conceptual framework in a literature review

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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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How to Make a Conceptual Framework

• 6-minute read
• 2nd January 2022

What is a conceptual framework? And why is it important?

A conceptual framework illustrates the relationship between the variables of a research question. It’s an outline of what you’d expect to find in a research project.

Conceptual frameworks should be constructed before data collection and are vital because they map out the actions needed in the study. This should be the first step of an undergraduate or graduate research project.

What Is In a Conceptual Framework?

In a conceptual framework, you’ll find a visual representation of the key concepts and relationships that are central to a research study or project . This can be in form of a diagram, flow chart, or any other visual representation. Overall, a conceptual framework serves as a guide for understanding the problem being studied and the methods being used to investigate it.

Steps to Developing the Perfect Conceptual Framework

• Pick a question
• Conduct a literature review
• Identify your variables
• Create your conceptual framework

1. Pick a Question

You should already have some idea of the broad area of your research project. Try to narrow down your research field to a manageable topic in terms of time and resources. From there, you need to formulate your research question. A research question answers the researcher’s query: “What do I want to know about my topic?” Research questions should be focused, concise, arguable and, ideally, should address a topic of importance within your field of research.

An example of a simple research question is: “What is the relationship between sunny days and ice cream sales?”

2. Conduct a Literature Review

A literature review is an analysis of the scholarly publications on a chosen topic. To undertake a literature review, search for articles with the same theme as your research question. Choose updated and relevant articles to analyze and use peer-reviewed and well-respected journals whenever possible.

For the above example, the literature review would investigate publications that discuss how ice cream sales are affected by the weather. The literature review should reveal the variables involved and any current hypotheses about this relationship.

3. Identify Your Variables

There are two key variables in every experiment: independent and dependent variables.

Independent Variables

The independent variable (otherwise known as the predictor or explanatory variable) is the expected cause of the experiment: what the scientist changes or changes on its own. In our example, the independent variable would be “the number of sunny days.”

Dependent Variables

The dependent variable (otherwise known as the response or outcome variable) is the expected effect of the experiment: what is being studied or measured. In our example, the dependent variable would be “the quantity of ice cream sold.”

Next, there are control variables.

Control Variables

A control variable is a variable that may impact the dependent variable but whose effects are not going to be measured in the research project. In our example, a control variable could be “the socioeconomic status of participants.” Control variables should be kept constant to isolate the effects of the other variables in the experiment.

Finally, there are intervening and extraneous variables.

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Intervening Variables

Intervening variables link the independent and dependent variables and clarify their connection. In our example, an intervening variable could be “temperature.”

Extraneous Variables

Extraneous variables are any variables that are not being investigated but could impact the outcomes of the study. Some instances of extraneous variables for our example would be “the average price of ice cream” or “the number of varieties of ice cream available.” If you control an extraneous variable, it becomes a control variable.

4. Create Your Conceptual Framework

Having picked your research question, undertaken a literature review, and identified the relevant variables, it’s now time to construct your conceptual framework. Conceptual frameworks are clear and often visual representations of the relationships between variables.

We’ll start with the basics: the independent and dependent variables.

Our hypothesis is that the quantity of ice cream sold directly depends on the number of sunny days; hence, there is a cause-and-effect relationship between the independent variable (the number of sunny days) and the dependent and independent variable (the quantity of ice cream sold).

Next, introduce a control variable. Remember, this is anything that might directly affect the dependent variable but is not being measured in the experiment:

Finally, introduce the intervening and extraneous variables. 

The intervening variable (temperature) clarifies the relationship between the independent variable (the number of sunny days) and the dependent variable (the quantity of ice cream sold). Extraneous variables, such as the average price of ice cream, are variables that are not controlled and can potentially impact the dependent variable.

Are Conceptual Frameworks and Research Paradigms the Same?

In simple terms, the research paradigm is what informs your conceptual framework. In defining our research paradigm we ask the big questions—Is there an objective truth and how can we understand it? If we decide the answer is yes, we may be working with a positivist research paradigm and will choose to build a conceptual framework that displays the relationship between fixed variables. If not, we may be working with a constructivist research paradigm, and thus our conceptual framework will be more of a loose amalgamation of ideas, theories, and themes (a qualitative study). If this is confusing–don’t worry! We have an excellent blog post explaining research paradigms in more detail.

Where is the Conceptual Framework Located in a Thesis?

This will depend on your discipline, research type, and school’s guidelines, but most papers will include a section presenting the conceptual framework in the introduction, literature review, or opening chapter. It’s best to present your conceptual framework after presenting your research question, but before outlining your methodology.

Can a Conceptual Framework be Used in a Qualitative Study?

Yes. Despite being less clear-cut than a quantitative study, all studies should present some form of a conceptual framework. Let’s say you were doing a study on care home practices and happiness, and you came across a “happiness model” constructed by a relevant theorist in your literature review. Your conceptual framework could be an outline or a visual depiction of how you will use this model to collect and interpret qualitative data for your own study (such as interview responses). Check out this useful resource showing other examples of conceptual frameworks for qualitative studies .

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How To Structure Your Literature Review

3 options to help structure your chapter.

By: Amy Rommelspacher (PhD) | Reviewer: Dr Eunice Rautenbach | November 2020 (Updated May 2023)

Writing the literature review chapter can seem pretty daunting when you’re piecing together your dissertation or thesis. As  we’ve discussed before , a good literature review needs to achieve a few very important objectives – it should:

• Demonstrate your knowledge of the research topic
• Identify the gaps in the literature and show how your research links to these
• Provide the foundation for your conceptual framework (if you have one)
• Inform your own  methodology and research design

To achieve this, your literature review needs a well-thought-out structure . Get the structure of your literature review chapter wrong and you’ll struggle to achieve these objectives. Don’t worry though – in this post, we’ll look at how to structure your literature review for maximum impact (and marks!).

But wait – is this the right time?

Deciding on the structure of your literature review should come towards the end of the literature review process – after you have collected and digested the literature, but before you start writing the chapter. 

In other words, you need to first develop a rich understanding of the literature before you even attempt to map out a structure. There’s no use trying to develop a structure before you’ve fully wrapped your head around the existing research.

Equally importantly, you need to have a structure in place before you start writing , or your literature review will most likely end up a rambling, disjointed mess. 

Importantly, don’t feel that once you’ve defined a structure you can’t iterate on it. It’s perfectly natural to adjust as you engage in the writing process. As we’ve discussed before , writing is a way of developing your thinking, so it’s quite common for your thinking to change – and therefore, for your chapter structure to change – as you write. 

Need a helping hand?

Like any other chapter in your thesis or dissertation, your literature review needs to have a clear, logical structure. At a minimum, it should have three essential components – an  introduction , a  body   and a  conclusion . 

Let’s take a closer look at each of these.

1: The Introduction Section

Just like any good introduction, the introduction section of your literature review should introduce the purpose and layout (organisation) of the chapter. In other words, your introduction needs to give the reader a taste of what’s to come, and how you’re going to lay that out. Essentially, you should provide the reader with a high-level roadmap of your chapter to give them a taste of the journey that lies ahead.

Here’s an example of the layout visualised in a literature review introduction:

Your introduction should also outline your topic (including any tricky terminology or jargon) and provide an explanation of the scope of your literature review – in other words, what you  will   and  won’t   be covering (the delimitations ). This helps ringfence your review and achieve a clear focus . The clearer and narrower your focus, the deeper you can dive into the topic (which is typically where the magic lies). 

Depending on the nature of your project, you could also present your stance or point of view at this stage. In other words, after grappling with the literature you’ll have an opinion about what the trends and concerns are in the field as well as what’s lacking. The introduction section can then present these ideas so that it is clear to examiners that you’re aware of how your research connects with existing knowledge .

2: The Body Section

The body of your literature review is the centre of your work. This is where you’ll present, analyse, evaluate and synthesise the existing research. In other words, this is where you’re going to earn (or lose) the most marks. Therefore, it’s important to carefully think about how you will organise your discussion to present it in a clear way. 

The body of your literature review should do just as the description of this chapter suggests. It should “review” the literature – in other words, identify, analyse, and synthesise it. So, when thinking about structuring your literature review, you need to think about which structural approach will provide the best “review” for your specific type of research and objectives (we’ll get to this shortly).

There are (broadly speaking)  three options  for organising your literature review.

Option 1: Chronological (according to date)

Organising the literature chronologically is one of the simplest ways to structure your literature review. You start with what was published first and work your way through the literature until you reach the work published most recently. Pretty straightforward.

The benefit of this option is that it makes it easy to discuss the developments and debates in the field as they emerged over time. Organising your literature chronologically also allows you to highlight how specific articles or pieces of work might have changed the course of the field – in other words, which research has had the most impact . Therefore, this approach is very useful when your research is aimed at understanding how the topic has unfolded over time and is often used by scholars in the field of history. That said, this approach can be utilised by anyone that wants to explore change over time .

For example , if a student of politics is investigating how the understanding of democracy has evolved over time, they could use the chronological approach to provide a narrative that demonstrates how this understanding has changed through the ages.

Here are some questions you can ask yourself to help you structure your literature review chronologically.

• What is the earliest literature published relating to this topic?
• How has the field changed over time? Why?
• What are the most recent discoveries/theories?

In some ways, chronology plays a part whichever way you decide to structure your literature review, because you will always, to a certain extent, be analysing how the literature has developed. However, with the chronological approach, the emphasis is very firmly on how the discussion has evolved over time , as opposed to how all the literature links together (which we’ll discuss next ).

Option 2: Thematic (grouped by theme)

The thematic approach to structuring a literature review means organising your literature by theme or category – for example, by independent variables (i.e. factors that have an impact on a specific outcome).

As you’ve been collecting and synthesising literature , you’ll likely have started seeing some themes or patterns emerging. You can then use these themes or patterns as a structure for your body discussion. The thematic approach is the most common approach and is useful for structuring literature reviews in most fields.

For example, if you were researching which factors contributed towards people trusting an organisation, you might find themes such as consumers’ perceptions of an organisation’s competence, benevolence and integrity. Structuring your literature review thematically would mean structuring your literature review’s body section to discuss each of these themes, one section at a time.

Here are some questions to ask yourself when structuring your literature review by themes:

• Are there any patterns that have come to light in the literature?
• What are the central themes and categories used by the researchers?
• Do I have enough evidence of these themes?

PS – you can see an example of a thematically structured literature review in our literature review sample walkthrough video here.

Option 3: Methodological

The methodological option is a way of structuring your literature review by the research methodologies used . In other words, organising your discussion based on the angle from which each piece of research was approached – for example, qualitative , quantitative or mixed  methodologies.

Structuring your literature review by methodology can be useful if you are drawing research from a variety of disciplines and are critiquing different methodologies. The point of this approach is to question  how  existing research has been conducted, as opposed to  what  the conclusions and/or findings the research were.

For example, a sociologist might centre their research around critiquing specific fieldwork practices. Their literature review will then be a summary of the fieldwork methodologies used by different studies.

Here are some questions you can ask yourself when structuring your literature review according to methodology:

• Which methodologies have been utilised in this field?
• Which methodology is the most popular (and why)?
• What are the strengths and weaknesses of the various methodologies?
• How can the existing methodologies inform my own methodology?

3: The Conclusion Section

Once you’ve completed the body section of your literature review using one of the structural approaches we discussed above, you’ll need to “wrap up” your literature review and pull all the pieces together to set the direction for the rest of your dissertation or thesis.

The conclusion is where you’ll present the key findings of your literature review. In this section, you should emphasise the research that is especially important to your research questions and highlight the gaps that exist in the literature. Based on this, you need to make it clear what you will add to the literature – in other words, justify your own research by showing how it will help fill one or more of the gaps you just identified.

Last but not least, if it’s your intention to develop a conceptual framework for your dissertation or thesis, the conclusion section is a good place to present this.

Example: Thematically Structured Review

In the video below, we unpack a literature review chapter so that you can see an example of a thematically structure review in practice.

Let’s Recap

In this article, we’ve  discussed how to structure your literature review for maximum impact. Here’s a quick recap of what  you need to keep in mind when deciding on your literature review structure:

• Just like other chapters, your literature review needs a clear introduction , body and conclusion .
• The introduction section should provide an overview of what you will discuss in your literature review.
• The body section of your literature review can be organised by chronology , theme or methodology . The right structural approach depends on what you’re trying to achieve with your research.
• The conclusion section should draw together the key findings of your literature review and link them to your research questions.

If you’re ready to get started, be sure to download our free literature review template to fast-track your chapter outline.

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

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27 Comments

Great work. This is exactly what I was looking for and helps a lot together with your previous post on literature review. One last thing is missing: a link to a great literature chapter of an journal article (maybe with comments of the different sections in this review chapter). Do you know any great literature review chapters?

I agree with you Marin… A great piece

I agree with Marin. This would be quite helpful if you annotate a nicely structured literature from previously published research articles.

Awesome article for my research.

I thank you immensely for this wonderful guide

It is indeed thought and supportive work for the futurist researcher and students

Very educative and good time to get guide. Thank you

Great work, very insightful. Thank you.

Thanks for this wonderful presentation. My question is that do I put all the variables into a single conceptual framework or each hypothesis will have it own conceptual framework?

Thank you very much, very helpful

This is very educative and precise . Thank you very much for dropping this kind of write up .

Pheeww, so damn helpful, thank you for this informative piece.

I’m doing a research project topic ; stool analysis for parasitic worm (enteric) worm, how do I structure it, thanks.

comprehensive explanation. Help us by pasting the URL of some good “literature review” for better understanding.

great piece. thanks for the awesome explanation. it is really worth sharing. I have a little question, if anyone can help me out, which of the options in the body of literature can be best fit if you are writing an architectural thesis that deals with design?

I am doing a research on nanofluids how can l structure it?

Beautifully clear.nThank you!

Lucid! Thankyou!

Brilliant work, well understood, many thanks

I like how this was so clear with simple language 😊😊 thank you so much 😊 for these information 😊

Insightful. I was struggling to come up with a sensible literature review but this has been really helpful. Thank you!

You have given thought-provoking information about the review of the literature.

Thank you. It has made my own research better and to impart your work to students I teach

I learnt a lot from this teaching. It’s a great piece.

I am doing research on EFL teacher motivation for his/her job. How Can I structure it? Is there any detailed template, additional to this?

You are so cool! I do not think I’ve read through something like this before. So nice to find somebody with some genuine thoughts on this issue. Seriously.. thank you for starting this up. This site is one thing that is required on the internet, someone with a little originality!

I’m asked to do conceptual, theoretical and empirical literature, and i just don’t know how to structure it

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On using conceptual frameworks to guide a systematic review: a comment on Berger-Tal et al.

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Andrew Sih, Lea Pollack, Emily Zepeda, On using conceptual frameworks to guide a systematic review: a comment on Berger-Tal et al., Behavioral Ecology , Volume 30, Issue 1, January/February 2019, Pages 12–13, https://doi.org/10.1093/beheco/ary153

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The core idea of systematic reviews as described by Berger-Tal et al. (2019) is undeniably exciting. Clearly, it is wonderful to “maximize objectivity, transparency, and comprehensiveness…while minimizing the bias at all stages of the review process”. In addition, involving stakeholders in the process is highly laudable both for their valuable input, and for facilitating “the uptake of these findings by managers and policy makers.” Of course, because the process is so systematic and rigorous, it poses challenges. As freely acknowledged by the authors, the devil is in the many details. Our focus will be on a point that the authors discussed briefly, but that we think deserves more detailed discussion: the need to have a strong conceptual framework to guide step one—the formulation of the questions.

We suggest that the answer to all interesting questions that might be the subject of a systematic review (in any field) is—it depends. The goal is to figure out what “it” depends on. What are the major factors that mediate the outcome of interest, and how might those factors interact to govern the outcome? The authors asked, for example: what are the fitness consequences of cage diving operations to sharks? The answer likely depends on the shark species, perhaps its social system or foraging mode, or even the individual shark’s personality. It might also depend on various aspects of the habitat, time of day, season, and details of the diving operation.

More generally, outcomes often depend on: 1) the focal organisms’ traits; 2) components of the environmental context; 3) specific aspects of the intervention or exposure; and 4) interactions among these. When doing a systematic review or meta-analysis, to gain insight on the importance of a given factor (or interaction), a key is to identify beforehand which ones seem likely to be important enough to include in the review (i.e., what information should be extracted, if available, from each paper?). We suggest that ideally authors first develop a clear, conceptual framework and use that framework to guide the formulation of explicit hypotheses on mediating factors. While a conceptual framework is often implicit in the investigators’ approach, in many cases, the review would benefit from a more explicit, rigorous, perhaps even theory-based, implementation.

In behavioral ecology, one common conceptual framework for predicting behavior is the cost-benefit approach. In a nice example of a conservation-relevant meta-analysis guided by a conceptual framework, Stankowich (2008) examined factors that influence ungulate flight responses from human disturbance. Using a cost-benefit framework as a guide, he looked at characteristics of the focal organisms (sex, age, reproductive status, past experience with humans), of the context (group size, environmental factors), and of the human disturbance (speed and direction of approach). To address unexplained variance, he suggested that interactions among factors might play an important role, emphasizing the need for an expanded conceptual framework for predicting interactions. Of course, the optimality approach is not always best. For example, if some animals behave adaptively, but others exhibit apparently maladaptive behavioral responses (e.g., to novel situations associated with human-induced rapid environmental change), Sih et al. (2011 , 2016 ) suggested an alternative framework for generating predictions, but the point remains that a conceptual framework is crucial for guiding a systematic review.

In contrast, many reviews or meta-analyses test for impacts of factors that are easy to extract from every paper, even if not necessarily connected to theory-based hypotheses. Some, for example, have focused largely on comparing taxonomic groups (e.g., birds vs. mammals) and broad ecosystem types (e.g., marine, freshwater or terrestrial systems) without clearly stated a priori hypotheses. In general, many if not most of the original studies in these reviews had information on at least some potential mediating factors—information that would have been valuable to extract while conducting the review. Berger-Tal’s suggested step one—careful formulation of the questions with broad, stakeholder engagement—should help alleviate this issue. Our point is to strongly emphasize that this key first step can be, in principle, more effectively handled by building the questions/hypotheses around an explicit conceptual framework. If nothing else, this process would bring behavioral ecology’s conceptual thinkers into the systematic review process—a move that we think should often prove valuable.

This paper was supported by the National Science Foundation: NSF IOS 1456724 grant to A.S. and NSF GRFP fellowships to E.Z. and L.P.

Berger-Tal O , Greggor AL , Macura B , Adams CA , Blumenthal A , Bouskila A , Candolin U , Doran A , FernÁndez-Juricic E , Gotanda KM , et al.  2019 . Systematic reviews and maps as tools for applying behavioral ecology to management and policy . Behav Ecol . 30:1–8.

Google Scholar

Sih A , Ferrari MC , Harris DJ. 2011 . Evolution and behavioural responses to human-induced rapid environmental change . Evol Appl . 4 : 367 – 387

Sih A , Trimmer PC , Ehlman SM. 2016 . A conceptual framework for understanding behavioral responses to HIREC . Curr Opin Behav Sci . 12 : 109 – 114

Stankowich T. 2008 . Ungulate flight responses to human disturbance: a review and meta-analysis . Biol Cons . 141 : 2159 – 2173

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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on 4 May 2022 by Bas Swaen and Tegan George. Revised on 18 March 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: visualise 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 visualising your expected cause-and-effect relationship.

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.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

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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Open Access

Peer-reviewed

Research Article

Development of a conceptual framework for defining trial efficiency

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Wolfson Institute of Population Health, Queen Mary University of London, London, England, United Kingdom

Roles Conceptualization, Methodology, Supervision, Validation

Affiliation Usher Institute, Asthma UK Centre for Applied Research, The University of Edinburgh, Edinburgh, Scotland, United Kingdom

• Charis Xuan Xie, 
• Anna De Simoni, 
• Sandra Eldridge, 
• Hilary Pinnock, 
• Clare Relton

• Published: May 23, 2024
• https://doi.org/10.1371/journal.pone.0304187
• Peer Review
• Reader Comments

Globally, there is a growing focus on efficient trials, yet numerous interpretations have emerged, suggesting a significant heterogeneity in understanding “efficiency” within the trial context. Therefore in this study, we aimed to dissect the multifaceted nature of trial efficiency by establishing a comprehensive conceptual framework for its definition.

To collate diverse perspectives regarding trial efficiency and to achieve consensus on a conceptual framework for defining trial efficiency.

From July 2022 to July 2023, we undertook a literature review to identify various terms that have been used to define trial efficiency. We then conducted a modified e-Delphi study, comprising an exploratory open round and a subsequent scoring round to refine and validate the identified items. We recruited a wide range of experts in the global trial community including trialists, funders, sponsors, journal editors and members of the public. Consensus was defined as items rated “without disagreement”, measured by the inter-percentile range adjusted for symmetry through the UCLA/RAND approach.

Seventy-eight studies were identified from a literature review, from which we extracted nine terms related to trial efficiency. We then used review findings as exemplars in the Delphi open round. Forty-nine international experts were recruited to the e-Delphi panel. Open round responses resulted in the refinement of the initial nine terms, which were consequently included in the scoring round. We obtained consensus on all nine items: 1) four constructs that collectively define trial efficiency containing scientific efficiency, operational efficiency, statistical efficiency and economic efficiency; and 2) five essential building blocks for efficient trial comprising trial design, trial process, infrastructure, superstructure, and stakeholders.

Conclusions

This is the first attempt to dissect the concept of trial efficiency into theoretical constructs. Having an agreed definition will allow better trial implementation and facilitate effective communication and decision-making across stakeholders. We also identified essential building blocks that are the cornerstones of an efficient trial. In this pursuit of understanding, we are not only unravelling the complexities of trial efficiency but also laying the groundwork for evaluating the efficiency of an individual trial or a trial system in the future.

Citation: Xie CX, De Simoni A, Eldridge S, Pinnock H, Relton C (2024) Development of a conceptual framework for defining trial efficiency. PLoS ONE 19(5): e0304187. https://doi.org/10.1371/journal.pone.0304187

Editor: Germain Honvo, University of Liege: Universite de Liege, BELGIUM

Received: December 4, 2023; Accepted: May 7, 2024; Published: May 23, 2024

Copyright: © 2024 Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its supporting information files.

Funding: CX is funded by the Wellcome Trust (224863/Z/21/Z). URL: https://wellcome.org/ . For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The funder does not play any role in the study design, data collection and analysis, decision to publish, and preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Worldwide, trial efficiency is a longstanding priority for the pharmaceutical industry [ 1 ], academia and funding bodies [ 2 , 3 ]. In 2004 in the US, the Clinical Trials Working Group of the National Cancer Advisory Board set the goal of improving operational efficiency to facilitate timely and cost-effective trial execution [ 4 ]. In the UK, the National Institute for Health and Care Research offers additional funding to support clinical trial units to advance the design and execution of efficient, innovative research, aiming to provide robust evidence to inform clinical practice and policy [ 5 ]. A recent article in The Lancet Global Health examined the challenges faced by current clinical trial research in low- and middle-income countries, and argued that efficient trials are needed to address research questions related to the increasing burden of non-communicable diseases in a timely and affordable way [ 6 ].

Currently, the concept of efficiency in healthcare trials has been used to refer to accelerated ethical approval [ 6 ], addressing multiple complex questions in a single trial [ 7 ] and with a minimised sample size [ 6 ], trials conducted with shorter duration [ 7 , 8 ], lower costs [ 9 ], and reduced resource requirements [ 10 ]. In addition, existing literature has discussed trial efficiency in terms of operational efficiency [ 11 – 13 ], scientific efficiency [ 11 ], statistical efficiency [ 13 , 14 ], and economic efficiency [ 15 ]. There is significant heterogeneity as to what is meant by efficiency in the context of trials, which may hinder effective communication and decision-making between stakeholders, and compromise the comparability of studies. Therefore, in this study we aimed to develop a conceptual framework for defining trial efficiency and to achieve expert consensus on the framework constructs.

Study design

We undertook a literature review to identify items that define and comprise trial efficiency. We then conducted an e-Delphi study to refine and validate those items and to achieve consensus on the constructs and the building blocks of trial efficiency. The ethics approval was obtained from Queen Mary University of London research ethics committee (QMERC22.316). This study follows the Guidance on Conducting and Reporting Delphi Studies (CREDES) [ 16 ].

Literature review for generating items

Our goal in the literature review was to collate existing discussions on efficiency in the context of trials, including definitions and attributes described as constituting an efficient trial. As discussions specifically focused on this subject are scarce, we included a broad range of study types, such as full trial papers or protocols, editorials, and opinion pieces that discussed trial efficiency. We considered all types of human trials evaluating medical, surgical, or behavioural interventions, including efficacy trials, effectiveness trials, and implementation trials. The search was limited to English-language articles, and there was no restriction on publication dates. To carry out the review, we searched MEDLINE (via Ovid) database, for terms such as ’trial’ and ’efficien*’ in article titles and keywords. As ’efficiency’ is a common word in literature, we searched for these two keywords only within article titles (rather than within the abstracts) ensuring the results’ relevance to the discussion of trial efficiency. The detailed inclusion and exclusion criteria are listed in S1 Table .

Panel selection and recruitment.

The aim was to recruit a diverse panel of experts from the trial community, encompassing a range of roles and perspectives. This included international researchers identified through the literature review, colleagues who are part of professional trial networks such as UK trial managers’ network, representatives from funding bodies, journal editors, and members of the public who have been involved in trials. Purposive sampling and snowball sampling methods were then used to identify additional participants. We approached those participants with known contact details by individual emails generated through Clinvivo [ 17 ], while for colleagues within professional networks, where we didn’t have individual contact details, we sent a generic recruitment email to the network’s mailing list. Recruitment began in November 2022 and continued until March 2023. Written informed consent was obtained online through the Clinvivo Delphi system.

Data collection.

We opted for two rounds of data collection because consensus was achieved by the end of the second round. These rounds were preceded by a pilot round to test the feasibility of the open round.

Pilot test . We pilot tested the feasibility of the open round questionnaire amongst colleagues with diverse experience in trial design and conduct at the Pragmatic Clinical Trial Unit of Queen Mary University of London. This provided valuable feedback on the clarity of the questions, the appropriateness of the response options, and the overall structure of the questionnaire. Based on the feedback received during the pilot testing, we made revisions and refinements to the questionnaire to enhance its usability.

Open round . In the open round, we invited panellists to share their thoughts on 1) their understanding of trial efficiency and 2) the most efficient or inefficient aspects they have encountered in the trials they have conducted or in which they have participated. These questions were designed as free-text to encourage detailed, narrative responses. To gain insights into the participants’ backgrounds, we collected information on countries of residence, and roles within the trials (see S1 File for the questionnaire). This open round allowed us to gather diverse viewpoints and experiences related to trial efficiency which contributed to the development of a comprehensive set of items for ranking in the subsequent round. The data collection for this round took place over four weeks, with reminder emails sent to participants after the second and third weeks.

Scoring round . Panel members from open round were emailed a link to the second questionnaire. They were asked to rate the importance of the proposed items on a scale of 1 to 9 (1: not at all important to 9: critically important). At the end of each question, there was a free text space for any comments they wished to share. The scoring round data collection spanned four weeks with weekly reminders to participants.

Data analysis and consensus.

Descriptive statistics were used to analyse quantitative demographics and thematic analysis was used to summarise free text responses from both Delphi rounds. To assess disagreement and appropriateness, we used the Research ANd Development (RAND)/ University of California Los Angeles (UCLA) appropriateness method [ 18 ]. It involves calculating the median score, the inter-percentile range (IPR) (30th and 70th), and the inter-percentile range adjusted for symmetry (IPRAS), for each item being rated. Consensus was defined as items rated “without disagreement”, measured by the IPRAS.

Patient and public involvement.

In this study, members of the public (n = 4) (including two who had participated in trials) were invited to share their thoughts, participate in the ranking process, provided with the outcomes of each round upon completion. They were considered experts due to their lived experience and offered £30 voucher as a compensation for their time.

Delphi participants

Out of 106 international experts approached, and 4 e-mails sent to network mailing lists, forty-nine participants responded to the open round (United Kingdom (n = 37), United States (n = 7), Canada (n = 2), Australia (n = 1), Ireland (n = 1), and Kenya (n = 1)). The panel included a diversity of roles including statisticians (n = 17), trial managers (n = 12), principal investigators (n = 7), funders (n = 4), journal editors (n = 3), member of the public (n = 4), data managers (n = 3), site staff (n = 2), sponsors (n = 2), researchers (n = 2), monitors (n = 2), ethicist (n = 1), clinician (n = 1), CTU manager (n = 1), trial support officer (n = 1), and trial methodologist (n = 1). Many participants had more than one role. See Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0304187.g001

Literature review

We included a total of 78 studies for data analysis (see S1 Fig ), including 6 (8%) reviews, 15 (19%) perspectives or commentaries, 1(1%) interview, 2 (3%) case studies, 2 (3%) surveys and 3 (4%) randomised trials, and 49 (63%) methodologies describing new trial designs. Only 8(10%) studies had explicitly defined or explained what ‘efficiency’ meant in the context of their trials (see S2 Table for details). We categorised discussions of efficiency from the literature into nine key items: 1)scientific efficiency [ 11 , 19 , 20 ], 2)operational efficiency [ 11 , 20 , 21 ], 3)statistical efficiency [ 14 , 22 – 24 ] and 4)economic efficiency [ 15 , 25 ], 5)efficiency in trial designs [ 7 , 8 , 23 , 26 – 45 ], 6)trial conduct [ 11 , 20 , 21 , 46 – 66 ], and other aspects such as 7)improving efficiency using information technologies and mobile apps [ 53 , 67 – 70 ]; 8)involving the public and stakeholders [ 20 , 71 ]; and 9)efficient trial reviews and regulatory approvals [ 28 , 66 , 72 – 74 ]. (see Table 1 for details). These results were included as exemplars in the Delphi open round questionnaire. The detailed description of the literature review has previously been made available [ 75 ] to ensure full transparency and to facilitate open scholarly dialogue.

https://doi.org/10.1371/journal.pone.0304187.t001

When asked to define trial efficiency, some participants referred to definitions from the literature review, while other cited similar definitions tailored to their trial context. When asked about the most efficient/inefficient facets of trial efficiency, the responses resonated closely with the findings from our literature review ( Fig 2 ). Specifically, trial design emerged as the facet most frequently cited as enhancing efficiency, whereas data collection was often highlighted as the element that most impeded efficiency.

The x-axis represents the frequency of responses.

https://doi.org/10.1371/journal.pone.0304187.g002

By incorporating findings from this round, we further refined the nine items identified from the literature review and divided them into two groups: 1) theoretical and abstract constructs: scientific efficiency, operational efficiency, statistical efficiency, and economic efficiency; 2) empirical and fundamental building blocks: trial design (including endpoints selection, statistical analysis plan, protocol development, etc.), trial process (including recruitment and retention, data collection and analysis, trial administration, etc.), superstructure (including regulatory approvals, funding application etc.), infrastructure (including financial and physical resources such as cost, information technologies, routine healthcare data, etc.), and stakeholders. This resulted in a total of nine items for rating in the scoring round (see Table 2 ).

https://doi.org/10.1371/journal.pone.0304187.t002

Scoring round and consensus

Forty participants responded (82%) to the scoring round and there was no disagreement on any items ( Table 2 ). We also conducted sub-analyses by five role groups: (1) funders and sponsors (n = 6); (2) statisticians (n = 13); (3) trial managers (n = 10); (4) principal investigators (n = 6); and (5) PPIs (n = 3). Group membership was not mutually exclusive. Stratified results showed widespread agreement that the items were appropriate, with the exception of one of the building blocks–superstructure. The funders and sponsors group disagreed this item was appropriate ( S3 Table ). As a result, no new items were added but we slightly modified the explanation of each proposed item, in line with free-text comments made by the participants.

Theoretical constructs of trial efficiency: Revised definitions incorporating Delphi comments

Scientific efficiency..

Some participants were confused by the provided definition ( Box 1 . quote 1); while some suggested expanding the definition with the inclusion of feasibility and implementation ( Box 1 . quotes 2–3). As such, we refined the definition as the balance of methodological rigour, relevance of the research question, and feasibility of trial design. It prioritises effective use of resources, including data, to minimise research waste, considers the alignment of design and statistical strategies, and underscores the importance of the study’s practical impact on stakeholders and delivering value to end-users.

Operational efficiency.

Some comments suggested the definition should be expanded to consider operation feasibility, bureaucracy, and ongoing evaluation ( Box 1 . quotes 4–6). Therefore, we modified operational efficiency as the optimal management, organisation, execution, and continuous evaluation of trial processes and procedures. It emphasises operational feasibility (such as ensuring there are enough workforce, managing delays, and working effectively with third-party providers), reducing unnecessary bureaucracy and duplication, and continuously assessing the trial for potential improvements.

Statistical efficiency.

The initial definition ( Table 1 ) was expanded based on the participants’ comments (Box1. quotes 7–8), as the application of design and analytical methods that result in more accurate estimates of treatment effects or other parameters of interest. This includes considerations of minimising the amount of data to be collected, accounting for missing data, and managing sources of bias or confounding; its focus is specifically on maximising the accuracy and reliability of results given the data collected.

Economic efficiency.

We increased the clarity of the initial definition according to scoring round feedback ( Box 1 .quotes 9–10): the optimal use of resources in the trial design, implementation and analysis, to ensure immediate and long-term cost-effectiveness of the trial. This focus on value ensures that resources are utilised to their fullest extent without compromising the quality of the research. It emphasises on the cost-effectiveness of conducting the trial.

Box 1. Scoring round exemplar free-text comments related to the construct definitions

Scientific efficiency.

• Quote 1 : “Not sure rigour equates to efficiency” (Participant n. 17, principal trial investigator)
• Quote 2 : “Feasibility of trial design needs to be included here. You could have the perfect trial design but no participants or high withdrawals and lack of site engagement.” (Participant n.2, trial manager)
• Quote 3 : “This may also need to include how important the findings will be to service users and the public and whether there are ways they are expected to be implemented in practice.” (Participant n.28, trial support officer)

Operational efficiency

• Quote 4 : “I’d make particular focus on the bureaucracy ‐ endless paperwork.” (Participant n.3, funder)
• Quote 5 : "Feasibility of operational efficiency. You may have participants and engaged sites but you need operational feasibility to align." (Participant n.2, trial manager)
• Quote 6 : “Would like to see reference to the ongoing assessment of a trial in the descriptor.” (Participant n.39, trial manager)

Statistical efficiency

• Quote 7 : “and accounting for missing data, and sources of bias or confounding” (Participant n.19, principal trial investigator)
• Quote 8 : “Also needs to encompass other aspects of analysis, e.g., health economics.” (Participant n.14, statistician)

Economic efficiency

• Quote 9 : “Allowing for the concept of data sharing beyond the life of the study” (Participant n.37, sponsor)
• Quote 10 : “Need to be clear that this is (I presume) related to the costs of delivering the trial and not the cost of the intervention (i.e. health economic analysis).” (Participant n.26, statistician)

Essential building blocks comprising an efficient trial

Overall, there was a strong consensus on the building blocks; the free-text comments did not suggest significant alterations, but recommended adding some details within each building block. Trial design concerns the planning and organisation of a trial, which may include the trial methodologies, research questions, sample size, interventions, control group, endpoints and outcomes; document development such as funding application; as well as planning feasibility and pilot studies. The trial process involves the setup, execution, and closeout phases of a trial (see S2 Fig for details). Stakeholders are the critical human factor, they are individuals or groups with an interest or concern in the design, execution, and outcomes of a trial. They could be trial participants (e.g. patients, practitioners, health system leaders, public health organisations, etc.), trialists (e.g. investigators, researchers, trial managers, statisticians, etc), funders, sponsors, trial sites and their staff, regulatory authorities, healthcare and clinical practitioners, the scientific community (researchers, academics, and clinicians interested in the trial’s outcomes and its implications for future research) and the general public (the broader population who may ultimately benefit from the knowledge generated by the clinical trial). Infrastructure is the underlying framework, systems, and resources required to design, implement, manage, and analyse a trial, such as resources (human, financial, physical), information systems and technologies, and healthcare data. Superstructure serves as the overarching structure of a trial, including laws, policy, and governance.

With these, we developed a Trial Efficiency Pentagon ( Fig 3 ) to place the five building blocks and to illustrate the multiple connections among them ‐ improvements in one block may potentially lead to trade-offs in one or more other blocks.

https://doi.org/10.1371/journal.pone.0304187.g003

The final conceptual framework for defining trial efficiency

Fig 4 represents the finalised framework. The term trial efficiency is complex and multifaceted, encompassing four conceptual constructs with five essential building blocks.

The outer blue circle outlines theoretical constructs of trial efficiency: Scientific Efficiency, Statistical Efficiency, Operational Efficiency and Economic Efficiency. At its core, the inner pentagon outlines the empirical building blocks: Superstructure, Stakeholders, Infrastructure, Trial Process, and Trial Design. The cyclical arrows indicate the necessity for a balanced consideration of each building block within each construct to optimise trial efficiency.

https://doi.org/10.1371/journal.pone.0304187.g004

Main findings

Consensus was achieved on the four constructs that together define trial efficiency: scientific efficiency, operational efficiency, statistical efficiency and economic efficiency; and the five essential building blocks for considering an efficient trial: trial design, trial process, infrastructure, superstructure, and stakeholder.

The conceptual constructs, empirical building blocks, and interrelationships

Overall there was no disagreement over the constructs that conceptually define trial efficiency. However, some concerns were raised regarding potential overlaps, between scientific efficiency and statistical efficiency, and between operational efficiency and economic efficiency ( S4 Table ). These four constructs share some common elements. However, they are conceptually distinct and each construct brings unique aspects to the concept of trial efficiency. Scientific efficiency, for instance, focuses primarily on the methodological rigour [ 77 ] and feasibility of trial design, while statistical efficiency is concerned with achieving the most accurate results possible with the smallest amount of data collected [ 78 ]. The overlap lies in the fact that both aim to optimize the quality and validity of the trial’s findings, yet their distinct focus underlines their separate roles within the overarching construct of trial efficiency. Similarly, while operational and economic efficiency both aim to make the best use of resources [ 11 ], they do so in different ways and in different contexts. Operational efficiency is about the effective management and organization of trial processes and procedures [ 11 , 13 ], while economic efficiency involves optimizing resource use in relation to the cost of delivering the trial. By maintaining these conceptually distinct constructs, we were able to capture the broad spectrum of abstract factors that define trial efficiency, thus offering a nuanced theoretical framework for its comprehension.

The proposed building blocks create a foundation for the formulation of an efficient trial. In the Delphi scoring round, there was strong consensus regarding the significance of these building blocks, with an average median score of 8.4 on a 1–9 scale. However, some participants perceived hierarchy among the building blocks, suggesting that some (e.g., trial design and process) hold more importance than others. This was reflected in the literature review and responses in the Delphi open round, where certain building blocks ‐ such as trial design ‐ were more frequently discussed as critical determinants of trial efficiency. Despite these observations, we propose that all five building blocks have equal importance and they mutually contribute to the overall efficiency of the trial. These foundational elements are also interconnected, for instance, even the most rigorous and feasible trial design is contingent upon the availability of suitable infrastructure support and requires inputs from stakeholders. Therefore, we advocate for a balanced view where no single building block takes precedence in the trial efficiency pentagon.

There is a layered connection between the constructs and the building blocks: the constructs were conceptualised to provide a broad, overarching view of efficiency within healthcare trials. In contrast, the building blocks were identified as the essential, practical components that operationalise efficiency in real-world settings. In addition to this relationship, we suggest that for a comprehensive understanding, each efficiency construct takes into account all five building blocks. For instance, while it may seem apparent that scientific efficiency is closely linked with trial design, focusing on how the study is conceptualised to ensure methodological soundness; it also intersects with stakeholder involvement, where patient and public engagement can improve the trial design and thus the trial outcomes’ relevance and applicability.

Implications

According to the results from the literature review, few studies explicitly defined efficiency in the context of trials and no effort has been made to develop a unified and agreed definition for trial efficiency. Linguistically, ‘efficiency’ is defined as “the production of the desired effects or results with minimum waste of time, effort, or skill” [ 79 ]. This definition shares similarities with those from the literature ( S2 Table ), wherein the outstanding characteristic corresponds to the balance between the inputs (e.g. resources) and the outputs (e.g. the objectives of the trial). Nevertheless, these interpretations are often narrowly tailored. In this study we hoped to offer a holistic view that captures the nuances and complex aspects of trial efficiency and which may benefit policymakers, funders, and researchers in making informed decisions, leading to improved trial implementation and patient care. Enhancing efficiency was emphasised in the UK Department of Health and Social Care’s 2022–2025 strategic plan for clinical research [ 80 ]. As of the drafting of this paper, the U.S. Food and Drug Administration is announcing the updated recommendations for good clinical practices advocating for greater efficiency in trials by modernising both design and conduct [ 81 ]. Therefore, it is evident that our study is timely, positioning the urgency of comprehensively understanding trial efficiency.

Strengths and limitations

Drawing on both literature review and expert opinion, our study followed a rigorous approach to develop a conceptual framework of trial efficiency. We included a wide range of experts in trial communities including members of the public, enhancing the comprehensiveness and richness of our study. Nevertheless, nine participants did not respond to the scoring round, which could have introduced potential biases in reaching a consensus or perhaps missed subtle distinctions regarding the significance of certain trial elements. However, given the diverse range of participants who did engage, coupled with the triangulation with existing literature, this non-response is not expected to significantly impact the overall validity and comprehensiveness of our Delphi findings.

While we have sought to delineate each construct and building block distinctly, we acknowledge the potential for different interpretations of qualitative data. The interplay between the identified themes is likely to be more intricate, reflecting the complex nature of trial efficiency. Future research could delve deeper into this interplay to unravel the connections.

The ’trial efficiency pentagon’, emerging as a novel concept from this study, is a promising tool for assessing trial efficiency (proactively and retrospectively). For example, it could be developed to support group discussions and/or calibrated as an evaluation instrument to measure the efficiency of a trial. However, it is limited by lacking robust theoretical foundation. To elucidate, while we’ve pieced together insights and perspectives to shape the pentagon, we have not rooted it in any established theory or conceptual model. This could mean that certain fundamental aspects of trial efficiency might be overlooked or not holistically represented. In the future, we aspire to hone the pentagon into an evidence-based, theory-informed tool and we welcome insights from our readers and remain open to potential collaborations to its further development.

This is the first attempt to dissect the concept of trial efficiency into theoretical constructs. In this pursuit of understanding, we are not only unravelling the complexities of trial efficiency but also laying the groundwork for evaluating the efficiency of an individual trial or a trial system in the future.

Supporting information

S1 fig. prisma flowchart..

https://doi.org/10.1371/journal.pone.0304187.s001

S2 Fig. Trial process in general.

https://doi.org/10.1371/journal.pone.0304187.s002

S1 Table. Literature review inclusion and exclusion criteria.

https://doi.org/10.1371/journal.pone.0304187.s003

S2 Table. Efficiency definitions/explanations in the literature.

https://doi.org/10.1371/journal.pone.0304187.s004

S3 Table. Scoring round stratified results.

https://doi.org/10.1371/journal.pone.0304187.s005

S4 Table. Scoring round exemplar quotes related to potential overlaps among the four constructs.

https://doi.org/10.1371/journal.pone.0304187.s006

S1 File. Open round questionnaire.

https://doi.org/10.1371/journal.pone.0304187.s007

Acknowledgments

We thank Prof. Shaun Treweek for his insightful discussion on trial efficiency, which has largely inspired this work. We thank Ann Thomson, Senior Trial Manager at Queen Mary University of London’s Pragmatic Clinical Trials Unit, for her valuable discussions and insights into the trial process. Our thanks also go to the Health Research Board ‐ Trials Methodology Research Network for their assistance in promoting our Delphi study through their email newsletter. We acknowledge the support of the UKCRC Registered CTU Network. The views expressed are those of the author(s) and not of the UKCRC or its members. We are immensely thankful to all participants of the Delphi study rounds for their invaluable contributions and willingness to share their expertise. We have received consent to acknowledge the following participants by name (with no particular order): Monica Taljaard, Lelia Duley, Sarah Markham, Deb Smith, Catey Bunce, Stephen Brealey, Steff Lewis, Laura Miller, Jacqueline French, Fiona Hogarth, Gail Holland, Nikki Totton, Nick Kisengese, Joanne Haviland, Matthew Burns, Richard Hooper, Claire Ayling, Catherine Arundel, Ines Rombach, Seonaidh Cotton, Paula Kareclas. Lastly, we appreciate the reviewer’s comments, which have been instrumental in enhancing the development of the conceptual framework.

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Synthesizing three decades of digital servitization: a systematic literature review and conceptual framework proposal

• Theoretical article
• Open access
• Published: 08 May 2024

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• Pedro E. Minaya   ORCID: orcid.org/0000-0002-1179-9378 1 ,
• Lucía Avella   ORCID: orcid.org/0000-0003-2598-7318 2 &
• Juan A. Trespalacios   ORCID: orcid.org/0000-0003-0658-4038 2  

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This study, through a systematic literature review spanning 1990 to 2023, interrogates how servitization, and nowadays digital servitization, enhances manufacturing competitiveness. It introduces the DASOBI (Drivers, Actors, Strategies, Obstacles, Benefits, and Impact) framework for navigating the digital servitization transition, emphasizing strategic adaptability and technological alignment. Analysis of 157 articles reveals a significant increase in research, highlighting digital servitization’s role in competitive enhancement and customer engagement. The DASOBI framework offers manufacturers a novel approach for managing this transition, marking a unique contribution by distilling extensive literature into actionable insights for both theory and practice in the evolving field of digital servitization.

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1 Introduction

1.1 context, motivation, and research topic.

In today’s dynamic manufacturing sector, companies are increasingly acknowledging the importance of complementing their product offerings with value-added services. This strategic shift, known as servitization—and more specifically digital servitization—marks a fundamental turn in the contemporary business paradigm. This transformation involves not only a shift from a product-centric to a service-centric focus but also a deep integration of advanced digital technologies. While considerable research has been conducted on individual aspects of servitization, a comprehensive analysis that encompasses all essential facets of this phenomenon, from its motivations to its final outcomes, remains relatively unexplored. This research proposal aims to develop a holistic conceptual framework that synthesizes and extends existing knowledge, thereby providing a more complete and nuanced understanding of digital servitization. This exhaustive review examines this evolving business model, highlighting its key benefits and challenges, its intersection with digital technologies, and its theoretical and practical implications.

The foundational premise, supported by Bustinza et al. ( 2015 ), suggests that manufacturing companies can achieve higher returns by offering services in conjunction with their products, a claim echoed in seminal works by Davies et al. ( 2007 ), Johnstone et al. ( 2009 ), Martín-Peña et al. ( 2017 ), and Leoni and Aria ( 2021 ). These services, ranging from maintenance and support to more sophisticated and customized solutions, expand the revenue streams of these firms. In this context, the contributions of Baines et al. ( 2007 ) and Neely et al. ( 2011 ) are pivotal, as they underscore how transitioning to a service-oriented market is driving strategic transformations in manufacturing firms, emphasizing value creation and differentiation in increasingly competitive markets (Brady et al. 2005 ).

The current market dynamics almost make this shift imperative. As noted by Sandström et al. ( 2008 ) and Tukker ( 2015 ), companies that limit their offerings to products alone face formidable challenges in maintaining profitability, driving them toward business model innovation that incorporates services into their product portfolios, as discussed in the literature by Gebauer and Fleisch ( 2007 ), Visnjic and Van Looy ( 2013 ), and Díaz-Garrido et al. ( 2018 ).

Servitization requires effective coordination among multiple stakeholders. Alghisi and Saccani ( 2015 ) address the critical importance of internal and external alignment, while Ayala et al. ( 2019 ) highlight the essential role of service providers in the successful adoption of servitization strategies. Moreover, Baines et al. ( 2011 ) and Lightfoot et al. ( 2013 ) explore how manufacturing firms can effectively integrate services into their product portfolio, emphasizing the importance of a strategically well-planned approach.

Beyond being a customer-facing strategy, the internal benefits are equally compelling. As delineated by Kamp and Alcalde ( 2014 ), servitization facilitates process optimization and extends the lifespan of machinery. These advantages are further enhanced with the incorporation of digital technologies, particularly in the era of Industry 4.0 (Kamp and Perry 2017 ). This digital servitization, explored in studies by Lee et al. ( 2014 ), Kans and Ingwald ( 2016 ), and Paiola and Gebauer ( 2020 ), offers an enhanced layer of value, encompassing innovative goods and services.

Researchers such as Favoretto et al. ( 2022 ) and Rabetino et al. ( 2023 ) have elucidated how technological advancements act as catalysts for developing differentiated products and services, thereby enhancing competitiveness (Müller et al. 2021 ). This leads to the formulation of hybrid business models, termed Product-Service Systems (PSS), which are economically, socially, and environmentally sustainable. This PSS model provides a more holistic solution, meeting specific customer needs beyond just providing functional products (Barquet et al. 2013 ).

In this process, a demand for specific organizational and technological capabilities is identified. Coreynen et al. ( 2017 ) and Schroeder et al. ( 2022 ) have pinpointed the importance of organizational structure and technological capabilities, particularly in the context of digitalization, as key factors for a successful transition to digital servitization (Parida et al. 2014 ; Kanninen et al. 2017 ).

Implementing servitization, as highlighted by Mathieu ( 2001 ) and Yu and Sung ( 2023 ), is not without its challenges, ranging from internal organizational resistance to external factors, such as customer reluctance. Brax ( 2005 ) and Benedettini et al. ( 2015 ) provide a comprehensive analysis of these risks, emphasizing the importance of effective management to navigate potential obstacles in achieving successful servitization (Windahl and Lakemond 2006 ; Pessôa and Becker 2017 ). The process demands a well-structured and strategically informed approach, incorporating both business and customer perspectives. Proper implementation of servitization can lead to substantial benefits, as demonstrated by Baines et al. ( 2009b , 2017 ) and Wang et al. ( 2018 ), highlighting its potential for long-term value creation (Brady et al. 2005 ).

The phenomenon of servitization, particularly in its digital form, has emerged as a prominent area of study, characterized by its complexity and multidimensionality. Academic literature has thoroughly explored this concept, from underlying motivations to implementation strategies, examining both inherent challenges and potential benefits (Raddats et al. 2016 ; Rabetino et al. 2021 ).

1.2 Research gap

Despite the extensive body of knowledge on servitization amassed by previous studies, there remains a discernible gap characterized by fragmented examinations rather than a consolidated analytical approach. This study pinpoints a need for a unified framework that can effectively guide servitization strategies, addressing this lacuna as a pivotal area for forthcoming research (Calabrese et al. 2019 ; Kohtamäki et al. 2020a ). The advent of the digital era has precipitated transformative shifts, underscoring the servitization concept—the transition from purely selling products to offering integrated product-service solutions. Nevertheless, the interaction between servitization and digital technologies, a realm referred to as digital servitization, remains a relatively uncharted territory. This area lacks a systematic and thorough review spanning the last three decades. This omission highlights the imperative need for an in-depth understanding of how servitization has evolved and the essential development of a framework to adeptly navigate the intricacies involved in implementing these strategies effectively.

1.3 Methodology proposed

To address the identified research gap, our study employs a comprehensive, multi-phased methodology structured as follows: Initially, we conduct an in-depth examination of the literature on servitization and digital servitization. This phase aims to develop an integrative theoretical framework that captures the evolution of servitization over the past three decades, emphasizing the shift toward digital service delivery within the manufacturing sector. Subsequently, the study undertakes a systematic literature review to classify the existing body of work. This review specifically focuses on selecting pertinent studies that encompass both traditional and digital servitization, aiming to identify trends, patterns, and existing research gaps. Following the review, we perform a detailed analysis of the selected articles to explore how various aspects of servitization and digital servitization interact and influence each other. In the final phase, we synthesize the findings from the study to deepen the conceptual understanding of the servitization phenomenon, including its digital components. This synthesis will provide valuable insights into effectively managing the transition toward servitization and digital servitization, highlighting its practical applicability in a business context.

1.4 Expected contributions

The primary goal of this research is to construct an integrative framework that captures the evolution, current state, and future trajectory of servitization and digital servitization. This framework will delineate both the theoretical underpinnings and practical ramifications of servitization, illuminating the challenges and opportunities that have surfaced. Particularly, it will explore the transformative influence of Industry 4.0 technologies—such as the Internet of Things, Big Data analytics, and Artificial Intelligence—on traditional servitization models, steering them toward more advanced digital practices. This examination is crucial for understanding how digital technologies can enhance the competitiveness and value proposition of manufacturing firms engaged in servitization.

The overarching aim of this study is to deepen the comprehension of servitization by exploring its interplay with digitalization, thus broadening its theoretical and managerial relevance. The research intends to offer an integrated perspective that not only advances the academic discourse in this field but also aids manufacturing companies in adeptly navigating the complexities of servitization and digital servitization. Furthermore, this review will articulate a roadmap for manufacturers considering this transition, conceptually enriching a domain that, despite its increasing importance, remains underexplored in scholarly research. By highlighting the enduring interest in adopting servitization correctly and underscoring the necessity for a unified theoretical framework, this study responds to calls for theoretical consolidation and a more comprehensive research agenda (Pettigrew 1988 ; Pye and Pettigrew 2005 ).

In summary, our proposed study aims to provide a detailed analysis that integrates insights from various studies into a cohesive narrative, with a particular focus on the servitization and digital servitization processes within the manufacturing sector. This synthesis will significantly contribute to both academic knowledge and practical applications, emphasizing the complex and evolving nature of servitization in manufacturing, and marking a key conclusion of this thorough examination.

2 Research aims

This study is dedicated to a comprehensive analysis of the servitization phenomenon and its progression toward digital servitization within the manufacturing sector, meticulously examining the most significant research from the past 30 years. The aim is to understand the development and various applications of servitization, along with the challenges and obstacles it entails. The study seeks to identify the motivations driving companies toward servitization, examine the various actors involved in the process and their interplay, and explore the strategies necessary for successful implementation. Furthermore, the organizational and technological capabilities required for transitioning to servitization will be analyzed, as well as the associated risks and challenges, including both internal and external hurdles that companies must overcome to reap the potential benefits of servitization. This analysis is guided by key research in the field (Zhang and Banerji 2017 ; Khanra et al. 2021 ) offering a comprehensive perspective on this significant shift in business dynamics within the manufacturing sector.

Essentially, this study seeks to answer the main research question: To what extent do servitization and digital servitization provide benefits that contribute to enhancing a company’s competitiveness? Alongside this primary question, the study intends to address the following aspects related to the development of servitization and digital servitization:

RQ1. Implementation of a digital servitization strategy. How it should be affected by the company’s business environment? How it should be the co-creation process in an international context? Which new knowledge and new skills need to be developed to be implemented correctly? Which benefits can be obtained by implementing the digital enablers of Industry 4.0? Which changes could it involve in the internal structure of the business? Which changes could it involve in the company’s business environment (relations with suppliers or strategic partners)? How could it face the challenges and obstacles that arise during the transition process?

RQ2. Benefits of developing an effective digital servitization strategy. How it provides greater value to the customer? How can product customization be optimized? How it encourages access to new markets? How it promotes gaining new customers? How it allows innovation in ideas or business models? How it allows the development of goods with novel services? How it effectively allows greater returns to be achieved? How it improves competitiveness?

The focus of this study is not only on analyzing servitization as a strategic shift for manufacturing companies but also on exploring how the integration of digital technologies can enrich and complicate this process. Additionally, the aim is to synthesize existing knowledge to provide a broader and more nuanced understanding of digital servitization, highlighting its key advantages, challenges, and intersection with digital technologies.

Four stages were established for this systematic literature review (Tranfield et al. 2003 ), one for each of the four phases outlined in the first section.

This collection focuses on four fields of research: business administration, marketing, operations management, and administration of services. The studies from the two main databases were examined: Web of Science and Scopus, as they are considered reference sources for the topic being analyzed. Once the information was screened, the most-cited studies were selected, which formed the basis for the present study.

3.1 Review process

In conducting a systematic literature review to gain a profound understanding of servitization and digital servitization within the manufacturing sector, our approach integrated multiple rigorous methodologies (Thomé et al. 2016 ). Initially, following the method proposed by Hertzberg and Rudner ( 1999 ), we conducted a meticulous keyword search in the Web of Science and Scopus databases, aiming to identify pertinent literature using terms like “servitization,” “digital servitization,” and their variants. This was instrumental in capturing the subject’s breadth and depth, allowing for the creation of search strings using the Boolean connector OR. The search strings were incorporated in titles, abstracts, and/or keywords, adhering to the time span of 1990 to 2023 in major databases, thus fulfilling the guidelines set by Tranfield et al. ( 2003 ) for inclusion criteria.

To further refine the search and ensure a robust database, we applied additional parameters and restrictions post-establishing the primary search strings for both databases. We limited our search to open access and hybrid gold journals, focusing on high-quality, readily available research outputs. Additionally, we set a citation threshold to include articles with significant field impact, thereby ensuring the inclusion of seminal works and recent influential studies. This strategy was pivotal in developing a comprehensive, relevant collection of literature, ensuring the inclusion of the most pertinent works in the field of digital servitization.

The approach was enhanced by strictly adhering to three key inclusion criteria: (a) considering publications from 1990 to 2023, to ensure a contemporary and comprehensive review, (b) prioritizing articles from prestigious academic journals within the relevant study areas, thus ensuring source quality and relevance, and (c) selecting articles focusing explicitly on key aspects of servitization and digital servitization. This approach, aligned with the study’s objectives and research questions, ensures a holistic and detailed understanding of the phenomenon, accurately reflecting the dynamics and transformations in the manufacturing sector.

The present study aimed to answer the research question and the various related questions. This was done via the PRISMA method (Preferred Reporting Items for Systemic Reviews and Meta-Analyses). The selection criteria produced 647 articles (from Web of Science) and 630 articles (from Scopus). Once identified, the abstracts of each article were read to screen and select only those in line with the fourth study phase: to help properly understand the concept, how it is managed, and how it is applied. 157 articles were ultimately identified that met all of the inclusion criteria. Figure  1 outlines the PRISMA method used.

Source: Authors’ own work from Web of Science and Scopus databases

Flow diagram, based on the PRISMA Method, for the selection of relevant documents for the systematic literature review.

3.2 Descriptive analysis

Figure 2 offers an analytical synthesis of the publication trends within the realms of servitization and digital servitization over a span of more than three decades, utilizing data harvested from the Web of Science and Scopus databases. The blue bars across all three charts articulate the volume of literature pertaining to servitization, encompassing its theoretical underpinnings, industry applications, and cross-disciplinary studies. This scholarly corpus embodies the foundational and evolutionary aspects of servitization as a strategic paradigm shift in manufacturing and service industries.

Source: Web of Science and Scopus databases and authors’ own work

Evolution of publications on Servitization and Digital Servitization (1990–2023).

In parallel, the orange bars specifically chart the trajectory of literature focused on digital servitization. This subset of research delves into the intricacies of embedding digital technologies within traditional servitization frameworks. It illuminates the burgeoning intersection of digital innovation and service strategies, reflecting a vibrant and rapidly advancing frontier of research.

The upward trend of both blue and orange bars in the separate charts for Web of Science and Scopus indicates a robust increase in scholarly output. This not only testifies to the growing academic and practical significance of servitization concepts but also their digital counterparts, which are pivotal in today’s technology-driven marketplaces.

The application of inclusion and exclusion criteria to the study of servitization and digital servitization clarifies the focus of academic research, emphasizing the most relevant and impactful studies in these areas. This refined approach highlights the critical and emerging conversations shaping the future of manufacturing industries through servitization and its digital augmentation. The graph reflects the scholarly community’s increasing investment in understanding these concepts and their application, suggesting a dual focus: the persistent importance of servitization in strengthening the interplay between manufacturing and services, and the transformative potential of digital technologies within this framework. Serving both as a retrospective and a forecast, the visualization indicates key areas for future research that promise to advance industrial practices and academic thought.

Regarding the countries in which the identified studies have been carried out, the visual data presented in Fig.  3 captures a comprehensive view of the global research output on servitization and digital servitization from 1990 to 2023, as indexed by the Web of Science and Scopus databases and further refined by the application of inclusion and exclusion criteria. The top section, shown in blue, delineates the Web of Science data, indicating a prominent concentration of scholarly activity within certain countries, possibly linked to their robust research infrastructures, funding provisions, or strong manufacturing sectors that are conducive to studies in servitization.

Source: Web of Science and Scopus databases

Number of publications by country on Servitization and Digital Servitization (1990–2023).

The middle section, in orange, portrays the Scopus data, revealing a parallel distribution pattern to that of the Web of Science but with slight variances that may be indicative of the different regional research emphases or variations in the databases’ indexing methodologies. The countries with the highest volume of publications are recognized as potential centers of excellence and innovation in the field of servitization.

The bottom section of the graph, in green, represents the distilled essence of this academic output following the application of the inclusion and exclusion criteria. This section emphasizes the refined and concentrated scholarly work that aligns more closely with the specific nuances and requirements of servitization and digital servitization research as defined by the study. It presents a narrower but more focused spectrum of publications, suggesting a curated body of knowledge that serves as a critical resource for understanding the current state and future directions of servitization in the manufacturing sector.

Together, these three segments of Fig.  3 not only illustrate the quantitative aspects of the research output but also underscore the qualitative focus and depth of scholarly exploration achieved through rigorous selection. This tripartite analysis offers a lens through which to view the international dissemination and development of knowledge in servitization and digital servitization, highlighting established leaders in the field as well as regions with the potential for increased research activity, international collaboration, and contribution to the servitization discourse.

In Fig.  4 , the Web of Science data (represented by the blue graph) lists Oscar Bustinza as the author with the highest number of publications, closely followed by Marko Kohtamäki and Vinit Parida. In contrast, the Scopus data (illustrated by the orange graph) also positions Vinit Parida prominently, yet Marko Kohtamäki’s publication count is lower than that reported in the Web of Science, presenting a notable discrepancy.

Number of publications by author on Servitization and Digital Servitization (1990–2023).

When the inclusion and exclusion criteria are applied (as shown in the green graph), there is a decrease in the number of publications, which aligns with expectations, given that these criteria aim to omit publications failing to meet the predetermined standards of quality and relevance. Following this filtration, Tim Baines emerges as the author with the most publications, indicating the significant relevance of his research work to the focused aims of this systematic literature review. Consequently, the filtration process underscores those authors whose contributions are particularly central or foundational to the field.

The comparison across the three graphs demonstrates the influence of database selection and methodological rigor on the perceived prominence of authors within the academic community. This analysis goes beyond merely highlighting the leading figures in servitization research; it underscores the importance of thorough evaluation in literature reviews to identify research of substantial impact.

Thus, the filtration process distinctly recognizes authors whose contributions are considered pivotal to the discipline.

Figure  5 provides a succinct overview of journal publication volumes on servitization and digital servitization from 1990 to 2023, based on data from Web of Science and Scopus databases. Prior to applying inclusion and exclusion criteria, the journals listed in the Web of Science (blue) and Scopus (orange) indicate a diverse quantity of publications.

Number of publication volume in journals with the highest frequency of articles on Servitization and Digital Servitization (1990–2023).

Post-application (green), the data are refined to highlight the top ten journals that are most aligned with the research criteria. It is noteworthy that the application of these criteria significantly alters the landscape of the considered literature. Some journals that initially (in the Web of Science or Scopus databases) had a high volume of publications appear to have fewer articles meeting the requirements, which may reflect on the specificity and relevance of their contributions to the field.

The graphic serves as an insightful metric of the research landscape, indicating not only the journals that are most prolific in the domain but also the robustness of articles surviving rigorous scholarly scrutiny. This visual representation is integral to the academic discourse, as it not only informs researchers of the core journals within the field but also reflects the evolving standards and focal areas within the literature on servitization and digital servitization.

The descriptive analyses included in this section serve as a pivotal foundation for the authors’ elaboration, shedding light on the trajectory of academic inquiry into servitization and digital servitization. It encapsulates the dual analysis conducted using the Web of Science and Scopus databases and the meticulous selection process leading to the corpus of papers employed in the systematic literature review. The synthesis of these findings offers valuable insights into the progression of research in this domain, indicating a maturing yet dynamically expanding field of study.

3.3 Classification process

Upon identifying studies that met the established selection criteria, a thorough examination of each was conducted to categorize them according to specific themes. These encompassed the motivations driving companies toward servitization, namely the reasons why manufacturers transition from producing solely goods to combining these with services, including the anticipated benefits of such a transformation. The various actors involved in the servitization process and the nature of their interactions were scrutinized, as well as the strategies necessary for successful implementation, which entailed identifying potential needs for external partners, commonly service providers (Martínez et al. 2010 ; Bastl et al. 2012 ; Spring and Araujo 2013 ; Ziaee et al. 2018 ). The types of services commonly offered were analyzed, categorized as basic, intermediate, or advanced, along with the specific servitization strategies adopted by the companies. Furthermore, the study delved into the organizational and technological capabilities required for an effective transition to servitization (Momeni et al. 2023 ), as well as the potential risks and challenges arising in these transition processes, including both internal and external obstacles that must be overcome to fully capitalize on the potential benefits of servitization (Raddats et al. 2017 ; Reim et al. 2019 ; Minaya et al. 2023 ).

4 Results: theoretical background

4.1 from servitization to digital servitization.

The concept of servitization, which has significantly evolved over the years, has achieved solid recognition in both the academic and industrial spheres. Initially defined by Levitt ( 1972 ) and Vandermerwe and Rada ( 1988 ) as the process of adding value through services (Johnson and Mena 2008 ; Baines et al. 2011 ; Lindman et al. 2016 ; Ruiz-Martín and Díaz-Garrido 2021 ), servitization has expanded to encompass multiple strategic objectives, such as competitive advantage (Baines et al. 2009a ; Raddats et al. 2019 ), financial goals, and marketing benefits (Khanra et al. 2021 ).

The shift toward servitization entails a redefinition of traditional business models, focusing on innovation (Sandström et al. 2008 ; Martín-Peña et al. 2018 ; Qi et al. 2020 ; Xing et al. 2023 ), and transforming manufacturers into service-centric companies (Cusumano 2008 ; Santamaría et al. 2012 ; Mosch et al. 2021 ). In this regard, manufacturing companies are fundamentally reorienting their business models and operational strategies to include value-added services (Gebauer and Kowalkowski 2012 ; Hyun and Kim 2021 ). Baines and Lightfoot ( 2013 ) and Luoto et al. ( 2017 ) highlight the widespread changes this implies in management, marketing, and operations. The change is so substantial that over 50% of a company’s activities and personnel can be involved in providing these newly implemented services, as indicated by multiple studies cited by Martín-Peña and Ziaee ( 2016 ). This is because research has shown that servitization not only adds value but also increases profitability with relatively low asset investments (Davies et al. 2007 ; Kharlamov and Parry 2021 ).

The types of services offered range from basic to advanced (Gebauer et al. 2013 ; Kindström and Kowalkowski 2014 ; Sousa and Da Silveira 2017 ), with advanced services contributing to greater profitability (Eggert et al. 2014 ) and generating higher customer satisfaction (Mont 2002 ; Ostrom et al. 2010 ), leading to improved competitive positioning (Oliva and Kallenberg 2003 ; Durugbo 2014 ). Baines et al. ( 2011 ) argue that servitization involves creating distinctive and sustainable capabilities (Raddats 2011 ; Kimita et al. 2022 ), requiring not just the provision of goods, but also the innovation of value through added services (Tukker and Tischner 2006 ; García Martín et al. 2019 ; Zighan and Abualqumboz 2022 ), enabling companies to maintain their competitive edge (Tuli et al. 2007 ; Brax and Jonsson 2009 ; Nordin and Kowalkowski 2010 ).

While the goal of servitization is to enrich product offerings and drive competitiveness (Neely et al. 2011 ; Gaiardelli et al. 2014 ; Benedettini et al. 2015 ), companies must avoid the “service paradox,” where the focus on new services undermines existing production capabilities (Gebauer et al. 2005 ; Hyun and Kim 2021 ). To this end, various researchers advocate for a comprehensive analysis covering customer needs, pricing strategies, delivery infrastructure, and organizational change (Manzini and Vezzoli 2003 ; Kohtamäki and Partanen 2016 ; Ziaee et al. 2017 ). In summary, moving away from product-centric thinking and engaging in product and servitization logic.

In this context, Santamaría et al. ( 2012 ) and Rabetino et al. ( 2017 ) underscore three fundamental considerations for a successful servitization strategy: the content, process, and context of organizational change. This involves determining what to change, how to change, and why the change is necessary (Kreye et al. 2015 ).

The complexity of servitization also demands internal and external alignments within companies (Gebauer 2008 ; Alghisi and Saccani 2015 ; Kohtamäki et al. 2019a ; Zhang et al. 2023 ). Internally, this involves harmonizing the organization’s strategy with the service portfolio and aligning this strategy throughout the organization (Oliva and Kallenberg 2003 ; Yan et al. 2020 ). Externally, alignment extends to the service provider network and customer expectations (Ceci and Masini 2011 ; Paiola et al. 2013 ). Similarly, servitization applies in B2B and B2C domains, serving as a differentiator and pathway to future alliances and customer loyalty (Baines et al. 2017 ; Pombo and Franco 2023 ).

On the other hand, technological advancements act as significant facilitators in the transition toward servitization, particularly the digital elements of Industry 4.0 (Dalenogare et al. 2018 ; Paschou et al. 2020 ; Opazo-Basáez et al. 2021 ; Tian et al. 2022 ; Le-Dain et al. 2023 ). This involves both internal and external organizational changes, focusing on disruptive innovations and addressing legal and financial challenges (Bustinza et al. 2018 ; Tronvoll et al. 2020 ; Kolagar et al. 2022 ), leading to what is known as digital servitization.

Digital servitization represents the integration of enabling technologies from Industry 4.0 into the servitization process, generating additional benefits and creating value for the customer (Ibarra et al. 2018 ; Grandinetti et al. 2020 ; Ciasullo et al. 2021 ; Bettiol et al. 2022 ). This digital transformation expands the scope of traditional services, allowing for greater customization and efficiency (Frank et al. 2019 ; Chen et al. 2021 ).

Digitalization facilitates data collection and analysis, improving decision-making, and enabling more predictive and proactive services (Lee et al. 2014 ; Chen et al. 2022a ; Rakic et al. 2022 ). Moreover, data-based digital capabilities are fundamental for the success of digital servitization, as they enhance both product support services and customer support services (Chen et al. 2023 ).

Digital servitization also promotes value co-creation and collaboration among manufacturers, suppliers, and customers, optimizing service delivery and strengthening relationships (Coreynen et al. 2017 ; Vendrell-Herrero et al. 2017 ; Kohtamäki et al. 2020b ; Sjödin et al. 2020 ). The business models of digital servitization are also influenced by Industry 4.0 technologies, such as Internet of Things and Big Data, enabling the development of more integrated and customer-centric solutions (Naik et al. 2020 ; Bortoluzzi et al. 2022 ; Minaya et al.  2023 ).

Furthermore, an integral aspect of the servitization landscape, especially in the digital era, is the evolution of Product-Service Systems (PSS). PSS represents a strategic approach that shifts the focus from selling products to offering a combination of products and services designed to fulfill specific customer needs more efficiently (Tukker and Tischner 2006 ; Baines et al. 2017 ). This transition to PSS reflects a broader industry movement toward sustainable and customer-centric business models, where the value proposition extends beyond the physical product to include personalized services. The advent of Industry 4.0 technologies has further propelled this evolution, leading to the development of Smart PSS. Smart PSS integrates digital technologies, such as the Internet of Things, Big Data, and Artificial Intelligence to enhance service delivery, improve customer experience and enable new forms of value creation (Chowdhury et al. 2018 ; Bortoluzzi et al. 2022 ). The adoption of these advanced technologies within PSS frameworks represents a significant leap in how companies’ approach servitization, allowing for greater customization, efficiency, and proactive engagement with customers. Therefore, understanding the role and impact of PSS, particularly Smart PSS, is crucial for comprehending the full scope of digital servitization and its implications for future business strategies.

4.2 Integrating smart product-service systems (smart PSS) into digital servitization: evolution, challenges, and opportunities

Product-Service Systems (PSS) epitomize an evolution in business models, integrating goods and services to fulfill customer needs sustainably and effectively (Galbraith 2002 ; Gebauer et al. 2011 ; Oliveira et al. 2015 ; Haase et al. 2017 ; Gaiardelli et al. 2021 ; Zhou and Song 2021 ). Tukker ( 2004 ) categorizes PSS into product oriented, use oriented, and result oriented, with each type offering distinct benefits, such as improved profit margins and differentiation from competitors (Tukker and Tischner 2006 ; Reim et al. 2015 ; Baines et al. 2017 ; Rabetino et al. 2017 ). Service-oriented PSS prioritize personalized customer experiences, requiring greater customer involvement (Matthyssens and Vandenbempt 2010 ; Cusumano et al. 2014 ; Zighan and Abualqumboz 2022 ).

The advent of Industry 4.0 technologies has given rise to Smart PSS, enhancing traditional PSS frameworks with digital capabilities and aligning with digital servitization’s goals to maximize customer value and competitive advantage (Chowdhury et al. 2018 ; Zheng et al. 2019 ; Wang et al. 2021 ; Bortoluzzi et al. 2022 ; Chen et al. 2023 ). Smart PSS incorporate Internet of Things, Big Data, and Artificial Intelligence to offer tailored services and predictive maintenance, thus improving product reliability and customer experience. However, transitioning to Smart PSS necessitates overcoming internal challenges, such as developing digital capabilities and adapting organizational culture, and external challenges like aligning strategies with customer and supplier expectations (Alghisi and Saccani 2015 ; Baines and Shi 2015 ; Ceci and Masini 2011 ; Mosch et al. 2021 ).

Business models in the context of Smart PSS vary from product centered to service oriented, depending on the company’s servitization maturity and technological capacity, leading to greater competitive differentiation and new market opportunities (Kowalkowski et al. 2017 ; Zheng et al. 2019 ; Baines et al. 2020 ; Chen et al. 2021 ). Implementing Smart PSS calls for a holistic approach, from strategic planning to system design and operational management, with a focus on how digital capabilities enhance PSS offerings and the overall value chain (Coreynen et al. 2017 ; Zheng et al. 2018 ).

In sum, the transition from traditional servitization to digital servitization, through the deployment of Smart PSS, marks a critical shift in value creation and sustaining customer loyalty, propelled by Industry 4.0 innovations (Vandermerwe and Rada 1988 ; Frank et al. 2019 ; Pinillos et al. 2022 ; Raddats et al. 2022 ; Schroeder et al. 2022 ; Chen et al. 2023 ; Martín-Peña et al. 2023 ). Realizing the potential of digital servitization demands an understanding of technological capabilities, fostering innovation, and market adaptability (Kohtamäki et al. 2019b ; Zhang et al. 2023 ). Successful digital servitization and Smart PSS rely on integrating technology with strategic vision and customer centricity, cultivating a business model focused on collaboration, innovation, and value co-creation (Naik et al. 2020 ; Chen et al. 2021 ; Zhou et al. 2021 ; Kolagar et al. 2022 ).

4.3 Digital servitization: crafting superior value in the modern era

As previously noted, servitization, as it evolves into digital servitization, catalyzes a profound and strategic transformation of business models and operational paradigms, emphasizing the importance of both internal and external strategic alignments. This process not only optimizes existing service offerings but also unlocks significant potential for service innovation and market competitiveness. Specifically, the integration of advanced technologies in digital servitization allows companies to create superior and customized value for their customers. This expanded value creation is achieved through a synergistic combination of technological resources and human capabilities, facilitating more predictive, personalized, and proactive services. Thus, digital servitization emerges as an essential and transformative step in business strategy, driving not only efficiency and strategic alignment but also fostering innovation and strengthening competitive positioning in the market.

Digital servitization, a contemporary evolution of traditional servitization, integrates Industry 4.0 technologies into the service domain, creating significant value for the customer. This value manifests in several key dimensions, all driven by digitalization and the emerging capabilities it offers.

Enhanced personalization and customer experience. The ability to collect and analyze large volumes of data using digital technologies enables companies to better understand the needs and preferences of their customers (Tao and Qi 2017 ; Chen et al. 2023 ). This leads to the creation of more personalized service offerings, tailored specifically to individual customer requirements. For instance, data analytics capabilities enhance servitization by enabling service personalization, which is fundamental for improving customer satisfaction and fostering long-term loyalty (Chen et al. 2022b ).

Efficiency and proactivity in service delivery. Digital servitization allows companies to be more efficient and proactive in delivering services. Technologies like the Internet of Things and Artificial Intelligence facilitate remote monitoring and predictive maintenance, anticipating problems before they occur and minimizing downtime (Lee et al. 2014 ; Tao and Qi 2017 ; Raddats et al. 2022 ). This not only improves product reliability but also reduces costs for the customer.

Creation of new opportunities and business models. The integration of digital services opens new avenues for innovative business models. For example, companies can offer usage-based solutions or subscriptions, where customers pay for performance or outcomes rather than the product itself (Vendrell-Herrero et al. 2017 ; Martín-Peña et al. 2020 ; Bortoluzzi et al. 2022 ). This can result in greater flexibility and more attractive cost options for the customer.

Enhanced customer–supplier relationships. Digital servitization fosters greater collaboration and value co-creation between suppliers and customers (Coreynen et al. 2017 ; Sjödin et al. 2020 ; Harrmann et al. 2023 ). This is because digital capabilities enable smoother communication and more transparent information exchange, resulting in stronger and more reliable relationships (Davies et al. 2023 ).

Continuous improvement of products and services. Ongoing feedback and data analysis enable continuous improvement of the products and services offered. Companies can quickly adjust their offerings in response to customer feedback or market changes, ensuring that their services remain relevant and of high quality (Chen et al. 2021 ).

Access to new markets. Digital servitization enables companies to access new markets and customer segments. By offering digital solutions, companies can overcome geographical and logistical barriers, reaching customers who were previously inaccessible (Münch et al. 2022 ; Rakic et al. 2022 ).

In summary, digital servitization not only enhances existing service offerings but also opens new opportunities for service innovation, strategic alignment, and market competitiveness. Its successful implementation is key to creating substantial value for the customer, highlighting the importance of a well-planned and executed strategy in the context of modern servitization.

5 Proposed conceptual framework: guiding the transition to digital servitization

Digital servitization represents a pivotal shift in the business landscape, where manufacturing companies evolve into providers of comprehensive solutions that seamlessly integrate products and services, augmented by digital technologies. This transformation is driven by the need for enhanced competitiveness, customer engagement, and value creation in a rapidly changing digital economy.

The development of our DASOBI conceptual framework, designed to guide the transition to digital servitization, is grounded in a rigorous methodological approach, underpinned by a comprehensive systematic literature review. This review meticulously synthesized three decades of academic research and industry insights, incorporating a total of 157 articles. Our comprehensive review process involved a deep analysis of the most influential and relevant publications in the field, among which notable contributions include Alghisi and Saccani ( 2015 ); Ayala et al. ( 2017 , 2019 ); Coreynen et al. ( 2017 ); Tao and Qi ( 2017 ); Vendrell-Herrero et al. ( 2017 ); Bustinza et al. ( 2018 ); Frank et al. ( 2019 ); Baines et al. ( 2020 ); Martín-Peña et al. ( 2020 ); Naik et al. ( 2020 ); Brax et al. ( 2021 ); Gaiardelli et al. ( 2021 ); Kohtamäki et al. ( 2021 ); Bettiol et al. ( 2022 ); Bortoluzzi et al. ( 2022 ); Marcon et al. ( 2022 ); Münch et al. ( 2022 ); Brekke et al. ( 2023 ); Chen et al. ( 2023 ); Chirumalla et al. ( 2023 ); Shen et al. ( 2023 ). These articles were particularly significant for identifying emerging trends, key challenges, and effective strategies in digital servitization. By systematically analyzing this extensive body of literature, we identified critical themes, challenges, strategies, and outcomes associated with the digital servitization journey. This analysis not only highlighted the multifaceted nature of digital servitization but also emphasized the critical importance of aligning strategic considerations, technological capabilities, and stakeholder roles to successfully navigate this complex transition. The structured framework presented herein not only reflects the evolution of the field but also provides clear guidance for manufacturing companies advancing toward more sophisticated and digitalized servitization practices.

The DASOBI framework, while empirically grounded in a comprehensive literature review, also draws extensively on classical and emerging theories to provide a robust theoretical foundation. For instance, diffusion of innovations theory (Rogers 2003 ) elucidates the “Drivers” and “Obstacles” in the adoption of digital servitization by explaining the rate and process through which new technological innovations spread within industries. Furthermore, the resource-based view (Barney 1991 ) is instrumental in understanding the “Strategies” component of the framework, emphasizing the importance of internal capabilities and resources in gaining a competitive advantage through digital transformation. These theoretical integrations not only enhance the academic rigor of our framework but also offer a deeper understanding of the multifaceted nature of digital servitization.

Therefore, the proposed DASOBI (Drivers, Actors, Strategies, Obstacles, Benefits, and Impact) model emerges as a synthesis of empirical evidence and theoretical insights, designed to offer a coherent and actionable guide for organizations seeking to embrace digital servitization.

This conceptual framework delineates a roadmap for organizations to navigate this complex transition. The framework identifies the core components essential for a successful journey toward digital servitization:

Underlying reasons for the shift (Drivers). Recognizing the strategic imperatives for transitioning toward a digital servitization model is critical. This includes understanding market dynamics, competitive pressures, and technological advancements driving this change.

Key actors involved (Actors). Successful digital servitization necessitates the involvement and alignment of various stakeholders, including internal teams, customers, technology partners, and suppliers. Their roles, expectations, and contributions are pivotal in shaping the servitization journey.

Strategic considerations and tools (Strategies). This encompasses adopting strategic frameworks, methodologies, and digital tools that are conducive to servitization. These tools and strategies should facilitate the integration of digital technologies with traditional product-service offerings, ensuring a seamless transition.

Potential challenges and obstacles (Obstacles). Identifying and addressing challenges such as cultural resistance, skill gaps, technological complexities, and integration issues with existing processes is crucial. Proactive strategies and contingency plans are essential to mitigate these barriers.

Anticipated benefits of the transition (Benefits). The transition to digital servitization should bring about significant benefits, including enhanced customer value, increased revenue streams, and improved competitive positioning. This component focuses on quantifying these benefits and aligning them with organizational goals.

Expected outcomes and impact (Impact). The final component of the framework revolves around the tangible outcomes and impacts of digital servitization. This includes enhanced customer satisfaction, increased market share, and improved operational efficiency.

In the digital servitization framework, the transition toward digital servitization, driven by market dynamics, competitive pressures, and technological advancements, is intrinsically linked to the roles and contributions of key stakeholders, such as internal teams, customers, and technology partners. Strategic considerations and tools must be selected in light of potential challenges, like cultural resistance and skill gaps, ensuring alignment with stakeholder capabilities and expectations for a seamless integration of digital technologies with traditional offerings. This strategic alignment is pivotal in overcoming obstacles and realizing anticipated benefits, such as enhanced customer value and competitive positioning. These benefits, in turn, lead to tangible outcomes, like improved customer satisfaction and operational efficiency, which feedback into the market, influencing ongoing strategic imperatives and shaping the evolution of digital servitization strategies. This dynamic interplay highlights a continuous feedback loop where outcomes inform underlying reasons, reinforcing the need for adaptability and strategic foresight in the digital servitization journey.

The contribution of the DASOBI framework to the existing literature is manifold. By synthesizing empirical findings with theoretical insights from servitization and digital transformation research, this framework addresses identified gaps, such as the integration of digital technologies in traditional servitization models and the management of organizational changes associated with such transitions (Baines and Lightfoot 2013 ; Vargo and Lusch 2008 ). Specifically, the DASOBI framework aids in conceptualizing how companies can strategically navigate the complexities of digital servitization, providing a structured approach that is missing in previous studies. This not only extends the theoretical discourse around servitization but also sets a foundation for future research to explore the dynamic interactions between digital technologies and service strategies in manufacturing sectors.

In conclusion, this conceptual framework serves as a comprehensive guide for firms embarking on the digital servitization journey. It provides a structured approach to understanding and implementing the necessary changes, ensuring a smooth transition and realization of the potential benefits of digital servitization. Figure  6 summarizes this meticulously formulated model (DASOBI), referred to as the Drivers (underlying reasons for the shift), Actors (key actors involved), Strategies (strategic considerations and tools), Obstacles (potential challenges and obstacles), Benefits (anticipated benefits of the transition), and Impact (expected outcomes and impact) of Digital Servitization Strategy, offers a robust framework for scholarly exploration, grounded in an exhaustive review of extant literature.

Source: Authors’ own work

Conceptual theoretical model for the analysis of Digital Servitization.

The DASOBI framework orchestrates the shift from traditional service strategies to digitally-enhanced service offerings, underpinned by the alignment of core elements: Drivers, Actors, Strategy, Obstacles, Benefits, and Impact. The model emphasizes a strategic approach, incorporating digital catalytic factors to augment adaptability, customer-centric analytics, and the pursuit of novel revenue streams through digital innovations.

Within this framework, the digital knowledge and capability development are crucial. Firms must harness Big Data to distill customer insights, leverage Artificial Intelligence for identifying opportunities, and increase the flexibility of their service offerings via digital platforms. The role of digital service providers is pivotal, offering expertise to mitigate transition risks, assure service quality, and bolster productivity with cutting-edge technological solutions.

However, the shift is not without its challenges. The resistance to digital transformation and the complexity of measuring profitability in the digital service landscape can impede progress. Moreover, the implications of Industry 4.0 are profound, necessitating organizational restructuring, workforce upskilling, and technological investments to realize the potential of digital servitization.

The anticipated benefits of this digital shift are manifold. Enhanced customer understanding through sophisticated data analytics, improved market positioning through digital innovation, and elevated creative capability with advanced technology are but a few of the advantages. Furthermore, embracing Industry 4.0 technologies within digital servitization amplifies these benefits, leading to superior product quality via smart manufacturing, greater adaptability in production, and increased operational efficiency ensuring timely delivery.

In summary, the DASOBI model meticulously integrates the transition to digital servitization with the digital economy’s imperatives, presenting a coherent roadmap for firms aspiring to harness the full spectrum of benefits offered by Industry 4.0 innovations.

6 Conclusions, limitations, and further research

This study embarked on an exhaustive journey through three decades of literature on servitization and its evolution toward digital servitization within the manufacturing sector. Through a systematic literature review, we explored the strategic transformation that involves integrating advanced services and digital technologies into product offerings, a change driven by the need to enhance competitiveness, customer engagement, and value creation in a rapidly evolving digital economy.

Our research findings have identified key drivers, actors, strategies, challenges, and benefits associated with the transition toward digital servitization. The DASOBI conceptual framework tries to provide a structured guide for understanding and managing this complex transition. This framework emphasizes the importance of recognizing the underlying reasons for adopting digital servitization models, the necessity of aligning and collaborating with diverse stakeholders, and the use of specific strategies to overcome the inherent challenges of this process.

Despite this study’s contribution to the body of knowledge on digital servitization, we acknowledge several limitations. The geographical concentration of the research activity analyzed might limit the generalizability of our findings across diverse cultural and economic contexts. The rapid evolution of digital technologies and business models also suggests that the relevance of our discoveries could be challenged by future developments. Additionally, our research focused primarily on manufacturing firms, which limits the applicability of the findings to other sectors.

These limitations open several avenues for future research. It is imperative to validate and test the generalizability of the DASOBI framework across various organizational and industry contexts. Further research is also needed to develop specific metrics that can measure the impacts of digital servitization. Longitudinal studies could provide a deeper understanding of how servitization strategies influence business outcomes over time.

This study contributes to the academic discussion by clarifying and deepening the concept of servitization and its intersection with digitalization, offering an integrative view that can assist manufacturing firms in navigating the complex landscape of servitization and digital servitization. Although we have tried to establish a solid foundation for future research, it is evident that the field of digital servitization remains dynamic and evolving, requiring ongoing examination to fully comprehend its impact on business strategy and practice.

Data availability

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

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Minaya, P.E., Avella, L. & Trespalacios, J.A. Synthesizing three decades of digital servitization: a systematic literature review and conceptual framework proposal. Serv Bus (2024). https://doi.org/10.1007/s11628-024-00559-x

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