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  • CBE Life Sci Educ
  • 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.


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.


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.


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.


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.

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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.


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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  • What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on November 15, 2022.


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

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

Table of contents

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

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

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

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

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

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

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

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

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

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


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

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

Moderating variables

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

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


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


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


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


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

Mediating variables

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

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


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

Moderator vs. mediator

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

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

Control variables

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


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

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

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

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

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

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

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

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

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

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

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

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A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

  • Original Article
  • Published: 25 January 2022
  • Volume 20 , pages 223–255, ( 2022 )

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  • Rajesh Chidananda Reddy 1 ,
  • Biplab Bhattacharjee   ORCID: orcid.org/0000-0002-3886-8409 2 ,
  • Debasisha Mishra 3 &
  • Anandadeep Mandal 4  

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While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.

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Avoid common mistakes on your manuscript.

1 Introduction

There is a digital revolution in businesses across sectors and geographies. There is a need for both traditional and new business models to capitalize on the digital technologies to overcome existential threats and gain access to game-changing opportunities. The digitalization revolution has gained momentum on the back of the Covid-19 pandemic which has forced businesses to reshape their business models. The availability of digital technologies and vastly spread Internet connectivity is leading to large-scale digital transformations, enabling organizations to redefine their business models (Van Tonder et al. 2020 ). Digital transformation involves two steps, namely, digitization and digitalization (Verhoef et al. 2021 ). Digitization is the process of transforming information from analogue to the digital mode. Digitalization refers to the process of leveraging digital technologies to redefine business models to benefit from digital business opportunities.

For successful implementation of digital transformation initiatives in shaping the next generation of products and services, organizations need a coherent and well-crafted digital strategy supported by technology and customer experience (Erceg and Zoranović 2020 ; Pappas et al. 2018 ; Sebastian et al. 2017 ; Zaki 2019 ). This strategy has to be enabled by digital capabilities (Westerman 2018 ). Any digital strategy encompasses the exploration, application, and large-scale adoption of technologies, such as Social, Mobile, Analytics, Cloud, and IoT (SMACIT), Virtual Reality, Blockchain, 3D Printing, Drones, and Augmented Reality. Adopting the right digital strategy will enable organizations in remaining competitive, overcoming the challenges of digitalization, and taking advantage of opportunities (Becker and Schmid 2020 ).

The digital strategy augments the organizational strategy by transitioning from predicting and planning to experimenting and responding. It also helps in agile planning, inclusive responsibilities, and utilization of IT capabilities. Digital solutions strategy often includes establishing ‘Data Science’ capabilities (Dremel et al. 2017 ). The rapid increase in digitization is opening up avenues for the large-scale capture, storage, and analysis of business data, which if optimally used with ‘Data Science’ tools and technologies can unlock significant business benefits. ‘Data Science’ plays a vital role in harnessing data from all organizational touchpoints and analyzing it to generate business insights across the domains of R&D, manufacturing, marketing, operations, supply chain, customer relationship management, strategy formulation, finance, human resource management, and others.

Driven by technological advancements, the increased ability to collect and process large amounts of data to extract business insights has led to a rush towards the implementation of ‘Data Science’ initiatives. It is expected that early adopters of Artificial Intelligence (AI) will share a global profit pool of $1 trillion by 2030 (Bughin 2018 ). Though the importance of ‘Data Science’ is widely acknowledged, most organizations are still in the early stages of adoption. About 75% to 80% of organizations (Deloitte’s Analytics Advantage Survey, 2013; Forbes report, 2015) have failed to successfully deploy ‘Data Science’ (Ghasemaghaei et al. 2018 ; Mazzei and Noble 2017 ).

In 2015, more than 75% of organizations either invested or planned to invest in ‘Data Science’ (Sun et al. 2020 ). However, more than 95% (2017 survey by McKinsey Global Institute and Digital@McKinsey) of the companies across domains and geography have not embraced AI to reinventing their business operations (Bughin 2018 ). As of 2017, while more than 80% of the organizations see an opportunity in adopting AI strategically, only 23% of them have adopted AI, and another 23% have started with pilot projects in AI (Ransbotham et al. 2017 ). By 2019, though AI adoption grew by 270% over the last four years (2015–2018), only 37% of the organizations have actually deployed AI or at least have short-term plans of embracing it (Rowell- Jones and Howard, 2019: 2019 Gartner CIO Survey). Amongst the ‘Data Science’ technology adopters, a large section has not achieved the desired Return on Investments (ROI) or repeated success from adoption (Braganza et al. 2017 ).

In the recent past, GE failed to implement a digital strategy planned towards digital transformation and as a result, had to lay off employees supporting its digital strategy (Colvin 2018 ). In similar lines, Nike could not reap benefits from customer digital data that was intended to provide feedback and suggestions on consumer lifestyle (de Swaan Arons et al. 2014 ). Nike had to discontinue its Nike + products (Correani et al. 2020 ). This implies that despite much hype (Fox and Do 2013 ) about ‘Data Science’ and its benefits, the successful implementation in organizations and in public sector (Vydra and Klievink 2019 ), and achieving the desired outcomes remain a challenge (Tabesh et al. 2019 ). We attribute this challenge to the lack of a well-crafted, holistic, distinct and robust 'Data Science Strategy' and/or failure in its implementation.

The evidence from reviewed articles suggests a lack of concise framework for understanding the enablers and barriers of ‘Data Science Strategy’ in enterprises. Therefore, the main purpose of this article is to synthesize all the enablers and barriers affecting the formulation and implementation of ‘Data Science Strategy’ in organizations across different industries, sectors, and geographies. To begin with, the structured literature survey is carried out to summarize the learnings from extant literature. The literary content is then segmented under appropriate themes. The study also proposes a conceptual framework for the successful implementation of ‘Data Science’ projects which would also be of use to future researchers with significant agenda for ‘Data Science Strategy’ research.

The learnings from this “Systematic Literature Review (SLR)” can be used by both academic researchers and practitioners in the field of ‘Data Science’ and ‘Information System’ Strategy. Academic researchers can explore the gap in existing literature in the area of ‘Data Science Strategy’ and attempt empirical studies on areas where the gaps have been identified. Practitioners such as ‘Data Science’ management, ‘Chief Data Science Officers’ can use our findings to understand the challenges, issues, best practices, and the possibilities of implementing ‘Data Science Strategy’ in their organizations.

Specifically, this paper focuses on the following research questions with respect to Data Science: What are the enablers and barriers that affect the strategic implementation and success of ‘Data Science’ in organizations?

The remaining sections of the paper are organized as follows: The background section presents the contextual information on ‘Data Science’ and its constituents, ‘Data Science Strategy’, and Contexts and associated factors. The research method section then discusses the methodology used in our literature review. This is followed by the results section, which presents the synthesized findings and conceptual framework. The discussion section details the various organizational approaches to ‘Data Science’ adoption, theoretical contributions and managerial implications. The limitations and future research section details the limitations of the study and the avenues for future research. Finally, the conclusion section summarizes the findings of the study.

2 Background

The definition of ‘Data Science’ is constantly evolving with the continuous advancement in technological trends. ‘Data Science’ is a cyclical process of capturing the business needs and acquiring the relevant data, storage, security, privacy, preparation, pre-processing, analytics; generating and communicating insights; and finally, actuating these actionable insights (Jägare 2019 ). ‘Data Science’ is an umbrella term that cuts across constituents such as Artificial Intelligence (AI), Machine Learning (ML), Big Data (BD), Big Data Analytics (BDA), Visualization, and Business Intelligence & Analytics (BI&A), Mathematics, Computer Science & Programming Skills, and Domain Knowledge (Fig.  1 ).

figure 1

‘Data Science’ constituents. The size and shape of each circle (or oval) in Fig. 1 is only representative, drawn in convenience to adjust the text and not indicative of any weightage to the ‘Data Science Constituents.’

‘Data Science Strategy (DSS)’ refers to the overall organizational strategy signifying its ‘Data Science' investment. It includes the overall Data Science objectives, strategic choices, regulatory requirements, data strategy (data management including, acquisition, storage, security, privacy, ethics, and data governance), resource management, competency build-up, infrastructure planning. This strategy also defines the means to track Key Performance Indicators (KPI) and Return on Investments (ROI) (Jägare 2019 ).

The adoption and successful implementation factors of ‘Data Science Strategy’ vary between industries and, more importantly, on the aligned business objectives. Currently, the Covid-19 pandemic is testing the resilience of organizations by challenging organizational design and work practices. In giant organizations (viz. Amazon, Google, AirBnB), Small and Medium Enterprises (SMEs), and start-ups, the key focus is on refining the current organizational strategy to integrate digital-enabled exponential systems (George et al. 2020 ). In context of the growing debate on automation replacing jobs, Soto-Acosta ( 2020 ) note that only 20% of activities are automated and the rest are augmented by digitalization. To make automation possible, more jobs need to be created in the area of ‘Data Science’ in order to perform the majority of tasks remotely. The Covid-19 pandemic is acting as a catalyst for the digital transformation of established firms as well as startups, forcing them to innovate in order to survive. Even traditional companies with no or limited digital experiences are adopting digital technologies in order to stay relevant in the market. This acceleration in the adoption of digital technologies finds reflection in the revised way of techniques for data collection, online appointments and therapies, work-from-home, smart-homes, schooling, learning and social interconnect activities (Hantrais et al. 2020 ; Maalsen and Dowling 2020 ).

Extant literature has studied ‘Data Science Strategy’ in the contexts of dynamic market places, organizational and dynamic capabilities (Knabke & Olbrich, 2018 ), innovations (Mikalef et al. 2018 , 2019a ), and new product development processes (Johnson et al. 2017 ). Studies have been conducted in the context of but not limited to healthcare (Chen and Banerjee 2020 ; Kamble et al. 2019 ; Kemppainen et al. 2019 ; Li et al. 2021 ; Newlands et al. 2020 ; Ramnath et al. 2020 ; Yang et al. 2015 ; Wang and Hajli 2017 ), B2B (Hallikainen et al. 2020 ), construction (Ahmed et al. 2018 ; Ram et al. 2019 ; Sang et al. 2020 ), supply chain management (Ali et al. 2020 ; Arunachalam et al. 2018 ; Brinch et al. 2018 ; Dubey et al. 2019a ; Khan 2019 ; Lai et al. 2018 ; Lamba and Singh 2018 ; Mandal 2019 ; Singh and Singh 2019 ; Wang et al. 2018c ), manufacturing (Popovič et al. 2018 ; Verma 2017 ), consumer goods (Rialti et al. 2018 ); e-commerce (Behl et al. 2019 ; Wamba et al. 2017 ), telecommunications (Saldžiūnas and Skyrius 2017 ; Walker and Brown 2019 ), banking and financial services (Lee et al. 2017 ; Lautenbach et al. 2017 ; Gregory 2011 ), automotive (Dremel et al. 2017 ), and airlines (Holland et al. 2020 ).

Attempts have also been made in the past to conduct SLR to identify various components of Data Science from a strategy point of view. These review articles from the strategic adoption point have focused highly on individual components of ‘Data Science’. In the recent past ‘Big Data’ (Ciampi et al. 2020 ; Günther et al. 2017 ; Mikalef et al. 2016 , 2020a ; Nelson and Olovsson 2016 ; Olszak and Mach-Król 2018 ; Wamba et al. 2015 ; Zhao-hong et al. 2018 ), ‘Big Data and dynamic capabilities’ (Rialti et al. 2019 ), ‘Big Data business models’ (Huberty 2015 ; Wiener et al. 2020 ), and ‘Big Data assessment models’ (Adrian et al. 2016 ) have been explored. Researchers have also focused upon ‘Big Data Analytics’ (Adrian et al. 2017 ; Al-Sai et al. 2020 ; Bogdan and Lungescu 2018 ; Inamdar et al. 2020 ; Maroufkhani et al. 2019 ; Mikalef et al. 2019a ; Singh et al. 2021 ; Sivarajah et al. 2017 ), Artificial Intelligence (Alsheiabni et al. 2020 ; Borges et al. 2021 ; Keding 2020 ; Kitsios and Kamariotou 2021 ; Markus 2017 ), Business Analytics (Cao and Duan 2017 ), and Business Intelligence and Analytics (BI&A) (Chen et al. 2012 ; Eggert and Alberts 2020 ; Lautenbach et al. 2017 ; Llave 2017 ; Moreno et al. 2019 ; Sang et al. 2020 ).

The literature on ‘Data Science Strategy’ under single-unified umbrella term that cuts across different technologies, such as ‘Big Data’, ‘Data Analytics’, ‘Artificial Intelligence’, ‘Machine Learning’, ‘Deep Learning’, ‘Visualization’, and ‘Business Intelligence’ is considerably scarce. In contrast, studies on ‘Data Science’ adoption and implementation in enterprises with corresponding barriers and enablers across various industry sectors and domains are limited. The conceptual framework for holistic ‘Data Science Strategy’ is absent to our limited understanding. To be successful in deriving desired business outcomes organizations need to recognize the need for a well-crafted and all-encompassing ‘Data Science Strategy’ augmenting digitalization and digital transformation.

3 Research method: a systematic literature review

The SLR is focused on identifying and synthesizing the knowledge on enablers and barriers influencing ‘Data Science Strategy’ in organizations. It takes into consideration the accessible scholarly business management research articles in the English language from four databases, namely, EBSCO business source ultimate, Scopus, Science Direct, and Pro-Quest databases. These databases are chosen since they have the most relevant and reputed peer-reviewed list of journals (by renowned publications like Springer, Emerald, IEEE, Elsevier, Taylor and Francis, etc.) where ‘Data Science Strategy’ research is traditionally published. Also, the large number of past SLRs (Mikalef et al., 2018 ; Dam et al., 2019 ; Sivarajah et al., 2017 ; Eggert and Alberts, 2020 ; Arunachalam et al., 2018 ) focused on the AI, ML, BDA domains too have considered these scholarly databases. The articles were identified based on the keyword search in title, abstract, and article keywords sections. In total, the search yielded 573 documents. The duplicates were excluded leaving a total of 480 articles for consideration. In the next step, the number of relevant articles was further narrowed down based on examination of title and abstracts and this brought down the number to 158 articles. The examination was focused towards characterizing factors influencing the ‘Data Science Strategy’ implementation in organizations. The systematic process (Fig.  2 ), based on further in-depth analyses for relevance and quality of articles, resulted in a final literature base comprising of 113 articles, leading to analyses of findings, synthesis, conceptual framework and research agenda for future studies.

figure 2

Methodology and output

4.1 Findings

This article summarizes the research in the field during the period 1998–2021.However, there have been more articles published on the subject during 2015–2021., Driven by the advancements in ‘Information Technology Capabilities’ and reduction in cost of computations, organizations of all sizes and types in the last quarter of twentieth century began to adopt digital technology to increase their competitiveness (Khosrowpour, 1990 ). Their focus was to implement business applications evolving from technological advancements in data associated fields. The ‘Big Data’ and ‘Data Science’ terms were coined in the year 2007 and 2008, respectively, and ‘Big Data Analytics’ gained importance in the year 2011 (Nguyen et al. 2018 ). As a result, both academic and practitioners shifted attention to the adoption and implementation of ‘Data Science’ strategies.

After the systematic process, as described in the research methodology section, this paper included 79 distinct journals in this literature review. Out of the 113 articles selected for the review, 17 were from A*-category (according to the 2019 ABDC journal quality list), followed by 37 in A category, 15 in B category, and 17 in C category journals. Likewise, 16 articles were indexed in the Scopus database. Further, 11 articles belonged neither to the ABDC quality list nor the Scopus database (Fig.  3 ).

figure 3

Year-wise distribution of articles with publication ranking

Figure  3 also represents the distribution of articles between the years 1998 and 2021 (till the date of search). The number of articles on ‘Data Science Strategy’ have increased over the years. The year 2019 recorded a maximum number of 31 papers. The steep increase in the number of articles over the past five years is testimony to the importance of the subject and the interest it is generating amongst academicians and practitioners worldwide. With more and more businesses investing in data-driven technologies, clear-cut business objectives are still far from realization. Hence, despite the increased number of articles, this research domain is still an emerging one.

Figure  4 shows the distribution of articles based on the industry type with corresponding research methods. The maximum number of articles, that is 48 articles, deal with multiple industries. Of these, 28 are empirical studies. Individually, the ‘Healthcare’ industry dominates the publications with 09 articles, out of which four are empirical studies, four are case study articles, and one SLR. ‘Financial Services’ and ‘Construction’ industries have received attention in 06 and 05 articles, respectively.

figure 4

Industry-wise distribution of articles with research methods. *Multi-Industry in the above plot refers to data collected from respondents (by means of interviews, case studies, and empirical investigations) belonging to multiple industries such as Technology & Entertainment, Web Service, Healthcare, Insurance, Manufacturing, Retail, Telecommunications, Agriculture, Banking, Transportation, Oil & Gas, Media, Consumer Goods, and many more

Figure  5 showcases the distribution of articles based on the domain of work with corresponding research methods. The supply chain management domain leads with 15 articles, of which eight articles are empirical studies. Manufacturing and policy studies are credited with 4 articles each Other domains with corresponding articles are also listed.

figure 5

Domain-wise distribution of articles with research methods

The distinct ‘Enablers and Barriers’ of ‘Data Science Strategy’ for different industry sectors, and domains are segregated in supplementary Table S1. The applicability and intensity of each barrier towards ‘Data Science’ adoption and its success varies based on the industry sector and the domain areas. Table S1 provides a comprehensive list of barriers in each of the major industries and domains varying from airlines to construction, healthcare to financial services, manufacturing to e-commerce, and so on. The underpinning theories used in the reviewed literature along with the key enablers of ‘Data Science Strategy’ implementation are documented in supplementary Table S2. Reviewed literature has drawn upon many theories including Resource-Based View (RBV), Dynamic Capability View (DCV), Knowledge-Based View (KBV), Technology-Organization- Environment (TOE) framework, Organizational Learning Theory (OLT), Diffusion of Innovation Theory (DIT), Technology Acceptance Model (TAM), Task-Technology Fit (TTF), Information Systems Success Model (ISSM), Agency Theory (AT), Stakeholders Theory (ST), Institutional Theory (ITh), and many more.

Different research methods adopted by the reviewed articles are graphically summarized in supplementary Fig. S1. The findings imply that nine different research methods are used in general. The majority of studies involved ‘Analytical or Empirical’ studies followed by ‘Case Study-based’ methodologies and ‘Interviewing’. The other research methods include Theoretical, Conceptual, Focus Group Discussions, Delphi Studies and Expert Viewpoints. The article search on the subject area resulted in articles from 79 distinct reputed journal publications. The journal publications with more than one article are documented in supplementary Fig. S2.’British Journal of Management ‘ has a maximum six number of articles published in the research area followed by five articles each in’Engineering, Construction and Architectural Management’, and’Industrial Marketing Management Journals ‘. Four articles are published in’Information and Management Journal ‘ followed by three articles each in’Information Systems & e-Business Management ‘,’Information Systems Frontiers ‘, and’International Journal of Logistics Management.’ The underpinning theories (49 numbers) for the reviewed articles on ‘Data Science Strategy’ is highlighted in supplementary Fig. S3. Resource-Based View is extensively used by 26% of the published research articles. Dynamic Capability Theory (19%) is the next prominent theory followed by Technology-Organization-Environment, which finds a place in 7% of the articles. Around 39% of the articles are drawn upon by different other theories as highlighted in supplementary Table S2. The study region of reviewed articles is spread across different geographies, as documented in supplementary Fig. S4. The United States of America dominates the list with 15 articles followed by 8 studies based on Indian industries. Europe and China follow the list with 7 articles each studied in the respective regions. Other regions including Asia, Singapore, Canada, Slovenia, Saudi Arabia, Pakistan, Norway, Iran, Finland, Dutch, and Brazil contribute one article each.

4.2 Synthesis of ‘data science’ components

The reviewed articles categorize the ‘Data Science’ components based on underlying theoretical frameworks. Under the lens of RBV, they are classified under Tangible, Human, and Intangible resources (Gupta and George 2016 ; Mikalef and Gupta 2021 ). Tangible resources comprise of data (internal, external, and combined data), technology (Hadoop, NoSQL, etc.), and basic resources, such as time and investments. The technical (pertaining to data-specific) and managerial (analytical and business acumen) skills include human resources. The intangible resources indicate organization culture and learning abilities, which include data-driven approaches and Knowledge-Management Systems. Few studies (Sun et al. 2020 ; Verma and Bhattacharyya 2017 ) have adopted the TOE framework to categorize the ‘Data Science’ components under the context of technology (resources, competence, complexity, compatibility, and relative advantage), organization (Firm size, perceived costs, and organizational support), and environment (competition, partner readiness, industry type, and regulations). Few more studies are carried out under the Motivation-Opportunity-Ability (MOA) theoretical framework (Wang et al. 2018c ). Motivation comprises of perceived ease and usefulness, external pressure, corporate culture and leadership support. Regulatory mechanisms, policies, information level, and associated risk define the opportunity aspect. The ability aspect points toward management capability, data talent, and infrastructure. Considering the merits of all these theoretical foundations and corresponding literature, in this SLR, the factors are synthesized (Fig.  6 ) into the following themes: 'Content' referring to Data Characteristics and Data Governance; 'Context' in technology and environmental aspects; 'Intent' toward aligning with the core strategy of the organization, managerial willingness, organizational agility, leadership, cultural aspects; and 'Outcome' as a business value in terms of 'Key Performance Indicators' (KPIs) and 'Return on Investments’ (RoI).

figure 6

Synthesized factors associated with ‘Data Science Strategy’ in organizations

4.2.1 Data characteristics

In the successful implementation of ‘Data Science’ in an organization, data characteristics can add value to the firm. Transforming these data characteristics into a valuable proposition relies upon the firms’ understanding of these characteristics and how to deliver value through their use (Wright et al. 2019 ). Ranjan ( 2019 ) analyzed the problems presented by data characteristics under the context of 10 Vs—Volume, Variety, Velocity, Veracity, Variability, Validity, Visualization, Vulnerability, Volatility, and Value. Whereas Ghasemaghaei et al. ( 2018 ) and Wamba et al. ( 2015 ) characterized data using 7Vs, namely Volume, Velocity, Variety, Veracity, Value, Variability, and Visualization. Each of these characteristics is important under different contexts including the industry sector. It is necessary to keep data as driver. Keeping data as driver demands certain business processes to be able to effectively use the data (de Medeiros et al. 2020 ). These business processes and procedures used in collecting and analyzing ‘data’ take the implementation of ‘Data Science Strategy’ further ahead (Rialti et al. 2019 ).

The characteristic of an incidence reflecting the raw facts defines the data quality. The data quality comprises of correctness, completeness, relevancy, timeliness, clarity, consistency, ease of understanding, and accessibility. It greatly affects the results of ‘Data Science’ (Ghasemaghaei et al. 2018 ). The application of ‘Data Science’ would positively influence the business value, once data quality challenges are addressed (Wamba et al. 2019 ). Intention to adopt ‘Data Science’ is positively affected by data quality and an understanding of the benefits of data.

4.2.2 Governance

For warranting the data quality and leveraging its value, data governance is a potential approach (Gregory 2011 ; Grover et al. 2018 ; Mir et al. 2020 ; Braganza et al. 2017 ; Wiener et al. 2020 ). Governance includes areas such as organizational strategy, data quality & security, innovation applications, ‘Data Science’ architecture, and lifecycle (Fakhri et al. 2020 ). Data policies are deployed by organizations to support product innovation (Lacam 2020 ).

The data governance aims at adding value to the enterprise through risk mitigation plans and helps achieving compliance (Gregory 2011 ). Bertot et al. ( 2014 ) highlight the need to develop a data governance model with respect to ‘Data Science’ in the context of privacy, reuse, accuracy, archival, curation, platforms, architecture, and sharing policies across sectors. In the finance sector, in protecting the consumer rights and interests, it is conducive to regulate the use of personal information (Li & Yu 2020 ). The magnitude of influence of data security, privacy, and accuracy are in descending order on ‘Data Science’ adoption and are 54%, 36%, and 8%, respectively in the total measurement (Latif et al. 2019 ). Effective data exchange within and among network partners is a necessity to benefit from ‘Data Science’. Due to security and privacy challenges, the organizations can be hesitant in sharing data with partners (Günther et al. 2017 ). To establish close connections and harmonization with partners there needs to be balanced and controlled sharing of data (Coombs et al. 2020 ; Sivarajah et al. 2017 ). There exists a positive interplay between governance of BDA infrastructure and BDA capabilities (Bertello et al. 2020 ).

4.2.3 Technology

Over the last few years, immense progress is observed in the technology related to ‘Data Science’ (Gupta and George 2016 ). The ‘Data Science’ concept emphasizes effective deployment of technology and talent to capture and manage data in order to generate insights (Mikalef et al. 2020b ). ‘Data Science’ technology capability includes infrastructure and human talent.

The core themes of technology capability in terms of infrastructure are connectivity, compatibility, and modularity (Akter et al. 2016 ). In import and export business enterprises, with the introduction of a new IT system, it is necessary to build an internal management system to improve operational efficiencies (Zhang 2021 ). Investing in infrastructure, such as data lake, analytics portfolio, and human talent generates business value (Grover et al. 2018 ). Bendre and Thool ( 2016 ) have highlighted the use of ‘Data Science’ in different domains and emphasized the need for technological platforms covering lifecycle management including data acquisition, processing and visualization challenges. The challenges arising from data characteristics in computing methods require different strategies and advanced techniques including technology usage on data segregation and accumulation, high-performance computing, incremental learning, scalability, and heuristics (Choi et al. 2018 ; Wang et al. 2018b ).

Amongst large and diverse firms, it is observed that investing in ‘Data Science’ which is IT-intensive and with a highly competitive nature leads to improvement in productivity (Müller et al. 2018 ). Many organizations have transitioned from traditional analytics (Analytics 1.0—business intelligence) to ‘Big Data Analytics’ (Analytics 2.0). Using the ‘Data Science’ platform that can combine the traditional analytics and ‘Big Data’ organizations are transitioning to a new synthesis, known as the Analytics 3.0- data enriched offering (Davenport 2013 ; Harlow 2018 ).

Human talent is arguably the most important and critical element in leveraging ‘Data Science’ investments (Grover et al. 2018 ; Wamba et al. 2015 ). Talent capability includes technical, relational, and business knowledge, along with the ability to manage technology (Akter et al. 2016 ). The challenges in talent management include cognitive and behavioural limitations, and lack of educational programs for the development of both analytical and communication skills with regards to translating data into business insights (de Medeiros et al. 2020 ).

The implementation of a ‘Data Science’ value chain is driven by overall business directions and strategy, including the knowledge management strategy. In this context, ‘knowledge management’ refers to the process of sharing internally held information in an easy and systematic approach for the benefit of the organization. Developing a strategic intent and knowledge management system for ‘Data Science’ will lead to a long-term sustainable competitive advantage by allowing enterprises to implement new business models and develop large-scale ability to experiment (Harlow 2018 ). Relative performance in firms that develop more ‘Data Science’ capabilities than others is higher. Further, knowledge management orientation plays a significant role in amplifying the effect of ‘Data Science’ capabilities (Ferraris et al. 2019 ). Knowledge management capabilities and organizational ambidexterity (exploration and exploitation) influence the relationship between ‘Data Science’ and strategic flexibility (Rialti et al. 2020 ). The synergy between organizational resources and capabilities in complementing ‘Data Science’, improves organizational objectives and outcomes (Wang et al. 2019 ). Poeppelbuss et al. ( 2011 ) studied maturity models describing organizational capabilities from the research, publication and practitioner’s perspectives. Grossman ( 2018 ) introduced the Analytic Processes Maturity Model (APMM) framework to evaluate the analytic maturity of an organization.

4.2.4 Business environment

The business environment reflects industry characteristics and government regulations. The industry is characterized by the firms' partners and competitors, and the government policies influence the macroeconomics context and the regulations (Sun et al. 2020 ). A favourable industry and regulatory environment (infrastructure, legal, regulations, directives, support, competitive pressure, and trading partner readiness) positively affects the ‘Data Science’ adoption intent (Sun et al. 2020 ; Wiener et al. 2020 ). Dubey et al. ( 2019b ) developed and tested the model describing the relation between resources, talent, culture, cost, and operational performance with institutional pressures (coercive, normative, and mimetic).

To successfully adopt ‘Data Science’ in the organizational context, firms should take the internal teams onboard the digital transformation journey, while reinforcing strategic initiatives and influencing employee behaviour towards their successful implementation (Boldosova 2019 ). In addition to its technical capacity, the power dynamics within a firm and its competitive landscape play a significant role in ‘Data Science’ build-up (Jha et al. 2020 ). An organization’s relationships with its stakeholders, including the public sector, and assimilation of social media facilitate organizational learning (Okwechime et al. 2018 ). This further leads to the absorption of ‘Data Science’ technologies (Bharati and Chaudhury 2019 ). Investment in ‘Data Science’ not only affects firms’ equilibrium price, market share, and profit but also the rivals’ performances (Wu et al. 2017 ). With ‘Data Science’ investments, market outcomes also vary based on the competition strategy used, either conservative or expansive. Depending on a fully covered or partially covered market structure, the impact on firms’ competition varies when consumer’s preference of ‘Data Science Strategy’ is heterogeneous.

There are limited studies exploring the factors for adoption of ‘Data Science’ in the start-up environment. These studies bring to light the fact that the key factors affecting adoption are different from established firms (Behl et al. 2019 ). Different enablers of ‘Data Science’ in start-ups are technical support from the vendors, attitude of the top management, competitive environment, infrastructure, skill enhancement, data access, quality of data, and perceived usefulness. Higher performance can be derived from firms when the Strategic Factor Market (SFM) is imperfect due to the differences in the expectation of the future value of strategic resources. The Information Management Capability (IMC) allows the firm to manage market needs and directions in line with core strategy (Macada et al. 2019 ).

4.2.5 Alignment with core strategy

‘Digital strategy’ being the superset of ‘Data Science Strategy’ needs to be aligned with the core organizational strategy. Most ‘digital strategies’ fail because they fail to re-imagine the vision of the organization and the journey of digitization. Alignment of digital strategy with core business strategy needs to be accompanied by the way the organization drives its vision (Trompenaars and Woolliams 2016 ). The digital strategy that an organization should embrace needs to be unique and be difficult enough for the competitors to replicate (Ross et al. 2017 ). Managerial and operational capabilities are necessary for realizing a digital business strategy (Ukko et al. 2019 ).

Adopting ‘Data Science Strategy’ in an organization does not mandatorily require building a new strategy. Rather, it requires a coherent alignment with the planned business objectives (Keding 2020 ). While formulating long-term business strategy, the inclusion of ‘Data Science Strategy’ facilitates business alignment and eventually leads to success (Grover et al. 2018 ).

Successful deployment of ‘Data Science Strategy’ requires good collaboration and alignment between the IT and business departments. This collaboration and alignment require diverse mechanisms of organizational governance (Dremel et al. 2017 ). Several stakeholders within and outside the organization mutually benefit either by combining and/or sharing data (Günther et al. 2017 ). Hence, it is important to align the ‘Data Science Strategy’ of an organization with that of core strategy (Biazzin and Castro-Carvalho 2019 ).

4.2.6 Managerial willingness

Behavioural intention to use ‘Data Science’ in organizations is determined by outcome expectations, social perception, enabling conditions, and resistance to change. Though ‘Data Science’ is perceived to be difficult (effort expectation), the influence of this perception on ‘Data Science’ adoption intention is small. The ‘Data Science’ adoption intention is contained to the relationship between facilitation conditions on behavioural intention (Cabrera-Sánchez and Villarejo-Ramos 2019 ). Based on the understanding of ‘Data Science’, the record of its success, and the logical reasoning provided by the technology itself, the managers' willingness to trust ‘Data Science’ varies (Keding 2020 ). Instead of working in business units in silos, leveraging ‘Data Science’ as a horizontal facilitator leads to an increased awareness at managerial levels. There is a different level of acceptance of ‘Data Science’ based on hierarchical layers in an organization. By customized design of algorithms, the acceptance level can be increased.

Leadership team support is a necessary ingredient in adopting ‘Data Science’. It provides the needed resources by actively promoting, endorsing, and fostering its use. Also, this helps manage change and remove organizational barriers related to Data Science usage (Lautenbach et al. 2017 ). An organization’s ‘Data Science’ adoption decision can be greatly influenced by highly motivated top managers inclined towards innovations. This happens because such personnel can only provide strategic direction, authority, and resources (Sun et al. 2020 ).

4.2.7 Organizational agility

Agility positively mediates the relationship between ‘Data Science’ and firm performance (Rialti et al. 2019 ). Organizational agility must be developed in order to contribute to the emergence of an overall ‘Data Science’ capability (Mikalef and Gupta 2021 ). Agility is often connected to an organization's dynamic capabilities (Rialti et al. 2019 ). For all the business processes including product development, resource allocation, knowledge creation, and/or ‘Data Science’ cycle, there is an association of organizational dynamic capabilities (Božič and Dimovski, 2020 ; Braganza et al. 2017 ). In a highly dynamic business environment, adopting ‘Data Science’ creates competitive advantage (Sun et al. 2020 ).

Work practices need to be realigned continuously to gain from ‘Data Science’ (Günther et al. 2017 ). At the work-practice level, organizations work with ‘Data Science’ in inductive (bottom-up) and deductive (top-down) approaches. For ‘Data Science’ success, both these approaches need to intertwine and complement each other. The intensity of organizational learning is one of the critical intangible resources needed to build ‘Data Science’ capability (Gupta and George 2016 ; Mikalef et al. 2020b ).

4.2.8 Leadership and culture

Cultural differences are a prime aspect in firms' managers' willingness for and resistance to ‘Data Science’ adoption (Keding 2020 ). Leadership with a clear ‘Data Science Strategy’ and vision can articulate the business cases that are likely to succeed (Grover et al. 2018 ).

In capitalizing on ‘Data Science’ capabilities, firms need power shift in the organization structure. The new teams of data and analytics need support from top management teams and also an inclusive data-based decision process (GalbRaith 2014 ). Behavioural change too is required in order to make data-driven decision and stimulating organizational readiness for capability restructuring is the need of the organizations (de Medeiros et al. 2020 ).

Along with increasing proficiency in sustainable design, 'Data Science' capability also directly enhances sustainable growth and performance (Zhang et al. 2020 ). ‘Data Science’ increases sustainable innovativeness in organizations as a measure of successful new product development (Song et al. 2020 ). For sustainable development, assessing the readiness of organizations to adopt 'Data Science' on the temporal dimension is important (Olszak and Mach-Król 2018 ).

As an antecedent of agility, ambidexterity improves organizations’ ability to respond effectively to market changes (Rialti et al. 2019 ). Ambidextrous organizational culture acts as a mediator between ‘Data Science’ capabilities and firm performance. Exploration orientation of a firm has a positive effect on ‘Data Science’ leading to long-term results such as generation of new product revenues (Johnson et al. 2017 ). Organizations can choose strategic pathways based on an assessment of their technical (exploiting suppliers) and analytical (exploring data) capabilities (Najjar and Kettinger 2013 ). ‘Data Science’ capability has a positive and significant effect on innovative capabilities, both incremental and radical. Moderation effect by information governance is significant on radical innovation than on incremental innovation (Mikalef et al. 2020a ). Increased exploration and exploitation capabilities of organizations may be considered related to implementing a 'Data Science capable’ business process management system within ambidextrous organizations (Rialti et al. 2018 ). Firms can realize organizational creativity and performance by fostering ‘Data Science’ (Mikalef and Gupta 2021 ). Organizational ambidexterity, which is data-driven with real-time responsiveness, increases firms’ ability toward new market opportunities.

4.2.9 Business value

‘Data Science’ has the potential to provide companies with high business value in terms of financial/market performance or customer satisfaction (Raguseo and Vitari 2018 ). The outcome of the 'Data Science' project lies in realizing business value, which could be transactional, strategic, and/or transformational (Ji-fan Ren et al. 2017 ). In enhancing business value, system quality and information quality are key factors (Ji-fan Ren et al. 2017 ). The strategic value of ‘Data Science’ could be functional (tangible: financial performance, market performance) and/or symbolic (intangible: reputation, brand image) (Grover et al. 2018 ). Based on industry characteristics and business goals, organizations need to have measures of Key Performance Indicators (KPIs) and RoIs.

The KPIs typically would involve data governance, decision quality, process efficiency, innovation contributions, business expansion, and stakeholder satisfaction (Chakravorty 2020 ; Grover et al. 2018 ). It is equally important to track the intermediate indicators, such as stakeholder sentiment and engagement, along with co-creation and value sharing (Libert et al. 2016 ). RoI refers to revenue, profitability, return on assets, share value, reduced costs, design-cycle optimization, symbolic value (image, reputation, first-mover), forecasting, and prediction (Grover et al. 2018 ; Shim et al. 2015 ). Early adopters of’Data Science', focused on the execution of projects as they knew that they had value to offer (Shim et al. 2015 ). With the integration of 'Data Science' into the mainstream, organizations need to devise the mechanism to substantiate the investments. To derive competitive advantage and add business value to organizations, ‘Data Science’ needs to be an integral part of the entire digital transformation journey and each organization needs a distinct ‘Data Science Strategy’ (DSS).

4.3 Conceptual framework of ‘Enablers and Barriers’ of successful ‘Data Science Strategy’

The SLR summarizes the status of research in the field of ‘Data Science Strategy’. Considering the fact that there is little effort so far in providing an eagle’s eye view on research in ‘Data Science’ encompassing the domains of ‘Big Data’, ‘Artificial Intelligence’, ‘BI&A’, and relevant technologies, this effort is a considerable leap in synthesizing all the enablers and barriers of ‘Data Science Strategy’ adoption in organizations. We propose a conceptual framework for developing and implementing successful ‘Data Science Strategy’ for enterprises (Refer Fig. 7 ).

figure 7

Conceptual framework for enterprise ‘Data Science Strategy’

The synthesized ‘Data Science’ components under the themes 'Content', 'Context', 'Intent' and 'Outcome' led to the development of a conceptual framework influenced by the 4I model by Crossan et al. ( 1999 ). The 4Is: Intuition, Interpretation, Integration and Institutionalization, take place across individual and organizational levels. Okwechime et al. ( 2018 ) have made use of the 4I model in deploying and integrating ‘Big Data’ and ‘Smart Cities’ from the organizational learning perspective. Mandlik and Kadirov ( 2018 ) studied the ‘Big Data’ ecosystem at the micro-level (Individual behavior), meso-level (Network of firms), and the macro-level (Institutional, Socio-political). The conceptual framework proposed for ‘Data Science’ success, connects the construct ‘Resources’ as the business input leading to ‘Strategic Business Value’ as the outcome. This transformation of input to outcome is driven by the enterprise ‘Data Science Strategy’. The strategy rests on the balancing of two competing constituents of the same continuum, which are ‘Barriers’ and ‘Enablers’ of ‘Data Science’ success. Drawn upon the Stimulus-Organism-Response (S-O-R) model (Mehrabian and Russell 1974 ), Contingency model (Fiedler 1964 ), and Institutional theory (Meyer and Rowan 1977 ), the constituents ‘Barriers’ and ‘Enablers’ are grouped under the themes Individual, Organizational and Institutional. The S-O-R model describes the influence of factors affecting individual, organizational and institutional behaviors that translate inputs in to business outcomes. The contingency model is appropriate in defining the optimal approach to balance the barriers and enablers of ‘Data Science Strategy’. The reference to the institutional theory is appropriate in emphasizing the role of institutional setup beyond the limited individual and organizational functional considerations.

At the individual-level, the tendency of resistance to ‘Data Science’ project execution either by an employee or by a mid-level manager is either due to fear of ‘failure’ or ‘loss of control’ or ‘operational disruption’ (Mikalef et al. 2020c ; Shahbaz et al. 2019 ). The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al. 2020 ; Behl et al. 2019 ; Chaurasia and Verma 2020 ; Foshay et al. 2015 ; Halaweh and Massry 2015 ; Lai et al. 2018 ; Lamba and Singh 2018 ; Lautenbach et al. 2017 ; Popovič et al. 2018 ; Ransbotham et al. 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al. 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success. Creating opportunities to interact with leadership team and adopting a deliberate storytelling technique (Boldosova 2019 ) would be helpful in overcoming the communication gap barriers. There is a significant effect of communicating to internal teams with deliberate storytelling, and reinforcing the strategic initiatives. This in turn influences employee behaviour towards ‘Data Science’ initiatives.

At organization-level barriers for ‘Data Science’ success range from data-related challenges to huge investment requirements to internal politics. Establishing ‘Data Science’ projects require huge investments on skills and infrastructure (Behl et al. 2019 ; De Luca et al. 2020 ; Holland et al. 2020 ; Lee et al. 2017 ; Wu et al. 2017 ), which is quite a big challenge for most organizations. Though it may not be avoided completely, infrastructure flexibility in identifying and using of compatible and complementary resources (Alaskar et al. 2020 ; Chaurasia and Verma 2020 ; Mikalef and Gupta 2021 ; Moreno et al. 2019 ; Shokouhyar et al. 2020 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ) already existing in the organization can considerably reduce the burden on new investments. Lack of skills and knowledge (Ahmed et al. 2018 ; Behl et al. 2019 ; Dubey et al. 2019b ; Lamba and Singh 2018 ; Foshay et al. 2015 ; GalbRaith 2014 ; Mikalef et al. 2020a , 2019b , 2020c ; Rialti et al. 2019 ) required to execute the ‘Data Science’ projects can be addressed by setting up ‘Training & Knowledge Management’ capabilities and processes (Calvard 2016 ; Dam et al. 2019 ; Ferraris et al. 2019 ; Harlow 2018 ; Rialti et al. 2020 ). Acquiring and retaining the right talent in the field of ‘Data Science’ is another challenge (Holland et al. 2020 ; Ransbotham et al. 2017 ) and needs to be addressed by rewarding and recognizing (GalbRaith 2014 ) the contributions in regular and timely manner.

The divergent actions towards ‘Data Science’ projects by different business units, including IT, in the same organization due to working in silos, and non-alignment (Calvard 2016 ; Foshay et al. 2015 ; Walker and Brown 2019 ) to business objectives are barriers for success. “Data Science Strategy’ must be aligned (Biazzin and Castro-Carvalho 2019 ; Comuzzi and Patel 2016 ; GalbRaith 2014 ; Ross et al. 2017 ; Mithas et al. 2013 ; Moreno et al. 2019 ; Trompenaars and Woolliams 2016 ; Ukko et al. 2019 ; Zaki, 2019 ) with ‘Digital Business Strategy’ which, in turn, should be aligned with ‘Organizational Strategy’. Further, there has to be seamless collaboration across the organization (Kache and Seuring 2017 ) to enable desired outcomes. Organizational inertness (Biazzin and Castro-Carvalho 2019 ), resistance to organizational flexibility (Ahmed et al. 2018 ; Cabrera-Sánchez and Villarejo-Ramos 2019 ; Dubey et al. 2019a ; Mikalef et al. 2019a , 2020c ; Wang et al. 2018b ) and denial (no support) to experimentation (Alaskar et al. 2020 ; Mikalef et al. 2020c ; Walker and Brown 2019 ) are other sets of organizational barriers. These challenges need to be addressed with a cultural shift within the enterprise. Organizations developing ability to absorb paradigm shifts (Walker and Brown 2019 ), by developing dynamic capabilities, such as dealing with complexity, facilitating reuse, enabling interoperability, client orientation, creating flexibility, adherence to privacy, facilitating communication, impact evaluation, decision-making support, and migration strategy (Gong and Janssen 2021 ) can overcome organizational inertness. Organizational agility (Dam et al. 2019 ; Verhoef et al. 2021 ) in learning and developing ambidextrous culture in exploring and exploiting the new technological advancements with strong data-driven analytics culture will help overcome the resistance to flexibility and lack of experimentation. The intensity of organizational learning is one of the critical intangible resources needed to build ‘Data Science’ capabilities. Barriers in the form of ‘data challenges’ (Brinch et al. 2018 ; Chakravorty 2020 ; Devasia 2018 ; Fakhri et al. 2020 ; Halaweh and Massry 2015 ; Lamba and Singh 2018 ; Lautenbach et al. 2017 ; Mikalef et al. 2020a ; Shahbaz et al. 2019 ; Saldžiūnas and Skyrius 2017 ; Sivarajah et al. 2017 ; Zaki 2019 ), such as collection, extraction, relevance, refinement, handling, ownership, documentation, communication, management, quality, trust, security, and privacy must be dealt with utmost care and by making deliberate use of characteristics of data (Behl et al. 2019 ; Choi et al. 2018 ; Ranjan 2019 ; Lacam 2020 ; Rialti et al. 2018 ; Sarker et al. 2019 ; Schroeder 2016 ; Wright et al. 2019 ; Yadav 2017 ). A proper data and information governance model (Bertot et al. 2014 ; Chakravorty 2020 ; Fakhri et al. 2020 ; Foshay et al. 2015 ; Gregory 2011 ; Mikalef et al. 2020a ; Wang et al. 2018a ) would help overcome these challenges to a significant effect. The intra-firm power dynamics (Jha et al. 2020 ; Schroeder 2016 ) also play a significant role in the success of ‘Data Science’ projects. The adverse effects must be handled in organizations by encouraging formal and informal network creation amongst the management team and ‘Data Science’ teams.

Barriers at the institutional-level are equally important to be addressed as much as individual and organizational-level in the success of ‘Data Science’ projects. Institutional pressures namely coercive, normative, and mimetic (Dubey et al. 2019b ) can act both as barriers and enablers based on the context and industry characteristics. Awareness of the institutional pressures help in the selection of tangible (infrastructure), intangible (culture), and human resources (‘Big Data’ skills). The competing business models with lack of transparency require compliance requirements (Lautenbach et al. 2017 ; Mikalef et al. 2020c ) to be established by industry-level collaborations along with a clear ‘target market’ definition (Holland et al. 2020 ). Creating effective institutional arrangement in facilitating innovative technologies enables organizations to benefit from ‘Data Science’ (Sang et al. 2020 ). External market factors (Lautenbach et al. 2017 ) adversely affecting the ‘Data Science’ projects need to be dealt by industry-government engagement by formulating a national-level strategy for the ‘Data Science’ domain. Information Management Capability (IMC) of an organization plays an important role in effectively handling external market factors (Macada et al, 2019 ). Lack of competency and support by vendors and alignment between client and service provider (Behl et al. 2019 ; Walker and Brown 2019 ) needs institutional arrangement in addressing the issue. There is also need to create a competence pool in the domain of ‘Data Science’. Dearth of adequate talent in the core domains, along with ‘Data Science’ skillsets (Akter et al. 2016 ; Chatterjee 2020 ; Devasia 2018 ; Halaweh and Massry 2015 ; Jha et al. 2020 ; Kache and Seuring 2017 ; Lautenbach et al. 2017 ; Mikalef et al. 2019a ; Ransbotham et al. 2017 ; Wang et al. 2018c ), is another challenge that organizations face in implementing ‘Data Science’. Collaboration between industry and academia on ‘Data Science’ topics would lead to the development of skilled talent during university education. This would help to address the above-mentioned institutional concerns to a considerable extent.

The proposed conceptual framework was validated and refined through an iterative feedback process based on expert opinions. The conceptual model was shared with ten different experts leading ‘Data Science’ initiatives in their organizations at senior executive levels. These organizations work in the Retail, Media, Healthcare, Fintech, Manufacturing, Automobile, Aerospace, and Telecommunications sectors. Their opinions were given due consideration and the model was appropriately modified.

5 Discussion

In adopting ‘Data Science’, organizational approaches can be either deductive (top-down) or inductive (bottom-up). The traditional approach is the top-down approach where the business problem is defined first followed by a search for the required data. With this approach, there could be challenges with the accessibility of data and a feeble understanding of data collection methods and processes. Unless there exists high data analysis competency in the organization, this approach could be risky. With limited exposure to ‘Data Science’, a bottom-up approach is suggested where available data is first analyzed, and further, the business problem is accordingly defined based. This approach would have the following steps: (i) control of data hygiene, (ii) staff training, (iii) data value estimation, (iv) development of new dimensions for data sets, (v) evaluation of data analysis consequences, and (vi) use of insights (Saldžiūnas and Skyrius 2017 ). On the contrary, Lautenbach et al. ( 2017 ) suggest organizations drive BI&A usage with the top-down approach. The leadership team must keep the employees informed of the value and benefits derived through BI&A use (Lautenbach et al. 2017 ).

‘Data Science Strategy’ by its very nature leads to asymmetric information in the technology market (Mandlik and Kadirov 2018 ). In equity markets, information on firms’ investment in ‘Data Science’ leads to positive stock market reactions. Moreover, investments in small vendors tend to bring in higher returns than big vendors. Also, investors assess ‘Data Science’ investments of big firms as compared to those of small firms (Lee et al. 2017 ). There is a different impact on the size of returns depending on firm size and vendor size. Reliability, ROI, real-time analytics must be kept in mind while dealing with ‘Data Science’ adoption from a business perspective (Yadav 2017 ).

This SLR provides an eagle’s eye view of enablers and challenges of adopting ‘Data Science’ in organizations. In harnessing the power of ‘Data Science’, policies are required that are collaborative and complementary across organizations in creating a stakeholder marketplace, facilitating the generation of annotated data sets, spreading awareness relating to the contribution of AI, and supporting the start-ups (Chatterjee 2020 ).

5.1 Theoretical contributions

The article makes quite a significant contribution to theory in the field of ‘Data Science Strategy’.

This SLR recognizes that prior studies have focused upon different aspects of ‘Data Science’ adoption in organizations predominantly focusing upon ‘Big Data Analytics’ and ‘Artificial Intelligence’. The dearth of studies on ‘Data Science’ as an umbrella term that cuts across different technologies, such as Big Data, Data Analytics, AI, ML, Deep Learning (DL), Visualization, and Business Intelligence (BI) is evident. This SLR has reviewed the most relevant articles and synthesized the knowledge on ‘Data Science’ strategies. The findings and synthesized components are further developed into a conceptual framework. Developing this conceptual framework is the main theoretical contribution of this article. The findings, therefore, contribute to answering the research question ‘What are the enablers and challenges that affect the strategic implementation and success of ‘Data Science’ (AI/ML/BD) in organizations?’ and fulfil a significant gap in the literature.

The existing literature on ‘Big Data Analytics’ and ‘Artificial Intelligence’ draws upon strong theoretical backgrounds (Refer Table S2). ‘Data Science’ adoption in organizations has extensive opportunity in generating novel theory, along with new management practices. This article contributes to the theories by drawing upon Stimulus-Organism-Response (S–O-R) model, Contingency model, and Institutional theory in formulation of the proposed conceptual framework.

The proposed framework sets the agenda for further research in ‘Data Science’ strategy. This SLR article could serve as reference for practitioners as well as research scholars. Through the proposed framework, literature can be further enriched by developing the constructs grouped under ‘barriers’ and ‘enablers’, and developing them into propositions and hypotheses. Research contributions towards validating these propositions and/or hypotheses using appropriate qualitative and quantitative techniques can add to the knowledge body.

5.2 Managerial implications

Managers need to note that ‘Data Science’ is a part of the wider ecosystem including ‘Big Data’, ‘Data Analytics’, ‘Machine Learning’, ‘Artificial Intelligence’, and ‘Business Intelligence’. It also provides significant benefits over traditional analytics (Business Intelligence) systems. The considerable effect of ‘Data Science’ on the workforce is quite eminent in the next few years both at the organizational and the personal levels.

The proposed framework of ‘Data Science Strategy’ can help organizations from various industries and domains in taking the right steps towards the successful implementation of ‘Data Science’ projects. We summarize the implications to managers below.

Every enterprise’s ‘Data Science’ journey should start with the right business questions. Be sure of a clear business strategy and an expected value that the ‘Data Science Strategy’ can relate to. In answering the business questions pertaining to ‘Data Science’, choose the strategic choices necessary to drive the ‘Data Science’ transformation forward with resources, such as Data, Technology, Infrastructure and Human talent (skills and knowledge). Without business-appropriate choice of these resources, the desired outcome would hardly be achieved.

For ‘Data Science’ project to successfully lead to business outcomes, managers need to be cognizant of the barriers. Though it may not be possible to completely eliminate them, they can be minimized. The proposed framework summarizes the comprehensive list of barriers that managers need to keep their focus upon. Prior analyses of these barriers much before implementation of ‘Data Science’, would help in the planning and distribution of resources. The barriers summarized in the framework are a result of our review on multiple industry sectors and domains. We believe many organizations would almost face all or few of these barriers. Depending on the specific industry and domain, specifically applicable barriers should be considered by the managers (also refer to Supplementary Table S1). In summary, good overview of barriers at the early stages of planning and implementation of ‘Data Science’ projects will help managers to overcome hindrances and realize expected outcomes.

Along with barriers to ‘Data Science’, the proposed framework also summarizes the comprehensive list of enablers which intend to minimize the barriers. Enablers and barriers of ‘Data Science’ projects are inversely related. The summary of enablers for various industry sectors and domains is presented in Supplementary Table S2. Detailed prior research on all the enablers and specific attention to the ones relevant to their industry would help them to optimize the resources and accelerate the ‘Data Science’ journey to realizing ‘business value’.

The outcomes of ‘Data Science’ projects can be either functional or symbolic. Organizations must focus upon clearly targeted outcomes which can be measured using process KPIs and business value in terms of profits and cost savings (RoI). The value in the form of competitive advantage and structural transformation must also be monitored as indicated in the framework. Managers must also recognize the importance of ‘Data Science’ adoption in the form of symbolic value add. As a symbolic business value, ‘Data Science’ adds to the organizations’ brand value, and recognition as technology-driver. ‘Data Science’ projects can also enhance and lead social, environmental and corporate friendly initiatives within the organization. Managers must realize that the business outcomes in most ‘Data Science’ project initiatives are iterative and prone to initial hiccups. Awareness and balance of barriers and enablers from the proposed framework will help to realize the desired results in a stipulated time span. Therefore, early-stage setbacks on ‘Data Science’ projects should not deter the purpose of successful utilization of ‘Data Science’. Despite the mentioned barriers, managers in organizations need to keep up their motivations and convince the stakeholders (sub-ordinates, partners, vendors and management teams), and count on the ‘Data Science’ enablers.

Knowledge acquired from the learnings of the proposed framework in the form of resources, barriers, enablers, and strategic business value would help organizations in adopting ‘Data Science’ and exploit new business opportunities.

6 Limitations and future research

We acknowledge the limitations of our study and the readers should interpret the content of this SLR article in the context of these limitations. Primarily, the SLR has been reliant on accessible research articles from four databases, namely, EBSCO Business Source Ultimate, Scopus, Science Direct, and Pro-Quest databases, in English language. Though we have conducted SLR by identifying all possible articles relevant, we could have missed some from other leading databases (Web of Science). In addition, the interpretation of findings and synthesis are subjective, a fact which we (authors) have attempted to overcome by examining the articles independently. Secondly, amongst the mentioned databases used, the articles are identified based on ‘keyword search’ in the Title, Abstract, and Article keywords sections. We could have failed to consider the relevant articles where the subject area of interest is embedded in the main text. Thirdly, the proposed conceptual framework needs to be developed into an hypotheses and validated with empirical studies.

In addressing the first and second limitations mentioned, it is recommended further to conduct literature reviews from other databases including keyword search throughout the article (not limiting only to Title, Abstract, and Article keywords). To address the third limitation mentioned above, the proposed framework could help future researchers push significant agenda for ‘Data Science Strategy’ research.

We have identified the following future research opportunities:

Developing constructs for each of the constituents in the framework, formulating and empirically validating hypotheses.

As highlighted in motivation for this SLR, the literature articles have focused highly on certain individual components of ‘Data Science Strategy’ in discrete (viz. Big Data Strategy, Big Data Analytics Strategy, Artificial Intelligence Strategy, and BI&A Strategy). Considerably less research attention is paid to other individual components of ‘Data Science strategies’ including data visualization strategies, Machine Learning and Deep Learning strategies in enterprises, and ‘Data Science’ strategies with ‘Small Data’. Data visualization is an important component in persuading the top management stakeholders. Future studies could focus upon examining the ‘Data Science’ strategies in these neglected areas.

A large number of ‘Data Science Strategy’ studies have been dedicated to ‘Big Data Analytics’. Apart from ‘Big Data Analytics’, there also exists relevance for ‘Small Data’ analytics. For many small and medium businesses, ‘Small Data’ also provides great business insights. There is dearth of studies on enablers, barriers and impact of ‘Small Data’ implementation and strategic constituents. Future researchers can focus upon this direction.

Extant literature predominantly examines the enablers and barriers of ‘Data Science’ adoption in certain industry sectors and business domains (viz. Healthcare, Manufacturing, Construction, E-commerce, Telecommunications, Banking and Finance, Automotive, Aerospace, and Entertainment) (Ahmed et al. 2018 ; Dremel et al. 2017 ; Holland et al. 2020 ; Lee et al. 2017 ; Saldžiūnas and Skyrius 2017 ; Yang et al. 2015 ; Wang and Hajli 2017 ; Wamba et al. 2017 ). However, many sectors are yet to be explored (viz. Offline and Online Education, Disaster management) in this context. Researchers could pay attention to these unexplored areas.

Sector-wise comparative analysis between enablers and barriers of ‘Data Science’ Strategy between enterprises has also not been attempted in extant literature. Studies in such direction can bring in key insights which can be used for peer-to-peer learning among industries. Owing to the current Covid-19 pandemic situation, organizations are speeding up their digital transformation journeys. As a result, they are capturing large amounts of data which is unprecedented and new AI use cases are also evolving. The impact of Covid-19 pandemic on the overall ‘Data Science’ ecosystem including strategic dimensions needs investigation.

‘Data Science Strategy’ studies have largely focused on industry sectors in countries such as the USA, India, China and Europe (Refer to Fig. S4). Except India and China, the industries in other Asia–Pacific regions have not been examined extensively. Future studies can be devoted to this aspect. Additionally, market-specific condition combination (developed & developing economies) along with organizational culture (explorative and exploitative, flexibility) can further be explored. The adoption of ‘Data Science’ in internationalization is still an emerging research interest. The organizational variables affecting the relationship between ‘Data Science’ and internationalization could be of interest to future researchers.

With many start-up companies mushrooming, studies exploring the factors associated with ‘Data Science’ adoption and success, and contrasting them with that of established firms is an area can be researched. Further research should be conducted to understand the role of ‘Data Science’ in digital transformation.

There may be scenarios, wherein the Human Resource Management (HRM) dimension has led to a failure in the ‘Data Science’ implementation. This dimension has not been studied extensively. Studies can be conducted specifically on the HRM dimension of the ‘Data Science’ implementation.

Consideration towards growing diversity of consumer preferences ethically is strategically relevant to deploy the ‘Data Science’ business models (Wiener et al. 2020 ). On the contrary, Biazzin and Castro-Carvalho ( 2019 ) studied the impact of buyers’ current behaviour and found it does not significantly impact their deployment. Societal impacts of different ‘Data Science’ strategies should involve the proper implementation of rules and policies to ensure ethical use. This could also be direction for future research.

Studies are also limited on the implementation of ‘Data Science Strategy’ in government sector organizations. As an example, the citizens’ data collected by Government of India through ‘Aadhar’ unique identification number, ‘Aarogya Sethu’, and ‘CoWIN’ can be used for building social welfare and health infrastructure development programs. The benefits and the intertwined challenges with ‘Data Science Strategy’ in the government sector offer further opportunities for research.

7 Conclusion

The SLR aimed at synthesizing the enablers and barriers of 'Data Science Strategy' adoption in different industries, sectors, and business domains. The review conducted an in-depth study of 113 published articles (1998- 2021) sourced from four different databases, namely, EBSCO Business Source Ultimate, Scopus, Science Direct, and Pro-Quest databases, respectively. The findings show that the enablers of ‘Data Science’ and barriers for adoption of ‘Data Science Strategy’ are synthesized. The SLR identified four themes namely ‘Content’ (Data Quality & Governance), ‘Context’ (Technology and Environmental), ‘Intent’ (Culture, Alignment, Agility), and ‘Outcomes’ (Functional and Symbolic business value) that influence the development of ‘Data Science Strategy’ in organizations.

The key lessons learnt are formulated in to a conceptual framework linking ‘Data Science’ resources to business outcomes, influenced by ‘barriers’ and ‘enablers’. In summary, the barriers and enablers are segregated under individual, organizational and institutional contexts. Organizations may not completely be able to eliminate the ‘barriers’, but capitalizing on ‘enablers’ would mitigate the risks of ‘Data Science’ adoption and success of projects leading to desired business outcomes. The study also summarizes the ‘barriers’ and ‘enablers’ of ‘Data Science Strategy’ in organizations depending on industry sectors and domains. Although most of the barriers and enablers might be common across businesses, organizations should prioritize those relevant to their areas of expertise.

The study also emphasizes on the significant role of 'Data Science Strategy' in organizations augmenting 'Data Strategy', 'digitalization' and, eventually 'digital transformation' leading to business success. Research gaps are identified for future attention. The academic and practitioner community should focus their efforts on promoting the development and implementation of a well-crafted, outcome-based, holistic 'Data Science Strategy' so as to reap the benefits of the digital revolution that’s happening globally. Additionally, the onset of Covid-19 pandemic has accentuated the challenges faced by enterprises across various domains which further pushes for a need for a ‘Data Science Strategy’ to aid in the development of a ‘Digital Strategy’ for an organization.

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Indian Institute of Management Shillong, Shillong, India

Rajesh Chidananda Reddy

Information Systems and Analytics Area, Indian Institute of Management Shillong, Umsawli, Shillong, 793018, India

Biplab Bhattacharjee

Strategic Management Area, Indian Institute of Management Shillong, Shillong, India

Debasisha Mishra

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Reddy, R.C., Bhattacharjee, B., Mishra, D. et al. A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy. Inf Syst E-Bus Manage 20 , 223–255 (2022). https://doi.org/10.1007/s10257-022-00550-x

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Received : 13 October 2021

Revised : 16 December 2021

Accepted : 05 January 2022

Published : 25 January 2022

Issue Date : March 2022

DOI : https://doi.org/10.1007/s10257-022-00550-x

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Livelihood – LITERATURE REVIEW Conceptual Framework

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Overview This chapter looks at the related literature on this research topic. It covers the definition of the term livelihood, livelihood diversification, highlights the indicators of livelihood, constraints of rural livelihood, and explains _________________.

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Livelihood means securing the necessities of life. People's capacities, assets, income and activities have always been the means of making a living. Livelihood as a body of knowledge and a field of practice is complex and diverse. The diversity of natural resource endowments and local cultures make it difficult to have a common approach across the country. Thus, the study of livelihoods is not only full of challenges so far as subjective evidence and progamme related information are concerned, but a scenario of single sub-sector is not available anywhere. Moreover, the quality of information available on livelihood scenario dealt with varies significantly. In the recent past, due to greater access to banking, technology adoption, urbanization and other structural reforms including livelihood promotion programmes India has witnessed a significant economic growth. However, amid its impressive growth rate, poverty continues to be widespread and disparities still seem difficult to be changed. The 2015 edition of the State of India's Livelihood (SOIL) Report under review is a rich engagement with an overview and analysis of the policies and funding framework through the lens of budget allocation, new policy pronouncements, large programmes initiated and legislative efforts that have a bearing on livelihoods. It tries to capture the impact of macroeconomic trends on livelihoods of the poor in India by exploring wide ranging themes keeping in mind the role of different actors or stakeholders. The book is spread out into nine chapters excluding foreword and preface. Besides dealing with income generating activities the report also includes some of the relevant issues related to quality of life. The opening chapter namely " Overview: Taking stock " (pp. 1-12), explores the macroeconomic context of livelihoods. It also describes other aspects of livelihoods apart from income enhancement by tracking the HDI and the progress on Millennium Development Goals. It highlights the significant changes in the pattern of funding by state

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