of fresh concepts. It also avoids assuming structures are stable (Charmaz, 2017). 
• Useful for when researchers wish to explain a process, not to test an existing theory. 

Limitations

• Inherently not useful for the application of received theory. 
• Not useful for testing hypotheses. 
• Analysis of data involves elements of researcher’s own subjective judgement.

Example questions

• How do perioperative nurses foster a culture of safety and risk aversion? 
• What is the impact of hand nerve disorders on a person’s function, activity and participation? 
• What are the barriers to health care access for a refugee population? 

Example studies

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   

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Peer-reviewed

Research Article

Explore an open-source value co-creation framework: A multiple case study

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

• Yang Luo, 
• Yongsheng Jin, 

• Published: September 20, 2024
• https://doi.org/10.1371/journal.pone.0310516
• Peer Review
• Reader Comments

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.

1 Introduction

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.

2 Theoretical underpinnings

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.

2.1 Value co-creation in open-source community

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 ).

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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.

2.2 Business ecosystem

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.

2.3 Open innovation

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.

3 Research design

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.

3.1 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

3.2 Research method

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.

3.3 Case selection

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:

• The selected cases must be OSCs that have been continuously operating since their establishment and have organized offline community activities at least once every six months;
• The selected cases must be typical and representative OSCs from different domains with global influence, organizing or participating in at least one global technology workshop or summit in this domain every year;
• The selected cases must have been established for more than two years and have sufficient online information for research.

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

3.4 Data collection

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

3.5 Data analysis

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:

• Examining the third-party research reports in chronological order, extracting the content of the relevant open-source cases, and analyzing the ecological maturity of the cases;
• Determining the ecological maturity of the cases by analyzing the objective indicators such as stars, pull requests (PR), and forks on GitHub and Gitee, in conjunction with the results of the first step;
• Determining the degree of commercialization of each case by analyzing information about its business-related cooperation from the official website, official WeChat account, corporate annual reports, and news;
• The form and content of VCC in each case were combined and summarized, and the cases were compared repeatedly. Discussion, supplementation, and optimization achieved a consensus determining the ecological maturity and degree of commercialization of each case.

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.

4 Case analysis

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.

4.1 High Commercial Maturity(HCM)

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

4.2 High Commercial Development(HCD)

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

4.3 Low Commercial Development(LCD)

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

4.4 Low Commercial Maturity(LCM)

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

4.5 Comparative analysis of primary participants

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:

• The dominant participant(DP), which is usually the initiator, exerts influence over the development of the OSCs. The DP plays a pivotal role in driving OSCs, reaping significant social and commercial benefits [ 46 ];
• Enterprise participants (EPs) consist of developers and users from enterprises in the OSCs. Enterprise developers (EDs) primarily consist of upstream and downstream companies in the supply chain of the DP. Enterprise users (EUs) typically prioritize the performance and usability of open-source products [ 46 ], and EUs with development capabilities are also EDs;
• Individual participants (IPs) consist of independent developers and users who are not affiliated with corporations. Generally, individual developers(IDs) also serve as individual users (IUs), whose engagement in OSCs is motivated by personal interests or career development [ 46 – 48 ].

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.

5.1 Conceptual framework of OSVCC

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 The OSVCC process

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:

• Usually, the DP is at the forefront of the IT industry, is adept at capturing technological and marketing changes, and is the first to identify potential opportunities and propose possibilities to create value;
• After conducting a thorough analysis, the DP decides to adopt an open-source strategy that considers both the internal and external company environments. The DP develops a VCC plan that revolves around the open-source strategy;
• The DP implements the VCC plan and actively communicates with other participants, establishing a consensual value proposition and collaborating on innovative activities;
• The DP evaluates the results of the implementation, conducts a joint analysis of the efficiency and effectiveness of VCC, and strives for continuous improvement.

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.

5.2.2 The EPs’ OSVCC process.

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.

• EPs are generally companies in the DP’s value chain, either upstream or downstream. This positioning enables EPs to effectively identify and seize opportunities for VCC that are initially discovered by the DP;
• EPs confirm the existence of opportunities through multiple communications with the DP, utilizing their cognitive abilities. EPs then formulate enterprise open-source strategies and engage in OSVCC;
• EPs exhibit distinct behavior compared with the other two types of participants. EPs have a dual motivation: on the one hand, EPs aspire to actively participate in OSVCC and contribute to the community; on the other hand, EPs aim to maximize their own interests, which may lead to "free riding."

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.

5.2.3 The IPs’ OSVCC process.

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:

• IPs develop a profound emotional connection to the community, and possess a strong passion for their technical field, willingly contributing their time and energy;
• IPs effectively leverage their cognitive abilities, encompassing technical and business skills, to enhance the vitality and diversity of OSCs;
• IPs exhibit self-motivated altruistic behavior, propelling the development and innovation of OSCs through collaborative and active contributions.

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.

5.2.4 Encounter processes.

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 ].

6 Conclusions and implications

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.

Acknowledgments

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.

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