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• v.21(3); Fall 2022

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.

Supplementary Material

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• Roberta Heale 1 ,
• Helen Noble 2
• 1 Laurentian University , School of Nursing , Sudbury , Ontario , Canada
• 2 Queens University Belfast , School of Nursing and Midwifery , Belfast , UK
• Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Ramsey Lake Road, Sudbury, P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/ebnurs-2019-103077

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Often the most difficult part of a research study is preparing the proposal based around a theoretical or philosophical framework. Graduate students ‘…express confusion, a lack of knowledge, and frustration with the challenge of choosing a theoretical framework and understanding how to apply it’. 1 However, the importance in understanding and applying a theoretical framework in research cannot be overestimated.

The choice of a theoretical framework for a research study is often a reflection of the researcher’s ontological (nature of being) and epistemological (theory of knowledge) perspective. We will not delve into these concepts, or personal philosophy in this article. Rather we will focus on how a theoretical framework can be integrated into research.

The theoretical framework is a blueprint for your research project 1 and serves several purposes. It informs the problem you have identified, the purpose and significance of your research demonstrating how your research fits with what is already known (relationship to existing theory and research). This provides a basis for your research questions, the literature review and the methodology and analysis that you choose. 1 Evidence of your chosen theoretical framework should be visible in every aspect of your research and should demonstrate the contribution of this research to knowledge. 2

What is a theory?

A theory is an explanation of a concept or an abstract idea of a phenomenon. An example of a theory is Bandura’s middle range theory of self-efficacy, 3 or the level of confidence one has in achieving a goal. Self-efficacy determines the coping behaviours that a person will exhibit when facing obstacles. Those who have high self-efficacy are likely to apply adequate effort leading to successful outcomes, while those with low self-efficacy are more likely to give up earlier and ultimately fail. Any research that is exploring concepts related to self-efficacy or the ability to manage difficult life situations might apply Bandura’s theoretical framework to their study.

Using a theoretical framework in a research study

Example 1: the big five theoretical framework.

The first example includes research which integrates the ‘Big Five’, a theoretical framework that includes concepts related to teamwork. These include team leadership, mutual performance monitoring, backup behaviour, adaptability and team orientation. 4 In order to conduct research incorporating a theoretical framework, the concepts need to be defined according to a frame of reference. This provides a means to understand the theoretical framework as it relates to a specific context and provides a mechanism for measurement of the concepts.

In this example, the concepts of the Big Five were given a conceptual definition, that provided a broad meaning and then an operational definition, which was more concrete. 4 From here, a survey was developed that reflected the operational definitions related to teamwork in nursing: the Nursing Teamwork Survey (NTS). 5 In this case, the concepts used in the theoretical framework, the Big Five, were the used to develop a survey specific to teamwork in nursing.

The NTS was used in research of nurses at one hospital in northeastern Ontario. Survey questions were grouped into subscales for analysis, that reflected the concepts of the Big Five. 6 For example, one finding of this study was that the nurses from the surgical unit rated the items in the subscale of ’team leadership' (one of the concepts in the Big Five) significantly lower than in the other units. The researchers looked back to the definition of this concept in the Big Five in their interpretation of the findings. Since the definition included a person(s) who has the leadership skills to facilitate teamwork among the nurses on the unit, the conclusion in this study was that the surgical unit lacked a mentor, or facilitator for teamwork. In this way, the theory of teamwork was presented through a set of concepts in a theoretical framework. The Theoretical Framework (TF)was the foundation for development of a survey related to a specific context, used to measure each of the concepts within the TF. Then, the analysis and results circled back to the concepts within the TF and provided a guide for the discussion and conclusions arising from the research.

Example 2: the Health Decisions Model

In another study which explored adherence to intravenous chemotherapy in African-American and Caucasian Women with early stage breast cancer, an adapted version of the Health Decisions Model (HDM) was used as the theoretical basis for the study. 7 The HDM, a revised version of the Health Belief Model, incorporates some aspects of the Health Belief Model and factors relating to patient preferences. 8 The HDM consists of six interrelated constituents that might predict how well a person adheres to a health decision. These include sociodemographic, social interaction, experience, knowledge, general and specific health beliefs and patient preferences, and are clearly defined. The HDM model was used to explore factors which might influence adherence to chemotherapy in women with breast cancer. Sociodemographic, social interaction, knowledge, personal experience and specific health beliefs were used as predictors of adherence to chemotherapy.

The findings were reported using the theoretical framework to discuss results. The study found that delay to treatment, health insurance, depression and symptom severity were predictors to starting chemotherapy which could potentially be adapted with clinical interventions. The findings from the study contribute to the existing body of literature related to cancer nursing.

Example 3: the nursing role effectiveness model

In this final example, research was conducted to determine the nursing processes that were associated with unexpected intensive care unit admissions. 9 The framework was the Nursing Role Effectiveness Model. In this theoretical framework, the concepts within Donabedian’s Quality Framework of Structure, Process and Outcome were each defined according to nursing practice. 10 11  Processes defined in the Nursing Role Effectiveness Model were used to identify the nursing process variables that were measured in the study.

A theoretical framework should be logically presented and represent the concepts, variables and relationships related to your research study, in order to clearly identify what will be examined, described or measured. It involves reading the literature and identifying a research question(s) while clearly defining and identifying the existing relationship between concepts and theories (related to your research questions[s] in the literature). You must then identify what you will examine or explore in relation to the concepts of the theoretical framework. Once you present your findings using the theoretical framework you will be able to articulate how your study relates to and may potentially advance your chosen theory and add to knowledge.

• Kalisch BJ ,
• Parent M , et al
• Strickland OL ,
• Dalton JA , et al
• Eraker SA ,
• Kirscht JP ,
• Lightfoot N , et al
• Harrison MB ,
• Laschinger H , et al

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Patient and public involvement Not required.

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Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

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Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

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• Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

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Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounded assumptions or predictions of behavior. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem. It is the structure of your paper that summarizes concepts, ideas, and theories derived from prior research studies and which was synthesized in order to form a conceptual basis for your analysis and interpretation of meaning found within your research.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (December 2018): 44-53; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013; Varpio, Lara, Elise Paradis, Sebastian Uijtdehaage, and Meredith Young. "The Distinctions between Theory, Theoretical Framework, and Conceptual Framework." Academic Medicine 95 (July 2020): 989-994.

Importance of Theory and a Theoretical Framework

Theories can be unfamiliar to the beginning researcher because they are rarely applied in high school social studies curriculum and, as a result, can come across as unfamiliar and imprecise when first introduced as part of a writing assignment. However, in their most simplified form, a theory is simply a set of assumptions or predictions about something you think will happen based on existing evidence and that can be tested to see if those outcomes turn out to be true. Of course, it is slightly more deliberate than that, therefore, summarized from Kivunja (2018, p. 46), here are the essential characteristics of a theory.

• It is logical and coherent
• It has clear definitions of terms or variables, and has boundary conditions [i.e., it is not an open-ended statement]
• It has a domain where it applies
• It has clearly described relationships among variables
• It describes, explains, and makes specific predictions
• It comprises of concepts, themes, principles, and constructs
• It must have been based on empirical data [i.e., it is not a guess]
• It must have made claims that are subject to testing, been tested and verified
• It must be clear and concise
• Its assertions or predictions must be different and better than those in existing theories
• Its predictions must be general enough to be applicable to and understood within multiple contexts
• Its assertions or predictions are relevant, and if applied as predicted, will result in the predicted outcome
• The assertions and predictions are not immutable, but subject to revision and improvement as researchers use the theory to make sense of phenomena
• Its concepts and principles explain what is going on and why
• Its concepts and principles are substantive enough to enable us to predict a future

Given these characteristics, a theory can best be understood as the foundation from which you investigate assumptions or predictions derived from previous studies about the research problem, but in a way that leads to new knowledge and understanding as well as, in some cases, discovering how to improve the relevance of the theory itself or to argue that the theory is outdated and a new theory needs to be formulated based on new evidence.

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.
• The theoretical framework adds context around the theory itself based on how scholars had previously tested the theory in relation their overall research design [i.e., purpose of the study, methods of collecting data or information, methods of analysis, the time frame in which information is collected, study setting, and the methodological strategy used to conduct the research].

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument. Writing@CSU. Colorado State University; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (2018): 44-53; Omodan, Bunmi Isaiah. "A Model for Selecting Theoretical Framework through Epistemology of Research Paradigms." African Journal of Inter/Multidisciplinary Studies 4 (2022): 275-285; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

• Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
• Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
• Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
• List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
• Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
• Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

• Means by which new research data can be interpreted and coded for future use,
• Response to new problems that have no previously identified solutions strategy,
• Means for identifying and defining research problems,
• Means for prescribing or evaluating solutions to research problems,
• Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
• Means of giving old data new interpretations and new meaning,
• Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
• Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
• Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

• Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
• Position your theoretical framework within a broader context of related frameworks, concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
• The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
• You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
• Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument. Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Other Disciplines

An increasingly important trend in the social and behavioral sciences is to think about and attempt to understand research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be more engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem or, if appropriate, how the theoretical framework was found to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

• A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted among a set of scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis. About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis. Slideshare presentation.

Still Yet Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis will likely include the expectation by your professor that you should offer modifications to the theory based on your research findings.

Indications that theoretical assumptions may need to be modified can include the following:

• Your findings suggest that the theory does not explain or account for current conditions or circumstances or the passage of time,
• The study reveals a finding that is incompatible with what the theory attempts to explain or predict, or
• Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors revealed from your analysis [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

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Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Learn More: Data Collection Methods: Types & Examples

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Module 2 Chapter 3: What is Empirical Literature & Where can it be Found?

In Module 1, you read about the problem of pseudoscience. Here, we revisit the issue in addressing how to locate and assess scientific or empirical literature . In this chapter you will read about:

• distinguishing between what IS and IS NOT empirical literature
• how and where to locate empirical literature for understanding diverse populations, social work problems, and social phenomena.

Probably the most important take-home lesson from this chapter is that one source is not sufficient to being well-informed on a topic. It is important to locate multiple sources of information and to critically appraise the points of convergence and divergence in the information acquired from different sources. This is especially true in emerging and poorly understood topics, as well as in answering complex questions.

What Is Empirical Literature

Social workers often need to locate valid, reliable information concerning the dimensions of a population group or subgroup, a social work problem, or social phenomenon. They might also seek information about the way specific problems or resources are distributed among the populations encountered in professional practice. Or, social workers might be interested in finding out about the way that certain people experience an event or phenomenon. Empirical literature resources may provide answers to many of these types of social work questions. In addition, resources containing data regarding social indicators may also prove helpful. Social indicators are the “facts and figures” statistics that describe the social, economic, and psychological factors that have an impact on the well-being of a community or other population group.The United Nations (UN) and the World Health Organization (WHO) are examples of organizations that monitor social indicators at a global level: dimensions of population trends (size, composition, growth/loss), health status (physical, mental, behavioral, life expectancy, maternal and infant mortality, fertility/child-bearing, and diseases like HIV/AIDS), housing and quality of sanitation (water supply, waste disposal), education and literacy, and work/income/unemployment/economics, for example.

Three characteristics stand out in empirical literature compared to other types of information available on a topic of interest: systematic observation and methodology, objectivity, and transparency/replicability/reproducibility. Let’s look a little more closely at these three features.

Systematic Observation and Methodology. The hallmark of empiricism is “repeated or reinforced observation of the facts or phenomena” (Holosko, 2006, p. 6). In empirical literature, established research methodologies and procedures are systematically applied to answer the questions of interest.

Objectivity. Gathering “facts,” whatever they may be, drives the search for empirical evidence (Holosko, 2006). Authors of empirical literature are expected to report the facts as observed, whether or not these facts support the investigators’ original hypotheses. Research integrity demands that the information be provided in an objective manner, reducing sources of investigator bias to the greatest possible extent.

Transparency and Replicability/Reproducibility.   Empirical literature is reported in such a manner that other investigators understand precisely what was done and what was found in a particular research study—to the extent that they could replicate the study to determine whether the findings are reproduced when repeated. The outcomes of an original and replication study may differ, but a reader could easily interpret the methods and procedures leading to each study’s findings.

What is NOT Empirical Literature

By now, it is probably obvious to you that literature based on “evidence” that is not developed in a systematic, objective, transparent manner is not empirical literature. On one hand, non-empirical types of professional literature may have great significance to social workers. For example, social work scholars may produce articles that are clearly identified as describing a new intervention or program without evaluative evidence, critiquing a policy or practice, or offering a tentative, untested theory about a phenomenon. These resources are useful in educating ourselves about possible issues or concerns. But, even if they are informed by evidence, they are not empirical literature. Here is a list of several sources of information that do not meet the standard of being called empirical literature:

• your course instructor’s lectures
• political statements
• advertisements
• newspapers & magazines (journalism)
• television news reports & analyses (journalism)
• many websites, Facebook postings, Twitter tweets, and blog postings
• the introductory literature review in an empirical article

You may be surprised to see the last two included in this list. Like the other sources of information listed, these sources also might lead you to look for evidence. But, they are not themselves sources of evidence. They may summarize existing evidence, but in the process of summarizing (like your instructor’s lectures), information is transformed, modified, reduced, condensed, and otherwise manipulated in such a manner that you may not see the entire, objective story. These are called secondary sources, as opposed to the original, primary source of evidence. In relying solely on secondary sources, you sacrifice your own critical appraisal and thinking about the original work—you are “buying” someone else’s interpretation and opinion about the original work, rather than developing your own interpretation and opinion. What if they got it wrong? How would you know if you did not examine the primary source for yourself? Consider the following as an example of “getting it wrong” being perpetuated.

Example: Bullying and School Shootings . One result of the heavily publicized April 1999 school shooting incident at Columbine High School (Colorado), was a heavy emphasis placed on bullying as a causal factor in these incidents (Mears, Moon, & Thielo, 2017), “creating a powerful master narrative about school shootings” (Raitanen, Sandberg, & Oksanen, 2017, p. 3). Naturally, with an identified cause, a great deal of effort was devoted to anti-bullying campaigns and interventions for enhancing resilience among youth who experience bullying.  However important these strategies might be for promoting positive mental health, preventing poor mental health, and possibly preventing suicide among school-aged children and youth, it is a mistaken belief that this can prevent school shootings (Mears, Moon, & Thielo, 2017). Many times the accounts of the perpetrators having been bullied come from potentially inaccurate third-party accounts, rather than the perpetrators themselves; bullying was not involved in all instances of school shooting; a perpetrator’s perception of being bullied/persecuted are not necessarily accurate; many who experience severe bullying do not perpetrate these incidents; bullies are the least targeted shooting victims; perpetrators of the shooting incidents were often bullying others; and, bullying is only one of many important factors associated with perpetrating such an incident (Ioannou, Hammond, & Simpson, 2015; Mears, Moon, & Thielo, 2017; Newman &Fox, 2009; Raitanen, Sandberg, & Oksanen, 2017). While mass media reports deliver bullying as a means of explaining the inexplicable, the reality is not so simple: “The connection between bullying and school shootings is elusive” (Langman, 2014), and “the relationship between bullying and school shooting is, at best, tenuous” (Mears, Moon, & Thielo, 2017, p. 940). The point is, when a narrative becomes this publicly accepted, it is difficult to sort out truth and reality without going back to original sources of information and evidence.

What May or May Not Be Empirical Literature: Literature Reviews

Investigators typically engage in a review of existing literature as they develop their own research studies. The review informs them about where knowledge gaps exist, methods previously employed by other scholars, limitations of prior work, and previous scholars’ recommendations for directing future research. These reviews may appear as a published article, without new study data being reported (see Fields, Anderson, & Dabelko-Schoeny, 2014 for example). Or, the literature review may appear in the introduction to their own empirical study report. These literature reviews are not considered to be empirical evidence sources themselves, although they may be based on empirical evidence sources. One reason is that the authors of a literature review may or may not have engaged in a systematic search process, identifying a full, rich, multi-sided pool of evidence reports.

There is, however, a type of review that applies systematic methods and is, therefore, considered to be more strongly rooted in evidence: the systematic review .

Systematic review of literature. A systematic reviewis a type of literature report where established methods have been systematically applied, objectively, in locating and synthesizing a body of literature. The systematic review report is characterized by a great deal of transparency about the methods used and the decisions made in the review process, and are replicable. Thus, it meets the criteria for empirical literature: systematic observation and methodology, objectivity, and transparency/reproducibility. We will work a great deal more with systematic reviews in the second course, SWK 3402, since they are important tools for understanding interventions. They are somewhat less common, but not unheard of, in helping us understand diverse populations, social work problems, and social phenomena.

Locating Empirical Evidence

Social workers have available a wide array of tools and resources for locating empirical evidence in the literature. These can be organized into four general categories.

Journal Articles. A number of professional journals publish articles where investigators report on the results of their empirical studies. However, it is important to know how to distinguish between empirical and non-empirical manuscripts in these journals. A key indicator, though not the only one, involves a peer review process . Many professional journals require that manuscripts undergo a process of peer review before they are accepted for publication. This means that the authors’ work is shared with scholars who provide feedback to the journal editor as to the quality of the submitted manuscript. The editor then makes a decision based on the reviewers’ feedback:

• Accept as is
• Accept with minor revisions
• Request that a revision be resubmitted (no assurance of acceptance)

When a “revise and resubmit” decision is made, the piece will go back through the review process to determine if it is now acceptable for publication and that all of the reviewers’ concerns have been adequately addressed. Editors may also reject a manuscript because it is a poor fit for the journal, based on its mission and audience, rather than sending it for review consideration.

Indicators of journal relevance. Various journals are not equally relevant to every type of question being asked of the literature. Journals may overlap to a great extent in terms of the topics they might cover; in other words, a topic might appear in multiple different journals, depending on how the topic was being addressed. For example, articles that might help answer a question about the relationship between community poverty and violence exposure might appear in several different journals, some with a focus on poverty, others with a focus on violence, and still others on community development or public health. Journal titles are sometimes a good starting point but may not give a broad enough picture of what they cover in their contents.

In focusing a literature search, it also helps to review a journal’s mission and target audience. For example, at least four different journals focus specifically on poverty:

• Journal of Children & Poverty
• Journal of Poverty
• Journal of Poverty and Social Justice
• Poverty & Public Policy

Let’s look at an example using the Journal of Poverty and Social Justice . Information about this journal is located on the journal’s webpage: http://policy.bristoluniversitypress.co.uk/journals/journal-of-poverty-and-social-justice . In the section headed “About the Journal” you can see that it is an internationally focused research journal, and that it addresses social justice issues in addition to poverty alone. The research articles are peer-reviewed (there appear to be non-empirical discussions published, as well). These descriptions about a journal are almost always available, sometimes listed as “scope” or “mission.” These descriptions also indicate the sponsorship of the journal—sponsorship may be institutional (a particular university or agency, such as Smith College Studies in Social Work ), a professional organization, such as the Council on Social Work Education (CSWE) or the National Association of Social Work (NASW), or a publishing company (e.g., Taylor & Frances, Wiley, or Sage).

Indicators of journal caliber.  Despite engaging in a peer review process, not all journals are equally rigorous. Some journals have very high rejection rates, meaning that many submitted manuscripts are rejected; others have fairly high acceptance rates, meaning that relatively few manuscripts are rejected. This is not necessarily the best indicator of quality, however, since newer journals may not be sufficiently familiar to authors with high quality manuscripts and some journals are very specific in terms of what they publish. Another index that is sometimes used is the journal’s impact factor . Impact factor is a quantitative number indicative of how often articles published in the journal are cited in the reference list of other journal articles—the statistic is calculated as the number of times on average each article published in a particular year were cited divided by the number of articles published (the number that could be cited). For example, the impact factor for the Journal of Poverty and Social Justice in our list above was 0.70 in 2017, and for the Journal of Poverty was 0.30. These are relatively low figures compared to a journal like the New England Journal of Medicine with an impact factor of 59.56! This means that articles published in that journal were, on average, cited more than 59 times in the next year or two.

Impact factors are not necessarily the best indicator of caliber, however, since many strong journals are geared toward practitioners rather than scholars, so they are less likely to be cited by other scholars but may have a large impact on a large readership. This may be the case for a journal like the one titled Social Work, the official journal of the National Association of Social Workers. It is distributed free to all members: over 120,000 practitioners, educators, and students of social work world-wide. The journal has a recent impact factor of.790. The journals with social work relevant content have impact factors in the range of 1.0 to 3.0 according to Scimago Journal & Country Rank (SJR), particularly when they are interdisciplinary journals (for example, Child Development , Journal of Marriage and Family , Child Abuse and Neglect , Child Maltreatmen t, Social Service Review , and British Journal of Social Work ). Once upon a time, a reader could locate different indexes comparing the “quality” of social work-related journals. However, the concept of “quality” is difficult to systematically define. These indexes have mostly been replaced by impact ratings, which are not necessarily the best, most robust indicators on which to rely in assessing journal quality. For example, new journals addressing cutting edge topics have not been around long enough to have been evaluated using this particular tool, and it takes a few years for articles to begin to be cited in other, later publications.

Beware of pseudo-, illegitimate, misleading, deceptive, and suspicious journals . Another side effect of living in the Age of Information is that almost anyone can circulate almost anything and call it whatever they wish. This goes for “journal” publications, as well. With the advent of open-access publishing in recent years (electronic resources available without subscription), we have seen an explosion of what are called predatory or junk journals . These are publications calling themselves journals, often with titles very similar to legitimate publications and often with fake editorial boards. These “publications” lack the integrity of legitimate journals. This caution is reminiscent of the discussions earlier in the course about pseudoscience and “snake oil” sales. The predatory nature of many apparent information dissemination outlets has to do with how scientists and scholars may be fooled into submitting their work, often paying to have their work peer-reviewed and published. There exists a “thriving black-market economy of publishing scams,” and at least two “journal blacklists” exist to help identify and avoid these scam journals (Anderson, 2017).

This issue is important to information consumers, because it creates a challenge in terms of identifying legitimate sources and publications. The challenge is particularly important to address when information from on-line, open-access journals is being considered. Open-access is not necessarily a poor choice—legitimate scientists may pay sizeable fees to legitimate publishers to make their work freely available and accessible as open-access resources. On-line access is also not necessarily a poor choice—legitimate publishers often make articles available on-line to provide timely access to the content, especially when publishing the article in hard copy will be delayed by months or even a year or more. On the other hand, stating that a journal engages in a peer-review process is no guarantee of quality—this claim may or may not be truthful. Pseudo- and junk journals may engage in some quality control practices, but may lack attention to important quality control processes, such as managing conflict of interest, reviewing content for objectivity or quality of the research conducted, or otherwise failing to adhere to industry standards (Laine & Winker, 2017).

One resource designed to assist with the process of deciphering legitimacy is the Directory of Open Access Journals (DOAJ). The DOAJ is not a comprehensive listing of all possible legitimate open-access journals, and does not guarantee quality, but it does help identify legitimate sources of information that are openly accessible and meet basic legitimacy criteria. It also is about open-access journals, not the many journals published in hard copy.

An additional caution: Search for article corrections. Despite all of the careful manuscript review and editing, sometimes an error appears in a published article. Most journals have a practice of publishing corrections in future issues. When you locate an article, it is helpful to also search for updates. Here is an example where data presented in an article’s original tables were erroneous, and a correction appeared in a later issue.

• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 12(8): e0181722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558917/
• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2018).Correction—A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 13(3): e0193937.  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193937

Search Tools. In this age of information, it is all too easy to find items—the problem lies in sifting, sorting, and managing the vast numbers of items that can be found. For example, a simple Google® search for the topic “community poverty and violence” resulted in about 15,600,000 results! As a means of simplifying the process of searching for journal articles on a specific topic, a variety of helpful tools have emerged. One type of search tool has previously applied a filtering process for you: abstracting and indexing databases . These resources provide the user with the results of a search to which records have already passed through one or more filters. For example, PsycINFO is managed by the American Psychological Association and is devoted to peer-reviewed literature in behavioral science. It contains almost 4.5 million records and is growing every month. However, it may not be available to users who are not affiliated with a university library. Conducting a basic search for our topic of “community poverty and violence” in PsychINFO returned 1,119 articles. Still a large number, but far more manageable. Additional filters can be applied, such as limiting the range in publication dates, selecting only peer reviewed items, limiting the language of the published piece (English only, for example), and specified types of documents (either chapters, dissertations, or journal articles only, for example). Adding the filters for English, peer-reviewed journal articles published between 2010 and 2017 resulted in 346 documents being identified.

Just as was the case with journals, not all abstracting and indexing databases are equivalent. There may be overlap between them, but none is guaranteed to identify all relevant pieces of literature. Here are some examples to consider, depending on the nature of the questions asked of the literature:

• Academic Search Complete—multidisciplinary index of 9,300 peer-reviewed journals
• AgeLine—multidisciplinary index of aging-related content for over 600 journals
• Campbell Collaboration—systematic reviews in education, crime and justice, social welfare, international development
• Google Scholar—broad search tool for scholarly literature across many disciplines
• MEDLINE/ PubMed—National Library of medicine, access to over 15 million citations
• Oxford Bibliographies—annotated bibliographies, each is discipline specific (e.g., psychology, childhood studies, criminology, social work, sociology)
• PsycINFO/PsycLIT—international literature on material relevant to psychology and related disciplines
• SocINDEX—publications in sociology
• Social Sciences Abstracts—multiple disciplines
• Social Work Abstracts—many areas of social work are covered
• Web of Science—a “meta” search tool that searches other search tools, multiple disciplines

Placing our search for information about “community violence and poverty” into the Social Work Abstracts tool with no additional filters resulted in a manageable 54-item list. Finally, abstracting and indexing databases are another way to determine journal legitimacy: if a journal is indexed in a one of these systems, it is likely a legitimate journal. However, the converse is not necessarily true: if a journal is not indexed does not mean it is an illegitimate or pseudo-journal.

Government Sources. A great deal of information is gathered, analyzed, and disseminated by various governmental branches at the international, national, state, regional, county, and city level. Searching websites that end in.gov is one way to identify this type of information, often presented in articles, news briefs, and statistical reports. These government sources gather information in two ways: they fund external investigations through grants and contracts and they conduct research internally, through their own investigators. Here are some examples to consider, depending on the nature of the topic for which information is sought:

• Agency for Healthcare Research and Quality (AHRQ) at https://www.ahrq.gov/
• Bureau of Justice Statistics (BJS) at https://www.bjs.gov/
• Census Bureau at https://www.census.gov
• Morbidity and Mortality Weekly Report of the CDC (MMWR-CDC) at https://www.cdc.gov/mmwr/index.html
• Child Welfare Information Gateway at https://www.childwelfare.gov
• Children’s Bureau/Administration for Children & Families at https://www.acf.hhs.gov
• Forum on Child and Family Statistics at https://www.childstats.gov
• National Institutes of Health (NIH) at https://www.nih.gov , including (not limited to):
• National Institute on Aging (NIA at https://www.nia.nih.gov
• National Institute on Alcohol Abuse and Alcoholism (NIAAA) at https://www.niaaa.nih.gov
• National Institute of Child Health and Human Development (NICHD) at https://www.nichd.nih.gov
• National Institute on Drug Abuse (NIDA) at https://www.nida.nih.gov
• National Institute of Environmental Health Sciences at https://www.niehs.nih.gov
• National Institute of Mental Health (NIMH) at https://www.nimh.nih.gov
• National Institute on Minority Health and Health Disparities at https://www.nimhd.nih.gov
• National Institute of Justice (NIJ) at https://www.nij.gov
• Substance Abuse and Mental Health Services Administration (SAMHSA) at https://www.samhsa.gov/
• United States Agency for International Development at https://usaid.gov

Each state and many counties or cities have similar data sources and analysis reports available, such as Ohio Department of Health at https://www.odh.ohio.gov/healthstats/dataandstats.aspx and Franklin County at https://statisticalatlas.com/county/Ohio/Franklin-County/Overview . Data are available from international/global resources (e.g., United Nations and World Health Organization), as well.

Other Sources. The Health and Medicine Division (HMD) of the National Academies—previously the Institute of Medicine (IOM)—is a nonprofit institution that aims to provide government and private sector policy and other decision makers with objective analysis and advice for making informed health decisions. For example, in 2018 they produced reports on topics in substance use and mental health concerning the intersection of opioid use disorder and infectious disease,  the legal implications of emerging neurotechnologies, and a global agenda concerning the identification and prevention of violence (see http://www.nationalacademies.org/hmd/Global/Topics/Substance-Abuse-Mental-Health.aspx ). The exciting aspect of this resource is that it addresses many topics that are current concerns because they are hoping to help inform emerging policy. The caution to consider with this resource is the evidence is often still emerging, as well.

Numerous “think tank” organizations exist, each with a specific mission. For example, the Rand Corporation is a nonprofit organization offering research and analysis to address global issues since 1948. The institution’s mission is to help improve policy and decision making “to help individuals, families, and communities throughout the world be safer and more secure, healthier and more prosperous,” addressing issues of energy, education, health care, justice, the environment, international affairs, and national security (https://www.rand.org/about/history.html). And, for example, the Robert Woods Johnson Foundation is a philanthropic organization supporting research and research dissemination concerning health issues facing the United States. The foundation works to build a culture of health across systems of care (not only medical care) and communities (https://www.rwjf.org).

While many of these have a great deal of helpful evidence to share, they also may have a strong political bias. Objectivity is often lacking in what information these organizations provide: they provide evidence to support certain points of view. That is their purpose—to provide ideas on specific problems, many of which have a political component. Think tanks “are constantly researching solutions to a variety of the world’s problems, and arguing, advocating, and lobbying for policy changes at local, state, and federal levels” (quoted from https://thebestschools.org/features/most-influential-think-tanks/ ). Helpful information about what this one source identified as the 50 most influential U.S. think tanks includes identifying each think tank’s political orientation. For example, The Heritage Foundation is identified as conservative, whereas Human Rights Watch is identified as liberal.

While not the same as think tanks, many mission-driven organizations also sponsor or report on research, as well. For example, the National Association for Children of Alcoholics (NACOA) in the United States is a registered nonprofit organization. Its mission, along with other partnering organizations, private-sector groups, and federal agencies, is to promote policy and program development in research, prevention and treatment to provide information to, for, and about children of alcoholics (of all ages). Based on this mission, the organization supports knowledge development and information gathering on the topic and disseminates information that serves the needs of this population. While this is a worthwhile mission, there is no guarantee that the information meets the criteria for evidence with which we have been working. Evidence reported by think tank and mission-driven sources must be utilized with a great deal of caution and critical analysis!

In many instances an empirical report has not appeared in the published literature, but in the form of a technical or final report to the agency or program providing the funding for the research that was conducted. One such example is presented by a team of investigators funded by the National Institute of Justice to evaluate a program for training professionals to collect strong forensic evidence in instances of sexual assault (Patterson, Resko, Pierce-Weeks, & Campbell, 2014): https://www.ncjrs.gov/pdffiles1/nij/grants/247081.pdf . Investigators may serve in the capacity of consultant to agencies, programs, or institutions, and provide empirical evidence to inform activities and planning. One such example is presented by Maguire-Jack (2014) as a report to a state’s child maltreatment prevention board: https://preventionboard.wi.gov/Documents/InvestmentInPreventionPrograming_Final.pdf .

When Direct Answers to Questions Cannot Be Found. Sometimes social workers are interested in finding answers to complex questions or questions related to an emerging, not-yet-understood topic. This does not mean giving up on empirical literature. Instead, it requires a bit of creativity in approaching the literature. A Venn diagram might help explain this process. Consider a scenario where a social worker wishes to locate literature to answer a question concerning issues of intersectionality. Intersectionality is a social justice term applied to situations where multiple categorizations or classifications come together to create overlapping, interconnected, or multiplied disadvantage. For example, women with a substance use disorder and who have been incarcerated face a triple threat in terms of successful treatment for a substance use disorder: intersectionality exists between being a woman, having a substance use disorder, and having been in jail or prison. After searching the literature, little or no empirical evidence might have been located on this specific triple-threat topic. Instead, the social worker will need to seek literature on each of the threats individually, and possibly will find literature on pairs of topics (see Figure 3-1). There exists some literature about women’s outcomes for treatment of a substance use disorder (a), some literature about women during and following incarceration (b), and some literature about substance use disorders and incarceration (c). Despite not having a direct line on the center of the intersecting spheres of literature (d), the social worker can develop at least a partial picture based on the overlapping literatures.

Figure 3-1. Venn diagram of intersecting literature sets.

Take a moment to complete the following activity. For each statement about empirical literature, decide if it is true or false.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is a Theoretical Framework? How to Write It (with Examples) 

Theoretical framework 1,2 is the structure that supports and describes a theory. A theory is a set of interrelated concepts and definitions that present a systematic view of phenomena by describing the relationship among the variables for explaining these phenomena. A theory is developed after a long research process and explains the existence of a research problem in a study. A theoretical framework guides the research process like a roadmap for the research study and helps researchers clearly interpret their findings by providing a structure for organizing data and developing conclusions.   

A theoretical framework in research is an important part of a manuscript and should be presented in the first section. It shows an understanding of the theories and concepts relevant to the research and helps limit the scope of the research.  

Table of Contents

What is a theoretical framework ?  

A theoretical framework in research can be defined as a set of concepts, theories, ideas, and assumptions that help you understand a specific phenomenon or problem. It can be considered a blueprint that is borrowed by researchers to develop their own research inquiry. A theoretical framework in research helps researchers design and conduct their research and analyze and interpret their findings. It explains the relationship between variables, identifies gaps in existing knowledge, and guides the development of research questions, hypotheses, and methodologies to address that gap.  

Now that you know the answer to ‘ What is a theoretical framework? ’, check the following table that lists the different types of theoretical frameworks in research: 3

Developing a theoretical framework in research can help in the following situations: 4

• When conducting research on complex phenomena because a theoretical framework helps organize the research questions, hypotheses, and findings  
• When the research problem requires a deeper understanding of the underlying concepts  
• When conducting research that seeks to address a specific gap in knowledge  
• When conducting research that involves the analysis of existing theories  

Summarizing existing literature for theoretical frameworks is easy. Get our Research Ideation pack  

Importance of a theoretical framework  

The purpose of theoretical framework s is to support you in the following ways during the research process: 2  

• Provide a structure for the complete research process  
• Assist researchers in incorporating formal theories into their study as a guide  
• Provide a broad guideline to maintain the research focus  
• Guide the selection of research methods, data collection, and data analysis  
• Help understand the relationships between different concepts and develop hypotheses and research questions  
• Address gaps in existing literature  
• Analyze the data collected and draw meaningful conclusions and make the findings more generalizable  

Theoretical vs. Conceptual framework  

While a theoretical framework covers the theoretical aspect of your study, that is, the various theories that can guide your research, a conceptual framework defines the variables for your study and presents how they relate to each other. The conceptual framework is developed before collecting the data. However, both frameworks help in understanding the research problem and guide the development, collection, and analysis of the research.  

The following table lists some differences between conceptual and theoretical frameworks . 5

How to write a theoretical framework  

The following general steps can help those wondering how to write a theoretical framework: 2

• Identify and define the key concepts clearly and organize them into a suitable structure.  
• Use appropriate terminology and define all key terms to ensure consistency.  
• Identify the relationships between concepts and provide a logical and coherent structure.  
• Develop hypotheses that can be tested through data collection and analysis.  
• Keep it concise and focused with clear and specific aims.  

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Examples of a theoretical framework  

Here are two examples of a theoretical framework. 6,7

Example 1 .   

An insurance company is facing a challenge cross-selling its products. The sales department indicates that most customers have just one policy, although the company offers over 10 unique policies. The company would want its customers to purchase more than one policy since most customers are purchasing policies from other companies.  

Objective : To sell more insurance products to existing customers.  

Problem : Many customers are purchasing additional policies from other companies.  

Research question : How can customer product awareness be improved to increase cross-selling of insurance products?  

Sub-questions: What is the relationship between product awareness and sales? Which factors determine product awareness?  

Since “product awareness” is the main focus in this study, the theoretical framework should analyze this concept and study previous literature on this subject and propose theories that discuss the relationship between product awareness and its improvement in sales of other products.  

Example 2 .

A company is facing a continued decline in its sales and profitability. The main reason for the decline in the profitability is poor services, which have resulted in a high level of dissatisfaction among customers and consequently a decline in customer loyalty. The management is planning to concentrate on clients’ satisfaction and customer loyalty.  

Objective: To provide better service to customers and increase customer loyalty and satisfaction.  

Problem: Continued decrease in sales and profitability.  

Research question: How can customer satisfaction help in increasing sales and profitability?  

Sub-questions: What is the relationship between customer loyalty and sales? Which factors influence the level of satisfaction gained by customers?  

Since customer satisfaction, loyalty, profitability, and sales are the important topics in this example, the theoretical framework should focus on these concepts.  

Benefits of a theoretical framework  

There are several benefits of a theoretical framework in research: 2  

• Provides a structured approach allowing researchers to organize their thoughts in a coherent way.  
• Helps to identify gaps in knowledge highlighting areas where further research is needed.  
• Increases research efficiency by providing a clear direction for research and focusing efforts on relevant data.  
• Improves the quality of research by providing a rigorous and systematic approach to research, which can increase the likelihood of producing valid and reliable results.  
• Provides a basis for comparison by providing a common language and conceptual framework for researchers to compare their findings with other research in the field, facilitating the exchange of ideas and the development of new knowledge.  

Frequently Asked Questions 

Q1. How do I develop a theoretical framework ? 7

A1. The following steps can be used for developing a theoretical framework :  

• Identify the research problem and research questions by clearly defining the problem that the research aims to address and identifying the specific questions that the research aims to answer.
• Review the existing literature to identify the key concepts that have been studied previously. These concepts should be clearly defined and organized into a structure.
• Develop propositions that describe the relationships between the concepts. These propositions should be based on the existing literature and should be testable.
• Develop hypotheses that can be tested through data collection and analysis.
• Test the theoretical framework through data collection and analysis to determine whether the framework is valid and reliable.

Q2. How do I know if I have developed a good theoretical framework or not? 8

A2. The following checklist could help you answer this question:  

• Is my theoretical framework clearly seen as emerging from my literature review?  
• Is it the result of my analysis of the main theories previously studied in my same research field?  
• Does it represent or is it relevant to the most current state of theoretical knowledge on my topic?  
• Does the theoretical framework in research present a logical, coherent, and analytical structure that will support my data analysis?  
• Do the different parts of the theory help analyze the relationships among the variables in my research?  
• Does the theoretical framework target how I will answer my research questions or test the hypotheses?  
• Have I documented every source I have used in developing this theoretical framework ?  
• Is my theoretical framework a model, a table, a figure, or a description?  
• Have I explained why this is the appropriate theoretical framework for my data analysis?  

Q3. Can I use multiple theoretical frameworks in a single study?  

A3. Using multiple theoretical frameworks in a single study is acceptable as long as each theory is clearly defined and related to the study. Each theory should also be discussed individually. This approach may, however, be tedious and effort intensive. Therefore, multiple theoretical frameworks should be used only if absolutely necessary for the study.  

Q4. Is it necessary to include a theoretical framework in every research study?  

A4. The theoretical framework connects researchers to existing knowledge. So, including a theoretical framework would help researchers get a clear idea about the research process and help structure their study effectively by clearly defining an objective, a research problem, and a research question.  

Q5. Can a theoretical framework be developed for qualitative research?  

A5. Yes, a theoretical framework can be developed for qualitative research. However, qualitative research methods may or may not involve a theory developed beforehand. In these studies, a theoretical framework can guide the study and help develop a theory during the data analysis phase. This resulting framework uses inductive reasoning. The outcome of this inductive approach can be referred to as an emergent theoretical framework . This method helps researchers develop a theory inductively, which explains a phenomenon without a guiding framework at the outset.  

Q6. What is the main difference between a literature review and a theoretical framework ?  

A6. A literature review explores already existing studies about a specific topic in order to highlight a gap, which becomes the focus of the current research study. A theoretical framework can be considered the next step in the process, in which the researcher plans a specific conceptual and analytical approach to address the identified gap in the research.  

Theoretical frameworks are thus important components of the research process and researchers should therefore devote ample amount of time to develop a solid theoretical framework so that it can effectively guide their research in a suitable direction. We hope this article has provided a good insight into the concept of theoretical frameworks in research and their benefits.  

References  

• Organizing academic research papers: Theoretical framework. Sacred Heart University library. Accessed August 4, 2023. https://library.sacredheart.edu/c.php?g=29803&p=185919#:~:text=The%20theoretical%20framework%20is%20the,research%20problem%20under%20study%20exists .  
• Salomao A. Understanding what is theoretical framework. Mind the Graph website. Accessed August 5, 2023. https://mindthegraph.com/blog/what-is-theoretical-framework/  
• Theoretical framework—Types, examples, and writing guide. Research Method website. Accessed August 6, 2023. https://researchmethod.net/theoretical-framework/  
• Grant C., Osanloo A. Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your “house.” Administrative Issues Journal : Connecting Education, Practice, and Research; 4(2):12-26. 2014. Accessed August 7, 2023. https://files.eric.ed.gov/fulltext/EJ1058505.pdf  
• Difference between conceptual framework and theoretical framework. MIM Learnovate website. Accessed August 7, 2023. https://mimlearnovate.com/difference-between-conceptual-framework-and-theoretical-framework/  
• Example of a theoretical framework—Thesis & dissertation. BacherlorPrint website. Accessed August 6, 2023. https://www.bachelorprint.com/dissertation/example-of-a-theoretical-framework/  
• Sample theoretical framework in dissertation and thesis—Overview and example. Students assignment help website. Accessed August 6, 2023. https://www.studentsassignmenthelp.co.uk/blogs/sample-dissertation-theoretical-framework/#Example_of_the_theoretical_framework  
• Kivunja C. Distinguishing between theory, theoretical framework, and conceptual framework: A systematic review of lessons from the field. Accessed August 8, 2023. https://files.eric.ed.gov/fulltext/EJ1198682.pdf  

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Theoretical vs Conceptual Framework

What they are & how they’re different (with examples)

By: Derek Jansen (MBA) | Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, sooner or later you’re bound to run into the terms theoretical framework and conceptual framework . These are closely related but distinctly different things (despite some people using them interchangeably) and it’s important to understand what each means. In this post, we’ll unpack both theoretical and conceptual frameworks in plain language along with practical examples , so that you can approach your research with confidence.

Overview: Theoretical vs Conceptual

What is a theoretical framework, example of a theoretical framework, what is a conceptual framework, example of a conceptual framework.

• Theoretical vs conceptual: which one should I use?

A theoretical framework (also sometimes referred to as a foundation of theory) is essentially a set of concepts, definitions, and propositions that together form a structured, comprehensive view of a specific phenomenon.

In other words, a theoretical framework is a collection of existing theories, models and frameworks that provides a foundation of core knowledge – a “lay of the land”, so to speak, from which you can build a research study. For this reason, it’s usually presented fairly early within the literature review section of a dissertation, thesis or research paper .

Let’s look at an example to make the theoretical framework a little more tangible.

If your research aims involve understanding what factors contributed toward people trusting investment brokers, you’d need to first lay down some theory so that it’s crystal clear what exactly you mean by this. For example, you would need to define what you mean by “trust”, as there are many potential definitions of this concept. The same would be true for any other constructs or variables of interest.

You’d also need to identify what existing theories have to say in relation to your research aim. In this case, you could discuss some of the key literature in relation to organisational trust. A quick search on Google Scholar using some well-considered keywords generally provides a good starting point.

Typically, you’ll present your theoretical framework in written form , although sometimes it will make sense to utilise some visuals to show how different theories relate to each other. Your theoretical framework may revolve around just one major theory , or it could comprise a collection of different interrelated theories and models. In some cases, there will be a lot to cover and in some cases, not. Regardless of size, the theoretical framework is a critical ingredient in any study.

Simply put, the theoretical framework is the core foundation of theory that you’ll build your research upon. As we’ve mentioned many times on the blog, good research is developed by standing on the shoulders of giants . It’s extremely unlikely that your research topic will be completely novel and that there’ll be absolutely no existing theory that relates to it. If that’s the case, the most likely explanation is that you just haven’t reviewed enough literature yet! So, make sure that you take the time to review and digest the seminal sources.

Need a helping hand?

A conceptual framework is typically a visual representation (although it can also be written out) of the expected relationships and connections between various concepts, constructs or variables. In other words, a conceptual framework visualises how the researcher views and organises the various concepts and variables within their study. This is typically based on aspects drawn from the theoretical framework, so there is a relationship between the two.

Quite commonly, conceptual frameworks are used to visualise the potential causal relationships and pathways that the researcher expects to find, based on their understanding of both the theoretical literature and the existing empirical research . Therefore, the conceptual framework is often used to develop research questions and hypotheses .

Let’s look at an example of a conceptual framework to make it a little more tangible. You’ll notice that in this specific conceptual framework, the hypotheses are integrated into the visual, helping to connect the rest of the document to the framework.

As you can see, conceptual frameworks often make use of different shapes , lines and arrows to visualise the connections and relationships between different components and/or variables. Ultimately, the conceptual framework provides an opportunity for you to make explicit your understanding of how everything is connected . So, be sure to make use of all the visual aids you can – clean design, well-considered colours and concise text are your friends.

Theoretical framework vs conceptual framework

As you can see, the theoretical framework and the conceptual framework are closely related concepts, but they differ in terms of focus and purpose. The theoretical framework is used to lay down a foundation of theory on which your study will be built, whereas the conceptual framework visualises what you anticipate the relationships between concepts, constructs and variables may be, based on your understanding of the existing literature and the specific context and focus of your research. In other words, they’re different tools for different jobs , but they’re neighbours in the toolbox.

Naturally, the theoretical framework and the conceptual framework are not mutually exclusive . In fact, it’s quite likely that you’ll include both in your dissertation or thesis, especially if your research aims involve investigating relationships between variables. Of course, every research project is different and universities differ in terms of their expectations for dissertations and theses, so it’s always a good idea to have a look at past projects to get a feel for what the norms and expectations are at your specific institution.

Want to learn more about research terminology, methods and techniques? Be sure to check out the rest of the Grad Coach blog . Alternatively, if you’re looking for hands-on help, have a look at our private coaching service , where we hold your hand through the research process, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

23 Comments

Thank you for giving a valuable lesson

good thanks!

VERY INSIGHTFUL

thanks for given very interested understand about both theoritical and conceptual framework

I am researching teacher beliefs about inclusive education but not using a theoretical framework just conceptual frame using teacher beliefs, inclusive education and inclusive practices as my concepts

good, fantastic

great! thanks for the clarification. I am planning to use both for my implementation evaluation of EmONC service at primary health care facility level. its theoretical foundation rooted from the principles of implementation science.

This is a good one…now have a better understanding of Theoretical and Conceptual frameworks. Highly grateful

Very educating and fantastic,good to be part of you guys,I appreciate your enlightened concern.

Thanks for shedding light on these two t opics. Much clearer in my head now.

Simple and clear!

The differences between the two topics was well explained, thank you very much!

Thank you great insight

Superb. Thank you so much.

Hello Gradcoach! I’m excited with your fantastic educational videos which mainly focused on all over research process. I’m a student, I kindly ask and need your support. So, if it’s possible please send me the PDF format of all topic provided here, I put my email below, thank you!

I am really grateful I found this website. This is very helpful for an MPA student like myself.

I’m clear with these two terminologies now. Useful information. I appreciate it. Thank you

I’m well inform about these two concepts in research. Thanks

I found this really helpful. It is well explained. Thank you.

very clear and useful. information important at start of research!!

Wow, great information, clear and concise review of the differences between theoretical and conceptual frameworks. Thank you! keep up the good work.

thank you so much. Educative and realistic.

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• Knowledge Base
• Methodology
• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualize your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, “hours of study,” is the independent variable (the predictor, or explanatory variable)
• The expected effect, “exam score,” is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (“hours of study”).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the “effect” component of the cause-and-effect relationship.

Let’s add the moderator “IQ.” Here, a student’s IQ level can change the effect that the variable “hours of study” has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our “IQ” moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the “IQ” moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs. mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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• Decide on a topic
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• Analyze the works of literature separately.
• Summarize the literature in table or concept map format.
• Synthesize the literature and then proceed to write your empirical review.

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Proposing Empirical Research

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• Provides step-by-step instructions for students who will be writing their first research proposal in the social and behavioral sciences.

• Each major section is divided into short topics. For each topic, students complete an exercise that leads them toward the goal of preparing a proposal.

• Numerous examples throughout the book make the recommendations for proposal writing come alive.

• The model proposals at the end of the book illustrate proposal writing and provide material for classroom discussions.

• Provides concrete guidance for students who will be writing proposals for both quantitative and qualitative research.

• The structure of this book enables students to work independently with confidence while writing the first drafts of their proposals.

• All steps in proposal writing are clearly described and illustrated.

• Ideal for use in:

· research methods classes where students write a proposal as a term project,

· thesis/dissertation preparation classes,

· senior research seminars where proposing and conducting research is a culminating undergraduate activity, and

· any graduate-level seminar in which the instructor wants to incorporate a project that will engage students in critical thinking about the content area.

• Written by our best-selling author, Mildred L. Patten. Your students will appreciate her clear and informative style.

TABLE OF CONTENTS

Part | 2  pages, part a: getting started, chapter 1 | 2  pages, what is empirical research, chapter 2 | 2  pages, identifying broad problem areas, chapter 3 | 2  pages, evaluating broad problem areas, chapter 4 | 2  pages, identifying and combining variables, chapter 5 | 2  pages, identifying treatment variables, chapter 6 | 2  pages, considering demographic variables, chapter 7 | 2  pages, writing purposes and hypotheses, part b: a closer look at problem selection, chapter 8 | 2  pages, finding ideas in the literature, chapter 9 | 2  pages, considering a body of literature, chapter 10 | 2  pages, considering theories, chapter 11 | 2  pages, determining feasibility, part c: selecting a research approach, chapter 12 | 2  pages, qualitative research, chapter 13 | 2  pages, survey research, chapter 14 | 2  pages, correlational research, chapter 15 | 2  pages, test development research, chapter 16 | 2  pages, experimental research, chapter 17 | 2  pages, causal-comparative research, chapter 18 | 2  pages, program evaluation, part d: organizing and evaluating literature, chapter 19 | 2  pages, organizing literature by topics, chapter 20 | 2  pages, evaluating research literature, chapter 21 | 2  pages, considering the history of a topic, part e: writing the introduction and literature review, chapter 22 | 2  pages, a separate introduction, chapter 23 | 2  pages, an integrated introduction and literature review, chapter 24 | 2  pages, writing the first paragraph(s), chapter 25 | 2  pages, using a topic outline, chapter 26 | 2  pages, being selective and critical, part f: proposing a sample, chapter 27 | 2  pages, sampling in qualitative research, chapter 28 | 2  pages, random sampling, chapter 29 | 2  pages, other methods of sampling: i, chapter 30 | 2  pages, other methods of sampling: ii, chapter 31 | 2  pages, sample size, part g: proposing instrumentation, chapter 32 | 2  pages, qualitative instrumentation, chapter 33 | 2  pages, proposing published instruments, chapter 34 | 2  pages, proposing new instruments, chapter 35 | 2  pages, proposing to measure demographics, chapter 36 | 2  pages, ethical issues in measurement, part h: proposing procedures, chapter 37 | 2  pages, nonexperimental procedures, chapter 38 | 2  pages, procedures in experiments, chapter 39 | 2  pages, ethical issues and procedures, part i: proposing methods of analysis, chapter 40 | 2  pages, qualitative analysis, chapter 41 | 2  pages, analysis of demographics, chapter 42 | 2  pages, relationships: nominal, chapter 43 | 2  pages, relationships: equal interval, chapter 44 | 2  pages, group differences, part j: concluding tasks, chapter 45 | 2  pages, writing a discussion section, chapter 46 | 2  pages, giving the proposal a title, chapter 47 | 2  pages, preparing an abstract, chapter 48 | 2  pages, developing a timeline, chapter 49 | 2  pages, preparing a reference list.

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Network Competition in the Airline Industry: An Empirical Framework

The Hub-and-Spoke network is a defining feature of the airline industry. This paper is among the first in the literature to introduce an empirical framework for analyzing network competition among airlines. Airlines make market entry decisions and choose flight frequencies in the first stage, followed by price competition to attract passengers in the second stage. A key feature of this model is the linkage between direct and indirect flights, which is described by a technological relationship (and estimated using data) that proxies the Hub-and-Spoke network. The paper estimates the marginal costs of serving passengers and operating flights using first-order conditions, bounds the entry costs using inequalities derived from the reveal-preference argument, and employs a state-of-the-art econometric method to conduct inference for entry cost parameters. Ignoring network externality underestimates the benefits of operating an additional flight by 13.2%, and airlines would schedule 21.53% fewer one-stop flights had they made flight operation decisions independently for each market. To evaluate the impact of a hypothetical merger, the paper proposes a novel equilibrium concept that makes it feasible to compute the industry equilibria. Counterfactual analyses indicate that a hypothetical merger between Alaska and Virgin America would increase consumer surplus as the merged airline would offer direct flights in 10% more markets while the overall post-merger price effect would likely be muted.

We thank the editor and anonymous referees whose comments significantly improved this paper. We are grateful to Victor Aguirregabiria, Heski Bar-Isaac, Dwayne Benjamin, Loren Brandt, Yanyou Chen, Andrew Ching, Rahul Deb, Mara Lederman, Yao Luo, Ismael Mourifi, Rob Porter, James Roberts, Marc Rysman, Xianwen Shi, Xiaoxia Shi, Eduardo Souza-Rodrigues, Junichi Suzuki, Yuanyuan Wan, Miaojun Wang, Mo Xiao, Daniel Xu, and seminar participants at Albany, Renmin University, Ryerson, Saskatchewan, Toronto, Western, Zhejiang University, CEA Annual Conference 2016, IIOC Annual Conference 2016, SHUFE IO Conference, EARIE Annual Conference 2016, and China Meeting for Econometric Society 2017 for helpful comments and discussions. We thank Siyu Chen and Jianjuan Chen for their computational support. All errors are our own. Zhe wants to acknowledge financial support from NSFC [Grants 72203202, 72192803 and 72141305]. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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• Published: 02 September 2024

Ecological care in nursing practice: a Walker and Avant concept analysis

• Golshan Moghbeli 1 ,
• Amin Soheili 2 ,
• Mansour Ghafourifard 1 , 3 ,
• Shahla Shahbazi 1 &
• Hanieh Aziz Karkan 1  

BMC Nursing volume  23 , Article number:  614 ( 2024 ) Cite this article

Metrics details

Today, the human population faces an increasing array of emerging environmental challenges. Despite its importance, nurses often neglect ecological issues, which can compromise patient health. While the ecological nursing perspective has the potential to lead to innovative care approaches that benefit patients, the nursing profession, and the environment, the concept of ecological care lacks a clear definition and its dimensions remain unclear. This study aimed to analyze and clarify the concept of ‘ecological care’ in the nursing discipline.

Walker and Avant’s analysis method was used to identify descriptions, antecedents, consequences, and empirical referents of the concept of ‘ecological care’ in nursing. We searched the databases (PubMed, Scopus, PsycINFO, CINAHL, ERIC, SID, and IranDoc) using the keywords “ecological,” “nurse,” and “nursing” using Boolean operators “AND” and “OR” in the title and abstract fields both in English and Persian to identify relevant literature on ecological care in nursing.

Ecological care, as a multidimensional concept, encompasses ecological thinking, ecological attitude, ecological awareness, ecological sensitivity, and ecological literacy. This entails the optimal utilization of environmental factors to provide patients with high-quality care and preserve ecological sustainability through environmentally friendly behaviors.

Conclusions

The findings highlight the need to elucidate, endorse, and solidify ecological thinking in all aspects of nursing care including nursing management, education, and research, which can lead to improved care quality, patient safety, and sustainability. Within this framework, nursing educators could play an essential role in integrating ecological care into nursing education. The study emphasizes the need to integrate ecological thinking into all aspects of nursing.

Peer Review reports

Ecology, the study of interactions between living organisms and their environments, encompasses physical and social surroundings that impact all living beings. From a human science perspective, ecology emphasizes these interconnected relationships, fostering a deeper understanding of nursing and caring practices [ 1 ]. Currently, environmental concerns are considered significant threats to public health. However, healthcare professionals often lack sufficient awareness of the importance of ecological issues [ 2 ].

As the largest group of healthcare professionals, nurses play a crucial role in decisions regarding product use, energy consumption, and chemical selection in healthcare settings. However, they face a significant challenge: balancing environmental concerns and ecological principles with their professional duties [ 3 ]. Although nurses can advocate reducing exposure to harmful chemicals and adopting less toxic products, their work environments often require high energy consumption and generate substantial medical waste [ 4 ]. This medical waste encompasses both hazardous (infectious, pathological, chemical, pharmaceutical, cytotoxic, and radioactive) and non-hazardous or general waste, posing potential risks to patients, communities, and broader ecological health [ 5 ]. Multiple studies have highlighted the critical role of ecological considerations within healthcare in the overall health of ecosystems [ 6 , 7 , 8 , 9 ]. Consequently, ecological issues have become a high priority for nurses, demanding attention and action [ 10 ].

The importance of environment, ecosystems, and ecology in nursing practice has been recognized by pioneers like Florence Nightingale as the founder of modern nursing (published in 1992, originally written in 1959) [ 11 ] and subsequently by Fawcett (1984) [ 12 ]. This vision is further reflected in the International Council of Nurses (ICN) Code of Ethics, which states that “nurses contribute to the population’s health and work to achieve the sustainable development goals.” By adopting sustainable practices, nurses can significantly reduce their environmental footprint and contribute to achieving the UN 2030 Agenda for Sustainable Development [ 9 ]. Recognizing this crucial role, nursing organizations such as the American Nurses Association actively promote nurses’ participation in environmental protection initiatives [ 13 ].

The concept of ecological care in nursing, as a multidimensional concept, encompasses several aspects. Lausten (2006) proposed a nursing ecological theory to broaden nurses’ perspectives by incorporating concepts of global ecosystems, communities, and interrelationships from the ecological sciences. This theory recognizes that human interactions with the environment extend beyond the personal sphere and encompass professional activities. Consequently, nurses can integrate ecological principles into their practice, fostering new directions in care that benefit patients, healthcare professionals, and the environment [ 14 ]. Dahlberg et al. (2016) conducted an empirical study to explore how a phenomenological life-world theory could expand the concept of holistic care into “ecological care.” They argued that the traditional approach to holistic care has neglected environmental and ecological dimensions. Their findings suggested that ecological care goes beyond fighting illnesses. It emphasizes understanding patients within the context of their world, a world that they both influence and are influenced by. This approach helps patients reintegrate into their rhythm of existence [ 1 ].

Al-Shamaly (2021) highlights “ecological awareness,” which emphasizes creating a safe and comfortable patient environment through noise, light, color, and temperature control [ 15 ]. Sattler (2013) adds another dimension, suggesting that nurses can act as catalysts for transforming hospitals into environmentally sustainable spaces. This can be achieved through practices such as adopting environmentally friendly purchasing policies (e.g., waste management strategies, reduced chemical use, and proper disposal of hazardous materials such as batteries), promoting healthy food options, and favoring mercury-free products [ 16 ].

Although ecological factors could influence the quality of care, patient safety, individual and community health, resource preservation, and sustainable practices [ 16 , 17 , 18 , 19 ], nurses’ awareness of ecological care and its dimensions remains limited [ 2 ]. Moreover, there is no universally accepted definition of ecological care as a complex concept [ 20 ]. Therefore, this study aimed to analyze and clarify the concept of ‘ecological care’ within the nursing discipline.

Walker and Avant’s concept analysis method was used as a rigorous and systematic approach to identify descriptions, antecedents, consequences, and empirical referents of the concept of ‘ecological care’ in nursing. Ecological care is a widely applicable concept that extends beyond the confines of nursing care. Therefore, the literature review encompasses all the various applications of ecological care, including both implicit and explicit aspects. The stages of the concept analysis method are as follows: (A) selecting a concept, (B) determining the aims or purposes of the analysis, (C) identifying all uses of the concept that you can discover, (D) determining the defining attributes, (E) identifying a model case, (F) identifying borderline, related, contrary, invented, and illegitimate cases, (G) identifying antecedents and consequences, and (H) defining empirical referents [ 21 ].

Literature search

A systematic literature review was conducted using multiple health databases, including PubMed, Scopus, PsycINFO, CINAHL, ERIC, SID, and IranDoc. The concepts “ecological,” “nurse” and “nursing” were searched using Boolean operators “AND” and “OR” in the title and abstract fields of each database. No temporal limits were applied and articles published in either English or Persian until July 2023 were retrieved.

Initially, 1083 records were identified by searching the titles and abstracts of these databases. Subsequently, 16 additional records were manually included, resulting in a total of 1099 records. Duplicate records were removed, leading to an initial selection of 1068 records. The titles and abstracts of these records were screened, and the eligibility criteria were applied to the full text of the selected records. Eventually, 36 records met the criteria and underwent a comprehensive review of concept analysis (Fig.  1 ). A detailed overview of the included studies, including publication year, title, country, and key findings, can be found in Appendix A.

Flow diagram of the study (data search and selection process)

Concept selection

The importance of a specific concept is influenced by a variety of factors both within and outside its field over time. Consequently, concepts lacking clear definitions warrant further analysis [ 21 ]. Considering the interconnectedness of ecosystems and human health, as well as the imperative to maintain environmental sustainability, particularly within healthcare, the concept of ecology has gained prominence in nursing and other health professions. Nightingale’s emphasis on the environment underscores this importance. Given the increasing significance of ecological care in healthcare and the lack of a clear, unified definition, this concept was selected for analysis to elucidate its dimensions and characteristics.

Determining the aims of the analysis

The concept of “ecological care” has been insufficiently analyzed within the healthcare context, resulting in a lack of a clear definition. This study aims to refine the meaning of ecological care in nursing by identifying its descriptions, antecedents, consequences, and empirical referents.

Identifying the use of the concept

To explore the concept of ecological care, it is crucial to understand the distinct meanings of each word from a variety of sources such as dictionaries, thesauruses, websites, and scholarly literature.

According to the Merriam-Webster dictionary, the term ‘ecological’ is an adjective related to the science of ecology. This refers to the environment of living things or the relationships between living things and their environments [ 22 ].

According to the Merriam-Webster dictionary, the term ‘care’ functions both as a noun, representing responsibility for or attention to health, well-being, safety, or solicitude, and as a verb, meaning to feel interest or concern and to provide care [ 23 ].

Ecological care in nursing literature

The concept of ecological care, originating from the theory of biological ecology, aims to offer solutions that effectively minimize the adverse impacts of nursing care on the ecosystem [ 14 ]. Ecological care can be classified into two types: individuals and professionals. The individual approach focuses on raising public awareness, shaping attitudes and behaviors, and promoting responsible actions regarding energy consumption, the production of toxic substances (such as greenhouse gases), chemical usage, and healthy and organic diet adoption. Conversely, the professional approach emphasizes the importance of sensitivity, awareness, attitude, behavior, and responsible actions among individuals when carrying out their professional responsibilities [ 9 , 24 ].

Clinical environments require ecological care, which can be achieved through two distinct approaches: environmental and organizational care. Environmental care involves maintaining equipment and machines, ensuring workplace safety, minimizing risks, managing noise levels, optimizing lighting conditions, regulating temperature, and employing creative designs to create a comfortable and relaxing environment. It also involves facilitating visits from family members and pets and improving patients’ sleep quality. Additionally, the use of digital technology helps ensure a healthy and safe treatment environment for patients in the Intensive Care Units (ICU). On the other hand, organizational care focuses on time and resource management. This includes strategies such as reducing paper and ink consumption by utilizing electronic records, which aids in efficient time management. Organizational care aims to streamline nurses’ tasks and improve overall work efficiency by minimizing their workload and improving access to patient information. Finally, waste management practices play a crucial role in maintaining an environmentally conscious approach in healthcare settings [ 15 ].

Determining the defining attributes

Ecological thinking.

According to Balgopal and Wallace (2009), ecological thinking is a combination of ecological understanding and ecological awareness [ 25 ]. Understanding ecology involves understanding concepts such as biotic, abiotic, and biological interactions. This serves as the initial stage of ecological thinking, which is further developed by comprehending the impact of human activities on the ecosystem [ 26 ]. Ecological understanding can be conceptualized as a continuum, with one end representing the capacity to identify problems and propose ecological decisions, considering their potential consequences. On the other end of the continuum is a lack of understanding, where the ability to explain the impact of human actions on the ecosystem is insufficient [ 25 ].

Ecological thinking causes a transformation in people’s presuppositions and attitudes towards the surrounding world, enabling them to recognize that we are interconnected and evolving alongside nature. Embracing an ecological perspective requires acknowledging ourselves as integral components of nature rather than being superior to it. This encompassing concept embodies various underlying principles such as ecology, wholeness, interdependence, diversity, partnership, energy flows, flexibility, cycles, and sustainability [ 17 , 27 ]. Hes and de Plessis (2014) refer to this set of principles as the ‘ecological worldview.’ Shifting towards an ecological perspective entail altering our perspective on the world and ourselves. The fundamental essence of this transformation involves moving away from egocentric and anthropocentric thinking, which emphasizes separateness, and instead adopting a holistic perception that aims to counterbalance environmental damage. Enhancing ecological thinking can be achieved through the instruction of ecological concepts and behaviors [ 28 ].

Ecological attitude

Ecological attitude is a complex construct that encompasses various key components such as emotions, perceptions, personal norms, values, and relationships with the environment. The emotional dimension of ecological attitude plays a pivotal role in preparing individuals to address environmental issues and cultivate ecological behaviors in all aspects of life [ 29 , 30 , 31 ], as it determines the extent to which individuals will act in environmentally responsible ways [ 32 ].

Predicting a specific behavior entails possessing a specific attitude towards that behavior, as attitudes alone do not guarantee behavior, but predict or influence it [ 2 , 33 ]. Ecological behavior can be defined as the actions taken by a nurse to protect the environment, and it varies depending on the individual’s context and circumstances. Achieving the goal of ecological behavior can be challenging in certain situations, but it is crucial to promote sustainable living and preserve the planet’s natural resources [ 31 ].

Ecological awareness

Ecological awareness refers to knowledge, attitudes, and behaviors related to the environment. Its focus is on increasing responsibility toward achieving ecological sustainability [ 34 ]. One of its important characteristics is the perception of natural objects from a subject’s perspective [ 35 ]. As a theoretical and practical science, ecological awareness includes two stages: awareness of environmental changes, and feelings of concern about environmental problems and trying to solve them. People with ecological awareness try to be actively responsible for their interactions with the environment and exhibit positive behaviors towards the surrounding environment [ 9 , 20 ].

Ecological awareness is also a level of cognitive thinking that enables nurses to focus on protecting the environment while providing nursing care. This concept requires nurses to pay attention to the potential of nature and the surrounding environment that promotes, maintains, and restores human health [ 9 , 14 ]. This raises important questions about whether nurses are aware of the positive effects of recycling medical equipment and materials, or the harmful effects of greenhouse gases (CO2, NO, etc.) caused by fossil fuels and smoke from medical waste incinerators. It also highlights how much nurses are aware of the impact of their care activities on ecosystem damage and public health [ 9 , 19 , 36 ]. The role of nurses with ecological awareness is crucial in raising awareness among colleagues, managers, patients, and students [ 8 , 37 , 38 , 39 ].

Ecological sensitivity

Ecological sensitivity refers to the inclination to actively address environmental threats and the extent to which healthcare providers demonstrate awareness of hazardous and protective circumstances [ 40 ]. Individuals with varying psychological traits, such as extroversion or introversion, exhibit distinct levels of sensitivity to environmental health [ 41 ].

Ecological sensitivity is a multidimensional concept that contributes significantly to sustainable development. This serves as an emotional foundation for cultivating an ecological worldview and establishing personal norms for pro-environmental actions. This dynamic framework takes shape within families during childhood and is strengthened throughout professional life. Therefore, an essential initial step in enhancing ecological sensitivity among healthcare providers is to impart ecological education and raise awareness levels [ 42 , 43 , 44 ]. The development of ecological sensitivity is influenced by various factors, including families, educational institutions, mass media, and non-governmental organizations [ 45 , 46 , 47 ]. In general, nurses who actively engage in staying informed about ecological news and trends, participate in ecological protection activities and events, and demonstrate awareness of ecologically detrimental behaviors, both in themselves and their colleagues exhibit higher levels of ecological sensitivity [ 42 , 43 ].

Ecological literacy

Ecological literacy is a crucial concept that includes three core components: cognitive, emotional, and behavioral. According to UNESCO, there are five key characteristics of ecological literacy: awareness and sensitivity to the environment; comprehension of environmental issues; having values and sentiments towards environmental concerns; possessing skills, desire, and commitment; and actively engaging in identifying and resolving ecological problems. Generally, ecological literacy can be defined as the integration of environmental sensitivity, knowledge, skills, attitudes, values, responsibilities, and active engagement, which enables nurses to make informed and responsible decisions to promote environmental sustainability [ 48 , 49 ].

Model and additional cases

A model case serves as a paradigmatic illustration of the application of a concept encompassing all its defining elements. In addition to the model case, two other types of cases are presented: (A) the borderline case, which shares most of the essential characteristics of the concept but exhibits some differences; and (B) the contrary case, which presents an apparent example that contrasts with the concept, highlighting what it is not [ 21 ].

A 65-year-old woman was admitted to the neurology ward with a diagnosis of transient ischemic attack during the night shift. The attending nurse approached the patient’s bedside and introduced herself and the inpatient department. During the evaluation, the nurse observed the patients’ uneasiness, homesickness, and concerns regarding sleep disturbance due to changes in sleeping arrangements. She addressed the situation by repositioning the patient’s bed next to the window, aiming to provide a more comfortable environment and alleviate feelings of homesickness. Careful attention was paid to ensure that the bed and equipment were securely locked. During medication administration, the nurse utilized a tablet for dosage calculations, opting for a paperless approach to reduce waste. Proper disposal procedures were followed after medication administration, with empty vials discarded in the chemical waste bin, and needles placed in a safety box. During the initiation of infusion, the nurse noticed loose screws on the electronic infusion device and promptly sought assistance from a colleague to rectify the issue. Toward the end of her tasks, the nurse dimmed unnecessary lights in the ward and adjusted the alarm range of the device to an audible level for more comfort. Immediately before leaving the ward, the nurse noticed a leaking water tap and promptly contacted the facility manager to initiate immediate remedial action.

Borderline case

The head nurse of the pediatric ward conducted a clinical round when she heard the cries of a hospitalized 4-year-old child who was upset due to the absence of her cherished doll. Regrettably, the nurses disregarded the situation and continued down the corridor. Several months later, the nurse was invited to join a committee responsible for making decisions regarding hospital equipment procurement. Drawing from the recent knowledge acquired through a TV program highlighting the hazards of mercury to human health, she recommended the acquisition of mercury-free medical equipment.

Contrary case

A nurse, aged 35, with ten years of experience in surgery, approached the patient who had undergone laparotomy to perform a dressing change. The nurse inadvertently wore a pair of sterile gloves instead of non-sterile gloves while removing the contaminated dressing and disposed of it in the general waste bin. Subsequently, sterile gloves were replaced with a fresh pair, the wound was cleansed using six sterile gauzes, and an additional seven gauzes were applied to dress the surgical site, although a smaller quantity would have sufficed. During the hand washing process, the nurse’s mobile phone rang, and without turning off the water tap, he engaged in a conversation until the patient’s family intervened and turned off the tap. Finally, despite the patient expressing mild pain at the surgical site, the nurse chose to administer a painkiller instead of utilizing non-pharmacological methods to alleviate pain.

Identify antecedents and consequences

Walker and Avant (2011) provided a clear definition of antecedents as events or attributes that precede the occurrence of a concept, whereas consequences refer to events that ensue from the concept’s occurrence [ 21 ]. In this study, it was crucial to identify and examine the associated antecedents and consequences (Fig.  2 ). Therefore, the antecedents and consequences investigated are as follows:

Attributes, antecedents, and consequences of ecological caring in nursing practice

Antecedents

The ecological care provided by nurses can be influenced by both personal characteristics and organizational policies. Personal characteristics include creativity, innovation, responsibility, environmental friendliness [ 41 ], kindness, empathy, and strong communication skills [ 9 ]. Meanwhile, organizational policies encompass the establishment of a supportive organizational culture, provision of training courses [ 14 ], and design of a creative and humanitarian environment within hospitals and healthcare facilities. Moreover, ensuring a safe environment equipped with adequate resources, services, technology, and competent human resources is essential for delivering ecological care in therapeutic settings [ 15 ].

Consequences

Ecological care yields numerous benefits to patients, their families, healthcare providers, healthcare systems, and the environment. Among these benefits, one of the most significant is the provision of high-quality holistic care, which leads to increased patient satisfaction. Additionally, ecological care contributes to patient and staff safety by minimizing hospital infections, conserving energy (electricity, gases, and water), optimizing equipment and time utilization, reducing employee workload, managing hospital procurement costs, and eliminating hospital waste. It also plays a vital role in preventing the entry of pathogens, chemical pollutants, and radioactive substances into the water, soil, and air. Furthermore, ecological care promotes ecological sustainability, safeguards the ecosystem, and helps protect food and agricultural resources by preventing food waste in the hospital setting. These considerations highlight the wide-ranging positive consequences of ecological care [ 14 , 41 ].

Empirical referents

According to Walker and Avant (2011), the final step in concept analysis is to identify the empirical referents of attributes. Empirical referents do not directly serve as instruments for measuring a concept, but they provide illustrations of how defining characteristics or attributes can be recognized or measured. By presenting real-world examples, empirical referents assist in measuring the concept and validating its significance [ 21 ]. Although this study did not identify a specific independent instrument for measuring ecological care in nursing, the following examples demonstrate instruments that measure the defining characteristics or attributes of the concept.

The Nurse’s Environmental Awareness Tool (NEAT) was developed by Schenk et al. in 2015 to measure nurses’ awareness of and behaviors associated with the environmental impact of their practices. The NEAT consists of 48 two-part items in six subscales and three paired subsets as follows: nurse awareness scales, nurse professional ecological behaviors scales, and personal ecological behaviors scales [ 9 ].

The Ecological Risk Perception Scale, developed by Slimak and Dietz in 2006, examines not only the attributes of the risk itself but also the characteristics of individuals perceiving the risk. Consisting of 24 ecological risk items, the scale encompasses four subscales: ecological, chemical, global, and biological [ 50 ].

The Environmental Literacy Questionnaire (ELQ) was derived from part of Michigan State University’s project and was originally used by Kaplowitz and Levine (2005) [ 51 ]. Later, Kahyaoğlu (2011) revised the ELQ. The revised version consisted of four components: knowledge (11 items), attitude (12 items), uses (19 items), and concern (9 items) [ 52 ].

Based on the current analysis, ecological care is a multidimensional integration of thinking, attitudes, awareness, sensitivity, and literacy to deliver high-quality holistic care while maintaining environmental sustainability and promoting energy conservation.

Analysis of the concept of ecological care has significant implications for the nursing profession. Given the limited exploration of ecological care within nursing practice, conducting an analysis can empower nurses to utilize ecological factors in delivering high-quality care and embracing environmentally friendly behaviors. The objective of this study was to present a comprehensive and practical definition of ecological care, thereby establishing a shared platform for not only nurses but also other healthcare professionals to promote pro-environmental behaviors.

Backes et al. (2011) conducted a study aiming to comprehend the meaning of ecological care from the perspective of students and teachers in the healthcare field at a Public Institution of Higher Education. The study revealed several categories, including (a) ecological care as a result of relationships, interactions, and communication with the global environment (main category); (b) the development of ecological awareness (causal conditions); (c) the connection of ecological care with different systems (context); (d) the perception of human-environment-health interaction (intervention); (e) the need to foster ecological consciousness through new references (strategy); and (f) a sense of motivation to understand ecological care (result). While this study acknowledged ecological awareness and conscience as integral components of ecological care, other attributes of the concept, such as adopting an ecological perspective; ecological literacy; and the impact of values, beliefs, and organizational culture on providing holistic care, were not extensively explained [ 20 ].

The findings of a study conducted by Dahlberg et al. (2016) revealed how ecological care facilitates patients to rediscover their place in a world characterized by interconnectedness. The role of ecological care extends beyond perceiving patients within a web of relationships; it encompasses assisting patients in re-establishing their sense of self and comprehending the world anew. Ecological care entails not only combating illness but also acknowledging patients as individuals influenced by and influencing the world. Such care endeavors to facilitate rhythmic movement and create space for activity and rest, being cared for and actively participating in one’s recovery, withdrawing from the world, and re-engaging with it. This study also highlights the use of the term ecological perspective to enhance the understanding of optimal care for patients. In this study, the novel attributes of the concept of ecological care are introduced. However, the potential impacts of constructive and destructive human activities on ecosystems remain unexplored [ 1 ]. In contrast, we refer to ecological sustainability and energy conservation as significant consequences of ecological care in nursing.

In a focused ethnographic study, Al-Shamaly (2021) explored the culture of multidimensional “caring-for” practice among ICU nurses. The inclusive nature of this culture encompasses caring for oneself, patients and their families, and colleagues (including nurses and other team members) as well as ecological consciousness within the ICU environment and organization. Ecological consciousness involves caring for equipment and machines, ensuring workplace safety, reducing hazards, transitioning towards a paperless unit, maintaining thorough documentation, and demonstrating commitment and concern for the organization’s budget regarding staff and resources [ 15 ]. While this study comprehensively addresses the practical aspects of the concept, it constrains the concept of ecological care solely to ecological consciousness. However, our study revealed that ecological care is a multidimensional, and complex phenomenon that extends beyond ecological consciousness. In another study, religious values were identified as a crucial factor in promoting an ecological care orientation that can be incorporated into daily life through religious education, considering the religious and cultural context of each country. These values are instilled into individuals from childhood to adulthood through various learning activities. Therefore, religious education plays a pivotal role in shaping individuals’ commitment to ecological care [ 53 ]. According to this study, religious values significantly contribute to the development of ecological thinking and the manifestation of ecological behavior.

Moreover, a previous study by Akkuzu (2016) introduced ecological intelligence as a new type of conscience, defined as a combination of environmental awareness and the sensitivity of human beings towards adverse global alterations in nature. This understanding empowers individuals to recognize the perils faced by their communities and comprehend the underlying causes. Furthermore, it equips them with the knowledge necessary to address these perils collectively and devise effective solutions [ 54 ].

Implications for nursing practice

While our analysis primarily focused on the ecological perspective, we contend that a profound understanding of this concept is imperative for establishing cultural and political frameworks within the healthcare system. This study contributes to the limited body of research on nursing by highlighting the essentiality of ecological and holistic thinking in the domains of caregiving, treatment, management, and education. Consequently, it has the potential to yield substantial impacts in ensuring the safety of patients and healthcare providers, enhancing the quality of care, and improving patient and family satisfaction.

Limitations

The conceptual analysis is subject to several limitations. Firstly, the literature search was confined to studies published in English and Persian, potentially limiting the diversity of perspectives from other countries, cultures, and languages. To mitigate this limitation, future studies should conduct a comprehensive search in multiple languages to ensure a more holistic understanding of ecological care in nursing practice. Secondly, the analysis is susceptible to selection bias, extraction bias, and analysis bias. To address these limitations, the study selection process, data extraction, and analysis were independently conducted by two researchers. Despite these limitations, the studies were described accurately and systematically, contributing valuable insights into the concept of ecological care in nursing practice.

The results of the present analysis provide a definition of ecological care in nursing that may guide the profession to new directions of care, striving for the greater good of the patient, the profession of caring, and the environment. It is clear that more research is needed to discover the neglected importance of the environment in holistic care and to identify phenomena related to this concept in practical nursing. The literature review shows that the educational field, as the most effective factor, plays a significant role in the formation of ecological literacy and worldviews and the creation of the perceptions, attitudes, and behaviors of ecological care. In this regard, nursing professors and instructors, as the most important role models, significantly contribute to the development of the identity and character of ecological care for today’s students and future nurses.

Data availability

The data supporting the findings of this study are available upon request from the corresponding author. The data were not publicly available because of privacy or ethical restrictions.

Abbreviations

Carbon dioxide

Nitric oxide

The United Nations Educational, Scientific and Cultural Organization

Nurse’s Environmental Awareness Tool

Environmental Literacy Questionnaire

Intensive Care Unit

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Acknowledgements

This study was extracted from a research project approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 73361). The authors would like to thank all those who spent valuable time participating in this research. We are also immensely grateful to the “anonymous” reviewers for their valuable insights.

The present study was financially supported by Tabriz University of Medical Sciences, Tehran, Iran.

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Golshan Moghbeli, Mansour Ghafourifard, Shahla Shahbazi & Hanieh Aziz Karkan

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GM, AS: original concept and study design; GM, HA, ShS: data collection; GM, HA, AS, MGh: data analysis and interpretation; GM, HA, AS, MGh, ShS: manuscript preparation and final critique; GM, MGh: study supervision.

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Moghbeli, G., Soheili, A., Ghafourifard, M. et al. Ecological care in nursing practice: a Walker and Avant concept analysis. BMC Nurs 23 , 614 (2024). https://doi.org/10.1186/s12912-024-02279-z

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Innovation Responds to Climate Change Proposals

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• Published: 02 September 2024

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• Greg Tindall 1 ,
• Rebel A. Cole 2 &
• David Javakhadze   ORCID: orcid.org/0000-0003-1580-6309 3  

Climate change is an ethical and moral challenge of a global scale due to its potentially catastrophic implications for human welfare. Understanding forces that drive corporate adaptation to climate change is an important research topic in business ethics. In this paper, we propose that shareholder climate-related proposals could be a catalyst for corporate innovations in technologies mitigating climate change. Our results, based on the analysis of US firms, indicate that corporations respond positively to these proposals by producing more climate-related patents and citations. We also uncover potential casual channels of influence. Further, we find that corporate governance moderates the documented effects. These proposals lead to a more efficient and valuable innovation output, but lower firm performance in the short term. The real effect that shareholder proposals have on innovation gains clarity in the context of climate change, contributing to the discussion of investor “voice.”

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Xiao and Shailer ( 2022 ) provide a novel systematic investigation of factors influencing stakeholders’ perceptions of the credibility of corporate sustainability reports.

What are shareholder proposals, and what makes them interesting? Established in 1942 (and amended several times), Rule 14a-8 was designed to give small shareholders a voice and managers ample opportunity to listen before being heard at annual meetings. The Rule now permits a shareholder to make a proposal of 500 words or less, if any of the following ownership amount and time requirements are met: 1) at least $2,000 in market value for at least three years; 2) or at least $15,000 for at least two years; 3) or at least $25,000 for at least one year. The proposal must be received at the company’s principal executive offices not less than 120 calendar days before the release of company's annual proxy statement, with shareholder intent to maintain the requisite interest through the annual meeting. For more formation, please see the Code of Federal Regulations, (Title 17, Volume 3, Sect. 240.14a-8, www.govinfo.gov ).

Theoretical perspectives on management’s response to stakeholder demands are influenced by corporate purpose.

Literature presents opposing views: Friedman’s ( 1970 ) profit-focused shareholder priority versus Stout’s ( 2013 )

inclusive stakeholder approach considering broader goals. See discussion on the subject in Clarke ( 2020 ).

The climate-related proposals to Chevron reflect this shift in emphasis toward a direct assessment of financial risk, from one of simple emission disclosure. From 1999 to 2009, requests for a “Report on Greenhouse Gas Emissions” were recurrent. Beginning in 2010, Chevron saw “Stockholder Proposals Regarding Financial Risks from Climate Change.”

Two examples from the 2016 proxy season highlight shareholder demands for innovation. Shareholders of Ameren Corp proposed “ITEM (4): SHAREHOLDER PROPOSAL RELATING TO A REPORT ON AGGRESSIVE RENEWABLE ENERGY ADOPTION.” Shareholders in AES Corp sponsored “PROPOSAL 4: A REPORT ON COMPANY POLICIES AND TECHNOLOGICAL ADVANCES” targeting the firm’s energy policies and emphasis on renewable sources.

In 2010, St. Joseph of the Capuchin Order requested a study “on how ExxonMobil, within a reasonable timeframe, can become the recognized industry leader in developing and making available the necessary technology (such as enhanced sequestration, engineered geothermal and the development of other renewable energy sources) to enable the U.S.A. to become energy independent in an environmentally sustainable way. By 2017, The New York State Common Retirement Fund sponsored the climate proposal that gained substantial press coverage, which essentially made a similar request: “…an annual assessment of the long-term portfolio impacts of technological advances and global climate change policies…” Further, the Board for Fluor Corporation has stated its opposition to repeated proposals from 2016 to 2018 requesting GHG reduction goals, by “Creating Technology to Reduce Greenhouse Gas Emissions,” more specifically, by investing in NuScale Power, LLC along with Rolls-Royce.

We emphasize that climate-friendly boards and heightened managerial perceptions of climate risk are potential mechanisms. We argue that shareholder proposals positively influence these factors. However, we acknowledge without direct demonstration that these mechanisms, in turn, enhance innovations, considering them as established facts based on prior research (Homroy and Slechten, 2019 ; Sautner et al., 2023 ).

We considered using alternate terms such as “greenhouse gases” or “carbon emissions,” but due to the content of the DEF14A filing, it is not possible to ensure that a term appears directly within a shareholder proposal or management’s response to one without visual inspection, thus hand-collection. Often, the proposals are only a small portion of the DEF14A which often presents year-end results at the annual meeting. Further, word lists invariably subject samples to gaming. “Climate Change” has fairly unambiguous meaning to management and is the phrase used by both the SEC and USPTO.

We also consider that firm innovation may not have a perfect memory of a pressure over the past 25 years of all proposals related to climate change. For robustness, we construct the same three-year, backward average but for only the last three years as well as the last five years. The results that follow remain unchanged. We also use lagged proposals as a proxy for shareholder pressure on climate-related issues for additional robustness, and our main findings are qualitatively similar. These results are not reported for brevity but are presented in online Appendix 1 .

In fact, of the 1.9 million patents we examine from 1994 to 2019, only 8 begin with the Y02 classification, even though 105,737 patents contain the Y02 classification in the CPC coding scheme. For example, patent 5,426,677 appears to be primarily concerned with Physics, the G classification, (G21C1/09; G21C17/00; G21Y2002/202; G21Y2002/204; G21Y2004/304; Y02E30/40), but also has a Climate Mitigation (Y02) component. Disentangling truncation bias by year-technology for the Y02 classification is not feasible for this paper. Further, from our discussions with the USPTO, the first classification tends to be more dominant than the last.

In unreported results, we also construct dependent variables looking forward five years to allow more time for the stockholder pressure to influence innovative behavior.

As Wooldridge ( 2012 ) explains, “sometimes log(1 + y) is used, but interpretation of the coefficients is difficult.” (p. 216) However, this practice is commonplace in corporate finance settings. For robustness, the inverse hyperbolic sine (IHS), as suggested by Burbidge et al. ( 1988 ) and proposed by Johnson ( 1949 ), for zero-value observations is used to log transform both the logged dependent variables and the independent variable of interest, Pressure . The IHS transformation is sinh-1(x) = log(x + (× 2 + 1)1/2). The results using IHS for OLS regressions suggest that the coefficients tend to overstate the economic impact of models (3) and (6) of Table  2 as well as models with Y02 Counts pct and Y02 Cites pct as dependent variables, while understate the coefficients of models with Y02 Top 1 pct and Y02 Top 10 pct as dependent variables (Appendix B ), but the statistical inference remains unchanged in sign or significance.

The Pope’s sentiment also intuitively satisfies the exclusion restriction as it is unlikely to directly influence corporate innovations. To gain some reassurance on the (notorious) exclusion restriction, we divide the sample along the lines of Religious Social Capital considered by Rupasingha et al. ( 2006 ) and obtained from the U.S. Census Bureau’s number of establishments in religious organizations (NAICS 813110), also examined by Grennan ( 2022 ) along with other donor-advised funds. In splitting the sample between More and Less Religious at the county level, we find that firms headquartered in less religious counties have a more acute influence on climate innovations when the Pope serves as an instrument. We would expect the Pope to have a stronger influence in more religious counties, if the Pope were directly influencing management to develop climate technologies and bypassing proposals made by shareholders who are not concentrated near headquarters. Since we find the opposite, we feel better about the exclusion restriction, instead of relying only on our (notorious) intuitions for justification.

We implement causal mediation analysis using the ivmediate command in Stata (e.g., Dippel, Ferrara, and Heblich, 2020 ), allowing us to estimate the treatment effect and determine the proportion attributable to a mediator. The primary advantage, as noted, is that despite both the treatment and mediator being endogenous, a single instrument can accurately detect both causal treatment and mediation effects. However, the method does not produce the first-stage result of the IV regression. Instead, it reports the F-test of excluded instruments directly from the first stage to assess instrument strength, which suffices to establish validity. In our models, detailed in Table  4 , the F-tests from the first stage across all models greatly exceed the conventional cutoff value of 10, ensuring the validity of the instrument. Nevertheless, we manually performed IV regressions and confirmed that our instrument, PopeUS, significantly and positively affects both Pressure and mediators.

In the results, not tabulated for brevity, we re-estimate the same model as in Panel A but with firm fixed effects. We find significant causal mediation effects of Pressure on Y02 Counts that pass through Ind Dir Exp. In parallel to Panel B, we re-estimated the same model with firm fixed effects using CC Bigrams as a mediator and found nearly full mediation. Additionally, we detected marginal mediation in the model with Y02 Cites as a dependent variable using CC Bigrams as a mediator, but not Ind Dir Exp. Thus, the results of firm fixed effects analysis are more suggestive in this case.

We also perform robustness checks of our mediation analysis using alternative measures of shareholder proposals (three-year backward averages for the last three and five years, and lagged proposals). We find statistically significant mediation in all cases, with the mediated effect ranging from 0.54 to 0.91 of the total effect. We also limit the sample to firms that have ever received a proposal related to climate change during our sample period and find the proportion of the total effect mediated varies from 0.62 to 0.74 of the total effect. Finally, using the percentage of votes at the annual meetings in favor of a climate-related proposal collected by ISS (ISS Vote For), the mediated effect ranges from 0.83 to 0.90 of the total effect. We estimate these models using industry fixed effects, with industries identified using 3-digit SIC codes. Overall, our results are in line with our main findings.

To ensure our results are not due to selection of matching estimator, we also employ entropy balancing, nearest neighbor, propensity score, and the CEM (Blackwell et al., 2009 ) and find our results to be robust. The main advantage of EBCT, of course, is that it allows us to match on our continuous treatment variable ( Pressure ), instead of a binary one required for the other estimators.

We note that, following the approach of Faleye et al., ( 2014 ), we also examined the short-term performance implications of the change in patent counts attributable to shareholder climate-related proposals. That is, we regress our performance metrics on predicted patent counts as well as patent cites, where the predicted values are from the regression of innovation variables in our shareholder proposal measures. Our findings remain consistent.

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Appendix a: description of variables and sources.

Appendix B: Shareholder Climate-Related Proposals and Corporate Innovations—Alternative Models

This table shows the results of ordinary least square regressions with Innovation as the dependent variable based on the patent data by date filed with the US Patent Office containing the Y02 (climate change). In Columns (1)–(4), dependent variables are Y02 Count Pct —the percent of a firm’s Y02 patents in a given year relative to all of that firm’s patents filed in the same year, Y02 Cite Pct —the percent of a firm’s Y02 patent citations in a given year relative to all of that firm’s patent citations filed in the same year, Y02 Top 1—the natural log of one plus the number of Y02 patents whose citations were in the top 1 percent of all Y02 patents in a given year, Y02 Top 10 —the natural log of one plus the number of Y02 patents whose citations were in the top 10 percent of all Y02 patents in a given year, respectively. Pressure is the natural log of one plus a three-year, backward average of an accumulated total of the climate-related shareholder proposals that a firm has received from 1994 to 2019. The control variables are also averaged over three years and include Size, R&D, Tobin’s Q, Age, Revenue Growth, Stock Returns, Leverage and Cash Surplus, as defined in Appendix A. t-statistic, based on robust standard errors, adjusted for heteroskedasticity and clustered at the industry-year level, are reported in brackets below the coefficients. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

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Networks and identity drive the spatial diffusion of linguistic innovation in urban and rural areas

• Aparna Ananthasubramaniam   ORCID: orcid.org/0000-0002-8307-3365 1 ,
• David Jurgens 1 , 2 &
• Daniel M. Romero   ORCID: orcid.org/0000-0002-8351-3463 1 , 2 , 3  

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Cultural innovation (e.g., music, beliefs, language) tends to be adopted regionally. The geographic area where innovation is adopted is often attributed to one of two factors: (i) speakers adopting new behaviors that signal their demographic identities (i.e., an identity effect), or (ii) these behaviors spreading through homophilous networks (i.e., a network effect). In this study, we show that network and identity play complementary roles in determining where new language is adopted; thus, modeling the diffusion of lexical innovation requires incorporating both network and identity. We develop an agent-based model of cultural adoption, and validate geographic properties in our simulations against a dataset of innovative words that we identify from a 10% sample of Twitter (e.g., fleeky, birbs, ubering). Using our model, we are able to directly test the roles of network and identity by comparing a model that combines network and identity against simulated network-only and identity-only counterfactuals. We show that both effects influence different mechanisms of diffusion. Specifically, network principally drives spread among urban counties via weak-tie diffusion, while identity plays a disproportionate role in transmission among rural counties via strong-tie diffusion. Diffusion between urban and rural areas, a key component in innovation spreading nationally, requires both network and identity. Our work suggests that models must integrate both factors in order to understand and reproduce the adoption of innovation.

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Introduction.

From new technologies 1 , 2 , to religious beliefs 3 , 4 to popular music 5 , 6 and memes on social media 7 , 8 , innovation is often adopted regionally within the USA (e.g., in the Deep South or the Mid-Atlantic) 9 , 10 . For instance, new words are often used in geographic areas that reflect their social, cultural, and historical significance 11 , 12 . In fact, many social science disciplines (e.g., sociology, anthropology, linguistics, cultural, and social geography) use linguistic variables as a proxy for culture change 13 , 14 , 15 , 16 , because shifts in culture often result in language change, and conversely, using new language sometimes signals adoption of new worldviews 17 , 18 , 19 . Specifically, researchers often use the geographic regions where new language is adopted to test putative mechanisms of diffusion 20 , 21 , 22 , 23 : To falsify a hypothesized mechanism, one could show that it does not predict where speakers would adopt a new word.

Existing mechanisms often fail to explain why cultural innovation is adopted differently in urban and rural areas 24 , 25 , 26 . Urban centers are larger, more diverse, and therefore often first to use new cultural artifacts 27 , 28 , 29 . Innovation subsequently diffuses to more homogenous rural areas, where it starts to signal a local identity 30 . Urban/rural dynamics in general, and diffusion from urban-to-rural areas in particular, are an important part of why innovation diffuses in a particular region 24 , 25 , 26 , 27 , 29 , 30 , 31 , including on social media 32 , 33 , 34 . However, these dynamics have proven challenging to model, as mechanisms that explain diffusion in urban areas often fail to generalize to rural areas or to urban-rural spread, and vice versa 30 , 31 , 35 .

Spatial properties of diffusion are often hypothesized to be the result of one of two mechanisms: the performance of demographic identity (henceforth referred to simply as identity) or the diffusion of innovation through a homophilous network (henceforth, network) 10 , 30 , 31 . On one hand, speakers may adopt language that allows them to perform their demographic identity—using certain words to signal what identities they hold (e.g., saying “pop” instead of “soda” to sounds Midwestern) 13 , 36 , 37 . For instance, mechanisms like strong-tie diffusion suggest that demographically similar speakers (often connected by strong, or close, ties) influence each others’ adoption 38 , 39 , 40 , explaining geographic variation as the byproduct of spatial assortativity in personal characteristics 11 , 35 , 41 . On the other hand, language regions may also be the result of network homophily—or the tendency for similar individuals to be connected in the social network (e.g., Michiganders tend to have ties to other Michiganders, Democrats to other Democrats) 28 , 40 , 42 , 43 . The amount of homophily in a network has been shown to determine both the extent of diffusion 44 , 45 , as well as specific geographic properties of cascades 46 . For instance, mechanisms like weak-tie diffusion suggest that new words tend to diffuse via the network, where weak ties, or more distant relationships, increase a word’s exposure 43 , 47 , 48 ; via this mechanism, geographically and demographically homophilous ties allow language regions to emerge 49 , 50 , 51 , 52 . As an example, let’s assume the phrase “no human is illegal” is more likely to be used in politically left-leaning states. Under the identity effect, this adoption geography is expected because using the phrase makes a speaker sound like a Democrat, and, therefore, it would likely diffuse in areas where many Democrats live and choose to use it 35 . Under the network effect, the phrase is thought to spread in left-leaning states because, once some Democrats start using it, their (largely Democratic) friends and neighbors start repeating it.

Existing theory tends to focus on either network or identity as the primary mechanism of diffusion. For instance, cultural geographers rarely explore the role of networks in mediating the spread of cultural artifacts 53 , and network simulations of diffusion often do not explicitly incorporate demographics 54 . Even within fields that acknowledge both network and identity as drivers of diffusion (e.g., sociology theories of diffusion or variationist sociolinguistics), any given model of adoption is often either identity-centered or network-centered, rather than offering an explanation of diffusion that connects the two 35 , 55 , 56 , 57 , 58 . Urban/rural dynamics are not well-explained using these network- or identity-only theories; in particular, in some cases, identity-only frameworks designed to model rural adoption do not explain urban diffusion 30 , while some network-only models capture urban but not rural dynamics 31 . However, a framework combining both of these effects may better explain how words spread across different types of communities 59 .

In this study, we test whether network and identity play complementary roles in creating key spatial properties of lexical diffusion. Specifically, we hypothesize that network tends to drive weak-tie diffusion between urban counties, while identity promotes strong-tie diffusion between rural counties. Testing our hypothesis requires comparing a combined network + identity model of diffusion to network-only and identity-only counterfactuals—and since network and identity are often correlated 50 , we cannot empirically observe these baselines. Instead, we develop an agent-based model, inspired by cognitive and social theory, to model the spread of new words through a network of speakers. Using agent-based models allows us to simulate the required counterfactuals and, therefore, directly test how network and identity interact 60 . Our simulations are validated using large-scale empirical data we curate, including a registry of new words on the microblog site Twitter (now known as \({\mathbb{X}}\) ) and the network and demographic identities of users on the site.

We find evidence supporting our hypothesis and, therefore, that key properties of linguistic diffusion—both the geographic regions that new words spread to and the spatiotemporal pathways through which they diffuse—are better approximated by network and identity together than by either one individually. Furthermore, urban/rural heterogeneity is an emergent property of our model: differences between urban and rural counties are present when taking network and identity into account, even though we do not explicitly model them. We conclude that models omitting either network or identity are missing a crucial dynamic in the adoption of innovation and drawing incomplete conclusions about the underlying diffusion process.

We develop an agent-based model to evaluate the roles of network and identity in the spatial patterns of cultural diffusion. To realistically model the adoption of innovation, our formulation draws heavily from social and cognitive theory, and underlying assumptions are empirically derived 61 , 62 , 63 , 64 . Our model simulates the diffusion of a new word w . The model begins with a set of initial adopters introducing the word to the lexicon (section “New words and initial adopters”), and spreads across a directed network of n agents \({\{j\}}_{j = 1}^{n}\) (section “Network” and section “Agent identity”). The new word connotes a particular identity ϒ w that is assigned based on the identities of its early users (section “Word identity”). In our simulations, the word continues to spread through the network over several subsequent timesteps (section “Diffusion”). Agents are exposed to the word when a network neighbor uses it. Agents are more likely to use the word if it signals an identity congruent with their own and if they were recently exposed by network neighbors with similar identities. We fit the model’s free parameters to empirical data about each word’s diffusion (section “Parameters and trials”), and compare how well this full model reproduces properties of empirical trials (section “Model evaluation” and section “Testing the hypotheses”) relative to network- and identity-only counterfactuals (section “Simulated counterfactuals”). See Supplementary Methods 1.2 for the full set of model equations and Supplementary Methods 1.3 for information about parameters and how they are inferred. Our model’s limitations, along with our attempts to address them, are listed in the Supplementary Discussion. Although we test our model against the diffusion of linguistic innovation (section “Hypotheses”), its formulation is sufficiently general to describe the adoption of other cultural innovations.

New words and initial adopters

We simulate the diffusion of widely used new words originating on Twitter between 2013 and 2020. Starting from all 1.2 million non-standard slang entries in the crowdsourced catalog UrbanDictionary.com, we systematically select 76 new words that were tweeted rarely before 2013 and frequently after (see Supplementary Methods 1.41 for details of the filtration process). Consistent with prior studies of online innovation 65 , 66 , 67 , 68 , 69 , the 76 new words in our study include terms describing popular culture phenomena (e.g., fanmix, sweaties), phonologically-motivated orthographical shifts (e.g., bawmb, whatchoo), part-of-speech changes (e.g., ubering, lebroning), abbreviations (e.g., ihml, profesh), concatenations (e.g., amaxing, sadboi), and even new coinages (e.g., gwuap, fleeky) (Supplementary Table 3 has more examples). These words often diffuse in well-defined geographic areas that mostly match prior studies of online and offline innovation 23 , 69 (see Supplementary Fig. 7 and Supplementary Methods 1.4.4 for a detailed comparison).

Each run of our model simulates the diffusion of one of these 76 words. The set of final adopters is often highly dependent on which users first adopted a practice (i.e., innovators and early adopters) 70 , including the level of homophily in their ties and the identities they hold 71 , 72 . Therefore, we seed the model with a set of empirical early adopters. Each simulation’s initial adopters are the corresponding word’s first ten users in our tweet sample (see Supplementary Methods 1.4.2) . Model results are not sensitive to small changes in the selection of initial adopters (Supplementary Methods 1.7.4) .

Patterns in the diffusion of innovation are often well-explained by the topology of speakers’ social networks 42 , 43 , 73 , 74 , 75 . Therefore, the word in our model diffuses through a network of agents. Nodes (agents) and edges (ties) in this network come from the Twitter Decahose, which includes a 10% random sample of tweets between 2012 and 2020. Agents in our model correspond to Twitter users in this sample who are located in USA. We draw an edge between two agents i and j if they mention each other at least once (i.e., directly communicated with each other by adding “@username” to the tweet), and the strength of the tie from i to j, w i j is proportional to the number of times j mentioned i from 2012 to 2019 76 , 77 . The edge drawn from agent i to agent j parametrizes i ’s influence over j ’s language style (e.g., if w i j is small, j weakly weighs input from i ; since the network is directed, w i j may be small while w j i is large to allow for asymmetric influence). Although Twitter users are exposed to content from more users than they reciprocally mention (e.g., unreciprocated ties, users they follow, public tweets), this network is particularly relevant to our study; prior research has shown that the mention network captures edges likely influential in information diffusion 78 , and reciprocal ties are often responsible for the diffusion of lexical items 79 and better predict properties of cascades 80 . Moreover, reciprocal ties are more likely to be structurally balanced and have stronger triadic closure 81 , both of which facilitate information diffusion 82 .

This directed network has nearly 4 million nodes and 30 million edges; the network evidences homophily (higher than expected levels of assortativity along all modeled aspects of identity) and exhibits some clustering within geographically localized regions as well as some clustering across regions (Supplementary Figs. 2 – 4) . The network also exhibits expected patterns in urban and rural tie strength. Consistent with prior studies of urban and rural areas 30 , 83 , ties between two urban counties tend to be weak ties (less demographic similarity and lower edge weight), while ties between two rural counties tend to be strong ties (more demographic similarity and higher edge weight) (Supplementary Figs. 18 , 19) . As expected, demographic similarity and edge weight are correlated: ties with lower edge-weight w i j tend to share fewer demographic similarities than edges with higher weight (Supplementary Table 6) .

Model results are robust to modest changes in network topology, including the Facebook Social Connectedness Index network (Supplementary Methods 1.7.1) 84 and the full Twitter mention network that includes non-reciprocal ties (Supplementary Methods 1.7.2) .

Agent identity

An individual often adopts innovation that signals their affiliation with some identity 37 , 85 , 86 , 87 . In our model, area demographics are proxies for each agent’s probable identity. Note that, although the term “identity” typically refers to how someone identifies along a range of markers 88 , our paper models solely demographic aspects. Agents are characterized by D  = 5 categories shown to be important to language style: (i) location within USA 21 , 89 , 90 , (ii) race/ethnicity 91 , 92 , 93 , 94 , (iii) socioeconomic status measured via income level, educational attainment, and workforce participation 47 , 95 , 96 , (iv) languages spoken 97 , 98 , 99 , and (v) political affiliation 14 , 100 . Each category is parametrized by several related registers (e.g., for political affiliation, “registers” are Democrat, Republican, and Third Party), for a total of d  = 26 registers.

We infer each agent’s location from their GPS-tagged tweets, using Compton et al. (2014)’s algorithm 101 . To ensure precise estimates, this procedure selects users with five or more GPS-tagged tweets within a 15-km radius, and estimates each user’s geolocation to be the geometric median of the disclosed coordinates (see Supplementary Methods 1.1.2 for details). By using conservative thresholds for frequency and dispersion, this algorithm has been shown to produce highly precise estimates of geolocation. Since Twitter does not supply demographic information for each user, agent identities must be inferred from their activity on the site. Automated demographic recognition tools often use network ties (or posts with mentions) as features, which would preclude independent measures of identity and network, and there are some debates around the methodological soundness and ethical acceptability of these methods 102 , 103 , 104 . Instead, we estimate each agent’s identity based on the Census tract and Congressional district they reside in refs. 105 , 106 . Similar to prior work studying sociolinguistic variation on Twitter 12 , 107 , each agent’s race/ethnicity, SES, and languages spoken correspond to the composition of their Census Tract in the 2018 American Community Survey. We also represent each agent’s political affiliation using their Congressional District’s results in the 2018 USA House of Representatives election. Since Census tracts are small (population between 1200 and 8000 people) and designed to be fairly homogeneous units of geography, we expect the corresponding demographic estimates to be sufficiently granular and accurate, minimizing the risk of ecological fallacies 108 , 109 . Due to limited spatial variation (Supplementary Methods 1.1.4) , age and gender are not included as identity categories even though they are known to influence adoption. However, adding age and gender (inferred using a machine learning classifier for the purposes of sensitivity analysis) does not significantly affect the performance of the model (Supplementary Methods 1.7.3) .

Since an agent may identify with each identity register to a different degree 37 , 110 and in order to capture spatial variation, each register of an agent’s identity ϒ j is represented as a value in the interval [0, 1] (e.g., in a district where 61% voted Republican and 39% Democrat, the Republican identity is represented by 0.61 and Democrat identity as 0.39, instead of the majority identity of 1 and 0, respectively), so ϒ j   ∈  [0, 1] d . Even though this procedure may underestimate some variation in demographics (e.g., in the example above, a Republican and a Democrat in the district are both represented with political identities of (0.61, 0.39)), our estimation strategy captures the spatial variation in identities that are hypothesized to drive geographic patterns in language diffusion. In particular, we did not randomly assign identities within Census tracts in order to avoid obscuring homophily in the network (i.e., because random assignment would not preferentially link similar users).

Word identity

Cultural innovation can be used to signal different aspects of an agent’s identity 111 , 112 , 113 . Each word may provide information about one or more of the identity categories like location, race, etc. 88 ; for each word, we denote the relative importance of each category with weight vector v w   ∈  [0, 1] D . Unlike agent identity, words often connote affiliation with a specific register of identity (e.g., in Eckert 2000, high schoolers may associate with multiple social groups, but each linguistic variable signals membership to a particular group 114 ). Therefore, word identities in our model are binary (i.e., a word either signals a given register of identity or it doesn’t), and we model word identities distributed in ϒ w   ∈  {0, 1} d unlike agents’ identities in ϒ j   ∈  [0, 1] d .

A word’s identity is often enregistered based on the demographics of a small number of its early adopters 110 , signaling that these speakers identify with certain registers of identity. For instance, if the initial adopters tend to come from disproportionately Republican, African American, French-speaking areas like Louisiana, the word signals this demographic identity: specifically, \({v}_{w}=\frac{1}{3}\) for the dimensions corresponding to the political affiliation, race, and language categories; ϒ w  = 1 for the dimensions corresponding to the Republican political affiliation, African American race, and French language registers; and other entries of both v w and ϒ w are 0 (see Supplementary Methods 1.2.2 – 1.2.3 for a more formal description). Agent identities remain unaltered by a word’s enregisterment. During the process of enregisterment, both online and offline, words often quickly develop a “stereotypic indexical value,” or universal understanding of the identity signaled by the word shared by all speakers and conveyed through context 71 , 115 , 116 . Therefore, a word’s identity is assigned based on the word’s first ten adopters.

After the initial adopters introduce the innovation and its identity is enregistered, the new word spreads through the network as speakers hear and decide to adopt it over time. In order to appropriately model the diffusion of language 18 , adoption is usage-based (i.e., agents can use the word more than once and adoption is influenced by frequency of exposure) 117 and the likelihood of adoption increases when there are multiple network neighbors using it 118 . Although we present a model for lexical adoption on Twitter, the cognitive and social processes on which our formalism is derived likely generalize well to other forms of cultural innovation and contexts 63 , 119 , 120 .

In our model, agents do not use the word until they have been exposed to it by a network neighbor at least once. Language change is better modeled in a usage-based rather than adopter-based framework (i.e., agents can use the word at each timestep rather than becoming and remaining an adopter one time) 18 . Accordingly, at each discrete timestep t , agent j decides whether they will use the word w with dynamic likelihood p j w t   ∈  [0, 1], reflecting whether the word is salient to them 121 . This probability changes at each timestep 71 , 122 , aggregating six pieces of information from agents’ exposures to the new word: (i) Attention Fading : If agent j was previously exposed to the word but is not exposed at timestep t , their attention to the new word, and their likelihood of adoption, fades 121 . If agent j ’s network neighbor i   ∈   N ( j ) uses the word at timestep t (i.e., i   ∈   a d o p t ( t )), j updates their likelihood of using the word at the next timestep p j , w , t +1 . At this point, agent j ’s mental representations are determined by five main characteristics: (ii) Novelty : With greater exposure, a word’s novelty wears off and its salience declines 123 . (iii) Stickiness : Some words are more likely to experience higher coinage and adoption because, for instance, they are related to topics of growing importance, used across a variety of semantic contexts, are associated with higher communicative need, or have notable linguistic properties 124 , 125 , 126 . (iv) Relevance : since speakers often use language to perform their own identity, agents may preferentially use words whose demographics more closely match their own 13 , 37 ; (v) Variety : In addition to common identity, diverse exposure, from multiple people across multiple contexts, improves a word’s salience and provides social affirmation for use of the word 118 , 127 , 128 ; and (vi) Relatability : Since self-expression and social engagement are key motivators for use of social networking sites, input from agents with similar identity may weigh more heavily 61 , 76 , 129 , 130 , 131 .

While many other factors may affect the diffusion of new words (cf. Supplementary Discussion), we do not include them in order to develop a parsimonious model that can be used to study specifically the effects of network and identity 132 . In particular, assumptions (iii)–(vi) are a fairly simple model of the effects of network and identity in the diffusion of lexical innovation. The network influences whether and to what extent an agent gets exposed to the word, using a linear-threshold-like adoption rule (assumption v) with a damping factor (assumption iii). Identity is modeled by allowing agents to both preferentially use words that match their own identity (assumption iv) and give higher weight to exposure from demographically similar network neighbors (assumption vi). Assumptions (i) and (ii) are optional to the study of network and identity and can be eliminated from the model when they do not apply (by removing Equation ( 1 ) or the η parameter from Equation ( 2 )). For instance, these assumptions may not apply to more persistent innovations, whose adoption grows via an S-curve 58 . Since new words that appear in social media tend to be fads whose adoption peaks and fades away with time (Supplementary Fig. 8) , we model the decay of attention theorized to underly this temporal behavior 133 , 134 . Without (i) and (ii), agents with a high probability of using the word would continue using it indefinitely. These assumptions allow the word to exit the lexicon and the cascade to stop.

Per Equation ( 1 ) and Equation ( 2 ), these six characteristics suggest that p j , w , t +1 should be proportionate to: (i) Attention Fading : an exponential decay in attention 134 , where agents retain fraction r   ∈  [0, 1] of their attention when not exposed to the word at time t :

When agents are exposed at time t , p j , w , t +1 is proportionate to (ii) Novelty : a cosine decaying function of the number of exposures j has had to the word η j w t ; (iii) Stickiness: the “stickiness” of the word S w , which scales the probability of adoption; (iv) Relevance: the similarity between j ’s identity and their understanding of the word’s identity, δ j w ; (v) Variety : the fraction of their network neighbors to have adopted the word at timestep t ; and (vi) Relatability : this fraction is weighted by the similarity in their identity δ i j and tie strength w i j .

In Equation ( 2 ), the network influences which words an agent has the opportunity to adopt and their likelihood of adopting those words by determining (1) the words an agent is exposed to and (2) the agents’ level of exposure to the word. Identity is modeled in two ways: (1) agents preferentially use words that match their own identity ( δ j w ), and (2) agents give higher weight to exposure from demographically similar network neighbors ( δ i j ). In both mechanisms, new adopters would more likely be demographically similar and geographically proximal to existing adopters, producing geographic regions. Notably, agents may have a relatively high likelihood of adopting words if either the identity effect (word signals their identity) or the network effect (enough of their ego network is using the word) is sufficiently strong; in other words, an agent may have a reasonably high probability of adopting a word that doesn’t signal their identity (which would make δ i w low) if many of their friends are using it (which would make the last term in Equation ( 2 ) high).

Identity comparisons ( δ j w , δ i j ) are done component-wise, and then averaged using the weight vector v w (section “Word identity”). Note that p j , w , t +1 implicitly takes into account the value of p j , w , t by accounting for all exposures overall time. See Supplementary Methods 1.2.4 for the full set of model equations.

We stop the model once the growth in adoption slows to under 1% increase over ten timesteps. Since early timesteps have low adoption, uptake may fall below this threshold as the word is taking off; we reduce the frequency of such false-ends by running at least 100 timesteps after initialization before stopping the model.

Simulated counterfactuals

We directly assess the roles of network and identity in linguistic diffusion by evaluating the impact of omitting each of these sets of variables from the model. We simulate three counterfactual conditions to the full Network+Identity model described above:

Network-only : eliminate agents performing identity by simulating the spread through just the weighted networks ( δ i j ,  δ j w  = 1).

Identity-only : shuffle the edges of the network. This configuration model-like procedure 135 preserves each agent’s degree, allowing us to isolate the impact of eliminating homophily, the characteristic of the network most often hypothesized to drive regionalization, while also holding constant other network-geographic confounds like population and degree distributions.

Null (Shuffled Network+No Identity) : shuffled network without identity variables. This holds constant several variables (e.g., population size, degree distribution, model formulation), thus isolating the impact of structural factors other than network and identity.

Parameters and trials

We evaluate each model by examining its performance across 25 random trials on each of the 76 neologisms described in the section “New words and initial adopters” (1900 trials in total). In a sequence of three steps, non-empirical model parameters are tuned to the data and simulations are run at these parameters:

Parameters Q , r , and θ are tuned to the number of adoptions in a random 20% sample of words using a grid search. As described in Supplementary Methods 1.3 , each parameter is assigned to the value that brings simulated usage (number of adoptions) closest to empirical usage; we do not maximize the study outcomes (e.g., Lee’s L, likelihood of model pathways) and use a 20% sample instead of all words in order to avoid overfitting the model. The optimal values for these parameters are Q  = 0.75, r  = 0.4, and θ  = 100.

S w is tuned separately for each word w , whereas in step #1, it is again fit to the number of adoptions using a grid search. As described in property (iii) of section “Diffusion”, some words may be inherently more likely to be adopted than others. Therefore, each word takes on a different value of stickiness.

Five trials are run for each word w at the value of S w from step #2.

Steps 2 and 3 are repeated five times, producing a total of 25 trials (five different stickiness values and five simulations at each value) per word, and a total of 1900 trials across all 76 words. This procedure is repeated on each of the four models from section “Simulated counterfactuals”.

Model evaluation

We evaluate whether models match the empirical (i) spatial distribution of each word’s usage and (ii) spatiotemporal pathways between pairs of counties.

First, we assess whether each model trial diffuses in a similar region as the word on Twitter. We compare the frequency of simulated and empirical adoptions per county using Lee’s L , an extension of Pearson’s R correlation that adjusts for the effects of spatial autocorrelation 136 . Based on Grieve et al. (2019)’s evaluation of this metric 107 , the simulated and empirical regions are “very similar” if the correlation between the two spatial distributions is L  ≥ 0.4, “broadly similar” if L  ≥ 0.13, and “not similar” otherwise (see Supplementary Methods 1.5.2 for details).

Second, we compare the strength of empirical pathways against simulated pathways from the four models. The strength of the pathway between counties i and j is j ’s propensity to adopt the word after i does—measured via the zero-inflated correlation τ 137 between i ’s level of adoption at timestep t and j ’s adoption at t  + 1. We compare empirical to simulated pathways by calculating the Bayesian likelihood of the empirical pathway strengths τ E given the corresponding model pathway strengths \(\hat{{{{{\boldsymbol{\tau }}}}}_{N+I}}\) , \(\hat{{{{{\boldsymbol{\tau }}}}}_{N}}\) , or \(\hat{{{{{\boldsymbol{\tau }}}}}_{I}}\) . To validate this measure, we show that it reproduces ground truth pathways in simulated data. See Supplementary Methods 1.5.2 for more details on the metric and validation.

All reported differences are statistically significant at the level α  = 0.05, using a two-tailed bootstrap hypothesis test.

Cultural artifacts like language often diffuse in well-known geographic regions. Our model formalizes two interacting mechanisms thought to generate this spatial heterogeneity: (1) network: edges tend to concentrate between demographically similar locales, meaning words may diffuse in regions well-connected by this network; and (2) identity: linguistic variants are selectively adopted in (and subsequently transmitted from) areas where speakers identify with their social signal (e.g., a word like “democrap” will likely get more use in a Republican-leaning area). Using this model, we test the roles of network and identity in diffusion.

In light of known urban/rural dynamics, our expectation is that network and identity are responsible for the spread of new words in different types of geographies. In particular, in diverse urban areas, we would expect new words to diffuse among dissimilar people via the network’s weak ties. On the other hand, in more homogenous rural areas, we would expect these words to spread along strong ties with a shared identity. Consistent with this proposed mechanism, we hypothesize that:

In the USA as a whole (across all urban and rural geographies), the Network+Identity model will outperform all other models, and the Null (Shuffled Network+No Identity) model will perform the worst.

In different subsets of the country, network and identity may play more important roles. Specifically:

Urban-Urban Diffusion : Transmission between two urban counties would be best approximated by the Network-only model.

Rural-Rural Diffusion : Transmission between two rural (i.e., non-urban) counties would be best approximated by the Identity-only model.

Urban-Rural Diffusion : Diffusion between an urban and a rural county (urban-to-rural or rural-to-urban) is best approximated by the Network+Identity model.

Note that, in testing these hypotheses, we do not penalize the Network+Identity model for added complexity. All models have the same number of free parameters that are tuned to the data. Moreover, our model predicts the spatial diffusion and pathways of a new word from first principles, unlike machine learning models that often learn these macroscopic patterns from the data. In a formal model, adding mechanisms that are unrelated to the process being simulated could result in a worse fit between the model’s output and empirical data 138 , so the Network+Identity model could have worse performance on a network- or identity-only process. Indeed, the Network+Identity model does not always outperform the Network- and Identity-only models: on average these counterfactuals better predict diffusion in urban and rural areas, respectively (see section “Network and identity play complementary, interacting roles”), and in 54% of the full-US simulations we ran, the Network- or Identity-only models had higher Lee’s L correlation with the empirical geographical distribution (Network+Identity was best in 46% of trials, Network-only in 34% of trials, Identity-only in 20% of trials).

Testing the hypotheses

We run identically-seeded trials on all four models from section “Simulated counterfactuals” and track the number of adopters of each new word per county at each timestep. To test H1, we compare the performance of all four models on both metrics in section “Model evaluation”.

To test H2, we classify each county as either urban or rural by adapting the US Office of Management and Budget’s operationalization of the urbanized or metropolitan area vs. rural area dichotomy (see Supplementary Methods 2.8 for details). Then, using the measures from section 2.8, we calculate pathway weights and likelihoods between pairs of two urban counties (urban-urban), pairs of two rural counties (rural-rural), and between urban and rural counties (urban-rural, encompassing urban-to-rural or rural-to-urban).

In order to test whether network and identity play the hypothesized roles, we evaluate each model’s ability to reproduce just urban-urban pathways, just rural-rural pathways, and just urban-rural pathways. Our hypotheses suggest that network or identity may better model urban and rural pathways alone rather than jointly. Our results are robust to removing location as a component of identity (Supplementary Methods 1.7.5) , suggesting that our results are not influenced by explicitly modeling geographic identity.

To more directly test the proposed mechanism, we check whether the spread of new words across counties is more consistent with strong- or weak-tie diffusion. While our proposed mechanism is consistent with a purely empirical evaluation (network characteristics explain a higher fraction of the variation in Twitter’s urban-urban pathway strength, while similarity in identity explains more in rural-rural empirical pathways (Supplementary Figs. 20 , 21 ), these empirical characteristics likely have a nonlinear relationship with the strength of network- and identity-only pathways. Since we cannot empirically disentangle the network from identity, we use our Network-only model to assess whether pairs of counties are connected via a heavy network pathway (i.e., when the Network-only model pathway weight is high, suggesting diffusion occurs on the basis of network ties) and the Identity-only model to determine whether they are connected via a heavy identity pathway (i.e., when the Identity-only model pathway weight is high, suggesting diffusion occurs on the basis of shared identity).

Depending on the weight of the network- and identity-influenced pathways, diffusion between a pair of counties may tend to be driven by high levels of strong-tie diffusion (heavy network, heavy identity—or diffusion along network ties with shared identity); high levels of weak-tie diffusion (heavy network, light identity—or diffusion along diverse network ties); lower levels of strong-tie diffusion (light network, heavy identity); or low levels of weak-tie diffusion (light network, light identity). To check which of these mechanisms is most common in each type of geography, we use linear regression to correlate the strength of each empirical pathway ( τ E ) to a three-way interaction between the strength of pathways in the Network- and Identity-only models ( \(\hat{{{{{\boldsymbol{\tau }}}}}_{N}}\) , \(\hat{{{{{\boldsymbol{\tau }}}}}_{I}}\) ) and the type of pathway (urban-urban, rural-rural, or urban-rural); see Supplementary Methods 1.5.3 for details.

Network and identity better predict spatial properties jointly

Consistent with H1, we find that geographic properties of new words are best explained by the joint contributions of network and identity. Key properties of spatial diffusion include the frequency of adoption of innovation in different parts of the USA 23 , 67 , 139 , as well as a new word’s propensity to travel from one geographic area (e.g., counties) to another 23 , 67 , 139 , 140 . In both the physical and online worlds, where words are adopted carries signals about their cultural significance 21 , 141 , while spread between pairs of counties acts like “pathways” along which, over time, variants diffuse into particular geographic regions 23 , 67 , 139 .

Figure 1 shows the performance of all four models. Overall, the Network+Identity model best predicts a word’s spatial diffusion. It is the only model whose adoption regions are, on average, “broadly similar” to those on Twitter (mean( L ) ≈ 0.15) (Fig. 1 a), and the likelihood of the pathways observed on Twitter is more than 50% higher given the Network+Identity model’s pathways than the other models’ pathways (Fig. 1 b). In turn, the Network- and Identity-only models far overperform the Null model on both metrics. These results suggest that spatial patterns of linguistic diffusion are the product of network and identity acting together. The Network- and Identity-only models have diminished capacity to predict geographic distributions of lexical innovation, potentially attributable to the failure to effectively reproduce the spatiotemporal mechanisms underlying cultural diffusion. Additionally, both network and identity account for some key diffusion mechanism that is not explained solely by the structural factors in the Null model (e.g., population density, degree distributions, and model formulation).

The Network+Identity model best reproduces spatial diffusion on Twitter. a Shows the distribution of Lee’s L correlations between simulated and empirical county maps, for all 1900 trials of each model; the black error bars show the 95% confidence interval for the mean correlation, and vertical lines are thresholds for “broadly” ( L  > 0.13) and “very similar” ( L  > 0.4) correlations. b Shows the likelihood of the pathways observed on Twitter given each of the simulations. c Shows the Lee’s L correlation between the empirical and simulated geographic distributions over time; each point represents the Lee’s L correlation between the geographies of adopters in each quintile (e.g., if there are 1000 empirical uses and 10,000 simulated of the word, the 20th–40th percentile of usage would be empirical uses #201–400 correlated with simulated uses #2001–4001). Error bars are 95% two-tailed bootstrap confidence intervals.

Note that, for the sake of interpretability, our model is very simple (e.g., built on first principles, one parameter S w tuned, and initialized with only the word’s first ten adopters), and a more complex model (e.g., better trained to the data) would likely have even higher performance. However, in spite of this, the Network+Identity model is able to capture many key spatial properties. Nearly 40% of Network+Identity simulations are at least “broadly similar,” and 12% of simulations are “very similar” to the corresponding empirical distribution (Fig. 1 a). The Network+Identity model’s Lee’s L distribution roughly matches the distribution Grieve et al. (2019) found for regional lexical variation on Twitter, suggesting that the Network+Identity model reproduces “the same basic underlying regional patterns” found on Twitter 107 . Compared to other models, the Network+Identity model was especially likely to simulate geographic distributions that are “very similar” to the corresponding empirical distribution (12.3 vs. 6.8 vs. 3.7%). These “very similar” distributions tended to occur among words whose adopters are highly localized (average Moran’s I of 0.84 among very similar vs. 0.66 among others) and where the Network- or Identity-only models tend to have a “very similar” distribution (34 and 20%, respectively—in these cases, the Network+Identity model almost always improves upon the performance of the Network- and Identity-only counterfactuals). These results suggest that network and identity are particularly effective at modeling the localization of language.

Figure 2 shows the strongest spatiotemporal pathways between pairs of counties in each model. Visually, the Network+Identity model’s strongest pathways correspond to well-known cultural regions (Fig. 2 a). Some pathways extend from the mid-Atlantic into the South, where African American Language is most spoken 94 ; from Atlanta to other urban hubs, along pathways defined by the Great Migrations 94 ; along and between both coasts, which are politically, linguistically, and racially distinctive from the middle of the country 14 , 100 ; within the economically significant Dallas-Austin-Houston “Texas triangle” 142 ; and between this Texas region and the West Coast 143 . These pathways likely capture the complementary effects of network and identity. The Network-only model does not capture the Great Migration or Texas-West Coast pathways (Fig. 2 b), while the Identity-only model only produces just these two sets of pathways but none of the others (Fig. 2 c). These results suggest that network and identity reproduce the spread of words on Twitter via distinct, socially significant pathways of diffusion. Our model appears to reproduce the mechanisms that give rise to several well-studied cultural regions.

The Network+Identity model’s pathways correspond to culturally significant regions. The maps depict the strongest pathways between pairs of counties in the a Network + Identity model, b Network-only model, and c Identity-only model. Pathways are shaded by their strength (purple is more strong, orange is less strong); if one county has more than ten pathways in this set, just the ten strongest pathways out of that county are pictured.

Notably, the Network+Identity model is best able to reproduce spatial distributions over the entire lifecycle of a word’s adoption. Figure 1 c shows how the correlation between the empirical and simulated geographic distributions changes over time. Early adoption is well-simulated by the network alone, but later adoption is better simulated by network and identity together as the Network-only model’s performance rapidly deteriorates over time. The Identity-only and Null models perform poorly at all times. These results are consistent with H2, since theory suggests that early adoption occurs in urban areas (which H2 suggests would be best modeled by network alone) and later adoption is urban-to-rural or rural-to-rural (best modeled by network+identity or identity alone, per H2) 25 . We will more directly test H2 in the next section.

Network and identity play complementary, interacting roles

Next, we show that network- and identity-influenced pathways between counties play distinct roles in the spread of innovation. As expected, pathway strengths in the Network- and Identity-only models are strongly correlated (Pearson’s R  = 0.78, Spearman’s ρ  = 0.81), since edges in the network often form between demographically similar individuals 49 (see Supplementary Methods 1.6.4 for details). Nonetheless, the Network- and Identity-only pathways exhibit important differences, and our hypothesis is that spatial diffusion in the USA consists of two interacting mechanisms: The adoption of innovation among urban counties tends to happen via weak-tie diffusion—because for multiple reasons, potentially including structural factors like the preponderance of weak and demographically dissimilar ties or behavioral factor like preferences for diverse input 144 , 145 , urban diffusion may tend to occur when demographically dissimilar speakers are exposed to words that have not yet entered their social circle. Among rural counties, on the other hand, we expect new cultural artifacts to spread via strong-tie diffusion; speakers are largely connected to demographically-like individuals via strong ties, and adopt words that signal an identity that both parties share. Evidence from social networking sites suggests that urban vs. rural heterogeneity persists online 146 , suggesting that this mechanism is testable in our setting.

We find that, although network- or identity-only models may show promising results in one type of geography, these same models will not work in all subsets of the USA. Figure 3 quantifies the efficacy of network and identity in urban and rural diffusion, while Fig. 4 shows the associations between the empirical pathway strength and the Network- and Identity-only strengths ( \(\hat{{{{{\boldsymbol{\tau }}}}}_{N}}\) , \(\hat{{{{{\boldsymbol{\tau }}}}}_{I}}\) ) in these different geographies. We find that H2.1) the Network-only model best explains the strength of urban-urban pathways; H2.2) the Identity-only model most closely approximates empirical rural-rural pathways; and H2.3) the strength of urban-rural pathways is best captured by the joint Network+Identity model. To elaborate:

Based on the likelihood of the pathways observed on Twitter given each of the simulations: a ) The Network-only model best matches pathways containing an urban county; b ) The Identity-only model best matches pathways among rural counties; and c ) the Network+Identity model best matches pathways connecting an urban county to a rural county. Error bars are 95% two-tailed bootstrap confidence intervals.

Based on standardized coefficients from a linear regression predicting empirical pathway strength ( τ E ) from a three-way interaction between the strength of the pathways in the Network- and Identity-only models ( \(\hat{{{{{\boldsymbol{\tau }}}}}_{N}}\) , \(\hat{{{{{\boldsymbol{\tau }}}}}_{I}}\) ) and the type of pathway (urban vs. rural county): a The strength of the Network-only model’s pathways have the largest effect on the strength of the urban-urban empirical pathways and are positively associated with all pathways; b Conversely, identity pathways have the largest effect on the strength of rural-rural pathways and is negatively associated with urban pathways; and c Urban heavy network pathways are weakened by heavy identity pathways—and conversely, rural-rural heavy identity pathways are strengthened by heavy network pathways. Error bars are 95% two-tailed bootstrap confidence intervals.

H2.1: Weak-tie diffusion along urban-urban pathways

Empirical pathways are heaviest when there is a heavy network and light identity pathway (high levels of weak-tie diffusion) and lightest when both network and identity pathways are heavy (high levels of strong-tie diffusion) (Fig. 4 , dark orange bars). In other words, diffusion between pairs of urban counties tends to occur via weak-tie diffusion—spread between dissimilar network neighbors connected by low-weight ties 76 . This is consistent with Fig. 3 a, where the Network-only model best reproduces the weak-tie diffusion mechanism in urban-urban pathways; conversely, the Identity-only and Network+Identity models perform worse in urban-urban pathways, amplifying strong-tie diffusion among demographically similar ties.

H2.2: Strong-tie diffusion along rural-rural pathways

Empirical rural-rural pathways tend to be heavier when both network and identity pathways are heavy (high levels of strong-tie diffusion), and lightest when both network and identity pathways are light (low levels of weak-tie diffusion) (Fig. 4 , dark blue bars). This suggests that transmission between two rural counties tends to occur via strong-tie diffusion. This is consistent with Fig. 3 b, where the Identity-only model best reproduces strong-tie diffusion among rural-rural pathways, increasing spread among only counties with relevant shared identities; conversely, the Network-only and Network+Identity models underperform by inflating levels of diffusion among strongly connected individuals who lack a relevant shared identity. For example, if two strongly tied speakers share a political but not linguistic identity, the identity-only model would differentiate between words signaling politics and language, but the network-only model would not.

H2.3: Network and identity required for diffusion between urban and rural areas

Finally, pathways between an urban and a rural county (urban-to-rural or rural-to-urban) tend to fall in between urban-urban and rural-rural pathways—relying more on identity than urban-urban pathways and more on the network than the rural-rural pathways (Fig. 4 , light orange/blue bars). As such, the Network+Identity model, which includes both factors, best predicts these pathway strengths in Fig. 3 c. These results suggest that network and identity may both be involved in a word spreading between urban and rural counties—for instance, a network- or identity-only model of diffusion may not explain urban-rural diffusion well, because words may travel from an urban center to a more sparsely populated rural area via both weak ties (diverse connections, bridging different geographic regions) and strong ties (geographically distal but socially proximal connections, perhaps remnants of migrations or other contact 27 ).

Although differences in cultural diffusion between urban and rural areas have been well-documented 24 , 25 , 26 , 27 , 29 , 30 , 31 , few prior studies could explain how these differences came to be. We offer a well-reasoned proposal as to how network and identity produce these patterns. Specifically, these two social structures take on complementary, interacting functions: identity pathways drive transmission among rural counties via strong-tie diffusion, while network pathways dominate urban-urban spread via weak-tie diffusion. The interaction of network, identity, and type of pathway explains a high fraction (almost 70%) of the variance in empirical pathway strength. Empirical pathways, then, are well-explained by our proposed mechanism, since most of the variance in the strength of pathways can be explained by urban/rural differences in weak- and strong-tie diffusion.

Furthermore, as shown in Supplementary Methods 1.6.5 , urban/rural dynamics are only partially explained by distributions of network and identity. The Network+Identity model was able to replicate most of the empirical urban/rural associations with network and identity (Supplementary Fig. 17) , so empirical distributions of demographics and network ties likely drive many urban/rural dynamics. However, unlike empirical pathways, the Network+Identity model’s urban-urban pathways tend to be heavier in the presence of heavy identity pathways, since agents in the model select variants on the basis of shared identity. These results suggest that urban-urban weak-tie diffusion requires some mechanism not captured in our model, such as urban speakers seeking diversity or being less attentive to identity than rural speakers when selecting variants 144 , 145 .

Finally, contrary to prior theories 24 , 25 , 147 , properties like population size and the number of incoming and outgoing ties were insufficient to reproduce urban/rural differences. The Null model, which has the same population and degree distribution, underperformed the Network+Identity model in all types of pathways. However, notably, the Null model predicts urban-urban pathway strengths better than identity alone and rural-rural pathway strengths better than network alone, suggesting that population distributions and other structural properties may be a better predictor of diffusion than network or identity alone in some geographies, and underscoring the fact that network and identity facilitate complementary mechanisms of diffusion that are each necessary in different parts of USA.

Overall, both network and identity are required to explain the adoption of innovation: omitting either one entails not only poorer prediction of spatial properties, but also losing a key determinant of diffusion. Because of these interacting mechanisms, innovation may be adopted less selectively in urban areas, where populations are more diverse and more likely connected by weak ties, and words may diffuse along strong ties in the more homogeneous rural areas if they signal a shared identity.

We demonstrate that many existing models of cultural diffusion are missing a key dynamic in the adoption of innovation: models that consider identity alone ignore weak-tie diffusion between an urban resident and their diverse contacts; while models that use network alone are unable to consider shared identity and, as a result, likely dilute the diffusion of local variants to and from rural areas. One direct consequence, as demonstrated by the simulated counterfactuals, is a loss of accuracy in reproducing spatial distributions and spatiotemporal pathways of diffusion. Moreover, the absence of either network or identity also hamstrings a model’s ability to reproduce key macroscopic dynamics like urban-rural diffusion that are likely the product of both strong-tie and weak-tie spread.

We also propose and test a mechanism through which words diffuse between and among urban or rural areas. Through this framework, we see that the adoption of cultural innovation is the product of complementary, interacting roles of network and identity. These ideas build on a rich literature on the mechanisms of spatial diffusion 148 , 149 , 150 and have powerful theoretic implications across disciplines. In the subfield of variationist sociolinguistics, our proposed mechanism for diffusion draws a link between identity- and network-based explanations of language change 35 : showing how strong- and weak-tie theory require information about network and identity to work together. In network theory, this idea suggests how strong ties may influence diffusion when reinforced by node characteristics like identity 47 , and integrate Granovetter’s theories on tie strength 76 with cultural theory about the role of urban centers and rural peripheries in diffusion 25 , 27 . Moreover, in cultural geography, our analysis provides a key contribution to theory: since urban vs. rural differences are emergent properties of our model’s minimal assumptions, urban/rural variation may not be the result of the factors to which it is commonly attributed (e.g., population size and edge distribution). Instead, people perform their spatially-correlated identities by choosing among variants that diffuse through homophilous networks; the differences in network topology and demographic distributions in urban and rural populations, then, may create the observed differences in adoption. Importantly, our results suggest that, urban and rural populations both contribute differently to the diffusion of cultural innovation, rather than there being one dominating culture online. The geographic regions found with our data also highlight that despite the ease of widespread dissemination of cultural artifacts in online settings which could lead to more universally-shared behaviors, pre-Internet geographic distinctions in culture still persist.

Although our hypotheses were tested on lexical diffusion in the USA, the results may apply to the spread of many other types of cultural innovation (e.g., music, beliefs) in a single country or even globally. Linguistic variants often serve as proxies for cultural variables, since their adoption tends to reflect broader societal shifts 10 , 11 , 12 , 13 , 14 , 17 . Although many of our assumptions about spatial patterns may not apply in every part of the world (e.g., in places that are less diverse or spatially segregated), the model may also apply to other countries or even international contexts where networks and identities are geographically correlated 146 . In these cases, however, it would be important to adapt how one estimates network and identity: e.g., the network may be better estimated using platforms other than Twitter or even surveys, and salient identities may not be demographic. Additionally, the type of geographic patterns we found relied on there being one type of geography where weak-tie (diverse) diffusion was more common and other where strong-tie (shared identity) diffusion was more common and our results are unlikely to generalize to areas where this is not the case. This sort of mechanism, combining strong and weak-tie diffusion, has been hypothesized in cross-country diffusion of business models 151 , and could be applicable to other forms of innovation as well.

Moreover, the assumptions of our model are sufficiently general to apply to the adoption of many social or cultural artifacts. However, since our model assumes a non-zero probability of adoption from the start, it likely would apply only to forms of innovation where the barriers to adoption are low enough for the effects of network and identity to be salient (e.g., not something like technological innovation where functional needs and accessibility are factors). We might also expect the Network-only model to perform best when weak-tie diffusion is the main mechanism (e.g., job information 76 ) and the Identity-only model to perform better when innovation spreads mainly through strong-tie diffusion (e.g., health behaviors, activism 152 , 153 ). Importantly, our conclusions about the importance of network and identity, and the mechanisms we have identified for their interaction, may have applicability across a range of social science disciplines—and future work can use the agent-based model developed in this paper to test whether these findings generalize to other cultural domains.

In order to make more accurate predictions about how innovation diffuses, we call on researchers across disciplines to incorporate both network and identity in their (conceptual or computational) models of diffusion. Scholars can develop and test theory about the ways in which other place-based characteristics (e.g., diffusion into specific cultural regions) emerge from network and identity. Our model has many limitations (detailed in Supplementary Discussion), including that our only data source was a 10% Twitter sample, our operationalization of network and identity, and several simplifying assumptions in the model. Nevertheless, our work offers one methodology, combining agent-based simulations with large-scale social datasets, through which researchers may create a joint network/identity model and use it to test hypotheses about mechanisms underlying cultural diffusion.

Data availability

The datasets pertaining to the new words identified in this study (word list, initial adopters, identities signaled, day/county-level spatial timeseries) are available on Github: https://github.com/aparna-ananth/network-identity-abm . The Twitter network (edgelist) and users (registry) that support the findings of this study are taken from our university’s Twitter Decahose, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available.

Code availability

All code for the models and analysis in this study are available on Github: https://github.com/aparna-ananth/network-identity-abm .

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Acknowledgements

We thank Abigail Z. Jacobs, Nick Ellis, and members of University of Michigan’s “Sociolinguistics, Language Contact, and Discourse Analysis” and “Measuring and Modeling Culture” Seminars for their detailed feedback and useful discussion on an early version of this work, leading to changes in experimental design, analysis, and framing. This work has been partially funded by the Air Force Office of Scientific Research under award number FA9550-19-1-0029 and by the National Science Foundation under Grant No 1850221.

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Ananthasubramaniam, A., Jurgens, D. & Romero, D.M. Networks and identity drive the spatial diffusion of linguistic innovation in urban and rural areas. npj Complex 1 , 14 (2024). https://doi.org/10.1038/s44260-024-00009-9

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