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Methodological Framework – Types, Examples and Guide

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Methodological Framework

Definition:

Methodological framework is a set of procedures, methods, and tools that guide the research process in a systematic and structured manner. It provides a structure for conducting research, collecting and analyzing data, and drawing conclusions. The framework outlines the steps to be taken in a research project, including the research question, hypothesis, data collection methods, data analysis techniques, and the interpretation of the results.

Types of Methodological Framework

There are different types of methodological frameworks that researchers can use depending on the nature of their research question, the type of data they want to collect, and the research methodology they want to employ. Some common types of methodological frameworks include:

Quantitative Research Framework

This type of framework uses numerical data and statistical analysis to test hypotheses and draw conclusions. It involves the collection of structured data through surveys, experiments, or other quantitative methods.

Qualitative Research Framework

This framework is used to explore complex social phenomena and involves the collection of non-numerical data through methods such as interviews, observation, and document analysis. Qualitative research typically involves the use of open-ended questions and in-depth analysis of data.

Mixed Methods Research Framework

This framework combines quantitative and qualitative research methods to address research questions from multiple angles. It involves collecting both numerical and non-numerical data and using both statistical analysis and interpretive techniques to analyze the data.

Action Research Framework

This framework involves the collaboration between researchers and participants to identify and address practical problems in real-world settings. It involves a cyclical process of planning, action, reflection, and evaluation to improve a specific situation or practice.

Case Study Research Framework

This framework involves the in-depth investigation of a specific case or phenomenon, often using qualitative methods. It aims to understand the complexity of the case and draw generalizations from the findings.

How to Develop a Methodological Framework

Developing a methodological framework involves a series of steps that help to guide the research process in a systematic and structured manner. Here are the general steps involved in developing a methodological framework:

• Define the research problem: The first step is to clearly define the research problem or question. This involves identifying the purpose of the research, the research objectives, and the scope of the study.
• Select an appropriate research methodology: The research methodology selected should align with the research problem and research question. Common research methodologies include quantitative, qualitative, mixed-methods, case study, or action research.
• Develop the research design: Once the research methodology is selected, the research design should be developed. This involves identifying the data collection methods, sampling strategy, and data analysis techniques.
• Identify and justify the data collection methods: The data collection methods should be chosen based on the research methodology and research design. For example, if the research methodology is qualitative, data collection methods such as interviews, observation, or document analysis may be used.
• Identify and justify the data analysis techniques: The data analysis techniques should also be chosen based on the research methodology and research design. For quantitative research, this may include statistical analysis techniques, while for qualitative research, this may include interpretive techniques such as thematic analysis.
• Consider ethical considerations: Ethical considerations should be taken into account throughout the research process. This includes obtaining informed consent, ensuring confidentiality and privacy, and protecting the rights of participants.
• Identify potential limitations: It is important to identify potential limitations or biases that may affect the research findings. This includes discussing potential sources of error or bias in the research design, data collection methods, or data analysis techniques.
• Consider the significance and implications of the research: The significance and implications of the research findings should be considered, including their potential contributions to theory, practice, or policy.
• Refine the framework: The methodological framework should be refined based on feedback from peers, experts, or other stakeholders. This involves identifying any areas for improvement in the research design, data collection methods, or data analysis techniques.

Applications of Methodological Framework

Here are some examples of how a methodological framework can be applied in various fields:

• Social sciences: In social sciences, a methodological framework can be used to conduct research on various topics, such as psychology, sociology, and anthropology. For example, a researcher may use a qualitative research methodology to investigate the experiences and perceptions of individuals living in poverty.
• Natural sciences: In natural sciences, a methodological framework can be used to conduct research on various topics, such as biology, chemistry, and physics. For example, a researcher may use a quantitative research methodology to investigate the effects of different fertilizers on crop yield.
• Engineering : In engineering, a methodological framework can be used to design and test new technologies or systems. For example, a researcher may use a mixed-methods research methodology to investigate the usability and effectiveness of a new software application.
• Business : In business, a methodological framework can be used to conduct research on various topics, such as marketing, management, and finance. For example, a researcher may use a quantitative research methodology to investigate the relationship between customer satisfaction and customer loyalty.

When to use Methodological Framework

Here are some specific situations when a methodological framework can be particularly useful:

• When conducting original research: If you are conducting original research, a methodological framework can help ensure that your study is designed in a structured and systematic manner, which increases the reliability and validity of the findings.
• When conducting a literature review: A methodological framework can be used when conducting a literature review to ensure that the review is conducted in a structured and systematic manner. This helps to identify relevant studies and synthesize the findings from multiple studies.
• When replicating previous research: If you are replicating previous research, a methodological framework can help ensure that the replication is conducted in a rigorous and systematic manner. This helps to ensure that the findings are consistent with the original study.
• When developing a research proposal : A methodological framework can be used when developing a research proposal to ensure that the proposal is designed in a structured and systematic manner. This helps to convince reviewers that the study is well-designed and likely to produce valid and reliable findings.
• When teaching research methods: A methodological framework can be used when teaching research methods to provide students with a structured approach to designing and conducting research. This helps to ensure that students understand the research process and are able to conduct research in a rigorous and systematic manner.

Examples of Methodological Framework

Here are some real-time examples of how methodological frameworks are used in various fields:

• In healthcare research, a mixed-methods research framework can be used to evaluate the effectiveness of a new treatment approach. The quantitative component may involve measuring the changes in patient outcomes, while the qualitative component may involve interviewing patients and healthcare providers to understand their perspectives on the treatment.
• In engineering, a design science research framework can be used to develop and test a new software application. The researchers may identify a problem with existing software, develop a new solution, and test it in a real-world setting.
• In business, a case study research framework can be used to understand the impact of a new marketing strategy on a particular company. The researcher may analyze data from the company’s financial statements, conduct interviews with key stakeholders, and observe the implementation of the strategy in order to understand its effectiveness.
• In education, an action research framework can be used to improve teaching practices. A teacher may identify a problem in their classroom, develop a plan to address the problem, implement the plan, and reflect on the results in order to improve their teaching practices.
• In social science research, a grounded theory framework can be used to develop a theory from qualitative data. A researcher may collect data from interviews or observations and use that data to develop a theory about a particular phenomenon.

Purpose of Methodological Framework

The purpose of a methodological framework is to provide a structured and systematic approach to designing, conducting, and analyzing research. The framework serves as a guide for researchers to follow, ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable, valid, and generalizable. Some key purposes of a methodological framework are:

• To provide a clear and concise description of the research process: The framework outlines the steps involved in conducting the research, including the research question, data collection methods, data analysis, and interpretation of results.
• To ensure that the research is conducted in a systematic and rigorous manner : The framework provides a structured approach to the research, helping to ensure that the research is conducted in a way that minimizes bias and maximizes the accuracy and reliability of the results.
• To improve the quality of the research: The framework helps to ensure that the research is of high quality and meets the standards of the field. This can help to increase the impact and relevance of the research.
• To increase transparency and replicability: The framework provides a clear and transparent description of the research process, making it easier for others to understand and replicate the research.
• To facilitate communication and collaboration: The framework provides a common language and structure for researchers to communicate their research findings and collaborate with others in the field.

Characteristics of Methodological Framework

Here are some common characteristics of a methodological framework:

• Systematic : A methodological framework is a systematic approach to research that provides a clear and structured guide for researchers to follow. It outlines the steps involved in conducting research, from developing a research question to analyzing and interpreting data.
• Transparent : A methodological framework promotes transparency in research by providing a clear and concise description of the research process. This helps to ensure that others can understand and replicate the research.
• Flexible : A methodological framework should be flexible enough to accommodate different research designs and methodologies. It should allow for modifications based on the specific research question, data collection methods, and analysis techniques.
• Contextual : A methodological framework should take into account the contextual factors that may impact the research. This includes the cultural, social, and historical context of the research, as well as the research setting and the characteristics of the participants.
• Rigorous : A methodological framework promotes rigor in research by ensuring that the research is conducted in a systematic and unbiased manner. It includes strategies for minimizing bias and ensuring the validity and reliability of the findings.
• Theory-driven: A methodological framework should be grounded in theoretical concepts and principles that guide the research. This helps to ensure that the research is relevant and meaningful, and that the findings can be applied to broader theoretical frameworks.

Advantages of Methodological Framework

There are several advantages to using a methodological framework in research:

• Structured approach: A methodological framework provides a clear and structured approach to conducting research, which helps to ensure that the research is conducted in a systematic and rigorous manner.
• Increased efficiency: A methodological framework can increase the efficiency of the research process by providing a clear roadmap for researchers to follow, reducing the time and resources required to conduct the research.
• Reproducibility: A methodological framework promotes reproducibility by providing a clear and transparent description of the research process, making it easier for others to replicate the research.
• Improved quality : A methodological framework can improve the quality of research by ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable and valid.
• Standardization : A methodological framework promotes standardization in research, helping to ensure that the research meets the standards of the field and is comparable to other research studies.
• Better communication : A methodological framework provides a common language and structure for researchers to communicate their research findings, facilitating communication and collaboration among researchers.
• Theory development: A methodological framework can contribute to the development of theory by providing a structured approach to data collection and analysis that is grounded in theoretical concepts and principles.

Limitations of Methodological Framework

While there are many advantages to using a methodological framework in research, there are also some limitations to be aware of:

• Flexibility : While a methodological framework can provide a structured approach to research, it may also limit flexibility in the research process. Researchers may feel constrained by the framework and unable to deviate from the prescribed steps, which may limit their ability to adapt to unexpected findings or changes in the research context.
• Applicability : Methodological frameworks may not be equally applicable to all research questions and contexts. Some frameworks may be more suitable for certain types of research than others, and researchers may need to modify or adapt the framework to fit their specific research question and context.
• Complexity : Some methodological frameworks can be complex and difficult to understand, particularly for novice researchers. This may limit their usefulness in certain contexts or for certain types of research.
• Time and resource constraints : Using a methodological framework may require additional time and resources to fully implement, which may not be feasible for all researchers or research projects.
• Overemphasis on methodology: While a methodological framework can provide a structured approach to research methodology, it may overemphasize the importance of methodology over other aspects of research, such as theoretical frameworks or ethical considerations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How methodological frameworks are being developed: evidence from a scoping review

Affiliations.

• 1 Health Economics and Health Technology Assessment (HEHTA), Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8RZ, UK. [email protected].
• 2 Health Economics and Health Technology Assessment (HEHTA), Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8RZ, UK.
• PMID: 32605535
• PMCID: PMC7325096
• DOI: 10.1186/s12874-020-01061-4

Background: Although the benefits of using methodological frameworks are increasingly recognised, to date, there is no formal definition of what constitutes a 'methodological framework', nor is there any published guidance on how to develop one. For the purposes of this study we have defined a methodological framework as a structured guide to completing a process or procedure. This study's aims are to: (a) map the existing landscape on the use of methodological frameworks; (b) identify approaches used for the development of methodological frameworks and terminology used; and (c) provide suggestions for developing future methodological frameworks. We took a broad view and did not limit our study to methodological frameworks in research and academia.

Methods: A scoping review was conducted, drawing on Arksey and O'Malley's methods and more recent guidance. We systematically searched two major electronic databases (MEDLINE and Web of Science), as well as grey literature sources and the reference lists and citations of all relevant papers. Study characteristics and approaches used for development of methodological frameworks were extracted from included studies. Descriptive analysis was conducted.

Results: We included a total of 30 studies, representing a wide range of subject areas. The most commonly reported approach for developing a methodological framework was 'Based on existing methods and guidelines' (66.7%), followed by 'Refined and validated' (33.3%), 'Experience and expertise' (30.0%), 'Literature review' (26.7%), 'Data synthesis and amalgamation' (23.3%), 'Data extraction' (10.0%), 'Iteratively developed' (6.7%) and 'Lab work results' (3.3%). There was no consistent use of terminology; diverse terms for methodological framework were used across and, interchangeably, within studies.

Conclusions: Although no formal guidance exists on how to develop a methodological framework, this scoping review found an overall consensus in approaches used, which can be broadly divided into three phases: (a) identifying data to inform the methodological framework; (b) developing the methodological framework; and (c) validating, testing and refining the methodological framework. Based on these phases, we provide suggestions to facilitate the development of future methodological frameworks.

Keywords: Framework; Methodological framework; Methodology; Scoping review.

Publication types

• Correspondence
• Open access
• Published: 18 September 2013

Using the framework method for the analysis of qualitative data in multi-disciplinary health research

• Nicola K Gale 1 ,
• Gemma Heath 2 ,
• Elaine Cameron 3 ,
• Sabina Rashid 4 &
• Sabi Redwood 2  

BMC Medical Research Methodology volume  13 , Article number:  117 ( 2013 ) Cite this article

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The Framework Method is becoming an increasingly popular approach to the management and analysis of qualitative data in health research. However, there is confusion about its potential application and limitations.

The article discusses when it is appropriate to adopt the Framework Method and explains the procedure for using it in multi-disciplinary health research teams, or those that involve clinicians, patients and lay people. The stages of the method are illustrated using examples from a published study.

Used effectively, with the leadership of an experienced qualitative researcher, the Framework Method is a systematic and flexible approach to analysing qualitative data and is appropriate for use in research teams even where not all members have previous experience of conducting qualitative research.

The Framework Method for the management and analysis of qualitative data has been used since the 1980s [ 1 ]. The method originated in large-scale social policy research but is becoming an increasingly popular approach in medical and health research; however, there is some confusion about its potential application and limitations. In this article we discuss when it is appropriate to use the Framework Method and how it compares to other qualitative analysis methods. In particular, we explore how it can be used in multi-disciplinary health research teams. Multi-disciplinary and mixed methods studies are becoming increasingly commonplace in applied health research. As well as disciplines familiar with qualitative research, such as nursing, psychology and sociology, teams often include epidemiologists, health economists, management scientists and others. Furthermore, applied health research often has clinical representation and, increasingly, patient and public involvement [ 2 ]. We argue that while leadership is undoubtedly required from an experienced qualitative methodologist, non-specialists from the wider team can and should be involved in the analysis process. We then present a step-by-step guide to the application of the Framework Method, illustrated using a worked example (See Additional File 1 ) from a published study [ 3 ] to illustrate the main stages of the process. Technical terms are included in the glossary (below). Finally, we discuss the strengths and limitations of the approach.

Glossary of key terms used in the Framework Method

Analytical framework: A set of codes organised into categories that have been jointly developed by researchers involved in analysis that can be used to manage and organise the data. The framework creates a new structure for the data (rather than the full original accounts given by participants) that is helpful to summarize/reduce the data in a way that can support answering the research questions.

Analytic memo: A written investigation of a particular concept, theme or problem, reflecting on emerging issues in the data that captures the analytic process (see Additional file 1 , Section 7).

Categories: During the analysis process, codes are grouped into clusters around similar and interrelated ideas or concepts. Categories and codes are usually arranged in a tree diagram structure in the analytical framework. While categories are closely and explicitly linked to the raw data, developing categories is a way to start the process of abstraction of the data (i.e. towards the general rather than the specific or anecdotal).

Charting: Entering summarized data into the Framework Method matrix (see Additional File 1 , Section 6).

Code: A descriptive or conceptual label that is assigned to excerpts of raw data in a process called ‘coding’ (see Additional File 1 , Section 3).

Data: Qualitative data usually needs to be in textual form before analysis. These texts can either be elicited texts (written specifically for the research, such as food diaries), or extant texts (pre-existing texts, such as meeting minutes, policy documents or weblogs), or can be produced by transcribing interview or focus group data, or creating ‘field’ notes while conducting participant-observation or observing objects or social situations.

Indexing: The systematic application of codes from the agreed analytical framework to the whole dataset (see Additional File 1 , Section 5).

Matrix: A spreadsheet contains numerous cells into which summarized data are entered by codes (columns) and cases (rows) (see Additional File 1 , Section 6).

Themes: Interpretive concepts or propositions that describe or explain aspects of the data, which are the final output of the analysis of the whole dataset. Themes are articulated and developed by interrogating data categories through comparison between and within cases. Usually a number of categories would fall under each theme or sub-theme [ 3 ].

Transcript: A written verbatim (word-for-word) account of a verbal interaction, such as an interview or conversation.

The Framework Method sits within a broad family of analysis methods often termed thematic analysis or qualitative content analysis. These approaches identify commonalities and differences in qualitative data, before focusing on relationships between different parts of the data, thereby seeking to draw descriptive and/or explanatory conclusions clustered around themes. The Framework Method was developed by researchers, Jane Ritchie and Liz Spencer, from the Qualitative Research Unit at the National Centre for Social Research in the United Kingdom in the late 1980s for use in large-scale policy research [ 1 ]. It is now used widely in other areas, including health research [ 3 – 12 ]. Its defining feature is the matrix output: rows (cases), columns (codes) and ‘cells’ of summarised data, providing a structure into which the researcher can systematically reduce the data, in order to analyse it by case and by code [ 1 ]. Most often a ‘case’ is an individual interviewee, but this can be adapted to other units of analysis, such as predefined groups or organisations. While in-depth analyses of key themes can take place across the whole data set, the views of each research participant remain connected to other aspects of their account within the matrix so that the context of the individual’s views is not lost. Comparing and contrasting data is vital to qualitative analysis and the ability to compare with ease data across cases as well as within individual cases is built into the structure and process of the Framework Method.

The Framework Method provides clear steps to follow and produces highly structured outputs of summarised data. It is therefore useful where multiple researchers are working on a project, particularly in multi-disciplinary research teams were not all members have experience of qualitative data analysis, and for managing large data sets where obtaining a holistic, descriptive overview of the entire data set is desirable. However, caution is recommended before selecting the method as it is not a suitable tool for analysing all types of qualitative data or for answering all qualitative research questions, nor is it an ‘easy’ version of qualitative research for quantitative researchers. Importantly, the Framework Method cannot accommodate highly heterogeneous data, i.e. data must cover similar topics or key issues so that it is possible to categorize it. Individual interviewees may, of course, have very different views or experiences in relation to each topic, which can then be compared and contrasted. The Framework Method is most commonly used for the thematic analysis of semi-structured interview transcripts, which is what we focus on in this article, although it could, in principle, be adapted for other types of textual data [ 13 ], including documents, such as meeting minutes or diaries [ 12 ], or field notes from observations [ 10 ].

For quantitative researchers working with qualitative colleagues or when exploring qualitative research for the first time, the nature of the Framework Method is seductive because its methodical processes and ‘spreadsheet’ approach seem more closely aligned to the quantitative paradigm [ 14 ]. Although the Framework Method is a highly systematic method of categorizing and organizing what may seem like unwieldy qualitative data, it is not a panacea for problematic issues commonly associated with qualitative data analysis such as how to make analytic choices and make interpretive strategies visible and auditable. Qualitative research skills are required to appropriately interpret the matrix, and facilitate the generation of descriptions, categories, explanations and typologies. Moreover, reflexivity, rigour and quality are issues that are requisite in the Framework Method just as they are in other qualitative methods. It is therefore essential that studies using the Framework Method for analysis are overseen by an experienced qualitative researcher, though this does not preclude those new to qualitative research from contributing to the analysis as part of a wider research team.

There are a number of approaches to qualitative data analysis, including those that pay close attention to language and how it is being used in social interaction such as discourse analysis [ 15 ] and ethnomethodology [ 16 ]; those that are concerned with experience, meaning and language such as phenomenology [ 17 , 18 ] and narrative methods [ 19 ]; and those that seek to develop theory derived from data through a set of procedures and interconnected stages such as Grounded Theory [ 20 , 21 ]. Many of these approaches are associated with specific disciplines and are underpinned by philosophical ideas which shape the process of analysis [ 22 ]. The Framework Method, however, is not aligned with a particular epistemological, philosophical, or theoretical approach. Rather it is a flexible tool that can be adapted for use with many qualitative approaches that aim to generate themes.

The development of themes is a common feature of qualitative data analysis, involving the systematic search for patterns to generate full descriptions capable of shedding light on the phenomenon under investigation. In particular, many qualitative approaches use the ‘constant comparative method’ , developed as part of Grounded Theory, which involves making systematic comparisons across cases to refine each theme [ 21 , 23 ]. Unlike Grounded Theory, the Framework Method is not necessarily concerned with generating social theory, but can greatly facilitate constant comparative techniques through the review of data across the matrix.

Perhaps because the Framework Method is so obviously systematic, it has often, as other commentators have noted, been conflated with a deductive approach to qualitative analysis [ 13 , 14 ]. However, the tool itself has no allegiance to either inductive or deductive thematic analysis; where the research sits along this inductive-deductive continuum depends on the research question. A question such as, ‘Can patients give an accurate biomedical account of the onset of their cardiovascular disease?’ is essentially a yes/no question (although it may be nuanced by the extent of their account or by appropriate use of terminology) and so requires a deductive approach to both data collection and analysis (e.g. structured or semi-structured interviews and directed qualitative content analysis [ 24 ]). Similarly, a deductive approach may be taken if basing analysis on a pre-existing theory, such as behaviour change theories, for example in the case of a research question such as ‘How does the Theory of Planned Behaviour help explain GP prescribing?’ [ 11 ]. However, a research question such as, ‘How do people construct accounts of the onset of their cardiovascular disease?’ would require a more inductive approach that allows for the unexpected, and permits more socially-located responses [ 25 ] from interviewees that may include matters of cultural beliefs, habits of food preparation, concepts of ‘fate’, or links to other important events in their lives, such as grief, which cannot be predicted by the researcher in advance (e.g. an interviewee-led open ended interview and grounded theory [ 20 ]). In all these cases, it may be appropriate to use the Framework Method to manage the data. The difference would become apparent in how themes are selected: in the deductive approach, themes and codes are pre-selected based on previous literature, previous theories or the specifics of the research question; whereas in the inductive approach, themes are generated from the data though open (unrestricted) coding, followed by refinement of themes. In many cases, a combined approach is appropriate when the project has some specific issues to explore, but also aims to leave space to discover other unexpected aspects of the participants’ experience or the way they assign meaning to phenomena. In sum, the Framework Method can be adapted for use with deductive, inductive, or combined types of qualitative analysis. However, there are some research questions where analysing data by case and theme is not appropriate and so the Framework Method should be avoided. For instance, depending on the research question, life history data might be better analysed using narrative analysis [ 19 ]; recorded consultations between patients and their healthcare practitioners using conversation analysis [ 26 ]; and documentary data, such as resources for pregnant women, using discourse analysis [ 27 ].

It is not within the scope of this paper to consider study design or data collection in any depth, but before moving on to describe the Framework Method analysis process, it is worth taking a step back to consider briefly what needs to happen before analysis begins. The selection of analysis method should have been considered at the proposal stage of the research and should fit with the research questions and overall aims of the study. Many qualitative studies, particularly ones using inductive analysis, are emergent in nature; this can be a challenge and the researchers can only provide an “imaginative rehearsal” of what is to come [ 28 ]. In mixed methods studies, the role of the qualitative component within the wider goals of the project must also be considered. In the data collection stage, resources must be allocated for properly trained researchers to conduct the qualitative interviewing because it is a highly skilled activity. In some cases, a research team may decide that they would like to use lay people, patients or peers to do the interviews [ 29 – 32 ] and in this case they must be properly trained and mentored which requires time and resources. At this early stage it is also useful to consider whether the team will use Computer Assisted Qualitative Data Analysis Software (CAQDAS), which can assist with data management and analysis.

As any form of qualitative or quantitative analysis is not a purely technical process, but influenced by the characteristics of the researchers and their disciplinary paradigms, critical reflection throughout the research process is paramount, including in the design of the study, the construction or collection of data, and the analysis. All members of the team should keep a research diary, where they record reflexive notes, impressions of the data and thoughts about analysis throughout the process. Experienced qualitative researchers become more skilled at sifting through data and analysing it in a rigorous and reflexive way. They cannot be too attached to certainty, but must remain flexible and adaptive throughout the research in order to generate rich and nuanced findings that embrace and explain the complexity of real social life and can be applied to complex social issues. It is important to remember when using the Framework Method that, unlike quantitative research where data collection and data analysis are strictly sequential and mutually exclusive stages of the research process, in qualitative analysis there is, to a greater or lesser extent depending on the project, ongoing interplay between data collection, analysis, and theory development. For example, new ideas or insights from participants may suggest potentially fruitful lines of enquiry, or close analysis might reveal subtle inconsistencies in an account which require further exploration.

Procedure for analysis

Stage 1: transcription.

A good quality audio recording and, ideally, a verbatim (word for word) transcription of the interview is needed. For Framework Method analysis, it is not necessarily important to include the conventions of dialogue transcriptions which can be difficult to read (e.g. pauses or two people talking simultaneously), because the content is what is of primary interest. Transcripts should have large margins and adequate line spacing for later coding and making notes. The process of transcription is a good opportunity to become immersed in the data and is to be strongly encouraged for new researchers. However, in some projects, the decision may be made that it is a better use of resources to outsource this task to a professional transcriber.

Stage 2: Familiarisation with the interview

Becoming familiar with the whole interview using the audio recording and/or transcript and any contextual or reflective notes that were recorded by the interviewer is a vital stage in interpretation. It can also be helpful to re-listen to all or parts of the audio recording. In multi-disciplinary or large research projects, those involved in analysing the data may be different from those who conducted or transcribed the interviews, which makes this stage particularly important. One margin can be used to record any analytical notes, thoughts or impressions.

Stage 3: Coding

After familiarization, the researcher carefully reads the transcript line by line, applying a paraphrase or label (a ‘code’) that describes what they have interpreted in the passage as important. In more inductive studies, at this stage ‘open coding’ takes place, i.e. coding anything that might be relevant from as many different perspectives as possible. Codes could refer to substantive things (e.g. particular behaviours, incidents or structures), values (e.g. those that inform or underpin certain statements, such as a belief in evidence-based medicine or in patient choice), emotions (e.g. sorrow, frustration, love) and more impressionistic/methodological elements (e.g. interviewee found something difficult to explain, interviewee became emotional, interviewer felt uncomfortable) [ 33 ]. In purely deductive studies, the codes may have been pre-defined (e.g. by an existing theory, or specific areas of interest to the project) so this stage may not be strictly necessary and you could just move straight onto indexing, although it is generally helpful even if you are taking a broadly deductive approach to do some open coding on at least a few of the transcripts to ensure important aspects of the data are not missed. Coding aims to classify all of the data so that it can be compared systematically with other parts of the data set. At least two researchers (or at least one from each discipline or speciality in a multi-disciplinary research team) should independently code the first few transcripts, if feasible. Patients, public involvement representatives or clinicians can also be productively involved at this stage, because they can offer alternative viewpoints thus ensuring that one particular perspective does not dominate. It is vital in inductive coding to look out for the unexpected and not to just code in a literal, descriptive way so the involvement of people from different perspectives can aid greatly in this. As well as getting a holistic impression of what was said, coding line-by-line can often alert the researcher to consider that which may ordinarily remain invisible because it is not clearly expressed or does not ‘fit’ with the rest of the account. In this way the developing analysis is challenged; to reconcile and explain anomalies in the data can make the analysis stronger. Coding can also be done digitally using CAQDAS, which is a useful way to keep track automatically of new codes. However, some researchers prefer to do the early stages of coding with a paper and pen, and only start to use CAQDAS once they reach Stage 5 (see below).

Stage 4: Developing a working analytical framework

After coding the first few transcripts, all researchers involved should meet to compare the labels they have applied and agree on a set of codes to apply to all subsequent transcripts. Codes can be grouped together into categories (using a tree diagram if helpful), which are then clearly defined. This forms a working analytical framework. It is likely that several iterations of the analytical framework will be required before no additional codes emerge. It is always worth having an ‘other’ code under each category to avoid ignoring data that does not fit; the analytical framework is never ‘final’ until the last transcript has been coded.

Stage 5: Applying the analytical framework

The working analytical framework is then applied by indexing subsequent transcripts using the existing categories and codes. Each code is usually assigned a number or abbreviation for easy identification (and so the full names of the codes do not have to be written out each time) and written directly onto the transcripts. Computer Assisted Qualitative Data Analysis Software (CAQDAS) is particularly useful at this stage because it can speed up the process and ensures that, at later stages, data is easily retrievable. It is worth noting that unlike software for statistical analyses, which actually carries out the calculations with the correct instruction, putting the data into a qualitative analysis software package does not analyse the data; it is simply an effective way of storing and organising the data so that they are accessible for the analysis process.

Stage 6: Charting data into the framework matrix

Qualitative data are voluminous (an hour of interview can generate 15–30 pages of text) and being able to manage and summarize (reduce) data is a vital aspect of the analysis process. A spreadsheet is used to generate a matrix and the data are ‘charted’ into the matrix. Charting involves summarizing the data by category from each transcript. Good charting requires an ability to strike a balance between reducing the data on the one hand and retaining the original meanings and ‘feel’ of the interviewees’ words on the other. The chart should include references to interesting or illustrative quotations. These can be tagged automatically if you are using CAQDAS to manage your data (N-Vivo version 9 onwards has the capability to generate framework matrices), or otherwise a capital ‘Q’, an (anonymized) transcript number, page and line reference will suffice. It is helpful in multi-disciplinary teams to compare and contrast styles of summarizing in the early stages of the analysis process to ensure consistency within the team. Any abbreviations used should be agreed by the team. Once members of the team are familiar with the analytical framework and well practised at coding and charting, on average, it will take about half a day per hour-long transcript to reach this stage. In the early stages, it takes much longer.

Stage 7: Interpreting the data

It is useful throughout the research to have a separate note book or computer file to note down impressions, ideas and early interpretations of the data. It may be worth breaking off at any stage to explore an interesting idea, concept or potential theme by writing an analytic memo [ 20 , 21 ] to then discuss with other members of the research team, including lay and clinical members. Gradually, characteristics of and differences between the data are identified, perhaps generating typologies, interrogating theoretical concepts (either prior concepts or ones emerging from the data) or mapping connections between categories to explore relationships and/or causality. If the data are rich enough, the findings generated through this process can go beyond description of particular cases to explanation of, for example, reasons for the emergence of a phenomena, predicting how an organisation or other social actor is likely to instigate or respond to a situation, or identifying areas that are not functioning well within an organisation or system. It is worth noting that this stage often takes longer than anticipated and that any project plan should ensure that sufficient time is allocated to meetings and individual researcher time to conduct interpretation and writing up of findings (see Additional file 1 , Section 7).

The Framework Method has been developed and used successfully in research for over 25 years, and has recently become a popular analysis method in qualitative health research. The issue of how to assess quality in qualitative research has been highly debated [ 20 , 34 – 40 ], but ensuring rigour and transparency in analysis is a vital component. There are, of course, many ways to do this but in the Framework Method the following are helpful:

Summarizing the data during charting, as well as being a practical way to reduce the data, means that all members of a multi-disciplinary team, including lay, clinical and (quantitative) academic members can engage with the data and offer their perspectives during the analysis process without necessarily needing to read all the transcripts or be involved in the more technical parts of analysis.

Charting also ensures that researchers pay close attention to describing the data using each participant’s own subjective frames and expressions in the first instance, before moving onto interpretation.

The summarized data is kept within the wider context of each case, thereby encouraging thick description that pays attention to complex layers of meaning and understanding [ 38 ].

The matrix structure is visually straightforward and can facilitate recognition of patterns in the data by any member of the research team, including through drawing attention to contradictory data, deviant cases or empty cells.

The systematic procedure (described in this article) makes it easy to follow, even for multi-disciplinary teams and/or with large data sets.

It is flexible enough that non-interview data (such as field notes taken during the interview or reflexive considerations) can be included in the matrix.

It is not aligned with a particular epistemological viewpoint or theoretical approach and therefore can be adapted for use in inductive or deductive analysis or a combination of the two (e.g. using pre-existing theoretical constructs deductively, then revising the theory with inductive aspects; or using an inductive approach to identify themes in the data, before returning to the literature and using theories deductively to help further explain certain themes).

It is easy to identify relevant data extracts to illustrate themes and to check whether there is sufficient evidence for a proposed theme.

Finally, there is a clear audit trail from original raw data to final themes, including the illustrative quotes.

There are also a number of potential pitfalls to this approach:

The systematic approach and matrix format, as we noted in the background, is intuitively appealing to those trained quantitatively but the ‘spreadsheet’ look perhaps further increases the temptation for those without an in-depth understanding of qualitative research to attempt to quantify qualitative data (e.g. “13 out of 20 participants said X). This kind of statement is clearly meaningless because the sampling in qualitative research is not designed to be representative of a wider population, but purposive to capture diversity around a phenomenon [ 41 ].

Like all qualitative analysis methods, the Framework Method is time consuming and resource-intensive. When involving multiple stakeholders and disciplines in the analysis and interpretation of the data, as is good practice in applied health research, the time needed is extended. This time needs to be factored into the project proposal at the pre-funding stage.

There is a high training component to successfully using the method in a new multi-disciplinary team. Depending on their role in the analysis, members of the research team may have to learn how to code, index, and chart data, to think reflexively about how their identities and experience affect the analysis process, and/or they may have to learn about the methods of generalisation (i.e. analytic generalisation and transferability, rather than statistical generalisation [ 41 ]) to help to interpret legitimately the meaning and significance of the data.

While the Framework Method is amenable to the participation of non-experts in data analysis, it is critical to the successful use of the method that an experienced qualitative researcher leads the project (even if the overall lead for a large mixed methods study is a different person). The qualitative lead would ideally be joined by other researchers with at least some prior training in or experience of qualitative analysis. The responsibilities of the lead qualitative researcher are: to contribute to study design, project timelines and resource planning; to mentor junior qualitative researchers; to train clinical, lay and other (non-qualitative) academics to contribute as appropriate to the analysis process; to facilitate analysis meetings in a way that encourages critical and reflexive engagement with the data and other team members; and finally to lead the write-up of the study.

We have argued that Framework Method studies can be conducted by multi-disciplinary research teams that include, for example, healthcare professionals, psychologists, sociologists, economists, and lay people/service users. The inclusion of so many different perspectives means that decision-making in the analysis process can be very time consuming and resource-intensive. It may require extensive, reflexive and critical dialogue about how the ideas expressed by interviewees and identified in the transcript are related to pre-existing concepts and theories from each discipline, and to the real ‘problems’ in the health system that the project is addressing. This kind of team effort is, however, an excellent forum for driving forward interdisciplinary collaboration, as well as clinical and lay involvement in research, to ensure that ‘the whole is greater than the sum of the parts’, by enhancing the credibility and relevance of the findings.

The Framework Method is appropriate for thematic analysis of textual data, particularly interview transcripts, where it is important to be able to compare and contrast data by themes across many cases, while also situating each perspective in context by retaining the connection to other aspects of each individual’s account. Experienced qualitative researchers should lead and facilitate all aspects of the analysis, although the Framework Method’s systematic approach makes it suitable for involving all members of a multi-disciplinary team. An open, critical and reflexive approach from all team members is essential for rigorous qualitative analysis.

Acceptance of the complexity of real life health systems and the existence of multiple perspectives on health issues is necessary to produce high quality qualitative research. If done well, qualitative studies can shed explanatory and predictive light on important phenomena, relate constructively to quantitative parts of a larger study, and contribute to the improvement of health services and development of health policy. The Framework Method, when selected and implemented appropriately, can be a suitable tool for achieving these aims through producing credible and relevant findings.

The Framework Method is an excellent tool for supporting thematic (qualitative content) analysis because it provides a systematic model for managing and mapping the data.

The Framework Method is most suitable for analysis of interview data, where it is desirable to generate themes by making comparisons within and between cases.

The management of large data sets is facilitated by the Framework Method as its matrix form provides an intuitively structured overview of summarised data.

The clear, step-by-step process of the Framework Method makes it is suitable for interdisciplinary and collaborative projects.

The use of the method should be led and facilitated by an experienced qualitative researcher.

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All authors were involved in the development of the concept of the article and drafting the article. NG wrote the first draft of the article, GH and EC prepared the text and figures related to the illustrative example, SRa did the literature search to identify if there were any similar articles currently available and contributed to drafting of the article, and SRe contributed to drafting of the article and the illustrative example. All authors read and approved the final manuscript.

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Gale, N.K., Heath, G., Cameron, E. et al. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 13 , 117 (2013). https://doi.org/10.1186/1471-2288-13-117

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Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

• Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
• Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
• Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• Open access
• Published: 13 May 2024

Sexual and reproductive health implementation research in humanitarian contexts: a scoping review

• Alexandra Norton 1 &
• Hannah Tappis 2  

Reproductive Health volume  21 , Article number:  64 ( 2024 ) Cite this article

85 Accesses

Metrics details

Meeting the health needs of crisis-affected populations is a growing challenge, with 339 million people globally in need of humanitarian assistance in 2023. Given one in four people living in humanitarian contexts are women and girls of reproductive age, sexual and reproductive health care is considered as essential health service and minimum standard for humanitarian response. Despite growing calls for increased investment in implementation research in humanitarian settings, guidance on appropriate methods and analytical frameworks is limited.

A scoping review was conducted to examine the extent to which implementation research frameworks have been used to evaluate sexual and reproductive health interventions in humanitarian settings. Peer-reviewed papers published from 2013 to 2022 were identified through relevant systematic reviews and a literature search of Pubmed, Embase, PsycInfo, CINAHL and Global Health databases. Papers that presented primary quantitative or qualitative data pertaining to a sexual and reproductive health intervention in a humanitarian setting were included.

Seven thousand thirty-six unique records were screened for inclusion, and 69 papers met inclusion criteria. Of these, six papers explicitly described the use of an implementation research framework, three citing use of the Consolidated Framework for Implementation Research. Three additional papers referenced other types of frameworks used in their evaluation. Factors cited across all included studies as helping the intervention in their presence or hindering in their absence were synthesized into the following Consolidated Framework for Implementation Research domains: Characteristics of Systems, Outer Setting, Inner Setting, Characteristics of Individuals, Intervention Characteristics, and Process.

This review found a wide range of methodologies and only six of 69 studies using an implementation research framework, highlighting an opportunity for standardization to better inform the evidence for and delivery of sexual and reproductive health interventions in humanitarian settings. Increased use of implementation research frameworks such as a modified Consolidated Framework for Implementation Research could work toward both expanding the evidence base and increasing standardization.

Plain English summary

Three hundred thirty-nine million people globally were in need of humanitarian assistance in 2023, and meeting the health needs of crisis-affected populations is a growing challenge. One in four people living in humanitarian contexts are women and girls of reproductive age, and provision of sexual and reproductive health care is considered to be essential within a humanitarian response. Implementation research can help to better understand how real-world contexts affect health improvement efforts. Despite growing calls for increased investment in implementation research in humanitarian settings, guidance on how best to do so is limited. This scoping review was conducted to examine the extent to which implementation research frameworks have been used to evaluate sexual and reproductive health interventions in humanitarian settings. Of 69 papers that met inclusion criteria for the review, six of them explicitly described the use of an implementation research framework. Three used the Consolidated Framework for Implementation Research, a theory-based framework that can guide implementation research. Three additional papers referenced other types of frameworks used in their evaluation. This review summarizes how factors relevant to different aspects of implementation within the included papers could have been organized using the Consolidated Framework for Implementation Research. The findings from this review highlight an opportunity for standardization to better inform the evidence for and delivery of sexual and reproductive health interventions in humanitarian settings. Increased use of implementation research frameworks such as a modified Consolidated Framework for Implementation Research could work toward both expanding the evidence base and increasing standardization.

Peer Review reports

Over the past few decades, the field of public health implementation research (IR) has grown as a means by which the real-world conditions affecting health improvement efforts can be better understood. Peters et al. put forward the following broad definition of IR for health: “IR is the scientific inquiry into questions concerning implementation – the act of carrying an intention into effect, which in health research can be policies, programmes, or individual practices (collectively called interventions)” [ 1 ].

As IR emphasizes real-world circumstances, the context within which a health intervention is delivered is a core consideration. However, much IR implemented to date has focused on higher-resource settings, with many proposed frameworks developed with particular utility for a higher-income setting [ 2 ]. In recognition of IR’s potential to increase evidence across a range of settings, there have been numerous reviews of the use of IR in lower-resource settings as well as calls for broader use [ 3 , 4 ]. There have also been more focused efforts to modify various approaches and frameworks to strengthen the relevance of IR to low- and middle-income country settings (LMICs), such as the work by Means et al. to adapt a specific IR framework for increased utility in LMICs [ 2 ].

Within LMIC settings, the centrality of context to a health intervention’s impact is of particular relevance in humanitarian settings, which present a set of distinct implementation challenges [ 5 ]. Humanitarian responses to crisis situations operate with limited resources, under potential security concerns, and often under pressure to relieve acute suffering and need [ 6 ]. Given these factors, successful implementation of a particular health intervention may require different qualities than those that optimize intervention impact under more stable circumstances [ 7 ]. Despite increasing recognition of the need for expanded evidence of health interventions in humanitarian settings, the evidence base remains limited [ 8 ]. Furthermore, despite its potential utility, there is not standardized guidance on IR in humanitarian settings, nor are there widely endorsed recommendations for the frameworks best suited to analyze implementation in these settings.

Sexual and reproductive health (SRH) is a core aspect of the health sector response in humanitarian settings [ 9 ]. Yet, progress in addressing SRH needs has lagged far behind other services because of challenges related to culture and ideology, financing constraints, lack of data and competing priorities [ 10 ]. The Minimum Initial Service Package (MISP) for SRH in Crisis Situations is the international standard for the minimum set of SRH services that should be implemented in all crisis situations [ 11 ]. However, as in other areas of health, there is need for expanded evidence for planning and implementation of SRH interventions in humanitarian settings. Recent systematic reviews of SRH in humanitarian settings have focused on the effectiveness of interventions and service delivery strategies, as well as factors affecting utilization, but have not detailed whether IR frameworks were used [ 12 , 13 , 14 , 15 ]. There have also been recent reviews examining IR frameworks used in various settings and research areas, but none have explicitly focused on humanitarian settings [ 2 , 16 ].

Given the need for an expanded evidence base for SRH interventions in humanitarian settings and the potential for IR to be used to expand the available evidence, a scoping review was undertaken. This scoping review sought to identify IR approaches that have been used in the last ten years to evaluate SRH interventions in humanitarian settings.

This review also sought to shed light on whether there is a need for a common framework to guide research design, analysis, and reporting for SRH interventions in humanitarian settings and if so, if there are any established frameworks already in use that would be fit-for-purpose or could be tailored to meet this need.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews was utilized to guide the elements of this review [ 17 ]. The review protocol was retrospectively registered with the Open Science Framework ( https://osf.io/b5qtz ).

Search strategy

A two-fold search strategy was undertaken for this review, which covered the last 10 years (2013–2022). First, recent systematic reviews pertaining to research or evaluation of SRH interventions in humanitarian settings were identified through keyword searches on PubMed and Google Scholar. Four relevant systematic reviews were identified [ 12 , 13 , 14 , 15 ] Table 1 .

Second, a literature search mirroring these reviews was conducted to identify relevant papers published since the completion of searches for the most recent review (April 2017). Additional file 1 includes the search terms that were used in the literature search [see Additional file 1 ].

The literature search was conducted for papers published from April 2017 to December 2022 in the databases that were searched in one or more of the systematic reviews: PubMed, Embase, PsycInfo, CINAHL and Global Health. Searches were completed in January 2023 Table 2 .

Two reviewers screened each identified study for alignment with inclusion criteria. Studies in the four systematic reviews identified were considered potentially eligible if published during the last 10 years. These papers then underwent full-text review to confirm satisfaction of all inclusion criteria, as inclusion criteria were similar but not fully aligned across the four reviews.

Literature search results were exported into a citation manager (Covidence), duplicates were removed, and a step-wise screening process for inclusion was applied. First, all papers underwent title and abstract screening. The remaining papers after abstract screening then underwent full-text review to confirm satisfaction of all inclusion criteria. Title and abstract screening as well as full-text review was conducted independently by both authors; disagreements after full-text review were resolved by consensus.

Data extraction and synthesis

The following content areas were summarized in Microsoft Excel for each paper that met inclusion criteria: publication details including author, year, country, setting [rural, urban, camp, settlement], population [refugees, internally displaced persons, general crisis-affected], crisis type [armed conflict, natural disaster], crisis stage [acute, chronic], study design, research methods, SRH intervention, and intervention target population [specific beneficiaries of the intervention within the broader population]; the use of an IR framework; details regarding the IR framework, how it was used, and any rationale given for the framework used; factors cited as impacting SRH interventions, either positively or negatively; and other key findings deemed relevant to this review.

As the focus of this review was on the approach taken for SRH intervention research and evaluation, the quality of the studies themselves was not assessed.

Twenty papers underwent full-text review due to their inclusion in one or more of the four systematic reviews and meeting publication date inclusion criteria. The literature search identified 7,016 unique papers. After full-text screening, 69 met all inclusion criteria and were included in the review. Figure  1 illustrates the search strategy and screening process.

Flow chart of paper identification

Papers published in each of the 10 years of the review timeframe (2013–2022) were included. 29% of the papers originated from the first five years of the time frame considered for this review, with the remaining 71% papers coming from the second half. Characteristics of included publications, including geographic location, type of humanitarian crisis, and type of SRH intervention, are presented in Table  3 .

A wide range of study designs and methods were used across the papers, with both qualitative and quantitative studies well represented. Twenty-six papers were quantitative evaluations [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], 17 were qualitative [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and 26 used mixed methods [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ]. Within the quantitative evaluations, 15 were observational, while five were quasi-experimental, five were randomized controlled trials, and one was an economic evaluation. Study designs as classified by the authors of this review are summarized in Table  4 .

Six papers (9%) explicitly cited use of an IR framework. Three of these papers utilized the Consolidated Framework for Implementation Research (CFIR) [ 51 , 65 , 70 ]. The CFIR is a commonly used determinant framework that—in its originally proposed form in 2009—is comprised of five domains, each of which has constructs to further categorize factors that impact implementation. The CFIR domains were identified as core content areas influencing the effectiveness of implementation, and the constructs within each domain are intended to provide a range of options for researchers to select from to “guide diagnostic assessments of implementation context, evaluate implementation progress, and help explain findings.” [ 87 ] To allow for consistent terminology throughout this review, the original 2009 CFIR domains and constructs are used.

Guan et al. conducted a mixed methods study to assess the feasibility and effectiveness of a neonatal hepatitis B immunization program in a conflict-affected rural region of Myanmar. Guan et al. report mapping data onto the CFIR as a secondary analysis step. They describe that “CFIR was used as a comprehensive meta-theoretical framework to examine the implementation of the Hepatitis B Virus vaccination program,” and implementation themes from multiple study data sources (interviews, observations, examination of monitoring materials) were mapped onto CFIR constructs. They report their results in two phases – Pre-implementation training and community education, and Implementation – with both anchored in themes that they had mapped onto CFIR domains and constructs. All but six constructs were included in their analysis, with a majority summarized in a table and key themes explored further in the narrative text. They specify that most concerns were identified within the Outer Setting and Process domains, while elements identified within the Inner Setting domain provided strength to the intervention and helped mitigate against barriers [ 70 ].

Sarker et al. conducted a qualitative study to assess provision of maternal, newborn and child health services to Rohingya refugees residing in camps in Cox’s Bazar, Bangladesh. They cite using CFIR as a guide for thematic analysis, applying it after a process of inductive and deductive coding to index these codes into the CFIR domains. They utilized three of the five CFIR domains (Outer Setting, Inner Setting, and Process), stating that the remaining two domains (Intervention Characteristics and Characteristics of Individuals) were not relevant to their analysis. They then proposed two additional CFIR domains, Context and Security, for use in humanitarian contexts. In contrast to Guan et al., CFIR constructs are not used nor mentioned by Sarker et al., with content under each domain instead synthesized as challenges and potential solutions. Regarding the CFIR, Sarker et al. write, “The CFIR guided us for interpretative coding and creating the challenges and possible solutions into groups for further clarification of the issues related to program delivery in a humanitarian crisis setting.” [ 51 ]

Sami et al. conducted a mixed methods case study to assess the implementation of a package of neonatal interventions at health facilities within refugee and internally displaced persons camps in South Sudan. They reference use of the CFIR earlier in the study than Sarker et al., basing their guides for semi-structured focus group discussions on the CFIR framework. They similarly reference a general use of the CFIR framework as they conducted thematic analysis. Constructs are referenced once, but they do not specify whether their application of the CFIR framework included use of domains, constructs, or both. This may be in part because they then applied an additional framework, the World Health Organization (WHO) Health System Framework, to present their findings. They describe a nested approach to their use of these frameworks: “Exploring these [CFIR] constructs within the WHO Health Systems Framework can identify specific entry points to improve the implementation of newborn interventions at critical health system building blocks.” [ 65 ]

Three papers cite use of different IR frameworks. Bolan et al. utilized the Theoretical Domains Framework in their mixed methods feasibility study and pilot cluster randomized trial evaluating pilot use of the Safe Delivery App by maternal and newborn health workers providing basic emergency obstetric and newborn care in facilities in the conflict-affected Maniema province of the Democratic Republic of the Congo (DRC). They used the Theroetical Domains Framework in designing interview questions, and further used it as the coding framework for their analysis. Similar to the CFIR, the Theoretical Domains Framework is a determinant framework that consists of domains, each of which then includes constructs. Bolan et al. utilized the Theoretical Domains Framework at the construct level in interview question development and at the domain level in their analysis, mapping interview responses to eight of the 14 domains [ 83 ]. Berg et al. report using an “exploratory design guided by the principles of an evaluation framework” developed by the Medical Research Council to analyze the implementation process, mechanisms of impact, and outcomes of a three-pillar training intervention to improve maternal and neonatal healthcare in the conflict-affected South Kivu province of the DRC [ 67 , 88 ]. Select components of this evaluation framework were used to guide deductive analysis of focus group discussions and in-depth interviews [ 67 ]. In their study of health workers’ knowledge and attitudes toward newborn health interventions in South Sudan, before and after training and supply provision, Sami et al. report use of the Conceptual Framework of the Role of Attitudes in Evidence-Based Practice Implementation in their analysis process. The framework was used to group codes following initial inductive coding analysis of in-depth interviews [ 72 ].

Three other papers cite use of specific frameworks in their intervention evaluation [ 19 , 44 , 76 ]. As a characteristic of IR is the use of an explicit framework to guide the research, the use of the frameworks in these three papers meets the intention of IR and serves the purpose that an IR framework would have in strengthening the analytical rigor. Castle et al. cite use of their program’s theory of change as a framework for a mixed methods evaluation of the provision of family planning services and more specifically uptake of long-acting reversible contraception use in the DRC. They describe use of the theory of change to “enhance effectiveness of [long-acting reversible contraception] access and uptake.” [ 76 ] Thommesen et al. cite use of the AAAQ (Availability, Accessibility, Acceptability and Quality) framework in their qualitative study assessing midwifery services provided to pregnant women in Afghanistan. This framework is focused on the “underlying elements needed for attainment of optimum standard of health care,” but the authors used it in this paper to evaluate facilitators and barriers to women accessing midwifery services [ 44 ]. Jarrett et al. cite use of the Centers for Disease Control and Prevention’s (CDC) Guidelines for Evaluating Public Health Surveillance Systems to explore the characteristics of a population mobility, mortality and birth surveillance system in South Kivu, DRC. Use of these CDC guidelines is cited as one of four study objectives, and commentary is included in the Results section pertaining to each criteria within these guidelines, although more detail regarding use of these guidelines or the authors’ experience with their use in the study is not provided [ 19 ].

Overall, 22 of the 69 papers either explicitly or implicitly identified IR as relevant to their work. Nineteen papers include a focus on feasibility (seven of which did not otherwise identify the importance of exploring questions concerning implementation), touching on a common outcome of interest in implementation research [ 89 ].

While a majority of papers did not explicitly or implicitly use an IR framework to evaluate their SRH intervention of focus, most identified factors that facilitated implementation when they were present or served as a barrier when absent. Sixty cite factors that served as facilitators and 49 cite factors that served as barriers, with just three not citing either. Fifty-nine distinct factors were identified across the papers.

Three of the six studies that explicitly used an IR framework used the CFIR, and the CFIR is the only IR framework that was used by multiple studies. As previously mentioned, Means et al. put forth an adaptation of the CFIR to increase its relevance in LMIC settings, proposing a sixth domain (Characteristics of Systems) and 11 additional constructs [ 2 ]. Using the expanded domains and constructs as proposed by Means et al., the 59 factors cited by papers in this review were thematically grouped into the six domains: Characteristics of Systems, Outer Setting, Inner Setting, Characteristics of Individuals, Intervention Characteristics, and Process. Within each domain, alignment with CFIR constructs was assessed for, and alignment was found with 29 constructs: eight of Means et al.’s 11 constructs, and 21 of the 39 standard CFIR constructs. Three factors did not align with any construct (all fitting within the Outer Setting domain), and 14 aligned with a construct label but not the associated definition. Table 5 synthesizes the mapping of factors affecting SRH intervention implementation to CFIR domains and constructs, with the construct appearing in italics if it is considered to align with that factor by label but not by definition.

Table 6 lists the CFIR constructs that were not found to have alignment with any factor cited by the papers in this review.

This scoping review sought to assess how IR frameworks have been used to bolster the evidence base for SRH interventions in humanitarian settings, and it revealed that IR frameworks, or an explicit IR approach, are rarely used. All four of the systematic reviews identified with a focus on SRH in humanitarian settings articulate the need for more research examining the effectiveness of SRH interventions in humanitarian settings, with two specifically citing a need for implementation research/science [ 12 , 13 ]. The distribution of papers across the timeframe included in this review does suggest that more research on SRH interventions for crisis-affected populations is taking place, as a majority of relevant papers were published in the second half of the review period. The papers included a wide range of methodologies, which reflect the differing research questions and contexts being evaluated. However, it also invites the question of whether there should be more standardization of outcomes measured or frameworks used to guide analysis and to facilitate increased comparison, synthesis and application across settings.

Three of the six papers that used an IR framework utilized the CFIR. Guan et al. used the CFIR at both a domain and construct level, Sarker et al. used the CFIR at the domain level, and Sami et al. did not specify which CFIR elements were used in informing the focus group discussion guide [ 51 , 65 , 70 ]. It is challenging to draw strong conclusions about the applicability of CFIR in humanitarian settings based on the minimal use of CFIR and IR frameworks within the papers reviewed, although Guan et al. provides a helpful model for how analysis can be structured around CFIR domains and constructs. It is worth considering that the minimal use of IR frameworks, and more specifically CFIR constructs, could be in part because that level of prescriptive categorization does not allow for enough fluidity in humanitarian settings. It also raises questions about the appropriate degree of standardization to pursue for research done in these settings.

The mapping of factors affecting SRH intervention implementation provides an example of how a modified CFIR framework could be used for IR in humanitarian contexts. This mapping exercise found factors that mapped to all five of the original CFIR domains as well as the sixth domain proposed by Means et al. All factors fit well within the definition for the selected domain, indicating an appropriate degree of fit between these existing domains and the factors identified as impacting SRH interventions in humanitarian settings. On a construct level, however, the findings were more variable, with one-quarter of factors not fully aligning with any construct. Furthermore, over 40% of the CFIR constructs (including the additional constructs from Means et al.) were not found to align with any factors cited by the papers in this review, also demonstrating some disconnect between the parameters posed by the CFIR constructs and the factors cited as relevant in a humanitarian context.

It is worth noting that while the CFIR as proposed in 2009 was used in this assessment, as well as in the included papers which used the CFIR, an update was published in 2022. Following a review of CFIR use since its publication, the authors provide updates to construct names and definitions to “make the framework more applicable across a range of innovations and settings.” New constructs and subconstructs were also added, for a total of 48 constructs and 19 subconstructs across the five domains [ 90 ]. A CFIR Outcomes Addendum was also published in 2022, based on recommendations for the CFIR to add outcomes and intended to be used as a complement to the CFIR determinants framework [ 91 ]. These expansions to the CFIR framework may improve applicability of the CFIR in humanitarian settings. Several constructs added to the Outer Setting domain could be of particular utility – critical incidents, local attitudes, and local conditions, each of which could help account for unique challenges faced in contexts of crisis. Sub-constructs added within the Inner Setting domain that seek to clarify structural characteristics and available resources would also be of high utility based on mapping of the factors identified in this review to the original CFIR constructs. As outcomes were not formally included in the CFIR until the 2022 addendum, a separate assessment of implementation outcomes was not undertaken in this review. However, analysis of the factors cited by papers in this review as affecting implementation was derived from the full text of the papers and thus captures content relevant to implementation determinants that is contained within the outcomes.

Given the demonstrated need for additional flexibility within an IR framework for humanitarian contexts, while not a focus of this review, it is worth considering whether a different framework could provide a better fit than the CFIR. Other frameworks have differing points of emphasis that would create different opportunities for flexibility but that do not seem to resolve the challenges experienced in applying the CFIR to a humanitarian context. As one example, the EPIS (Exploration, Preparation, Implementation, Sustainment) Framework considers the impact of inner and outer context on each of four implementation phases; while the constructs within this framework are broader than the CFIR, an emphasis on the intervention characteristics is missing, a domain where stronger alignment within the CFIR is also needed [ 92 ]. Alternatively, the PRISM (Practical, Robust Implementation and Sustainability Model) framework is a determinant and evaluation framework that adds consideration of context factors to the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) outcomes framework. It has a stronger emphasis on intervention aspects, with sub-domains to account for both organization and patient perspectives within the intervention. While PRISM does include aspects of context, external environment considerations are less robust and intentionally less comprehensive in scope, which would not provide the degree of alignment possible between the Characteristics of Systems and Outer Setting CFIR domains for the considerations unique to humanitarian environments [ 93 ].

Reflecting on their experience with the CFIR, Sarker et al. indicate that it can be a “great asset” in both evaluating current work and developing future interventions. They also encourage future research of humanitarian health interventions to utilize the CFIR [ 51 ]. The other papers that used the CFIR do not specifically reflect on their experience utilizing it, referring more generally to having felt that it was a useful tool [ 65 , 70 ]. On their use of an evaluation framework, Berg et al. reflected that it lent useful structure and helped to identify aspects affecting implementation that otherwise would have gone un-noticed [ 67 ]. The remaining studies that utilized an IR framework did not specifically comment on their experience with its use [ 72 , 83 ]. While a formal IR framework was not engaged by other studies, a number cite a desire for IR to contribute further detail to their findings [ 21 , 37 ].

In their recommendations for strengthening the evidence base for humanitarian health interventions, Ager et al. speak to the need for “methodologic innovation” to develop methodologies with particular applicability in humanitarian settings [ 7 ]. As IR is not yet routinized for SRH interventions, this could be opportune timing for the use of a standardized IR framework to gauge its utility. Using an IR framework to assess factors influencing implementation of the MISP in initial stages of a humanitarian response, and interventions to support more comprehensive SRH service delivery in protracted crises, could lend further rigor and standardization to SRH evaluations, as well as inform strategies to improve MISP implementation over time. Based on categorizing factors identified by these papers as relevant for intervention evaluation, there does seem to be utility to a modified CFIR approach. Given the paucity of formal IR framework use within SRH literature, it would be worth conducting similar scoping exercises to assess for explicit use of IR frameworks within the evidence base for other health service delivery areas in humanitarian settings. In the interim, the recommended approach from this review for future IR on humanitarian health interventions would be a modified CFIR approach with domain-level standardization and flexibility for constructs that may standardize over time with more use. This would enable use of a common analytical framework and vocabulary at the domain level for stakeholders to describe interventions and the factors influencing the effectiveness of implementation, with constructs available to use and customize as most appropriate for specific contexts and interventions.

This review had a number of limitations. As this was a scoping review and a two-part search strategy was used, the papers summarized here may not be comprehensive of those written pertaining to SRH interventions over the past 10 years. Papers from 2013 to 2017 that would have met this scoping review’s inclusion criteria may have been omitted due to being excluded from the systematic reviews. The review was limited to papers available in English. Furthermore, this review did not assess the quality of the papers included or seek to assess the methodology used beyond examination of the use of an IR framework. It does, however, serve as a first step in assessing the extent to which calls for implementation research have been addressed, and identify entry points for strengthening the science and practice of SRH research in humanitarian settings.

With one in 23 people worldwide in need of humanitarian assistance, and financing required for response plans at an all-time high, the need for evidence to guide resource allocation and programming for SRH in humanitarian settings is as important as ever [ 94 ]. Recent research agenda setting initiatives and strategies to advance health in humanitarian settings call for increased investment in implementation research—with priorities ranging from research on effective strategies for expanding access to a full range of contraceptive options to integrating mental health and psychosocial support into SRH programming to capturing accurate and actionable data on maternal and perinatal mortality in a wide range of acute and protracted emergency contexts [ 95 , 96 ]. To truly advance guidance in these areas, implementation research will need to be conducted across diverse humanitarian settings, with clear and consistent documentation of both intervention characteristics and outcomes, as well as contextual and programmatic factors affecting implementation.

Conclusions

Implementation research has potential to increase impact of health interventions particularly in crisis-affected settings where flexibility, adaptability and context-responsive approaches are highlighted as cornerstones of effective programming. There remains significant opportunity for standardization of research in the humanitarian space, with one such opportunity occurring through increased utilization of IR frameworks such as a modified CFIR approach. Investing in more robust sexual and reproductive health research in humanitarian contexts can enrich insights available to guide programming and increase transferability of learning across settings.

Availability of data and materials

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

Abbreviations

Availability, Accessibility, Acceptability and Quality

Centers for Disease Control and Prevention

Consolidated Framework for Implementation Research

Democratic Republic of the Congo

Exploration, Preparation, Implementation, Sustainment

• Implementation research

Low and middle income country

Minimum Initial Service Package

Practical, Robust Implementation and Sustainability Model

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Reach, Effectiveness, Adoption, Implementation, Maintenance

• Sexual and reproductive health

World Health Organization

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Consumer Trust: Meta-Analysis of 50 Years of Empirical Research

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Mansur Khamitov, Koushyar Rajavi, Der-Wei Huang, Yuly Hong, Consumer Trust: Meta-Analysis of 50 Years of Empirical Research, Journal of Consumer Research , Volume 51, Issue 1, June 2024, Pages 7–18, https://doi.org/10.1093/jcr/ucad065

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Trust is one of the highly important concepts of consumer research; yet it is characterized by a striking lack of generalizations and consensus regarding the relative strength of its antecedents, consequences, and moderators. To close this important gap, the current research reports a comprehensive large-scale meta-analysis shedding light on a wide variety of the antecedents, consequences, and moderators of the individual consumer’s trust and their relative importance. Empirical generalizations are based on 2,147 effect sizes from 549 studies across 469 manuscripts in numerous disciplines, representing a total of 324,834 respondents in 71 countries over a five-decade span (1970–2020). The key findings are thus that (1) integrity-based (vs. reliability-based) antecedents are more effective in driving trust, and (2) trust is more effective in improving primarily attitudinal (vs. primarily behavioral) outcomes. Moderation analyses unpack further heterogeneity. Notably, both integrity-based and reliability-based antecedents have become stronger drivers of consumer trust in recent years. Theoretical and practical contributions are discussed in addition to advancing important future directions.

Trust is one of the highly important concepts of consumer research. Trust is crucial in all aspects of our daily lives, such as commercial and social transactions, because it reduces perceived uncertainty regarding intentions and capabilities of other entities. Past research in marketing recognizes the significance of trust—albeit with a slightly richer tradition in the B2B setting, from extensive study of the nature of trust between business customers, to the point that two meta-analyses on that topic have emerged ( Geyskens, Steenkamp, and Kumar 1998 ; Palmatier et al. 2006 ). Given the synthesized and illustrious evidence accumulated in the B2B setting, it is puzzling that no systematic meta-analysis was conducted on the nuanced role of the individual consumer’s trust. 1

RQ1: What is the (relative) impact of a broad set of antecedents of consumer trust? RQ2: Under what conditions do antecedents of consumer trust become more effective?

RQ3: What is the (relative) impact of consumer trust on a broad set of downstream consequences?

Overall, our meta-analysis of 2,147 individual effects derived from 549 studies across 469 manuscripts from 1970 to 2020 offers generalizable insights into antecedents and consequences of consumer trust, along with future implications. The work provides a big-tent investigation of consumer-trust research that highlights its multi-disciplinary nature using the meta-analytic lens.

Consumer trust is defined as “a consumer’s confidence in […] reliability and integrity” of the target of trust ( De Wulf, Odekerken-Schröder, and Iacobucci 2001 , 36). In order to identify drivers of consumer trust, it is important to consider what trust consists of. While there are small differences in how consumer trust is conceptualized in past research, it is commonly accepted that it encompasses consumers’ beliefs about how reliably and with integrity an entity would deliver on its stated promise(s) ( Garbarino and Johnson 1999 ; McKnight, Choudhury, and Kacmar 2002 ). Thus, factors that drive inferences of an entity’s reliability or integrity are particularly relevant in generating consumer trust. That is, a systematic classification of prior consumer trust literature simply cannot be considered complete without accounting for both integrity-based trust antecedents (IBTA) and reliability-based trust antecedents (RBTA). Theoretical support for this underlying grouping can be found in numerous seminal consumer trust papers: intentions toward the consumers versus reliability ( Delgado‐Ballester and Munuera‐Alemán 2001 ), benevolence and integrity versus ability and dependability ( Sirdeshmukh, Singh, and Sabol 2002 ), honesty versus reliability and safety ( Chaudhuri and Holbrook 2001 ), can be counted on to be good to the consumer versus confidence and reliability ( Garbarino and Johnson 1999 ), and behaving in the long-term interest of the customer versus confidence and reliability ( Crosby, Evans, and Cowles 1990 ). 2 , 3 Furthermore, our review of past studies on consumer trust led us to two general groups of outcomes associated with consumer trust: primarily attitudinal consequences (PAC) and primarily behavioral consequences (PBC).

After determining general groups of antecedents and consequences based on past research, we chose specific antecedents and consequences based on (1) how frequently a construct appears in past research on individual consumer’s trust and (2) whether a construct fits our theoretical framework (e.g., IBTA or RBTA for antecedents). 4 Therefore, we objectively focus on the most prevalent antecedents and consequences of consumer trust, as determined by past research. In doing so, in accordance with Palmatier et al. (2006) , we retain antecedents that appear in at least 10% of the past studies on consumer trust. 5

These considerations led us to eight antecedents of consumer trust: IBTA include three constructs (attachment, ethicality and social responsibility [SR], reputation) and RBTA encompass five constructs (marketing investments, perceived value, competence, perceived risk, perceived quality). 6 For the nine consequences in our study, PAC include five constructs (self-concept connection, evaluation, engagement, attitudinal loyalty, satisfaction) and PBC have four constructs (behavioral loyalty, willingness to pay, purchase intention, market performance). 7

In web appendix A , we define and describe these constructs, report their common aliases, and highlight sample studies that examined their respective relationship with consumer trust. Figure 1 illustrates our theoretical framework. We next briefly discuss how and why the antecedents fit within the two buckets and how they affect consumer trust (for a more detailed review of research on drivers of consumer trust, see web appendix B ). The discussion on the relationships between consumer trust and its consequences can be found in web appendix C .

THEORETICAL FRAMEWORK

Antecedents of Consumer Trust

Turning to how the eight underlying antecedents fit within the two buckets and in turn drive consumer trust, we start with the three IBTA. Through ongoing encounters and interactions, consumers often form a connection with the business entity and develop attachment to the entity, which has been shown to impact consumer trust ( Bidmon 2017 ) by affecting consumers’ perceptions of the sincere relational motives of the cherished entity ( Khamitov et al. 2019 ).

Growing consumer consciousness in the 21st century has encouraged businesses to focus on ethicality and SR . The ethicality of a business entity (i.e., the commitment to doing the right thing) and investments in CSR activities influence consumers’ trust by signaling to them that the entity is moral, honest, benevolent, less likely to cheat, and likely to be of high integrity ( Diallo and Lambey-Checchin 2017 ). Relatedly, the reputation of a business entity—being highly respected and getting known for having the consumer’s best interests at heart—has been shown to significantly enhance consumer trust ( Johnson and Grayson 2005 ).

In terms of the five RBTA, businesses invest in various marketing activities to create and communicate value and expertise to their consumers. Different forms of sale-independent marketing investments influence consumer trust through conveying capability (e.g., signaling superiority; Rajavi, Kushwaha, and Steenkamp 2019 ). Perceived value has been shown to affect consumer trust by making consumers presume that the entity has the reliability and resources to come up with offerings that provide superior value to them ( Wu and Huang 2023 ).

Consumers’ interactions with a business entity, and the information that consumers obtain via different sources (e.g., news, social media) affect customers’ beliefs about competence , perceived quality , and perceived risk of these entities. Competence affects consumer trust by influencing perceptions regarding the entity’s ability to deliver and reliably satisfy consumers’ needs ( Sung and Kim 2010 ). Perceived quality drives trust by enhancing perceptions regarding the overall excellence of an offering and improving public assessments of its attributes ( Hennig-Thurau, Langer, and Hansen 2001 ). Finally, perceived risk can erode consumers’ beliefs regarding the likelihood that the business entity will reliably fulfill its promises, because an entity that increases perceived risk for consumers sends negative signals about its ability to deliver ( Pappas 2016 ).

According to the considerable amount of work focused on morality in the marketplace ( Campbell and Winterich 2018 ; Grayson 2014 ; Philipp-Muller et al. 2022 ), a vast majority of ordinary consumers are guided by moral beliefs and intuitions in the marketplace, with a huge importance placed on marketplace actors acting responsibly and with integrity, making integrity-related levers particularly influential when it comes to consumer trust. We thus posit antecedents based on integrity, on average, outperform antecedents based on reliability in driving trust.

In light of the seeming evidence that consumer trust has undergone dramatic changes in recent times ( Edelman 2021 ; Gallup 2023 ; Khamitov et al. 2019 ), we utilize year of publication to examine how the impacts of trust antecedents have changed recently. Additionally, given that the research in the consumer-trust domain has evolved from an early focus on brands and firms ( Garbarino and Johnson 1999 ) to encompass trust toward specific offerings ( Johnson and Grayson 2005 ), industries ( Diallo and Lambey-Checchin 2017 ), and even technologies ( Kim and Peterson 2017 ), we include a target of trust moderator to unpack this heterogeneity. Lastly, as extant work hinted at the potential moderating role of search, experience, and credence attributes in the context of consumer trust ( Pan and Chiou 2011 ), we employ a type of attribute moderator.

Overview of Data Collection and Coding Procedure

To ensure extensive coverage of articles that examined drivers or consequences of the individual consumer’s trust, we systematically searched several databases, including Google Scholar, ProQuest, EBSCOhost, and Web of Science. We used individual keyword phrases and their combinations such as “consumer trust,” “firm trust,” “customer mistrust,” etc., to identify studies related to consumer trust (see web appendix D for the complete list of keywords). We also manually reviewed leading journals in marketing and other disciplines to uncover additional work. We retained studies that (1) examined the individual consumer’s trust rather than an organization’s, (2) were published between 1970 and 2020, (3) had an empirical focus, and (4) reported sufficient information for direct use or indirect computation of our focal effects. We also made an effort to incorporate unpublished work (“file drawer”) by soliciting unpublished manuscripts in a blind, anonymous, confidential manner via the Association for Consumer Research’s ACR-L and American Marketing Association’s ELMAR listservs over a period of four weeks. This led to a final sample that includes 2,147 effect sizes from 549 studies across 469 manuscripts, representing a total of 324,834 respondents in 71 countries over a five-decade span. 8 Our benchmarking review of consumer research suggests that our final dataset’s scope and magnitude compare very favorably to those of other recent meta-analytic datasets (290 studies in Khamitov et al. 2019 ; 141 studies in Weingarten and Goodman 2021 ). 9

Following other meta-analytic studies ( Gremler et al. 2020 ; Palmatier et al. 2006 ), we use Pearson’s correlation coefficient as the focal effect size metric in our study. As needed, we employed conversion formulas to transform other available statistics into correlation coefficients ( Lipsey and Wilson 2001 ). We adjusted the effect sizes for measurement error using the square root of the products of the reliabilities of the two constructs, that is, consumer trust and its respective antecedent or consequence ( Hunter and Schmidt 1990 ). Finally, we weighted the resulting reliability-adjusted correlations by sample size ( Hunter and Schmidt 1990 ). For a detailed description of our data collection (i.e., literature search, inclusion criteria, PRISMA flow chart of the screening process and outcomes, coding procedure, control variables), see web appendix D .

Methodology: Hierarchical Linear Modeling

Following Raudenbush and Bryk’s (2001) recommendation, we specify a three-level hierarchical linear model (HLM) that accounts for the nested structure of data. The first level represents observations belonging to each study (i.e., the within-study effect sizes), the second level stands for different studies belonging to a paper, and the third level incorporates the distinct papers in our dataset. In our HLM model with maximum-likelihood estimation, the dependent variable represents adjusted and weighted effect sizes (correlations). The focal independent variables are eight dummies corresponding to the eight antecedents of consumer trust (RQ1). Following Gremler et al. (2020) , for each effect size, we set all dummy variables to 0, except the dummy variable corresponding to the antecedent of consumer trust, whose correlation the focal effect size is capturing (it gets a value of 1). We also control for several sample-, study-, and paper-level characteristics that we briefly discuss in the Discussion section. Additionally, we present the moderator subgroup analyses (year of publication, target of trust, type of attribute) to decompose heterogeneity (RQ2). In web appendices E and F , we detail our model specifications as well as robustness checks and publication bias analyses/corrections. We use a similar three-level HLM to examine the consequences of consumer trust.

Antecedents of Consumer Trust (RQ1)

The antecedent results appear in table 1 . The focal effects are robust to inclusion or exclusion of covariates in models A0 and A1. We focus on the results from the full model (A1).

RESULTS FOR ANTECEDENTS OF CONSUMER TRUST

p < .05;

p < .01;

p < .001; ψ: number of effect sizes. Although the reported coefficients are unstandardized, because the effect size captures correlations, magnitude of estimates are directly comparable across the antecedents of consumer trust. Robust standard errors are reported. For the shaded rows, we combined the absolute effect sizes for the three integrity-based (the five reliability-based) antecedents to construct the aggregate variables. For the aggregate analyses presented here and in subsequent analyses, we utilize absolute values of effect sizes since some effect sizes are positive and others are negative. The estimates for the covariates and the deviance values are based on models with the eight antecedents included.

Most importantly, when we compare aggregated integrity-based antecedents with aggregated reliability-based ones, we observe a stronger magnitude for integrity-based antecedents ( b IBTA = 0.432, SE = 0.021 vs. b RBTA = 0.353, SE = 0.014, p = .002). That is, integrity-based antecedents have stronger influence on consumer trust than reliability-based antecedents. Thus, the most important aspect of trust building is establishing and conveying integrity and honesty aligned with morality in the marketplace stream ( Campbell and Winterich 2018 ; Grayson 2014 ; Philipp-Muller, Teeny, and Petty 2022 ), central premise of which is that consumers perceive and care about the business morality.

When looking at specific antecedents of trust, reputation emerges as the strongest driver ( b = 0.460, SE = 0.031, p < .001), followed by ethicality and SR ( b = 0.426, SE = 0.032, p < .001). Reputation is likely the strongest driver of consumer trust, since reputation is and has for a while been the most valuable marketplace currency according to the notion of reputation economy ( Rifkin, Corus, and Kirk 2022 ), which underscores that the consumption marketplace is an environment where trust toward firms and brands is built on reputational considerations of track record and the promise(s) they deliver. The ethicality and SR results are in line with the importance and relevance of moral theories and concepts in marketplace environment ( Diallo and Lambey-Checchin 2017 ) and are consistent with the theme of a recent issue of JCP on marketplace morality ( Campbell and Winterich 2018 ).

The next three antecedents, while relatively weaker in terms of their strength, also emerge as strong and positive: attachment ( b = 0.408, SE = 0.030, p < .001), perceived quality ( b = 0.407, SE = 0.034, p < .001), and perceived value ( b = 0.353, SE = 0.021, p < .001). Attachment’s strong effect reinforces consumer–brand relationship theory as it pertains to attachment figures ( Khamitov et al. 2019 ). The quality finding reinforces the relationship marketing theories ( Palmatier et al. 2006 ), whereas the relatively strong positive effect for perceived value highlights the importance of ensuring that consumer needs and wants are fulfilled. Marketing investments ( b = 0.256, SE = 0.032, p < .001), competence ( b = 0.209, SE = 0.087, p = .016), and the non-significant perceived risk ( b  =   −0.120, SE = 0.073, p = .102) are the weakest drivers of consumer trust. The last finding is especially surprising given that risk is traditionally strongly linked to trust in the extant consumer-trust literature ( Elliott and Yannopoulou 2007 ). This outcome suggests that the identified risk is a weaker determinant of trust than expected, likely because a vast majority of consumption situations in our meta-analytic dataset entail minimal levels of risk 10 ; hence, the risk-reducing capability may not be particularly relevant when it comes to driving trust. 11

Moderating Conditions (RQ2)

Trust across time.

We conducted the year of publication moderation analyses on antecedents of consumer trust by comparing meta-analytic coefficients in recent studies (published after 2015) versus older studies (published before 2015). 12 We conjectured that the change in trends in older versus more recent studies would be manifested in the future: antecedents that have recently become stronger determinants of trust will continue to play an even more important role in the future. We present the detailed results in table 2 . We find that the magnitude of the effectiveness of IBTA ( p = .031) and RBTA ( p = .033) has both significantly increased over time, although less so for the RBTA. That is, different antecedents of trust are more effective in driving consumer trust in today’s marketplace than in the past. This outcome is consistent with the observation of consumer scholars that the roles of trust and other consumer relationship constructs have strengthened over time ( Khamitov et al. 2019 ) and is a silver lining for practitioners and managers who strive to enhance trust.

CHANGE IN EFFECTIVENESS OF ANTECEDENTS OF CONSUMER TRUST ACROSS TIME

p < .001; Because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Absolute values of effect sizes were used in aggregating antecedents to IBTA and RBTA.

We report the results for each specific antecedent in web appendix H . Interestingly, we find that marketing investments have grown in importance in recent years (although in a marginally significant way: p = .098), which is a testament to the continued effects of the positive signals that marketing mix instruments convey ( Rajavi et al. 2019 ).

Target of Trust

Target of trust plays an important role when it comes to the relative influence of antecedents on consumer trust ( table 3 ). We focus primarily on big-picture differences in (magnitude of) effects of drivers of trust by comparing average IBTA versus RBTA effects. Though on average there is no significant difference in the strength of effects of IBTA versus RBTA for specific offerings and technologies, IBTA are significantly more effective in driving trust toward brands/firms and industries as compared to RBTA. Being intangible entities, brands are increasingly viewed by many consumers as a series of normatively binding expectations that are ethically akin to brand promises ( Bhargava and Bedi 2022 ) and are expected to be honest and well-intentioned relational agents ( Khamitov et al. 2019 ), making it easier to drive trust by conveying integrity. As for industries, because a number of industries (fuel and energy, banking, aviation, tobacco, and alcohol) over the years have left consumers with the impression that some industries lack integrity ( Darke and Ritchie 2007 ), if and when a certain industry can convince consumers of its moral uprightness, such efforts are particularly effective in driving trust.

SPLIT-SAMPLE ANALYSIS OF ANTECEDENTS OF CONSUMER TRUST BASED ON TYPE OF TRUST ENTITY

p < .001; ψ: number of effect sizes. For average IBTA and RBTA effects, we focused on absolute value of effect sizes. Because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Out of 983 effect sizes for antecedents, we were not able to categorize 134 of them into any of the above four categories (e.g., target of trust was an employee). A full table with estimates for each antecedent is presented in web appendix H .

Comparing average IBTA and RBTA effects across different entities is also worthwhile. While there is no significant difference in IBTA effects across brands/firms and specific offerings (all pairwise p -values >.10), IBTA are significantly stronger (weaker) in driving trust toward industries (technologies). This implies a particularly strong role for industry integrity (aligned with the discussion above), which is unlike the relatively weaker technology benevolence mandate. Also, while RBTA are similarly effective in driving trust toward brands/firms and technologies (all pairwise p -values >.10), they are stronger (weaker) in driving trust toward specific offerings (industries). We conjecture that unlike with other trust entities, consumers’ responses to specific product/service offerings are influenced more heavily by an offering’s perceived practical and functional reliability in meeting their requirements.

Type of Attribute

We performed the type of attribute moderator analyses by comparing IBTA and RBTA meta-analytic coefficients for not-search versus search, not-experience versus experience, and not-credence versus credence attributes. We provide the results in table 4 . There is only one statistically significant difference: the magnitude of the effectiveness of IBTA is significantly stronger for non-experience attributes than for experience attributes ( p = .006). That is, if quality or other characteristics remain unknown until consumption (i.e., experience attributes), whether a good has higher or lower integrity is unlikely be diagnostic when it comes to trusting the good.

ANALYSIS OF ANTECEDENTS OF CONSUMER TRUST BASED ON TYPE OF ATTRIBUTE

p < .001; because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Absolute values of effect sizes were used in aggregating antecedents to IBTA and RBTA.

Put differently, if the consumer can evaluate a good only by way of experience, communicating integrity and ethicality may not be that meaningful for trust-building ( Grabner-Kraeuter 2002 ).

Consequences of Consumer Trust (RQ3)

When we compare aggregated PAC with aggregated primarily behavioral ones in table 5 , we observe a stronger magnitude of effect for attitudinal consequences ( b PAC = 0.431, SE = 0.010 vs. b PBC = 0.353, SE = 0.015, p < .001), which makes sense because behavioral outcomes are further down the purchase funnel and might be strongly affected by other variables (e.g., price, availability, etc.), hence lowering the overall importance of trust in driving them. This finding reinforces the hierarchy of effects and attitude-behavior gap theories ( Barry and Howard 1990 ). When it comes to individual consequences of trust, the most notable results are for satisfaction ( b = 0.494, SE = 0.027, p < .001; top consequence) and attitudinal loyalty ( b = 0.404, SE = 0.014, p < .001; third strongest consequence), which are in line with the classic tripartite relationship quality theory ( Connors et al. 2021 ; Fletcher, Simpson, and Thomas 2000 ).

RESULTS FOR CONSEQUENCES OF CONSUMER TRUST

p < .001; ψ: number of effect sizes. Although the reported coefficients are unstandardized, because the effect size captures correlations, the magnitude of coefficient estimates is directly comparable across the consequences of consumer trust. Robust standard errors are reported. For the shaded rows, we combined the absolute effect sizes for the five attitudinal-based (the four behavioral-based) consequences to construct the aggregate variables. The estimates for the covariates and the deviance values are based on models with the nine consequences included.

Trust remains the most important currency in lasting relationships … . In times of turbulence and volatility, trust is what holds society together. (Edelman “Trust Barometer” 2021)

Theoretical and Practical Contributions

Closing the consumer trust gap.

Over the last five decades, numerous articles from various disciplines have expanded our understanding of the individual consumer’s trust. Although the extant research demonstrates the crucial role played by consumer trust, no consensus has been reached regarding which antecedents and consequences of the individual consumer’s trust are most powerful. Furthermore, a vast majority of such studies employ a singular focus, context, operationalization, and/or sample and, hence, have been unable to examine conditions under which antecedents of consumer trust become more rather than less effective. The present research is the first to systematically investigate the antecedents and consequences of consumer trust, as well as important moderators across a very broad body of multidisciplinary work, and to shed light on the differential strength of these antecedents and consequences. In so doing, we advance the extant literatures on both consumer trust ( Chaudhuri and Holbrook 2001 ; Darke and Ritchie 2007 ; Engeler and Barasz 2021 ; Sirdeshmukh et al. 2002 ) and empirical generalizations in consumer research ( Khamitov et al. 2019 ; Weingarten and Goodman 2021 ).

Integrity Over Reliability

From a practical standpoint, the empirical generalizations distilled by the current research can and should be used as managerial benchmarks when it comes to driving and benefiting from consumer trust. For instance, managers are encouraged to prioritize establishing integrity over conveying reliability, to strategically prioritize top drivers of consumer trust (e.g., reputation, ethicality and SR, perceived quality, attachment), and to allocate resources accordingly. Such an approach is warranted, as businesses typically have limited resources, which is why effective trust-building approaches are critical. To this end, in web appendix H , we also provide granular trust-driver results that can be used by managers in charge of a brand/firm (ethicality/SR, reputation, attachment), specific offering (competence, attachment, perceived value), industry (reputation, ethicality/SR, perceived quality), or technology (perceived value, reputation, perceived quality).

Strong Effect of Consumer Trust on Attitudinal and Behavioral Outcomes

On the surface, consumer trust is logically expected to lead to strong market performance. However, lack of systematic and generalizable evidence on the exact nature of benefits associated with consumer trust has led some experts to draw on anecdotal evidence and undermine the importance of fostering consumer trust ( Marketing Week 2021 ). Our findings stand in contrast to such claims and highlight the strong effect of consumer trust on desirable outcomes. Not only does consumer trust result in enhanced attitudinal consequences of satisfaction, attitudinal loyalty, self-concept connection, evaluations, and engagement, but it also boosts behavioral consequences like purchase intentions, behavioral loyalty, willingness to pay, and even market performance.

Consumer Trust in the Future

The increasing importance of the right antecedent levers.

The cross-time findings, alongside recent industry reports regarding change in baseline trust yield interesting insights. While reports by Edelman (2021) , Gallup (2023) , and Millward Brown (2018) suggest that baseline consumer trust has declined, our findings imply that all is not doom and gloom, and that managerial actions now have more power to move the needle and improve consumers’ trust. In other words, although, in general, many consumers have lost trust in brands, brands can more easily make up for that loss in baseline trust by engaging in the right activities (conveying integrity via a reputation campaign or CSR, increasing the quality of their offerings).

The Nuanced Impact on Downstream Consequences Over Time

How has the importance of consumer trust in driving outcomes changed? Both researchers and practitioners would benefit greatly from insights regarding the future influence of consumer trust on different outcomes. To speak to the future role of trust, we conducted additional exploratory analyses on consequences of consumer trust by comparing meta-analytic coefficients in recent versus older studies. We likewise conjectured that the change in trends in older versus more recent studies would be manifested in the future: the outcomes that trust more strongly affects in recent studies will be impacted by it strongly in the future as well. On the aggregate, we do not find significant evidence for change in the effectiveness of consumer trust in driving PAC and PBC. However, when looking at individual consequences, we find that in recent years, the effect of consumer trust on behavioral loyalty and market performance has strongly increased. Interestingly, and contrary to the claims made by some practitioners ( Marketing Week 2021 ), trust has recently become (and will most likely continue to be) more important in driving consumer purchase decisions. Additionally, the effect of consumer trust in enhancing behavioral loyalty has also increased in recent years. We present the detailed results in web appendix H .

Implications and Future Research Agenda

Probing integrity further.

The current article opens avenues for further research. First, the impressively strong impact of reputation, ethicality, and SR on consumer trust speaks to the effectiveness of inherently moral precursors of generating trust and the importance of doing the right thing . These integrity antecedents emerged the strongest among a number of contenders. Thus, scholars are encouraged to pay increased attention to studying various nuances related to how and why reputational and moral considerations influence consumer trust as well as studying the apparent importance of establishing integrity over establishing reliability in the marketplace (which is particularly meaningful amid the growing proliferation of unsuccessful sociopolitical activism efforts, greenwashing, and CSI). Related to this, past research has shown that when it comes to choosing between service providers, consumers prioritize competent ones over moral ones ( Kirmani et al. 2017 ). Our findings paint a different picture when it comes to consumer trust. Future research on tradeoffs between consumers’ trust and choice across settings is needed.

Rethinking Certain Antecedents

Second, the relatively low average capacity of perceived risk and marketing investments to influence trust is interesting and rather surprising, implying that their effects on trust are likely to be weaker than previously thought. This former finding is different than Geyskens et al.’s (1998) finding regarding the importance of risk and uncertainty in driving trust in the B2B context. This might be because the individual consumer is less calculative than the organizational customer. In this connection, future research should investigate conditions under which risk and marketing investments hold the ground and serve as more effective drivers of the individual consumer’s trust (e.g., types or magnitude of risk and marketing investments).

Digging Deeper into the Moderators

Further, the finding that different trust entities have differential effectiveness of their respective antecedents implies that there is likely no one-size-fits-all approach to driving consumer trust. That is, depending on which target trust is directed at (brands/firms vs. specific offerings vs. industries vs. technologies), the impact of different antecedents varies quite dramatically. This is consistent with the idea of the increasingly nuanced marketplace wherein nowadays consumers have to put trust in both humans and machines, whereas humans were more of the focus in the past. Therefore, future researchers must carefully select a particular trust entity context of interest and avoid expecting uniform effects. Depending on the context, consumer trust scholars should be able to calibrate their expectations and shortlist a handful of manipulations holding the highest potential when it comes to predicting trust (e.g., manipulating competence to drive trust in crowdfunding requestors; Wang et al. 2021 ).

Interestingly, looking at temporal patterns and trajectories within our meta-analytic data for recent versus older years as well as attribute type differences spurs a number of research pathways. These trends naturally prompt the following questions: Why do we observe such increases and decreases, respectively? What are some of the factors driving this evolution over time and this IBTA effectiveness gap for experience attributes? Can scholars expect the same patterns moving forward? Future work is urged in this regard, and we explicitly call for research identifying certain conditions where trust is still highly impactful on consumer outcomes.

Consumer Trust in a Post-Truth World

Importantly, one can argue that consumers are increasingly distrustful of media in general and of social media in particular, especially in the United States with the prevalence of fake news and one’s inability to distinguish truth from lies in these contexts. Is it likely that this distrust finds its way into a general distrust of products and brands? Has time come to determine more latent ways in which trust might affect consumers’ decisions even when they do not explicitly state it as important? Relatedly, would the increasing levels of nationalism being observed across the globe lead to a distrust of foreign brands?

Calling for Greater Ecological Validity

Lastly, a fairly strong trust-market performance link warrants elaboration. On the one hand, this effect is reassuring, as it implies that the positive substantial effects of trust are not limited to attitudes and behavioral intentions. On the other hand, only a handful of included studies (i.e., 28 effect sizes) focused on market performance. Against this background, more studies of ecologically valid downstream financial and market consequences are urgently needed going forward because of their (1) superior representation of the real-world marketplace, (2) current lower sample size, and (3) higher potential to arrive at realistic, non-inflated effect sizes.

Examining Understudied Constructs

To keep the scope of our work manageable, following other meta studies we focused on the most prevalent antecedents of our focal construct. However, many other antecedents of consumer trust have been discussed in past research. A few examples are propensity to trust ( Yamagishi and Yamagishi 1994 ), warmth ( Kirmani et al. 2017 ), and familiarity ( Garbarino and Johnson 1999 ). Future research could look through other theoretical lenses and meta-analyze another set of understudied antecedents not examined in our research.

Trust Dimensionality

The dimensionality of trust warrants further investigation. Our review of past research indicates that trust is predominantly conceptualized as two-dimensional (65% of papers, web appendix I ), aligning with our findings regarding the differential effects of IBTA versus RBTA. The most commonly studied dimensions are reliability and integrity, although other dimensions such as sympathy and familiarity are also mentioned in the literature, albeit rarely ( web appendix I ). While we adopted a two-dimensional conceptualization of trust, given the limited available data in prior papers, our empirical modeling treated trust as a unidimensional construct with two groups of antecedents inspired by the most commonly examined dimensions of trust. Future work could explore the relationships between antecedents and different dimensions of trust in greater detail.

The collection and coding of data for the meta-analysis were administered at Indiana University and Georgia Institute of Technology between Fall of 2020 and Summer of 2023. The first two authors designed the coding protocol and conducted data analyses. The third and fourth authors carried out data collection under supervision of the first two authors. Data and coding were discussed on multiple occasions by all authors. The final article was jointly authored. The data are currently stored in a project directory on the Open Science Framework.

Mansur Khamitov ( [email protected] ) is an assistant professor of marketing at the Kelley School of Business, Indiana University, 1309 E 10th St, Bloomington, IN 47405, USA.

Koushyar Rajavi ( [email protected] ) is an assistant professor of marketing at the Scheller College of Business, Georgia Institute of Technology, 800 W Peachtree St NW, Atlanta, GA 30308, USA.

Der-Wei Huang ( [email protected] ) is an assistant professor of marketing at the School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) 518172, China.

Yuly Hong ( [email protected] ) is an assistant professor of marketing at NEOMA Business School, 59 rue Pierre Taittinger, 51100 Reims, France.

All authors contributed equally. The authors are grateful for funding from RATS Grant (2236360/MKHAM) to Mansur Khamitov provided by the Kelley School of Business, Indiana University. Supplementary materials are included in the web appendix accompanying the online version of this article.

We acknowledge that there have been meta-analytic studies on the role of consumer trust in specific contexts (e.g., Kim and Peterson’s study (2017) of the role of online trust in e-commerce); yet such studies are context-specific, smaller in scale, and their conclusions might not be generalizable to consumer trust in other settings or across settings. We also acknowledge Khamitov, Wang, and Thomson’s study (2019) meta-analyzing the link between brand relationships and customer loyalty that (1) focuses only on trust towards a single, specific entity (brand), (2) does not study any brand-trust antecedents, (3) explores a single brand-trust consequence (customer brand loyalty), and (4) includes fewer effect sizes (216).

As can be seen in our discussion of prior literature, different labels have been used to refer to similar and/or closely related components of trust (e.g., reliability, capability, ability). We acknowledge that there might be slight conceptual differences between these constructs. We utilize the integrity versus reliability dichotomy, which, in our view, most succinctly and parsimoniously represents the literature on consumer trust across different domains.

It should be noted that some perspectives on trust place more emphasis on the inherent characteristics of the trusting entity (e.g., propensity to trust in the literature on individual trust). In the current work, following a large body of research on consumer trust, we view trust as a temporary state experienced by consumers when they examine brands, products, services, etc., rather than a stable personality trait. This perspective is pertinent to practitioners, for it focuses on antecedents that business entities can modify to enhance consumer trust. Thus, ** we do not examine factors that are related to the stable nature of trust that practitioners have little or no influence over (e.g., consumers’ general propensity to trust).

On the basis of these two criteria, for instance, we excluded perceived warmth (appeared in <2% of the past studies on consumer trust) and familiarity/experience (lack of fit with our theoretical framework).

Using the 10% threshold led to fewer consequences compared to antecedents (seven vs. eight). To have more balance between the number of antecedents and consequences, and to provide more insights with respect to different marketplace outcomes tied to trust, we also included the next two commonly studied consequence variables: market performance and willingness to pay.

Our assignment of certain antecedents to IBTA versus RBTA is based on the primary mechanism in the literature. Our framework is not meant to suggest that a variable categorized as IBTA (RBTA) has no impact at all on the reliability (integrity) trust aspect.

To further justify the categorization of trust consequences/outcomes as primarily attitudinal versus primarily behavioral, we refer the reader to past research like Chaudhuri and Holbrook (2001) , Boonlertvanich (2019) , or Liu et al. (2021) where this attitudinal versus behavioral distinction is apparent and central.

We also caution that our framework does not suggest that trust would never drive any of our antecedents, such as attachment and/or reputation. To assign a construct to antecedents or consequences of consumer trust, we relied on past research and determined its role in the nomological framework based on the majority of the past research. Resultantly, our antecedents and consequences were used in the same role in more than 80% of past research. As such, our focal relationship specification between constructs represents a better-fitting depiction of the extant literature (and not a universal depiction).

Of the overall sample, 983 effect sizes from 347 studies across 310 manuscripts correspond to antecedents of trust, while 1,164 effect sizes from 459 studies across 414 manuscripts capture consequences of trust.

A full list of included papers is available at https://researchbox.org/1335&PEER_REVIEW_passcode=YPKQTP .

Our follow-up interaction analysis based on low versus high level of financial risk suggests that risk does not influence trust in the low-risk subset ( b = −0.101, p = .186), whereas in the high financial risk subset, the effect of risk is substantial ( b = −0.592, p < .001). Relatedly, while in the low physical risk subset the impact of risk on trust is b = −0.102 ( p = .176), the influence of risk on trust is much stronger under high physical risk ( b = −0.355, p = .038).

We present pairwise significance tests across coefficients of antecedents (and consequences) in web appendix G .

The 2015 year of publication threshold leads to a good balance of effect sizes for recent and older studies, as well as allowing us to focus specifically on the most recent studies that are pertinent to understanding what the future might look like.

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• Published: 08 May 2024

Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective

• Chenyu Gu   ORCID: orcid.org/0000-0001-6059-0573 1 &
• Qiuting Duan 2  

Humanities and Social Sciences Communications volume  11 , Article number:  587 ( 2024 ) Cite this article

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Influencer advertising has emerged as an integral part of social media marketing. Within this realm, consumer engagement is a critical indicator for gauging the impact of influencer advertisements, as it encompasses the proactive involvement of consumers in spreading advertisements and creating value. Therefore, investigating the mechanisms behind consumer engagement holds significant relevance for formulating effective influencer advertising strategies. The current study, grounded in self-determination theory and employing a stimulus-organism-response framework, constructs a general model to assess the impact of influencer factors, advertisement information, and social factors on consumer engagement. Analyzing data from 522 samples using structural equation modeling, the findings reveal: (1) Social media influencers are effective at generating initial online traffic but have limited influence on deeper levels of consumer engagement, cautioning advertisers against overestimating their impact; (2) The essence of higher-level engagement lies in the ad information factor, affirming that in the new media era, content remains ‘king’; (3) Interpersonal factors should also be given importance, as influencing the surrounding social groups of consumers is one of the effective ways to enhance the impact of advertising. Theoretically, current research broadens the scope of both social media and advertising effectiveness studies, forming a bridge between influencer marketing and consumer engagement. Practically, the findings offer macro-level strategic insights for influencer marketing.

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Introduction.

Recent studies have highlighted an escalating aversion among audiences towards traditional online ads, leading to a diminishing effectiveness of traditional online advertising methods (Lou et al., 2019 ). In an effort to overcome these challenges, an increasing number of brands are turning to influencers as their spokespersons for advertising. Utilizing influencers not only capitalizes on their significant influence over their fan base but also allows for the dissemination of advertising messages in a more native and organic manner. Consequently, influencer-endorsed advertising has become a pivotal component and a growing trend in social media advertising (Gräve & Bartsch, 2022 ). Although the topic of influencer-endorsed advertising has garnered increasing attention from scholars, the field is still in its infancy, offering ample opportunities for in-depth research and exploration (Barta et al., 2023 ).

Presently, social media influencers—individuals with substantial follower bases—have emerged as the new vanguard in advertising (Hudders & Lou, 2023 ). Their tweets and videos possess the remarkable potential to sway the purchasing decisions of thousands if not millions. This influence largely hinges on consumer engagement behaviors, implying that the impact of advertising can proliferate throughout a consumer’s entire social network (Abbasi et al., 2023 ). Consequently, exploring ways to enhance consumer engagement is of paramount theoretical and practical significance for advertising effectiveness research (Xiao et al., 2023 ). This necessitates researchers to delve deeper into the exploration of the stimulating factors and psychological mechanisms influencing consumer engagement behaviors (Vander Schee et al., 2020 ), which is the gap this study seeks to address.

The Stimulus-Organism-Response (S-O-R) framework has been extensively applied in the study of consumer engagement behaviors (Tak & Gupta, 2021 ) and has been shown to integrate effectively with self-determination theory (Yang et al., 2019 ). Therefore, employing the S-O-R framework to investigate consumer engagement behaviors in the context of influencer advertising is considered a rational approach. The current study embarks on an in-depth analysis of the transformation process from three distinct dimensions. In the Stimulus (S) phase, we focus on how influencer factors, advertising message factors, and social influence factors act as external stimuli. This phase scrutinizes the external environment’s role in triggering consumer reactions. During the Organism (O) phase, the research explores the intrinsic psychological motivations affecting individual behavior as posited in self-determination theory. This includes the willingness for self-disclosure, the desire for innovation, and trust in advertising messages. The investigation in this phase aims to understand how these internal motivations shape consumer attitudes and perceptions in the context of influencer marketing. Finally, in the Response (R) phase, the study examines how these psychological factors influence consumer engagement behavior. This part of the research seeks to understand the transition from internal psychological states to actual consumer behavior, particularly how these states drive the consumers’ deep integration and interaction with the influencer content.

Despite the inherent limitations of cross-sectional analysis in capturing the full temporal dynamics of consumer engagement, this study seeks to unveil the dynamic interplay between consumers’ psychological needs—autonomy, competence, and relatedness—and their varying engagement levels in social media influencer marketing, grounded in self-determination theory. Through this lens, by analyzing factors related to influencers, content, and social context, we aim to infer potential dynamic shifts in engagement behaviors as psychological needs evolve. This approach allows us to offer a snapshot of the complex, multi-dimensional nature of consumer engagement dynamics, providing valuable insights for both theoretical exploration and practical application in the constantly evolving domain of social media marketing. Moreover, the current study underscores the significance of adapting to the dynamic digital environment and highlights the evolving nature of consumer engagement in the realm of digital marketing.

Literature review

Stimulus-organism-response (s-o-r) model.

The Stimulus-Response (S-R) model, originating from behaviorist psychology and introduced by psychologist Watson ( 1917 ), posits that individual behaviors are directly induced by external environmental stimuli. However, this model overlooks internal personal factors, complicating the explanation of psychological states. Mehrabian and Russell ( 1974 ) expanded this by incorporating the individual’s cognitive component (organism) into the model, creating the Stimulus-Organism-Response (S-O-R) framework. This model has become a crucial theoretical framework in consumer psychology as it interprets internal psychological cognitions as mediators between stimuli and responses. Integrating with psychological theories, the S-O-R model effectively analyzes and explains the significant impact of internal psychological factors on behavior (Koay et al., 2020 ; Zhang et al., 2021 ), and is extensively applied in investigating user behavior on social media platforms (Hewei & Youngsook, 2022 ). This study combines the S-O-R framework with self-determination theory to examine consumer engagement behaviors in the context of social media influencer advertising, a logic also supported by some studies (Yang et al., 2021 ).

Self-determination theory

Self-determination theory, proposed by Richard and Edward (2000), is a theoretical framework exploring human behavioral motivation and personality. The theory emphasizes motivational processes, positing that individual behaviors are developed based on factors satisfying their psychological needs. It suggests that individual behavioral tendencies are influenced by the needs for competence, relatedness, and autonomy. Furthermore, self-determination theory, along with organic integration theory, indicates that individual behavioral tendencies are also affected by internal psychological motivations and external situational factors.

Self-determination theory has been validated by scholars in the study of online user behaviors. For example, Sweet applied the theory to the investigation of community building in online networks, analyzing knowledge-sharing behaviors among online community members (Sweet et al., 2020 ). Further literature review reveals the applicability of self-determination theory to consumer engagement behaviors, particularly in the context of influencer marketing advertisements. Firstly, self-determination theory is widely applied in studying the psychological motivations behind online behaviors, suggesting that the internal and external motivations outlined within the theory might also apply to exploring consumer behaviors in influencer marketing scenarios (Itani et al., 2022 ). Secondly, although research on consumer engagement in the social media influencer advertising context is still in its early stages, some studies have utilized SDT to explore behaviors such as information sharing and electronic word-of-mouth dissemination (Astuti & Hariyawan, 2021 ). These behaviors, which are part of the content contribution and creation dimensions of consumer engagement, may share similarities in the underlying psychological motivational mechanisms. Thus, this study will build upon these foundations to construct the Organism (O) component of the S-O-R model, integrating insights from SDT to further understand consumer engagement in influencer marketing.

Consumer engagement

Although scholars generally agree at a macro level to define consumer engagement as the creation of additional value by consumers or customers beyond purchasing products, the specific categorization of consumer engagement varies in different studies. For instance, Simon and Tossan interpret consumer engagement as a psychological willingness to interact with influencers (Simon & Tossan, 2018 ). However, such a broad definition lacks precision in describing various levels of engagement. Other scholars directly use tangible metrics on social media platforms, such as likes, saves, comments, and shares, to represent consumer engagement (Lee et al., 2018 ). While this quantitative approach is not flawed and can be highly effective in practical applications, it overlooks the content aspect of engagement, contradicting the “content is king” principle of advertising and marketing. We advocate for combining consumer engagement with the content aspect, as content engagement not only generates more traces of consumer online behavior (Oestreicher-Singer & Zalmanson, 2013 ) but, more importantly, content contribution and creation are central to social media advertising and marketing, going beyond mere content consumption (Qiu & Kumar, 2017 ). Meanwhile, we also need to emphasize that engagement is not a fixed state but a fluctuating process influenced by ongoing interactions between consumers and influencers, mediated by the evolving nature of social media platforms and the shifting sands of consumer preferences (Pradhan et al., 2023 ). Consumer engagement in digital environments undergoes continuous change, reflecting a journey rather than a destination (Viswanathan et al., 2017 ).

The current study adopts a widely accepted definition of consumer engagement from existing research, offering operational feasibility and aligning well with the research objectives of this paper. Consumer engagement behaviors in the context of this study encompass three dimensions: content consumption, content contribution, and content creation (Muntinga et al., 2011 ). These dimensions reflect a spectrum of digital engagement behaviors ranging from low to high levels (Schivinski et al., 2016 ). Specifically, content consumption on social media platforms represents a lower level of engagement, where consumers merely click and read the information but do not actively contribute or create user-generated content. Some studies consider this level of engagement as less significant for in-depth exploration because content consumption, compared to other forms, generates fewer visible traces of consumer behavior (Brodie et al., 2013 ). Even in a study by Qiu and Kumar, it was noted that the conversion rate of content consumption is low, contributing minimally to the success of social media marketing (Qiu & Kumar, 2017 ).

On the other hand, content contribution, especially content creation, is central to social media marketing. When consumers comment on influencer content or share information with their network nodes, it is termed content contribution, representing a medium level of online consumer engagement (Piehler et al., 2019 ). Furthermore, when consumers actively upload and post brand-related content on social media, this higher level of behavior is referred to as content creation. Content creation represents the highest level of consumer engagement (Cheung et al., 2021 ). Although medium and high levels of consumer engagement are more valuable for social media advertising and marketing, this exploratory study still retains the content consumption dimension of consumer engagement behaviors.

Theoretical framework

Internal organism factors: self-disclosure willingness, innovativeness, and information trust.

In existing research based on self-determination theory that focuses on online behavior, competence, relatedness, and autonomy are commonly considered as internal factors influencing users’ online behaviors. However, this approach sometimes strays from the context of online consumption. Therefore, in studies related to online consumption, scholars often use self-disclosure willingness as an overt representation of autonomy, innovativeness as a representation of competence, and trust as a representation of relatedness (Mahmood et al., 2019 ).

The use of these overt variables can be logically explained as follows: According to self-determination theory, individuals with a higher level of self-determination are more likely to adopt compensatory mechanisms to facilitate behavior compared to those with lower self-determination (Wehmeyer, 1999 ). Self-disclosure, a voluntary act of sharing personal information with others, is considered a key behavior in the development of interpersonal relationships. In social environments, self-disclosure can effectively alleviate stress and build social connections, while also seeking societal validation of personal ideas (Altman & Taylor, 1973 ). Social networks, as para-social entities, possess the interactive attributes of real societies and are likely to exhibit similar mechanisms. In consumer contexts, personal disclosures can include voluntary sharing of product interests, consumption experiences, and future purchase intentions (Robertshaw & Marr, 2006 ). While material incentives can prompt personal information disclosure, many consumers disclose personal information online voluntarily, which can be traced back to an intrinsic need for autonomy (Stutzman et al., 2011 ). Thus, in this study, we consider the self-disclosure willingness as a representation of high autonomy.

Innovativeness refers to an individual’s internal level of seeking novelty and represents their personality and tendency for novelty (Okazaki, 2009 ). Often used in consumer research, innovative consumers are inclined to try new technologies and possess an intrinsic motivation to use new products. Previous studies have shown that consumers with high innovativeness are more likely to search for information on new products and share their experiences and expertise with others, reflecting a recognition of their own competence (Kaushik & Rahman, 2014 ). Therefore, in consumer contexts, innovativeness is often regarded as the competence dimension within the intrinsic factors of self-determination (Wang et al., 2016 ), with external motivations like information novelty enhancing this intrinsic motivation (Lee et al., 2015 ).

Trust refers to an individual’s willingness to rely on the opinions of others they believe in. From a social psychological perspective, trust indicates the willingness to assume the risk of being harmed by another party (McAllister, 1995 ). Widely applied in social media contexts for relational marketing, information trust has been proven to positively influence the exchange and dissemination of consumer information, representing a close and advanced relationship between consumers and businesses, brands, or advertising endorsers (Steinhoff et al., 2019 ). Consumers who trust brands or social media influencers are more willing to share information without fear of exploitation (Pop et al., 2022 ), making trust a commonly used representation of the relatedness dimension in self-determination within consumer contexts.

Construction of the path from organism to response: self-determination internal factors and consumer engagement behavior

Following the logic outlined above, the current study represents the internal factors of self-determination theory through three variables: self-disclosure willingness, innovativeness, and information trust. Next, the study explores the association between these self-determination internal factors and consumer engagement behavior, thereby constructing the link between Organism (O) and Response (R).

Self-disclosure willingness and consumer engagement behavior

In the realm of social sciences, the concept of self-disclosure willingness has been thoroughly examined from diverse disciplinary perspectives, encompassing communication studies, sociology, and psychology. Viewing from the lens of social interaction dynamics, self-disclosure is acknowledged as a fundamental precondition for the initiation and development of online social relationships and interactive engagements (Luo & Hancock, 2020 ). It constitutes an indispensable component within the spectrum of interactive behaviors and the evolution of interpersonal connections. Voluntary self-disclosure is characterized by individuals divulging information about themselves, which typically remains unknown to others and is inaccessible through alternative sources. This concept aligns with the tenets of uncertainty reduction theory, which argues that during interpersonal engagements, individuals seek information about their counterparts as a means to mitigate uncertainties inherent in social interactions (Lee et al., 2008 ). Self-disclosure allows others to gain more personal information, thereby helping to reduce the uncertainty in interpersonal relationships. Such disclosure is voluntary rather than coerced, and this sharing of information can facilitate the development of relationships between individuals (Towner et al., 2022 ). Furthermore, individuals who actively engage in social media interactions (such as liking, sharing, and commenting on others’ content) often exhibit higher levels of self-disclosure (Chu et al., 2023 ); additional research indicates a positive correlation between self-disclosure and online engagement behaviors (Lee et al., 2023 ). Taking the context of the current study, the autonomous self-disclosure willingness can incline social media users to read advertising content more attentively and share information with others, and even create evaluative content. Therefore, this paper proposes the following research hypothesis:

H1a: The self-disclosure willingness is positively correlated with content consumption in consumer engagement behavior.

H1b: The self-disclosure willingness is positively correlated with content contribution in consumer engagement behavior.

H1c: The self-disclosure willingness is positively correlated with content creation in consumer engagement behavior.

Innovativeness and consumer engagement behavior

Innovativeness represents an individual’s propensity to favor new technologies and the motivation to use new products, associated with the cognitive perception of one’s self-competence. Individuals with a need for self-competence recognition often exhibit higher innovativeness (Kelley & Alden, 2016 ). Existing research indicates that users with higher levels of innovativeness are more inclined to accept new product information and share their experiences and discoveries with others in their social networks (Yusuf & Busalim, 2018 ). Similarly, in the context of this study, individuals, as followers of influencers, signify an endorsement of the influencer. Driven by innovativeness, they may be more eager to actively receive information from influencers. If they find the information valuable, they are likely to share it and even engage in active content re-creation to meet their expectations of self-image. Therefore, this paper proposes the following research hypotheses:

H2a: The innovativeness of social media users is positively correlated with content consumption in consumer engagement behavior.

H2b: The innovativeness of social media users is positively correlated with content contribution in consumer engagement behavior.

H2c: The innovativeness of social media users is positively correlated with content creation in consumer engagement behavior.

Information trust and consumer engagement

Trust refers to an individual’s willingness to rely on the statements and opinions of a target object (Moorman et al., 1993 ). Extensive research indicates that trust positively impacts information dissemination and content sharing in interpersonal communication environments (Majerczak & Strzelecki, 2022 ); when trust is established, individuals are more willing to share their resources and less suspicious of being exploited. Trust has also been shown to influence consumers’ participation in community building and content sharing on social media, demonstrating cross-cultural universality (Anaya-Sánchez et al., 2020 ).

Trust in influencer advertising information is also a key predictor of consumers’ information exchange online. With many social media users now operating under real-name policies, there is an increased inclination to trust information shared on social media over that posted by corporate accounts or anonymously. Additionally, as users’ social networks partially overlap with their real-life interpersonal networks, extensive research shows that more consumers increasingly rely on information posted and shared on social networks when making purchase decisions (Wang et al., 2016 ). This aligns with the effectiveness goals of influencer marketing advertisements and the characteristics of consumer engagement. Trust in the content posted by influencers is considered a manifestation of a strong relationship between fans and influencers, central to relationship marketing (Kim & Kim, 2021 ). Based on trust in the influencer, which then extends to trust in their content, people are more inclined to browse information posted by influencers, share this information with others, and even create their own content without fear of exploitation or negative consequences. Therefore, this paper proposes the following research hypotheses:

H3a: Information trust is positively correlated with content consumption in consumer engagement behavior.

H3b: Information trust is positively correlated with content contribution in consumer engagement behavior.

H3c: Information trust is positively correlated with content creation in consumer engagement behavior.

Construction of the path from stimulus to organism: influencer factors, advertising information factors, social factors, and self-determination internal factors

Having established the logical connection from Organism (O) to Response (R), we further construct the influence path from Stimulus (S) to Organism (O). Revisiting the definition of influencer advertising in social media, companies, and brands leverage influencers on social media platforms to disseminate advertising content, utilizing the influencers’ relationships and influence over consumers for marketing purposes. In addition to consumer’s internal factors, elements such as companies, brands, influencers, and the advertisements themselves also impact consumer behavior. Although factors like the brand image perception of companies may influence consumer behavior, considering that in influencer marketing, companies and brands do not directly interact with consumers, this study prioritizes the dimensions of influencers and advertisements. Furthermore, the impact of social factors on individual cognition and behavior is significant, thus, the current study integrates influencers, advertisements, and social dimensions as the Stimulus (S) component.

Influencer factors: parasocial identification

Self-determination theory posits that relationships are one of the key motivators influencing individual behavior. In the context of social media research, users anticipate establishing a parasocial relationship with influencers, resembling real-life relationships. Hence, we consider the parasocial identification arising from users’ parasocial interactions with influencers as the relational motivator. Parasocial interaction refers to the one-sided personal relationship that individuals develop with media characters (Donald & Richard, 1956 ). During this process, individuals believe that the media character is directly communicating with them, creating a sense of positive intimacy (Giles, 2002 ). Over time, through repeated unilateral interactions with media characters, individuals develop a parasocial relationship, leading to parasocial identification. However, parasocial identification should not be directly equated with the concept of social identification in social identity theory. Social identification occurs when individuals psychologically de-individualize themselves, perceiving the characteristics of their social group as their own, upon identifying themselves as part of that group. In contrast, parasocial identification refers to the one-sided interactional identification with media characters (such as celebrities or influencers) over time (Chen et al., 2021 ). Particularly when individuals’ needs for interpersonal interaction are not met in their daily lives, they turn to parasocial interactions to fulfill these needs (Shan et al., 2020 ). Especially on social media, which is characterized by its high visibility and interactivity, users can easily develop a strong parasocial identification with the influencers they follow (Wei et al., 2022 ).

Parasocial identification and self-disclosure willingness

Theories like uncertainty reduction, personal construct, and social exchange are often applied to explain the emergence of parasocial identification. Social media, with its convenient and interactive modes of information dissemination, enables consumers to easily follow influencers on media platforms. They can perceive the personality of influencers through their online content, viewing them as familiar individuals or even friends. Once parasocial identification develops, this pleasurable experience can significantly influence consumers’ cognitions and thus their behavioral responses. Research has explored the impact of parasocial identification on consumer behavior. For instance, Bond et al. found that on Twitter, the intensity of users’ parasocial identification with influencers positively correlates with their continuous monitoring of these influencers’ activities (Bond, 2016 ). Analogous to real life, where we tend to pay more attention to our friends in our social networks, a similar phenomenon occurs in the relationship between consumers and brands. This type of parasocial identification not only makes consumers willing to follow brand pages but also more inclined to voluntarily provide personal information (Chen et al., 2021 ). Based on this logic, we speculate that a similar relationship may exist between social media influencers and their fans. Fans develop parasocial identification with influencers through social media interactions, making them more willing to disclose their information, opinions, and views in the comment sections of the influencers they follow, engaging in more frequent social interactions (Chung & Cho, 2017 ), even if the content at times may be brand or company-embedded marketing advertisements. In other words, in the presence of influencers with whom they have established parasocial relationships, they are more inclined to disclose personal information, thereby promoting consumer engagement behavior. Therefore, we propose the following research hypotheses:

H4: Parasocial identification is positively correlated with consumer self-disclosure willingness.

H4a: Self-disclosure willingness mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H4b: Self-disclosure willingness mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H4c: Self-disclosure willingness mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Parasocial identification and information trust

Information Trust refers to consumers’ willingness to trust the information contained in advertisements and to place themselves at risk. These risks include purchasing products inconsistent with the advertised information and the negative social consequences of erroneously spreading this information to others, leading to unpleasant consumption experiences (Minton, 2015 ). In advertising marketing, gaining consumers’ trust in advertising information is crucial. In the context of influencer marketing on social media, companies, and brands leverage the social connection between influencers and their fans. According to cognitive empathy theory, consumers project their trust in influencers onto the products endorsed, explaining the phenomenon of ‘loving the house for the crow on its roof.’ Research indicates that parasocial identification with influencers is a necessary condition for trust development. Consumers engage in parasocial interactions with influencers on social media, leading to parasocial identification (Jin et al., 2021 ). Consumers tend to reduce their cognitive load and simplify their decision-making processes, thus naturally adopting a positive attitude and trust towards advertising information disseminated by influencers with whom they have established parasocial identification. This forms the core logic behind the success of influencer marketing advertisements (Breves et al., 2021 ); furthermore, as mentioned earlier, because consumers trust these advertisements, they are also willing to share this information with friends and family and even engage in content re-creation. Therefore, we propose the following research hypotheses:

H5: Parasocial identification is positively correlated with information trust.

H5a: Information trust mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H5b: Information trust mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H5c: Information trust mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Influencer factors: source credibility

Source credibility refers to the degree of trust consumers place in the influencer as a source, based on the influencer’s reliability and expertise. Numerous studies have validated the effectiveness of the endorsement effect in advertising (Schouten et al., 2021 ). The Source Credibility Model, proposed by the renowned American communication scholar Hovland and the “Yale School,” posits that in the process of information dissemination, the credibility of the source can influence the audience’s decision to accept the information. The credibility of the information is determined by two aspects of the source: reliability and expertise. Reliability refers to the audience’s trust in the “communicator’s objective and honest approach to providing information,” while expertise refers to the audience’s trust in the “communicator being perceived as an effective source of information” (Hovland et al., 1953 ). Hovland’s definitions reveal that the interpretation of source credibility is not about the inherent traits of the source itself but rather the audience’s perception of the source (Jang et al., 2021 ). This differs from trust and serves as a precursor to the development of trust. Specifically, reliability and expertise are based on the audience’s perception; thus, this aligns closely with the audience’s perception of influencers (Kim & Kim, 2021 ). This credibility is a cognitive statement about the source of information.

Source credibility and self-disclosure willingness

Some studies have confirmed the positive impact of an influencer’s self-disclosure on their credibility as a source (Leite & Baptista, 2022 ). However, few have explored the impact of an influencer’s credibility, as a source, on consumers’ self-disclosure willingness. Undoubtedly, an impact exists; self-disclosure is considered a method to attempt to increase intimacy with others (Leite et al., 2022 ). According to social exchange theory, people promote relationships through the exchange of information in interpersonal communication to gain benefits (Cropanzano & Mitchell, 2005 ). Credibility, deriving from an influencer’s expertise and reliability, means that a highly credible influencer may provide more valuable information to consumers. Therefore, based on the social exchange theory’s logic of reciprocal benefits, consumers might be more willing to disclose their information to trustworthy influencers, potentially even expanding social interactions through further consumer engagement behaviors. Thus, we propose the following research hypotheses:

H6: Source credibility is positively correlated with self-disclosure willingness.

H6a: Self-disclosure willingness mediates the impact of Source credibility on content consumption in consumer engagement behavior.

H6b: Self-disclosure willingness mediates the impact of Source credibility on content contribution in consumer engagement behavior.

H6c: Self-disclosure willingness mediates the impact of Source credibility on content creation in consumer engagement behavior.

Source credibility and information trust

Based on the Source Credibility Model, the credibility of an endorser as an information source can significantly influence consumers’ acceptance of the information (Shan et al., 2020 ). Existing research has demonstrated the positive impact of source credibility on consumers. Djafarova, in a study based on Instagram, noted through in-depth interviews with 18 users that an influencer’s credibility significantly affects respondents’ trust in the information they post. This credibility is composed of expertise and relevance to consumers, and influencers on social media are considered more trustworthy than traditional celebrities (Djafarova & Rushworth, 2017 ). Subsequently, Bao and colleagues validated in the Chinese consumer context, based on the ELM model and commitment-trust theory, that the credibility of brand pages on Weibo effectively fosters consumer trust in the brand, encouraging participation in marketing activities (Bao & Wang, 2021 ). Moreover, Hsieh et al. found that in e-commerce contexts, the credibility of the source is a significant factor influencing consumers’ trust in advertising information (Hsieh & Li, 2020 ). In summary, existing research has proven that the credibility of the source can promote consumer trust. Influencer credibility is a significant antecedent affecting consumers’ trust in the advertised content they publish. In brand communities, trust can foster consumer engagement behaviors (Habibi et al., 2014 ). Specifically, consumers are more likely to trust the advertising content published by influencers with higher credibility (more expertise and reliability), and as previously mentioned, consumer engagement behavior is more likely to occur. Based on this, the study proposes the following research hypotheses:

H7: Source credibility is positively correlated with information trust.

H7a: Information trust mediates the impact of source credibility on content consumption in consumer engagement behavior.

H7b: Information trust mediates the impact of source credibility on content contribution in consumer engagement behavior.

H7c: Information trust mediates the impact of source credibility on content creation in consumer engagement behavior.

Advertising information factors: informative value

Advertising value refers to “the relative utility value of advertising information to consumers and is a subjective evaluation by consumers.” In his research, Ducoffe pointed out that in the context of online advertising, the informative value of advertising is a significant component of advertising value (Ducoffe, 1995 ). Subsequent studies have proven that consumers’ perception of advertising value can effectively promote their behavioral response to advertisements (Van-Tien Dao et al., 2014 ). Informative value of advertising refers to “the information about products needed by consumers provided by the advertisement and its ability to enhance consumer purchase satisfaction.” From the perspective of information dissemination, valuable advertising information should help consumers make better purchasing decisions and reduce the effort spent searching for product information. The informational aspect of advertising has been proven to effectively influence consumers’ cognition and, in turn, their behavior (Haida & Rahim, 2015 ).

Informative value and innovativeness

As previously discussed, consumers’ innovativeness refers to their psychological trait of favoring new things. Studies have shown that consumers with high innovativeness prefer novel and valuable product information, as it satisfies their need for newness and information about new products, making it an important factor in social media advertising engagement (Shi, 2018 ). This paper also hypothesizes that advertisements with high informative value can activate consumers’ innovativeness, as the novelty of information is one of the measures of informative value (León et al., 2009 ). Acquiring valuable information can make individuals feel good about themselves and fulfill their perception of a “novel image.” According to social exchange theory, consumers can gain social capital in interpersonal interactions (such as social recognition) by sharing information about these new products they perceive as valuable. Therefore, the current study proposes the following research hypothesis:

H8: Informative value is positively correlated with innovativeness.

H8a: Innovativeness mediates the impact of informative value on content consumption in consumer engagement behavior.

H8b: Innovativeness mediates the impact of informative value on content contribution in consumer engagement behavior.

H8c: Innovativeness mediates the impact of informative value on content creation in consumer engagement behavior.

Informative value and information trust

Trust is a multi-layered concept explored across various disciplines, including communication, marketing, sociology, and psychology. For the purposes of this paper, a deep analysis of different levels of trust is not undertaken. Here, trust specifically refers to the trust in influencer advertising information within the context of social media marketing, denoting consumers’ belief in and reliance on the advertising information endorsed by influencers. Racherla et al. investigated the factors influencing consumers’ trust in online reviews, suggesting that information quality and value contribute to increasing trust (Racherla et al., 2012 ). Similarly, Luo and Yuan, in a study based on social media marketing, also confirmed that the value of advertising information posted on brand pages can foster consumer trust in the content (Lou & Yuan, 2019 ). Therefore, by analogy, this paper posits that the informative value of influencer-endorsed advertising can also promote consumer trust in that advertising information. The relationship between trust in advertising information and consumer engagement behavior has been discussed earlier. Thus, the current study proposes the following research hypotheses:

H9: Informative value is positively correlated with information trust.

H9a: Information trust mediates the impact of informative value on content consumption in consumer engagement behavior.

H9b: Information trust mediates the impact of informative value on content contribution in consumer engagement behavior.

H9c: Information trust mediates the impact of informative value on content creation in consumer engagement behavior.

Advertising information factors: ad targeting accuracy

Ad targeting accuracy refers to the degree of match between the substantive information contained in advertising content and consumer needs. Advertisements containing precise information often yield good advertising outcomes. In marketing practice, advertisers frequently use information technology to analyze the characteristics of different consumer groups in the target market and then target their advertisements accordingly to achieve precise dissemination and, consequently, effective advertising results. The utility of ad targeting accuracy has been confirmed by many studies. For instance, in the research by Qiu and Chen, using a modified UTAUT model, it was demonstrated that the accuracy of advertising effectively promotes consumer acceptance of advertisements in WeChat Moments (Qiu & Chen, 2018 ). Although some studies on targeted advertising also indicate that overly precise ads may raise concerns about personal privacy (Zhang et al., 2019 ), overall, the accuracy of advertising information is effective in enhancing advertising outcomes and is a key element in the success of targeted advertising.

Ad targeting accuracy and information trust

In influencer marketing advertisements, due to the special relationship recognition between consumers and influencers, the privacy concerns associated with ad targeting accuracy are alleviated (Vrontis et al., 2021 ). Meanwhile, the informative value brought by targeting accuracy is highlighted. More precise advertising content implies higher informative value and also signifies that the advertising content is more worthy of consumer trust (Della Vigna, Gentzkow, 2010 ). As previously discussed, people are more inclined to read and engage with advertising content they trust and recognize. Therefore, the current study proposes the following research hypotheses:

H10: Ad targeting accuracy is positively correlated with information trust.

H10a: Information trust mediates the impact of ad targeting accuracy on content consumption in consumer engagement behavior.

H10b: Information trust mediates the impact of ad targeting accuracy on content contribution in consumer engagement behavior.

H10c: Information trust mediates the impact of ad targeting accuracy on content creation in consumer engagement behavior.

Social factors: subjective norm

The Theory of Planned Behavior, proposed by Ajzen ( 1991 ), suggests that individuals’ actions are preceded by conscious choices and are underlain by plans. TPB has been widely used by scholars in studying personal online behaviors, these studies collectively validate the applicability of TPB in the context of social media for researching online behaviors (Huang, 2023 ). Additionally, the self-determination theory, which underpins this chapter’s research, also supports the notion that individuals’ behavioral decisions are based on internal cognitions, aligning with TPB’s assertions. Therefore, this paper intends to select subjective norms from TPB as a factor of social influence. Subjective norm refers to an individual’s perception of the expectations of significant others in their social relationships regarding their behavior. Empirical research in the consumption field has demonstrated the significant impact of subjective norms on individual psychological cognition (Yang & Jolly, 2009 ). A meta-analysis by Hagger, Chatzisarantis ( 2009 ) even highlighted the statistically significant association between subjective norms and self-determination factors. Consequently, this study further explores its application in the context of influencer marketing advertisements on social media.

Subjective norm and self-disclosure willingness

In numerous studies on social media privacy, subjective norms significantly influence an individual’s self-disclosure willingness. Wirth et al. ( 2019 ) based on the privacy calculus theory, surveyed 1,466 participants and found that personal self-disclosure on social media is influenced by the behavioral expectations of other significant reference groups around them. Their research confirmed that subjective norms positively influence self-disclosure of information and highlighted that individuals’ cognitions and behaviors cannot ignore social and environmental factors. Heirman et al. ( 2013 ) in an experiment with Instagram users, also noted that subjective norms could promote positive consumer behavioral responses. Specifically, when important family members and friends highly regard social media influencers as trustworthy, we may also be more inclined to disclose our information to influencers and share this information with our surrounding family and friends without fear of disapproval. In our subjective norms, this is considered a positive and valuable interactive behavior, leading us to exhibit engagement behaviors. Based on this logic, we propose the following research hypotheses:

H11: Subjective norms are positively correlated with self-disclosure willingness.

H11a: Self-disclosure willingness mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H11b: Self-disclosure willingness mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H11c: Self-disclosure willingness mediates the impact of subjective norms on content creation in consumer engagement behavior.

Subjective norm and information trust

Numerous studies have indicated that subjective norms significantly influence trust (Roh et al., 2022 ). This can be explained by reference group theory, suggesting people tend to minimize the effort expended in decision-making processes, often looking to the behaviors or attitudes of others as a point of reference; for instance, subjective norms can foster acceptance of technology by enhancing trust (Gupta et al., 2021 ). Analogously, if a consumer’s social network generally holds positive attitudes toward influencer advertising, they are also more likely to trust the endorsed advertisement information, as it conserves the extensive effort required in gathering product information (Chetioui et al., 2020 ). Therefore, this paper proposes the following research hypotheses:

H12: Subjective norms are positively correlated with information trust.

H12a: Information trust mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H12b: Information trust mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H12c: Information trust mediates the impact of subjective norms on content creation in consumer engagement behavior.

Conceptual model

In summary, based on the Stimulus (S)-Organism (O)-Response (R) framework, this study constructs the external stimulus factors (S) from three dimensions: influencer factors (parasocial identification, source credibility), advertising information factors (informative value, Ad targeting accuracy), and social influence factors (subjective norms). This is grounded in social capital theory and the theory of planned behavior. drawing on self-determination theory, the current study constructs the individual psychological factors (O) using self-disclosure willingness, innovativeness, and information trust. Finally, the behavioral response (R) is constructed using consumer engagement, which includes content consumption, content contribution, and content creation, as illustrated in Fig. 1 .

Consumer engagement behavior impact model based on SOR framework.

Materials and methods

Participants and procedures.

The current study conducted a survey through the Wenjuanxing platform to collect data. Participants were recruited through social media platforms such as WeChat, Douyin, Weibo et al., as samples drawn from social media users better align with the research purpose of our research and ensure the validity of the sample. Before the survey commenced, all participants were explicitly informed about the purpose of this study, and it was made clear that volunteers could withdraw from the survey at any time. Initially, 600 questionnaires were collected, with 78 invalid responses excluded. The criteria for valid questionnaires were as follows: (1) Respondents must have answered “Yes” to the question, “Do you follow any influencers (internet celebrities) on social media platforms?” as samples not using social media or not following influencers do not meet the study’s objective, making this question a prerequisite for continuing the survey; (2) Respondents had to correctly answer two hidden screening questions within the questionnaire to ensure that they did not randomly select scores; (3) The total time taken to complete the questionnaire had to exceed one minute, ensuring that respondents had sufficient time to understand and thoughtfully answer each question; (4) Respondents were not allowed to choose the same score for eight consecutive questions. Ultimately, 522 valid questionnaires were obtained, with an effective rate of 87.00%, meeting the basic sample size requirements for research models (Gefen et al., 2011 ). Detailed demographic information of the study participants is presented in Table 1 .

Measurements

To ensure the validity and reliability of the data analysis results in this study, the measurement tools and scales used in this chapter were designed with reference to existing established research. The main variables in the survey questionnaire include parasocial identification, source credibility, informative value, ad targeting accuracy, subjective norms, self-disclosure willingness, innovativeness, information trust, content consumption, content contribution, and content creation. The measurement scale for parasocial identification was adapted from the research of Schramm and Hartmann, comprising 6 items (Schramm & Hartmann, 2008 ). The source credibility scale was combined from the studies of Cheung et al. and Luo & Yuan’s research in the context of social media influencer marketing, including 4 items (Cheung et al., 2009 ; Lou & Yuan, 2019 ). The scale for informative value was modified based on Voss et al.‘s research, consisting of 4 items (Voss et al., 2003 ). The ad targeting accuracy scale was derived from the research by Qiu Aimei et al., 2018 ) including 3 items. The subjective norm scale was adapted from Ajzen’s original scale, comprising 3 items (Ajzen, 2002 ). The self-disclosure willingness scale was developed based on Chu and Kim’s research, including 3 items (Chu & Kim, 2011 ). The innovativeness scale was formulated following the study by Sun et al., comprising 4 items (Sun et al., 2006 ). The information trust scale was created in reference to Chu and Choi’s research, including 3 items (Chu & Choi, 2011 ). The scales for the three components of social media consumer engagement—content consumption, content contribution, and content creation—were sourced from the research by Buzeta et al., encompassing 8 items in total (Buzeta et al., 2020 ).

All scales were appropriately revised for the context of social media influencer marketing. To avoid issues with scoring neutral attitudes, a uniform Likert seven-point scale was used for each measurement item (ranging from 1 to 7, representing a spectrum from ‘strongly disagree’ to ‘strongly agree’). After the overall design of the questionnaire was completed, a pre-test was conducted with 30 social media users to ensure that potential respondents could clearly understand the meaning of each question and that there were no obstacles to answering. This pre-test aimed to prevent any difficulties or misunderstandings in the questionnaire items. The final version of the questionnaire is presented in Table 2 .

Data analysis

Since the model framework of the current study is derived from theoretical deductions of existing research and, while logically constructed, does not originate from an existing research model, this study still falls under the category of exploratory research. According to the analysis suggestions of Hair and other scholars, in cases of exploratory research model frameworks, it is more appropriate to choose Smart PLS for Partial Least Squares Path Analysis (PLS) to conduct data analysis and testing of the research model (Hair et al., 2012 ).

Measurement of model

In this study, careful data collection and management resulted in no missing values in the dataset. This ensured the integrity and reliability of the subsequent data analysis. As shown in Table 3 , after deleting measurement items with factor loadings below 0.5, the final factor loadings of the measurement items in this study range from 0.730 to 0.964. This indicates that all measurement items meet the retention criteria. Additionally, the Cronbach’s α values of the latent variables range from 0.805 to 0.924, and all latent variables have Composite Reliability (CR) values greater than the acceptable value of 0.7, demonstrating that the scales of this study have passed the reliability test requirements (Hair et al., 2019 ). All latent variables in this study have Average Variance Extracted (AVE) values greater than the standard acceptance value of 0.5, indicating that the convergent validity of the variables also meets the standard (Fornell & Larcker, 1981 ). Furthermore, the results show that the Variance Inflation Factor (VIF) values for each factor are below 10, indicating that there are no multicollinearity issues with the scales in this study (Hair, 2009 ).

The current study then further verified the discriminant validity of the variables, with specific results shown in Table 4 . The square roots of the average variance extracted (AVE) values for all variables (bolded on the diagonal) are greater than the Pearson correlation coefficients between the variables, indicating that the discriminant validity of the scales in this study meets the required standards (Fornell & Larcker, 1981 ). Additionally, a single-factor test method was employed to examine common method bias in the data. The first unrotated factor accounted for 29.71% of the variance, which is less than the critical threshold of 40%. Therefore, the study passed the test and did not exhibit serious common method bias (Podsakoff et al., 2003 ).

To ensure the robustness and appropriateness of our structural equation model, we also conducted a thorough evaluation of the model fit. Initially, through PLS Algorithm calculations, the R 2 values of each variable were greater than the standard acceptance value of 0.1, indicating good predictive accuracy of the model. Subsequently, Blindfolding calculations were performed, and the results showed that the Stone-Geisser Q 2 values of each variable were greater than 0, demonstrating that the model of this study effectively predicts the relationships between variables (Dijkstra & Henseler, 2015 ). In addition, through CFA, we also obtained some indicator values, specifically, χ 2 /df = 2.528 < 0.3, RMSEA = 0.059 < 0.06, SRMR = 0.055 < 0.08. Given its sensitivity to sample size, we primarily focused on the CFI, TLI, and NFI values, CFI = 0.953 > 0.9, TLI = 0.942 > 0.9, and NFI = 0.923 > 0.9 indicating a good fit. Additionally, RMSEA values below 0.06 and SRMR values below 0.08 were considered indicative of a good model fit. These indices collectively suggested that our model demonstrates a satisfactory fit with the data, thereby reinforcing the validity of our findings.

Research hypothesis testing

The current study employed a Bootstrapping test with a sample size of 5000 on the collected raw data to explore the coefficients and significance of the paths in the research model. The final test data results of this study’s model are presented in Table 5 .

The current study employs S-O-R model as the framework, grounded in theories such as self-determination theory and theory of planned behavior, to construct an influence model of consumer engagement behavior in the context of social media influencer marketing. It examines how influencer factors, advertisement information factors, and social influence factors affect consumer engagement behavior by impacting consumers’ psychological cognitions. Using structural equation modeling to analyze collected data ( N  = 522), it was found that self-disclosure willingness, innovativeness, and information trust positively influence consumer engagement behavior, with innovativeness having the largest impact on higher levels of engagement. Influencer factors, advertisement information factors, and social factors serve as effective external stimuli, influencing psychological motivators and, consequently, consumer engagement behavior. The specific research results are illustrated in Fig. 2 .

Tested structural model of consumer engagement behavior.

The impact of psychological motivators on different levels of consumer engagement: self-disclosure willingness, innovativeness, and information trust

The research analysis indicates that self-disclosure willingness and information trust are key drivers for content consumption (H1a, H2a validated). This aligns with previous findings that individuals with a higher willingness to disclose themselves show greater levels of engagement behavior (Chu et al., 2023 ); likewise, individuals who trust advertisement information are more inclined to engage with advertisement content (Kim, Kim, 2021 ). Moreover, our study finds that information trust has a stronger impact on content consumption, underscoring the importance of trust in the dissemination of advertisement information. However, no significant association was found between individual innovativeness and content consumption (H3a not validated).

Regarding the dimension of content contribution in consumer engagement, self-disclosure willingness, information trust, and innovativeness all positively impact it (H1b, H2b, and H3b all validated). This is consistent with earlier research findings that individuals with higher self-disclosure willingness are more likely to like, comment on, or share content posted by influencers on social media platforms (Towner et al., 2022 ); the conclusions of this paper also support that innovativeness is an important psychological driver for active participation in social media interactions (Kamboj & Sharma, 2023 ). However, at the level of consumer engagement in content contribution, while information trust also exerts a positive effect, its impact is the weakest, although information trust has the strongest impact on content consumption.

In social media advertising, the ideal outcome is the highest level of consumer engagement, i.e., content creation, meaning consumers actively join in brand content creation, seeing themselves as co-creators with the brand (Nadeem et al., 2021 ). Our findings reveal that self-disclosure willingness, innovativeness, and information trust all positively influence content creation (H1c, H2c, and H3c all validated). The analysis found that similar to the impact on content contribution, innovativeness has the most significant effect on encouraging individual content creation, followed by self-disclosure willingness, with information trust having the least impact.

In summary, while some previous studies have shown that self-disclosure willingness, innovativeness, and information trust are important factors in promoting consumer engagement (Chu et al., 2023 ; Nadeem et al., 2021 ; Geng et al., 2021 ), this study goes further by integrating and comparing all three within the same research framework. It was found that to trigger higher levels of consumer engagement behavior, trust is not the most crucial psychological motivator; rather, the most effective method is to stimulate consumers’ innovativeness, thus complementing previous research. Subsequently, this study further explores the impact of different stimulus factors on various psychological motivators.

The influence of external stimulus factors on psychological motivators: influencer factors, advertisement information factors, and social factors

The current findings indicate that influencer factors, such as parasocial identification and source credibility, effectively enhance consumer engagement by influencing self-disclosure willingness and information trust. This aligns with prior research highlighting the significance of parasocial identification (Shan et al., 2020 ). Studies suggest parasocial identification positively impacts consumer engagement by boosting self-disclosure willingness and information trust (validated H4a, H4b, H4c, and H5a), but not content contribution or creation through information trust (H5b, H5c not validated). Source credibility’s influence on self-disclosure willingness was not significant (H6 not validated), thus negating the mediating effect of self-disclosure willingness (H6a, H6b, H6c not validated). Influencer credibility mainly affects engagement through information trust (H7a, H7b, H7c validated), supporting previous findings (Shan et al., 2020 ).

Advertisement factors (informative value and ad targeting accuracy) promote engagement through innovativeness and information trust. Informative value significantly impacts higher-level content contribution and creation through innovativeness (H8b, H8c validated), while ad targeting accuracy influences consumer engagement at all levels mainly through information trust (H10a, H10b, H10c validated).

Social factors (subjective norms) enhance self-disclosure willingness and information trust, consistent with previous research (Wirth et al., 2019 ; Gupta et al., 2021 ), and further promote consumer engagement across all levels (H11a, H11b, H11c, H12a, H12b, and H12c all validated).

In summary, influencer, advertisement, and social factors impact consumer engagement behavior by influencing psychological motivators, with influencer factors having the greatest effect on content consumption, advertisement content factors significantly raising higher-level consumer engagement through innovativeness, and social factors also influencing engagement through self-disclosure willingness and information trust.

Implication

From a theoretical perspective, current research presents a comprehensive model of consumer engagement within the context of influencer advertising on social media. This model not only expands the research horizon in the fields of social media influencer advertising and consumer engagement but also serves as a bridge between two crucial themes in new media advertising studies. Influencer advertising has become an integral part of social media advertising, and the construction of a macro model aids researchers in understanding consumer psychological processes and behavioral patterns. It also assists advertisers in formulating more effective strategies. Consumer engagement, focusing on the active role of consumers in disseminating information and the long-term impact on advertising effectiveness, aligns more closely with the advertising effectiveness measures in the new media context than traditional advertising metrics. However, the intersection of these two vital themes lacks comprehensive research and a universal model. This study constructs a model that elucidates the effects of various stimuli on consumer psychology and engagement behaviors, exploring the connections and mechanisms through different mediating pathways. By differentiating levels of engagement, the study offers more nuanced conclusions for diverse advertising objectives. Furthermore, this research validates the applicability of self-determination theory in the context of influencer advertising effectiveness. While this psychological theory has been utilized in communication behavior research, its effectiveness in the field of advertising requires further exploration. The current study introduces self-determination theory into the realm of influencer advertising and consumer engagement, thereby expanding its application in the field of advertising communication. It also responds to the call from the advertising and marketing academic community to incorporate more psychological theories to explain the ‘black box’ of consumer psychology. The inclusion of this theory re-emphasizes the people-centric approach of this research and highlights the primary role of individuals in advertising communication studies.

From a practical perspective, this study provides significant insights for adapting marketing strategies to the evolving media landscape and the empowered role of audiences. Firstly, in the face of changes in the communication environment and the empowerment of audience communication capabilities, traditional marketing approaches are becoming inadequate for new media advertising needs. Traditional advertising focuses on direct, point-to-point effects, whereas social media advertising aims for broader, point-to-mass communication, leveraging audience proactivity to facilitate the viral spread of content across online social networks. Secondly, for brands, the general influence model proposed in this study offers guidance for influencer advertising strategy. If the goal is to maximize reach and brand recognition with a substantial advertising budget, partnering with top influencers who have a large following can be an effective strategy. However, if the objective is to maximize cost-effectiveness with a limited budget by leveraging consumer initiative for secondary spread, the focus should be on designing advertising content that stimulates consumer creativity and willingness to innovate. Thirdly, influencers are advised to remain true to their followers. In influencer marketing, influencers attract advertisers through their influence over followers, converting this influence into commercial gain. This influence stems from the trust followers place in the influencer, thus influencers should maintain professional integrity and prioritize the quality of information they share, even when presented with advertising opportunities. Lastly, influencers should assert more control over their relationships with advertisers. In traditional advertising, companies and brands often exert significant control over the content. However, in the social media era, influencers should negotiate more creative freedom in their advertising partnerships, asserting a more equal relationship with advertisers. This approach ensures that content quality remains high, maintaining the trust influencers have built with their followers.

Limitations and future directions

while this study offers valuable insights into the dynamics of influencer marketing and consumer engagement on social media, several limitations should be acknowledged: Firstly, constrained by the research objectives and scope, this study’s proposed general impact model covers three dimensions: influencers, advertisement information, and social factors. However, these dimensions are not limited to the five variables discussed in this paper. Therefore, we call for future research to supplement and explore more crucial factors. Secondly, in the actual communication environment, there may be differences in the impact of communication effectiveness across various social media platforms. Thus, future research could also involve comparative studies and explorations between different social media platforms. Thirdly, the current study primarily examines the direct effects of various factors on consumer engagement. However, the potential interaction effects between these variables (e.g., how influencers’ credibility might interact with advertisement information quality) are not extensively explored. Future research could investigate these complex interrelationships for a more holistic understanding. Lastly, our study, being cross-sectional, offers preliminary insights into the complex and dynamic nature of engagement between social media influencers and consumers, yet it does not incorporate the temporal dimension. The diverse impacts of psychological needs on engagement behaviors hint at an underlying dynamism that merits further investigation. Future research should consider employing longitudinal designs to directly observe how these dynamics evolve over time.

The findings of the current study not only theoretically validate the applicability of self-determination theory in the field of social media influencer marketing advertising research but also broaden the scope of advertising effectiveness research from the perspective of consumer engagement. Moreover, the research framework offers strategic guidance and reference for influencer marketing strategies. The main conclusions of this study can be summarized as follows.

Innovativeness is the key factor in high-level consumer engagement behavior. Content contribution represents a higher level of consumer engagement compared to content consumption, as it not only requires consumers to dedicate attention to viewing advertising content but also to share this information across adjacent nodes within their social networks. This dissemination of information is a pivotal factor in the success of influencer marketing advertisements. Hence, companies and brands prioritize consumers’ content contribution over mere viewing of advertising content (Qiu & Kumar, 2017 ). Compared to content consumption and contribution, content creation is considered the highest level of consumer engagement, where consumers actively create and upload brand-related content, and it represents the most advanced outcome sought by enterprises and brands in advertising campaigns (Cheung et al., 2021 ). The current study posits that to pursue better outcomes in social media influencer advertising marketing, enhancing consumers’ willingness for self-disclosure, innovativeness, and trust in advertising information are effective strategies. However, the crux lies in leveraging the consumer’s subjective initiative, particularly in boosting their innovativeness. If the goal is simply to achieve content consumption rather than higher levels of consumer engagement, the focus should be on fostering trust in advertising information. There is no hierarchy in the efficacy of different strategies; they should align with varying marketing contexts and advertising objectives.

The greatest role of social media influencers lies in attracting online traffic. information trust is the core element driving content consumption, and influencer factors mainly affect consumer engagement behaviors through information trust. Therefore, this study suggests that the primary role of influencers in social media advertising is to attract online traffic, i.e., increase consumer behavior regarding ad content consumption (reducing avoidance of ad content), and help brands achieve the initial goal of making consumers “see and complete ads.” However, their impact on further high-level consumer engagement behaviors is limited. This mechanism serves as a reminder to advertisers not to overestimate the effects of influencers in marketing. Currently, top influencers command a significant portion of the ad budget, which could squeeze the budget for other aspects of advertising, potentially affecting the overall effectiveness of the campaign. Businesses and brands should consider deeper strategic implications when planning their advertising campaigns.

Valuing Advertising Information Factors, Content Remains King. Our study posits that in the social media influencer marketing context, the key to enhancing consumer contribution and creation of advertising content lies primarily in the advertising information factors. In other words, while content consumption is important, advertisers should objectively assess the role influencers play in advertising. In the era of social media, content remains ‘king’ in advertising. This view indirectly echoes the points made in the previous paragraph: influencers effectively perform initial ‘online traffic generation’ tasks in social media, but this role should not be overly romanticized or exaggerated. Whether it’s companies, brands, or influencers, providing consumers with advertisements rich in informational value is crucial to achieving better advertising outcomes and potentially converting consumers into stakeholders.

Subjective norm is an unignorable social influence factor. Social media is characterized by its network structure of information dissemination, where a node’s information is visible to adjacent nodes. For instance, if user A likes a piece of content C from influencer I, A’s follower B, who may not follow influencer I, can still see content C via user A’s page. The aim of marketing in the social media era is to influence a node and then spread the information to adjacent nodes, either secondarily or multiple times (Kumar & Panda, 2020 ). According to the Theory of Planned Behavior, an individual’s actions are influenced by significant others in their lives, such as family and friends. Previous studies have proven the effectiveness of the Theory of Planned Behavior in influencing attitudes toward social media advertising (Ranjbarian et al., 2012 ). Current research further confirms that subjective norms also influence consumer engagement behaviors in influencer marketing on social media. Therefore, in advertising practice, brands should not only focus on individual consumers but also invest efforts in groups that can influence consumer decisions. Changing consumer behavior in the era of social media marketing doesn’t solely rely on the company’s efforts.

As communication technology advances, media platforms will further empower individual communicative capabilities, moving beyond the era of the “magic bullet” theory. The distinction between being a recipient and a transmitter of information is increasingly blurred. In an era where everyone is both an audience and an influencer, research confined to the role of the ‘recipient’ falls short of addressing the dynamics of ‘transmission’. Future research in marketing and advertising should thus focus more on the power of individual transmission. Furthermore, as Marshall McLuhan famously said, “the medium is the extension of man.” The evolution of media technology remains human-centric. Accordingly, future marketing research, while paying heed to media transformations, should emphasize the centrality of the ‘human’ element.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy issues. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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The authors thank all the participants of this study. The participants were all informed about the purpose and content of the study and voluntarily agreed to participate. The participants were able to stop participating at any time without penalty. Funding for this study was provided by Minjiang University Research Start-up Funds (No. 324-32404314).

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Conceptualization: CG; methodology: CG and QD; software: CG and QD; validation: CG; formal analysis: CG and QD; investigation: CG and QD; resources: CG; data curation: CG and QD; writing—original draft preparation: CG; writing—review and editing: CG; visualization: CG; project administration: CG. All authors have read and agreed to the published version of the manuscript.

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Gu, C., Duan, Q. Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective. Humanit Soc Sci Commun 11 , 587 (2024). https://doi.org/10.1057/s41599-024-03127-w

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An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping

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• Published: 16 May 2024

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• Zhiqiang Zhang 1 , 2 , 3 ,
• Gongwen Wang   ORCID: orcid.org/0000-0002-0141-7209 4 ,
• Emmanuel John M. Carranza 5 ,
• Jingguo Du 6 ,
• Yingjie Li 1 , 2 , 3 ,
• Xinxing Liu 1 , 2 , 3 &
• Yongjun Su 7  

The uncertainty inherent in three-dimensional (3D) mineral prospectivity mapping (MPM) encompasses (a) mineral system conceptual model uncertainty stemming from geological conceptual frameworks, (b) aleatoric uncertainty, attributable to the variability and noise due to multi-source geoscience datasets collection and processing, as well as 3D geological modeling process, and (c) epistemic uncertainty due to predictive algorithm modeling. Quantifying the uncertainty of 3D MPM is a prerequisite for accepting predictive models in exploration. Previous MPM studies were centered on addressing the mineral system conceptual model uncertainty. To the best of our knowledge, few studies quantified the aleatoric and epistemic uncertainties of 3D MPM. This study proposes a novel uncertainty-quantification machine learning framework to qualify aleatoric and epistemic uncertainties in 3D MPM by the uncertainty-quantification random forest. Another innovation of this framework is utility of the accuracy–rejection curve to provide a quantitative uncertainty threshold for exploration target delineation. The Bayesian hyperparameter optimization tunes the hyperparameters of the uncertainty-quantification random forest automatically. The case study of 3D MPM for exploration target delineation in the Wulong gold district of China demonstrated the practicality of our framework. The aleatoric uncertainty of the 3D MPM indicates that the 3D Early Cretaceous dyke model is the main source of this uncertainty. The 3D exploration targets delineated by the uncertainty-quantification machine learning framework can benefit subsurface gold exploration in the study area.

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Acknowledgments

This research is supported by Hebei Natural Science Foundation (No. D2023403051), Open Project Program of Hebei Province Collaborative Innovation Center for Strategic Critical Mineral Research, Hebei GEO University, China (No. HGUXT-2023-13), and the MNR Key Laboratory for Exploration Theory & Technology of Critical Mineral Resources (No. 202405).

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Hebei Province Collaborative Innovation Center for Strategic Critical Mineral Research, Hebei GEO University, Shijiazhuang, 050031, People’s Republic of China

Zhiqiang Zhang, Yingjie Li & Xinxing Liu

Hebei Key Laboratory of Strategic Critical Mineral Resources, Hebei GEO University, Shijiazhuang, 050031, People’s Republic of China

School of Earth Sciences, Hebei GEO University, Shijiazhuang, 050031, People’s Republic of China

MNR Key Laboratory for Exploration Theory and Technology of Critical Mineral Resources, China University of Geosciences, Beijing, 100083, People’s Republic of China

Gongwen Wang

Geological of Geology, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa

Emmanuel John M. Carranza

School of Earth Science and Resources, Chang’An University, Xi’an, 710054, People’s Republic of China

Tianjin Center, China Geological Survey, Tianjin, 300170, People’s Republic of China

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Zhang, Z., Wang, G., Carranza, E.J.M. et al. An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping. Nat Resour Res (2024). https://doi.org/10.1007/s11053-024-10349-x

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DOI : https://doi.org/10.1007/s11053-024-10349-x

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