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Research Design in Business and Management pp 171–186 Cite as

Multiple Case Research Design

  • Stefan Hunziker 3 &
  • Michael Blankenagel 3  
  • First Online: 10 November 2021

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This chapter addresses the peculiarities, characteristics, and major fallacies of multiple case research designs. The major advantage of multiple case research lies in cross-case analysis. A multiple case research design shifts the focus from understanding a single case to the differences and similarities between cases. Thus, it is not just conducting more (second, third, etc.) case studies. Rather, it is the next step in developing a theory about factors driving differences and similarities. Also, researchers find relevant information on how to write a multiple case research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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When you have finished studying this chapter, you will be able to:

understand the purpose of multiple case research: in-depth analysis of a small sample in its environmental context

explain why there is a trade-off between in-depth analysis and sample size

embrace the contextual conditions as part of the research process

understand that multiple case research is based on non-random sampling

understand that this design focuses on differences and similarities between cases

in-depth analysis of a small sample in its environmental context

trade-off between in-depth and size of sample

contextual conditions are part of the research process

non-random sampling of cases

focuses on differences and similarities between cases

9.1 General Description of Multiple Case Research Design

We see potential advantages of multiple case research in the cross-case analysis. A comprehensive comparison in cross-case analysis reveals similarities and differences and how they impact findings. Every case is analyzed as a stand-alone case to compare the mechanisms identified, contributing to potential theoretical conclusions (Vaughan, 1992 ). Case study research is a means of advancing theories by comparing similarities and differences among multiple cases (Ridder, 2017 ).

Regarding the number of cases examined, we distinguish between single case studies and multiple case studies (Stake, 2005 ). A single case can be, for example, the operation of one drug-rehab clinic. A multiple case might involve looking at several drug-rehab clinics operating in the health care industry. Individual cases may represent criticality, extremeness, uniqueness, representativeness, and typicality. Individual case studies are useful to examine conflicting theoretical findings. Also, they serve as a basis to gain new insights into an unexplored phenomenon. With a multiple case study, researchers conduct an in-depth analysis of several cases. First, an investigation of individual cases is conducted. Later, these individual results are combined. Researchers try to find similarities and differences. A major advantage of multiple case studies over individual case studies is that researchers can compare their findings. Thus, the results of multiple case research are more robust but might not be as detailed as in single case research. The major disadvantage compared to single case research is that they tie up more resources and are more costly if they try to achieve a similar depth of analysis. Sometimes, a comparative analysis is not possible because of a lack of comparable cases.

Yin ( 2014 ) differentiates not only between single and multiple case research. He adds two further distinctions “holistic” (a comprehensive analysis of a system) and “embedded” (the unit of analysis part of another system, e.g., the nursery within a hospital). Yin ( 2014 ) suggests five rationales for conducting only single case research, that is for critical, extreme, typical, revelatory, or longitudinal cases. Thus, we cannot provide a general answer to whether a single case study or multiple case study is preferable. This always depends on the specific aim, the cases, and the resources. We distinguish several units of analysis within a case. This allows the object of analysis to be analyzed from different perspectives. We conclude that there is no ideal number of cases. It always depends on the research question, the resources, and the accessibility of the cases. Yet, a multiple case research design may lead to more robust results. This specifically holds true in inductive theory building (Eisenhardt & Graebner, 2007 ). We believe that gaining access to suitable cases (e.g., companies of a specific industry) represents one of the most challenging steps in the entire research process (Walsham, 2006 ). Thus, looking for cases and checking accessibility is an important step that must be done before honing the research question(s).

Yin states that in multiple case research, each case must be selected so that it predicts similar results (literal replication) or predicts contrasting results but for expected reasons (theoretical replication). If multiple cases lead to contradictory results, the preliminary theory must be revised and tested with other cases. Both single and multiple case designs can be holistic (one unit of analysis per case) or embedded (multiple units of analysis per case) (Yin, 2014 ).

Opportunistic and convenient sampling

A general challenge in empirical studies is the access to a sufficiently large number of interview partners who reserve time for an interview (or other methods of data retrieval). To find companies for a research project, we can apply an opportunistic and convenient sampling strategy based on the approach of Bruns and McKinnon ( 1993 ): “the corporations taking part in our study comprise a non-random sample selected on the basis of location and accessibility, personal contacts and expected willingness to help with the research process” (p. 90). This obviously constitutes a sample bias, but at least the researcher can retrieve information. Thus, personal contacts and contacts through the researcher’s network can be used for the selection of the sample. Also, companies can be contacted by letter with the request for a possible interview. Finally, we may search interview partners publishing articles in relevant academic and professional journals.

Purposive sampling.

For example, a research project about risk management only considers non-financial companies for sampling purposes. This is reasoned as follows. Financial companies can be understood as so-called “risk management entities”. Their business activities and their stricter regulatory environment require significantly different risk management approaches. Therefore, they are not comparable to non-financial companies and are excluded from the sample.

9.2 Particularities of Multiple Case Research Design

In this section, we specifically address the elements that make a multiple case a discrete research design. Next to the characteristics of multiple case research, we address the main issues and decisions to be made within this research design, and the major pitfalls.

9.2.1 Characteristics of Multiple Case Research

In this section, we elaborate on the key characteristics of multiple case research along the steps of the research process.

The typical conclusion of a multiple case study is that cases are similar or different from certain elements, relationships, and conditions. This might be expressed in a categorization of cases. The elements, relationships and conditions might need to be included in a general theory.

Intellectual contribution

The intellectual contribution of a multiple case study is establishing preliminary categories of elements, relationships and conditions and elaborating theories by including these categories (either by adding them to the theory or by differentiating existing elements, relationships, and conditions) by logical generalization. This might be condensed into establishing a testable theory.

The key argument in multiple case studies is that we can explain differences in phenomena by differences in elements, relationships, and conditions in different (categories of) cases. Hence, these differences are important for a comprehensive description and explanation of the cases and potentially for more or all cases.

Results are similarities and differences, grouped into categories of elements, relationships, and conditions from rich and comprehensive case descriptions.

Methods involve mainly categorization and clustering (apart from the methods used in single case studies) and can be qualitative and quantitative.

Data used are varied and from multiple sources that provide information about potential categories, either guided by theory or with no guidance. The data is basically the same as in single case studies, but the search might be more focused by theories or preconceptions about similarities and differences. All data necessary for an envisioned categorization need to be collected.

Research question

The typical research question of a multiple case study is “what are the similarities and differences or different categories of cases that might explain variations in a phenomenon?”.

9.2.2 Issues to Address in Multiple Case Research

In detailing the research design, you face many multiple case research specific problems and decisions. We list the main options you have in the following.

Multiple case as a research design on its own

A multiple case research design shifts the focus from understanding a single case to the differences and similarities between cases. Thus, it is not just conducting another (second, third, etc.) case study. Rather, it is the next step in developing a theory about factors driving differences and similarities. Often case studies result from tackling research gaps left from models with unsatisfying explanatory power. The case study then tries to understand one phenomenon comprehensively, looking for elements that can contribute to explain the case.

The multiple case research design aims at finding and establishing systematics or taxonomies to group and classify these elements. These classifications might be rather tentative, based on the similarities and differences between two cases or already approaching the operationalization of variables that can be used in the next generation of a testable model.

Number of cases

How many cases form a multiple case research design? We suggest anything between two and (a bit arbitrary) roughly 20. It rarely makes sense to examine over twenty cases as your approach sample sizes where applications of testable models become workable. This does not mean that quantitative methods are better. They are just used in distinct steps of gathering knowledge and establishing and refining theories. The number of cases is only limited by the in-depth analysis you can conduct.

In-depth analysis of the cases

Multiple case research is still case research as it tries to understand the cases comprehensively. However, as more empirical evidence emerges about which elements are (more) important for comprehensive understanding, targeting these elements becomes easier and more manageable. The better defined your theory about these elements is, the faster you can draw a comprehensive picture up to the point where you can define variables and proxies to measure them. If there are many unique elements and interrelationships between them, this might call for many cases to increase the likelihood of integrating many combinations. However, if you have a limited number of possible combinations of elements, then another research design (e.g., cross-sectional research) might yield additional arguments than just adding more of the same (e.g., there is no or only limited added value to analyze the 45th organization where a combination of X, Y and Z prevented the successful introduction of U).

Theory about important elements

Based on existing empirical research, you establish a theory about what elements of the unit of analysis or its environment are relevant in the sense that they contribute to the understanding of the system and the system’s behavior. Often you can be guided by the following question: would the system and its behavior be understandable without integrating this element? This theory guides your description of your cases. The more refined the theory is, the more you can focus on obtaining the proper information.

Even in a single case study, your search for information is often driven by an underlying theory about the importance of elements. In the end, it is more a question of focus: the comprehensive description of a case (see single case study) or the establishing of a theory about the important elements for a comprehensive description (multiple case study).

Data analysis methods

Usually, we do not refer to specific methods as they are well known and better described elsewhere, we like to stress the possibility to use “qualitative” and “quantitative” methods in various research methods. In multiple case research analysis, this looks like a spurious claim. In fact, the qualitative comparative analysis (QCA) straddles this chasm (Ragin, 2009 ). Qualitative comparative analysis uses categorical variables and the respective n-tuples to classify units of analysis. The potential combinations are then compared to the actual observations. In a second step, inferential logic or Boolean algebra is used to reduce the number of relationships. So, you can arrive at the minimum set of necessary and sufficient conditions to predict values of a category. This method was specially developed to study samples that are too small for linear regression. Qualitative comparative analysis fits nicely with the purpose of multiple case research designs. We like to use this example as teaser to encourage you to look for appropriate new methods.

9.2.3 Major Fallacies in Conducting Multiple Case Research

While providing guidance and support for research projects, these are the major pitfalls students encounter in their multiple case research projects.

Multiple cases as an excuse to forego in-depth analysis

Many studies use a multiple case study research design to solve insufficient access to a single case. As more cases means not as much in-depth analysis, perhaps even only a single interview. This is a fallacy as this only works if you already know very well what information you need to gain a comprehensive picture. Only conducting ten interviews to “have” ten cases is not enough. First you need to start with a very good guide (based on empirical evidence) about the information you like to get. Second, you need to verify the information, making sure that the information pertains to the case and is not just the opinion of the interviewee.

Jumping the chain with insufficient evidence

The feasibility of conducting successful multiple case research is very much depending on the availability of empirical evidence and the research gap left by this evidence. Jumping from single case research as empirical evidence to multiple case research with about 20 cases rarely works. This is because the single case rarely offers enough evidence to establish a theory about the relevance of elements and their interdependencies. It is usually better to introduce a multiple case research project with perhaps three to eight cases as an intermediate step.

Staggered design versus multiple case research

It is difficult to differentiate between multiple case research and a staggered design comprising (several) single cases followed by a multiple case research design. The (one stage) multiple case research design would limit or prune the data collection and the analysis of the cases to specific categories based on existing theory. The staggered design, on the other hand, uses fully fledged single case research (first stage) as input for categorization and comparison in the multiple case research (second stage). For example, Yin ( 2014 ) states that a multiple case study basically means conducting case studies and a comparison of the cases. In fact, we consider this a staggered design combining different research designs to answer different questions. We first conduct case study research and then we look for the similarities and differences (i.e., the relevant elements that describe the cases sufficiently). Not realizing the staggered design leads to confusion regarding the research question and lacks the required comprehensiveness of the in-depth analysis.

9.3 Writing a Multiple Case Research Paper

Writing a multiple case research paper follows the principles and structure detailed in Chap. 4 . However, there are some aspects especially important for reports about multiple case projects or (partly) different from reports about other research projects. We address these idiosyncrasies following the standard structure of a scientific paper.

Introduction

Introducing a multiple case research means to clarify the focus of the research and its reasoning. Often a multiple case research design acts as a bridge between single case research and cross-sectional (or longitudinal) research. Their initial set-ups or starting points are rather clearly defined. However, regarding the multiple case research, it is important that we delineate which part of the bridge–from where to where–our research design establishes. This is in line with the general purpose of expectation management in the introduction.

In multiple case research, the purpose is usually theory elaboration. What will be elaborated? What is the starting point? This needs to be defined in the introduction. The outline of the preliminary aims affects the expectations for the theoretical background and the literature review.

Theoretical background

Here, you describe the theory that is elaborated in your research project. At one extreme of the theory continuum, there is no theoretical foundation available (apart from some underlying grand theories, see Sect. 2.3.1 ). This goes together with the research aim to identify similarities and preliminary categories.

At the other extreme, there might be a rather well-developed theory lacking only nuances to develop testable hypotheses. Then the research aim is to define constructs and categories and to confirm the construct and instrument validity.

As mentioned, these are the two extremes. So, anything in between is also possible. This makes a detailed guidance on this section impossible, apart from the necessity to check carefully for consistency between the theoretical background and the research objective. Any mismatch here seriously impairs the intellectual contribution.

Literature review

The same holds true for the literature review. The literature review of all multiple case research studies comprises no or only few studies that aim to generalize a theory. And those few usually feature a low power of explanation. Here, you demonstrate that no or only limited studies exist about your research topic.

There is a large continuum across existing case research that corresponds to the state of theory development. If there is little or no theory, we would expect only few case studies in the literature review. If the theory is already more developed, there are likely to be more case studies available that have already started to elaborate the theory by establishing categories for elements, conditions, and relationships.

The third part of the literature review is optional and depends on your experience as a researcher. Talking about constructs and categories, you do not have to reinvent the wheel. There might be other research available that deals with similar issues in another context or refers to other phenomena that help you get ideas, concepts, and instruments for your own research projects.

Typical research gap

The typical research gap of multiple case research follows directly from the issues addressed in the theoretical background and the literature review:

the power of explanation of statistically testable models is too low,

there are no statistically testable models,

there is no or no satisfying theory about the system, elements, and relationships that can be tested or applied to specific types of systems or environments (cases),

there are no case studies, only few case studies, but there has been no theory established what elements are relevant for describing and explaining the system, or

there is only an initial idea (starting point) about a theory, but it is yet unclear about details and operationalization and can thus not yet be tested.

The extent (small to large) of the identified research gap has a tremendous impact on your research aim.

Typical research aims

The typical research aim of a multiple case research design is to contribute to establishing a (refined) theory by classifying and characterizing cases and their relevant elements and relationships.

As already mentioned, the specifics and the extent of the aim depend on the existing theories, research, and the corresponding research gap.

Typical research question

Typical research questions of multiple case research design projects are:

what are the similarities and differences of [cases] about [dimension(s)]?

what are the relevant dimensions for describing [cases] about [problem or situation]?

We can illustrate this further. [Cases] could, for example, mean one of the following.

organizations,

departments of organization,

industries, or

By [dimensions] we mean any kind of criteria or characteristics of the cases, for example:

ownership structure,

management structure,

organization,

products and services,

innovation process, or

degree of outsourcing.

By [problem or situation] we mean any kind of condition that is relevant for the cases, for example:

during a pandemic,

after a financial crisis,

in a recession, or

victim of a cyber-attack.

The difference between [dimension] and [problem / situation] is not clearly cut, which in our opinion is not an obstacle to multiple case research (i.e., you can largely ignore it).

The sample needs to be described. As the focus is on similarities and differences between cases, the selection process needs to be described.

Data collection methods also use an information guideline like the single case research, that needs to be developed and substantiated based on the theory so far. The data collection is basically the same as in single case research but tends to be more standardized to facilitate comparison and the larger sample.

Data analysis methods are split in two parts. One part of the analysis methods deals with the data analysis of each case individually, which again is basically the same as in single case research. Yet, the multiple case specific analysis methods need to be described. Those refer to categorization and comparison, mainly operationalization of the establishing of categories (which categories and how to allocate to a category), and what constitutes being “similar” and “different” in each respect.

As the differentiation of the analysis methods, the results section covers the results of the individual cases, the results of the case comparison, and the case categorization. You present the rich individual case descriptions (probably not as comprehensive as compared to single case research designs) providing a complete picture about the relevant dimensions. The less standardized and more comprehensive the individual case descriptions are, the more important they become.

The results on the level of categorization and comparison should differentiate in three parts:

identifying criteria for the comprehensive description of the units of analysis,

comparing the units of analysis regarding these criteria, and

comparing and clustering the units of analysis.

The relative importance and meaningfulness of the results differ depending on the data collection (obviously, data collected to allow a categorization about certain criteria is less suited to identify additional criteria than, for example, single case research).

With qualitative data, there is already some interpretation involved in the data analysis. You discuss the results in this section.

Here are some issues you must discuss, among others:

can the criteria sufficiently describe the units of analysis?

is the comparison of the units of analysis based on these criteria meaningful (in relation to the comparison of the entire units)? Do we understand differences in the units of analysis as differences in distinct criteria? Are these criteria-related differences sufficient for understanding the differences and the similarities between units of analysis?

can you identify clusters? Can you use distinct criteria for allocating units of analysis to clusters (categories)?

can you elaborate on the existing theory? Has the theory now reached a state where it might be tested? What lacks to arrive at a testable hypothesis?

Referring to the picture of the bridge from “no theory at all to theory confirmation”, how far have you come?

The typical conclusion of a multiple case research is that the units of analysis are similar or different regarding certain elements, conditions, and relationships (summarized as criteria) and that the units of analysis can be grouped into similar and delineated from different clusters of cases, using certain criteria.

An additional conclusion may be the elaboration of an existing theory with the addition, elimination or differentiation of certain elements, conditions and relationships and preparing theory testing by identifying testable hypotheses.

9.4 Related Research Designs

In this section, we briefly describe or cross-reference research designs that are similar to the multiple case research design. They might partially overlap with or considered to be adjacent to multiple case research or in fact be a multiple case research design that has its own label in the literature. If the multiple case research design does not fully meet your intentions and expectations, look here for direction where to continue your search.

Single case research

Single-case research (see Chap. 8 ) draws on a comprehensive holistic picture of a phenomenon. The purpose is to understand “how” and “why” the unit of analysis behaves and acts in real life. This does not require an underlying theory (but it also does not preclude its existence). So, if there is no preconception about categories that denote similarities and differences, focus on one unit of analysis to understand the relevant elements, conditions, and relationships better. These results can-at a later stage (in a staggered design)-be used to compare these elements with those derived from other units of analysis.

Cross-sectional field study

A cross-sectional field study (CSFS) is a type of multiple case research and is within the peculiarities and issues of multiple case research designs. Cross-sectional field studies were introduced by Lillis and Mundy ( 2005 ) and intended to bridge the gaps between qualitative and quantitative research. We deem it worthwhile to present it in a bit more detail.

The major characteristics and properties of cross-sectional field study are:

it uses a larger sample than (multiple) case studies with less in-depth data, usually retrieved by relatively shorter interviews,

it is better suited to deal with case typical “how” and “why”-questions than surveys (used in cross-sectional research) (Eisenhardt 1991),

it can provide a better understanding of “complex phenomena” than surveys (used in cross-sectional research), and

it may help to discover ambiguities or the need for additional differentiation or categorization in prior research.

In short, it addresses the missing dialogue between “pure” case studies and cross-sectional (survey based quantitative) research. In line with our argumentation for the intellectual contribution of multiple case research, a cross-sectional field study tries to bridge case studies’ problems of generalizability with the insufficient explanatory power of cross-sectional research (high degree of unexplained variation of the dependent variable). The relation of these three designs can be depicted in Fig. 9.1 .

Three boxes depict the relation between the, case study, cross-sectional field study, and survey.

Main differences between case study, cross-sectional field study (CSFS) and cross-sectional study (Lillis & Mundy, 2005 )

A cross-sectional field study attempts a compromise by combining somewhat “in-depth” analysis with somewhat “large” samples of around 12 - 40 to identify categories and patterns. Cross-sectional field study allocates this sample size well beyond the sample sizes of multiple case studies and distinguishes between the two, which we, the authors of this textbook, do not (see Fig. 9.2 ).

A depth of analysis versus sample size plot depicts that a cross-sectional field study allocates the sample size well beyond the sample sizes of multiple case studies.

Classification of research designs based on depth and sample size (Lillis & Mundy, 2005 )

Arguing with Keating’s ( 1995 ) critique that theory elaboration (refinement) is neglected in literature, a cross-sectional field study aims to address especially this type of intellectual contribution. Based on existing, but not yet testable theory, the topic should be researched in more depth and the theory should be refined to allow for future theory testing (Ferreira & Merchant, 1992 ). This again is in line with our understanding of the purpose of multiple case research.

Table 9.1 summarizes the suitability and the methods used in cross-sectional field study and clarifies each criterion with examples.

Cross-sectional research

Cross-sectional research (see Chap. 10 ) focuses on generalizable observations at one point in time. If the number of potentially relevant variables is sufficiently low, the variables rather well defined (because of existing theories or studies), and the number of data sets rather large, you might consider a cross-sectional research.

Key Aspects to Remember

Understand the advantages and disadvantages of multiple case designs

A major advantage of multiple case research over individual case studies is that researchers can compare their findings. A systematic comparison by the means of a cross-case analysis reveals similarities and differences and how they affect findings. Thus, the results of multiple case studies are more convincing, trustworthy, and robust. Yet, the disadvantage compared to single case studies is that they tie up more resources and are more costly.

Do not confuse interviews with cases

Many studies use a multiple case research design to solve insufficient access to a single case. Because of more cases they analyze each one not in as much depth, and perhaps even conduct only a single interview. This is a fallacy as this only works if you already know very well what information you need for a rather comprehensive picture. So just conducting ten interviews to “have” ten cases is not enough. First you need to start with a very good guide (based on empirical evidence) about the information you would like to get and secondly you need to verify the information, making sure that the information pertains to the case and is not just the opinion of the interviewee.

Differentiate between holistic and embedded cases

In multiple case research designs, each case must be selected so that it predicts similar results (literal replication) or predicts contrasting results, but for expected reasons (theoretical replication). If multiple cases lead to contradictory results, the preliminary theory must be revised and tested with other cases. Both single and multiple designs can be holistic (one unit of analysis per case) or embedded (multiple units of analysis per case).

Understand the intellectual contribution of multiple case research designs

Critical Thinking Questions

Is there an ideal number of cases in multiple case research designs?

Why is an interview with a company representative not a case?

What major challenges do you face when applying multiple case research design?

Why is this research design sometimes used as an excuse to forego in-depth analysis?

How does a multiple research design produce intellectual contributions?

Recommendations for further Readings

If you are still unsure whether multiple case research design is suitable for your research project, you might find the following literature and readings helpful.

Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches. Los Angeles, CA: Sage.

Günes, E., & Bahçivan, E. (2016). A multiple case study of preservice science teachers’ TPACK: Embedded in a comprehensive belief system. International Journal of Environmental and Science Education, 11(15), 8040–8054.

Keating, P. J. (1995). A framework for classifying and evaluating the theoretical contributions of case research in management accounting. In Journal of management accounting research 7, p. 66–86.

Lillis, A. M. & Mundy, J. (2005). Cross-Sectional Field Studies in Management Accounting Research—Closing the Gaps between Surveys and Case Studies. In Journal of management accounting research 17 (1), pp. 119–141.

Ridder, H-G. (2017). The theory contribution of case study research designs. In Bus Res 10 (2), pp. 281–305.

Williams, J. J. & Seaman, Alfred E. (2002). Management accounting systems change and departmental performance: the influence of managerial information and task uncertainty. In Management Accounting Research 13 (4), pp. 419–445.

Yin, R. K. (2014). Case study research. Design and methods. 5. edition. Los Angeles, London, New Delhi, Singapore, Washington, DC: SAGE.

Bruns, W. J., & McKinnon, S. M. (1993). Information and managers: A field study. Journal of Management Accounting Research, 5 , 84–108.

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Keating, P. J. (1995). A framework for classifying and evaluating the theoretical contributions of case research in management accounting. Journal of Management Accounting Research, 7 , 66–86.

Lillis, A. M., & Mundy, J. (2005). Cross-sectional field studies in management accounting research—closing the gaps between surveys and case studies. Journal of Management Accounting Research, 17 (1), 119–141.

Ragin, C. C. (2009). Reflections on casing and case-oriented research (pp. 522–534). The Sage handbook of case-based method.

Ridder, H.-G. (2017). The theory contribution of case study research designs. Business Research, 10 (2), 281–305.

Stake, R. E. (2005). Qualitative case studies. In N.K. Denzin & Y.S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed., pp. 443–466).

Vaughan, D. (1992). Theory elaboration: The heuristics of case analysis. What is a case?. In C.C. Ragin & H.S. Becker (Eds.), Exploring the foundations of social inquiry (pp. 173–202). Cambridge University Press.

Walsham, G. (2006). Doing interpretive research. European Journal of Information Systems, 15 (3), 320–330.

Yin, R. K. (2014). Case study research. Design and methods (5th ed.). SAGE.

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Hunziker, S., Blankenagel, M. (2021). Multiple Case Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_9

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"This is an excellent book that serves an important purpose. It should become a valuable resource in research methods courses covering issues of case research. Doctoral students especially, should find the book particularly helpful. The conceptual material and methods of knowledge integration presented in this book provide scholars with the background and tools necessary to conduct case studies that meet the field's most rigorous scientific standards."

This book should be required reading for anyone involved with case study analysis.

This is a bold contribution to case study methodology, perhaps more suitable for postgraduate student considering a mixed methods approach, or a research team collaborating on a new type of project. Scholz and team introduce the transdisciplinary case study approach (TCS), which they developed at the Swiss Federal Institute of Technology (ETH). There are case studies from 5 different disciplines: neuropsychology, education, law, business, and environmental sciences. An excellent guide for any researcher contemplating a trans-disciplinary or complex case study.

The book is at the forefront of assessing good case study approaches, how they can be managed, and the conditions under which they are effective. The book makes academics aware of useful theories to explore ways in which case studies can be useful.

Embedded case study methods: Integrating quantitative and qualitative knowledge focuses on different aspects of the case study research approach, and argues that systematic embedded case studies can be used as a research methodology in its own right. The authors show how embedded case studies can be employed to qualitative, quantitative and mixed research approaches. The text provides examples of how embedded case studies can be used in different fields to solve complex research problems in areas such as: neuropsychology, educational sciences, law, business and environmental science. The book consists of 20 chapters which are divided into four parts. Part one covers case study design and synthesis which introduces the reader to different types of case studies and their design, as well as the purpose and methods of knowledge integration. Part two discusses methods of knowledge integration in relation to the different methods which can be applied to different types of embedded case studies in areas such as: integrated risk management, life cycle assessment, mediation – area development, bio-ecological potential analysis, methods for medical cases, and more cases are listed in the book, as well as how to choose the right method for different areas of research. Part three contains the largest amount of chapters within the book and its main focus is about discussing in detail the methods which were briefly present to the reader previously in part two of the text. Part four covers validation perspectives in terms of setting out the rationale for research projects and providing validation of embedded case studies. The text includes useful tables and illustrations throughout the book to aid the read and is assessable for both undergraduate and postgraduate students planning to employ embedded case study research.

This is a wonderfully in-depth reference for case study research. It does a fine job of presenting the analytic nature of case study research, which is suitable for doctoral level study.

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Research Design Review

A discussion of qualitative & quantitative research design, qualitative data analysis: the unit of analysis.

embedded unit of analysis case study

As discussed in two earlier articles in Research Design Review (see “The Important Role of ‘Buckets’ in Qualitative Data Analysis” and “Finding Connections & Making Sense of Qualitative Data” ), the selection of the unit of analysis is one of the first steps in the qualitative data analysis process. The “unit of analysis” refers to the portion of content that will be the basis for decisions made during the development of codes. For example, in textual content analyses, the unit of analysis may be at the level of a word, a sentence (Milne & Adler, 1999), a paragraph, an article or chapter, an entire edition or volume, a complete response to an interview question, entire diaries from research participants, or some other level of text. The unit of analysis may not be defined by the content per se but rather by a characteristic of the content originator (e.g., person’s age), or the unit of analysis might be at the individual level with, for example, each participant in an in-depth interview (IDI) study treated as a case. Whatever the unit of analysis, the researcher will make coding decisions based on various elements of the content, including length, complexity, manifest meanings, and latent meanings based on such nebulous variables as the person’s tone or manner.

Deciding on the unit of analysis is a very important decision because it guides the development of codes as well as the coding process. If a weak unit of analysis is chosen, one of two outcomes may result: 1) If the unit chosen is too precise (i.e., at too much of a micro-level than what is actually needed), the researcher will set in motion an analysis that may miss important contextual information and may require more time and cost than if a broader unit of analysis had been chosen. An example of a too-precise unit of analysis might be small elements of content such as individual words. 2) If the unit chosen is too imprecise (i.e., at a very high macro-level), important connections and contextual meanings in the content at smaller (individual) units may be missed, leading to erroneous categorization and interpretation of the data. An example of a too-imprecise unit of analysis might be the entire set of diaries written by 25 participants in an IDI research study, or all the comments made by teenagers on an online support forum. Keep in mind, however, that what is deemed too precise or imprecise will vary across qualitative studies, making it difficult to prescribe the “right” solution for all situations.

Although there is no perfect prescription for every study, it is generally understood that researchers should strive for a unit of analysis that retains the context necessary to derive meaning from the data. For this reason, and if all other things are equal, the qualitative researcher should probably err on the side of using a broader, more contextually based unit of analysis rather than a narrowly focused level of analysis (e.g., sentences). This does not mean that supra-macro-level units, such as the entire set of transcripts from an IDI study, are appropriate; and, to the contrary, these very imprecise units, which will obscure meanings and nuances at the individual level, should be avoided. It does mean, however, that units of analysis defined as the entirety of a research interview or focus group discussion are more likely to provide the researcher with contextual entities by which reasonable and valid meanings can be obtained and analyzed across all cases.

In the end, the researcher needs to consider the particular circumstances of the study and define the unit of analysis keeping in mind that broad, contextually rich units of analysis — maintained throughout coding, category and theme development, and interpretation — are crucial to deriving meaning in qualitative data and ensuring the integrity of research outcomes.

Milne, M. J., & Adler, R. W. (1999). Exploring the reliability of social and environmental disclosures content analysis. Accounting, Auditing & Accountability Journal , 12 (2), 237–256.

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Embedded Case Study Methods

  • By: Roland W. Scholz & Olaf Tietje
  • Publisher: SAGE Publications, Inc.
  • Publication year: 2002
  • Online pub date: January 01, 2011
  • Discipline: Anthropology
  • Methods: Case study research , Evaluation , Theory
  • DOI: https:// doi. org/10.4135/9781412984027
  • Keywords: agents , knowledge , life cycle , mediation , risk , risk management , teams Show all Show less
  • Print ISBN: 9780761919452
  • Online ISBN: 9781412984027
  • Buy the book icon link

Subject index

In an embedded case study, the starting and end point is the comprehension of the case as a whole in its real-world context. However, in the course of analysis the case will be faceted either by different perspectives of inquiry or by several sub-units. The book presents different methodological approaches to organize this faceting process. It uses the power of the system approach in order to apply methods, which allow a scientific treatment of complex cases in a way that will be also acknowledged by the quantitative research community. The authors emphasize that a qualitative analysis starting from the real-world level is an indispensable part of case analysis. Thus the book bridges the gap between quantitatve and qualitative approaches to complex problems when using the case study methodology.

Front Matter

  • LIST OF BOXES, FIGURES, AND TABLES
  • Types of Case Studies
  • The Use of Case Studies in Different Disciplines
  • The Architecture of Knowledge Integration in Embedded Case Studies
  • The ETH-UNS Case Study Zurich North
  • The Methods in Brief
  • How to Choose the Right Method
  • Formative Scenario Analysis
  • System Dynamics
  • Multi-Attribute Utility Theory
  • Integrated Risk Management
  • Mediation: Area Development Negotiations
  • Future Workshops
  • Experiential Case Encounter
  • Synthesis Moderation and Group Techniques
  • Material Flux Analysis
  • Life Cycle Assessment
  • Bio-Ecological Potential Analysis
  • The Validation of Embedded Case Studies

Back Matter

  • ABOUT THE AUTHORS

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Embedded case study methods: Integrating quantitative and qualitative knowledge

Profile image of Olaf Tietje

In an embedded case study, the starting and end point is the comprehension of the case as a whole in its real-world context. However, in the course of analysis the case will be faceted either by different perspectives of inquiry or by several sub-units. Preparing the books to read every day is enjoyable for ... Embedded case study methods: Integrating quantitative and qualitative knowledge focuses on different aspects of the case study research approach, and argues that systematic embedded case studies can be used as a research methodology in its own right.

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Embedded case study design

Boundary work in adaptive watershed management, 3.3 analysis of the case study, 3.4.2 embedded case study design.

3.4.2.1 Spatial and temporal limits, and sub-units of analysis

As shown in Figure 3.6, the embedded case study was conceived as an ongoing process of transfer of scientific knowledge into action, identifying its four most crucial stages. Broadly, I considered: (i) late 1970s, beginning of joint research project between HWU and FRISSH; (ii) late 1980s, decision by HWU to launch an advisory extension service for farmers; (iii) early 1990s, start of voluntary agreements with farmers to implement groundwater protection measures; and (iv) early 2000s, inclusion of biodiversity and nature conservation objectives, through a participatory landscape planning processes.

Shown in Figure 3.6 are the four stakeholders, used as sub-units for analysis in the embedded case study. They were identified based on the extent that they have affected the process of knowledge transfer and their contribution to adaptive watershed management in the Fuhrberg

watershed. They represent the stakeholders typically involved in adaptive watershed management. For both clarity and generality, the four sub-units were labelled as the “Scientific community”, “Water utility”, “Farmers’ community”, and “Landscape planning”. Following are some specification about the stakeholders in the Fuhrberg watershed.

Figure 3.6: Embedded case study representing transfer of knowledge into action in the Fuhrberg watershed management, highlight of the four stages of the process and the five most significant boundaries considered for empirical investigation.

The Scientific community comprised the then head of HWU’s water laboratories and the soil scientists from FRISSH. During 1980-1985, this group jointly applied for funding and carried out extensive scientific research, which led to a detailed understanding of the biochemical processes that determine groundwater quality and quantity in the Fuhrberg watershed. Water utility refers to HWU and its advisory extension service, whose primary purpose was to promote groundwater protection, through voluntary agreements with farmers. HWU is part of Hannover Public Utility (i.e. “Enercity” or in German “Stadtwerke Hannover AG” ) , a long-lived institution that provides electricity, gas, and district heating. Since 1971, Hannover Public Utility has become a joint-stock company owned by the city of Hannover. Today, with close to 2600 employees and an annual revenue of almost two billion Euros, it is one of the largest companies in Germany (Enercity 2011a). Due to HWU’s organizational autonomy, however, in this study Hannover Public Utility was considered only as part of the embedding context. Instead, the focus was on HWU and its proactive involvement in scientific research projects; particularly, on how it succeeded in integrating new scientific findings in its systems of watershed management and decision-making. To this end, it was found it useful to distinguish between “Management” and “Extension service”. The Farmers’ community includes nearly 200 agricultural holdings that today cover more than 13.000 ha, roughly 43% of the Fuhrberg watershed. In fact, despite the initially strained relations between HWU and farmers (i.e. during 1960s-1990s), today, 70% of the agricultural land is

37 covered by voluntary agreements of groundwater protection management. Finally, Landscape planning consists of both involved experts and the approach of landscape planning. The former were landscape planners from the Institute of Environmental Planning in Hannover (“Institut für Umweltplanung” - IUP) at the Leibniz University of Hanover. They had been invited by HWU to take over the coordination of the abovementioned “drinking area co-operations”, which at that time had failed in fully meeting their initial expectations.

In the above described embedded case study design, two aspects need further clarification. Firstly, the fact that the four sub-units of analysis are not the only stakeholders in the process of knowledge transfer in the case study. Indeed, given the relatively long time-period and scale of our analysis, there were also other stakeholders, including residents and civil society organizations. However, their impact on the process of knowledge transfer investigated here had been indirect or relatively limited. Therefore, they were included only as part of the embedding context. Secondly, the fact those three sub-units of the analysis were arguably associated with three different stages of the process of knowledge transfer in the Fuhrberg. More specifically, (i) production of scientific knowledge - Knowledge, (ii) translation into policy and decisions - Policy, and finally (iii) implementation on the ground – Action, were associated to “Scientific Community”, “Water Utility” and “Farmers’ Community” respectively, while landscape planning went across the three stages (Figure 3.6). However, it is worth clarifying that what may appear in the scheme as a linear flow of knowledge was only a helpful frame for analyzing the phenomenon over time. Generally, the sub-unit as well as the embedding socio-ecological context feedback to each other.

3.4.2.2 Embedding socio-ecological context

Diverse factors affected to different extents the process of knowledge transfer in the case study, including the regulatory framework, the relative influence of actors, the main societal concerns, and the historic pathways. Accordingly, literature addressing relevant issues, such as implementation of the Water Framework Directive (WFD) in Lower Saxony, integrated landscape planning in the Fuhrberg watershed, and evolution of metropolitan governance in Hannover Region was analyzed. Moreover, content analysis of newspaper articles about the Fuhrberg watershed, since the start of water abstraction in the mid-1950s, was performed. Arguably, newspapers were regarded as a reliable proxy of the general societal concerns, especially when covering such a long time-period. Among other things, they allowed gaining an overall understanding of trends: water-related stakeholders’ interactions and level of societal awareness (see Figure 3.5).

3.4.2.3 Data collection: interviews, workshop, and field

Three interview protocols were designed in order to cover the five representative boundaries and stakeholders engaged in knowledge use and production (see

Table 3.4 ). Semi-structured interview questions were used to address relevant issues, including the organizational structure of HWU, critical moments in the implementation of an adaptive watershed management (for e.g. beginning of the joint-scientific research, decision regarding installment of a treatment plant, acquisition of land and launching of advisory service), and interaction between stakeholders. The questionnaires were designed according to Harrell and Bradley (2009) and administered to other researches as a pre-test. (See Appendix 2).

The questionnaires were used to interview both primary and secondary sources with several years of direct involvement in the case study (Scholz and Tietje, 2002). Interviewees were selected in a purposive and snowball fashion (Bryman, 2001), to cover all sub-units of analysis, at different phases of the process of knowledge transfer. It was made sure to include “numerous and highly knowledgeable informants, who view the focal phenomena from diverse perspectives” (Eisenhardt and Graebner 2007).

Table 3.4: Topics addressed to investigate the five most significant boundaries for knowledge into action transfer in the Fuhrberg watershed management case study (A.1, A.2, B.1, B.2 and C.1). Questionnaire A Farmers Questionnaire B Water managers Questionnaire C Landscape planners  Involvement with HWU

 Main driver for cooperation  Advantages and

disadvantages of cooperation

 Learning from cooperation

 Introduction to HWU and the advisory extension service

 Decision about water treatment plant (1985)  Role of the then head of

HWU's water laboratory  Negotiation for abstraction

rights (1980s-90s)  Land acquisition (late

 Cooperation with Farmers (1988-90s)

 Scaling Up and/or

Replication of the Fuhrberg experience (today)

 Farmers perception of previous collaboration with HWU (-2000)

 Participatory planning process to integrate water protection with nature and biodiversity conservation, and agricultural production (2003)

39 Nine interviews, a focus group and a field visit were conducted during June-November, 2014. The focus group, in particular, involved three senior water managers from HWU and a knowledgeable M.Sc. student from the IUP. The workshop, which had been facilitated by an expert landscape planner and myself, aimed at getting a closer look at HWU’s decision-making processes and their implications from a boundary work perspective. All the interviews and workshops have been recorded, hence, transcribed using software f4 ©.

3.4.2.4 Analysis and generalization: synthesis and triangulation of findings

Data from the interviews and the workshop was triangulated with other documentary sources. Hence, the empirical evidence of boundary work was characterized based on “Barriers” and the boundary work attributes of “Participation”, “Accountability” and “Boundary object”. In the analysis, the context (i.e. use and source of knowledge) was considered as an independent variable, and empirical evidence of boundary work as the dependent variable. Thus, the empirical evidences were assessed against the theoretical potential for interaction among users and producers of knowledge, in accordance with the framework by Clark et al. (2011). Finally, the case study findings were critically discussed to proceed with an analytical generalization (Yin 2008). Noteworthy was how, to further enhance the construct validity of the research (Yin, 2008), three key informants (all professors) were asked to review the draft of this chapter, and hence their comments were duly integrated.

3.5 Results

This section is organized in three parts. Firstly, the key informants, which are representative of the main stakeholders involved in knowledge use and production in the Fuhrberg case study, are introduced (3.5.1). Secondly, the findings about the embedding socio-ecological context are presented, including the implementation of the Water Framework Directive in Lower Saxony and content analysis of local newspaper articles (3.5.2). Thirdly, an account of the empirical evidences of boundary work is provided (3.5.3). For the five illustrative boundaries, the main barriers to knowledge transfer and boundary work put in place to overcome them are described, specifying the attributes of participation, accountability and boundary object. Here, the results were presented in a narrative format instead of tables, to give an idea of the dynamic process in which boundary work took place.

  • Relationship with other frameworks
  • Illustrative application of the framework
  • Theoretical background
  • Scientific research and implementation in the Fuhrberg watershed
  • Embedded case study design (You are here)
  • Embedding context
  • Empirical evidence of boundary work in Fuhrberg watershed management Table 3.8 shows the five representative boundaries, which were investigated empirically to
  • Discussions and conclusions
  • Case study: The Toker watershed (Eritrea)
  • Setting the agenda
  • Opportunities for real-life application in the Toker Watershed
  • Water utilities as learning organizations
  • Bibliography

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  • v.94(1); 2023

Logo of actabiomedica

Role and challenges to digital technologies in community health promotion programs in Italy during the COVID-19 pandemic: a multiple embedded case study protocol

Marco del riccio.

1 University of Florence, Department of Health Sciences, Florence, Italy

Luigi Costantini

2 University of Modena and Reggio Emilia, Department of Medical and Surgical Sciences, School of Specialization in Community Medicine and Primary Care, Italy

Massimo Guasconi

3 University of Parma, Department of Medicine and Surgery, Parma, Italy

4 Azienda USL (Local Health Service) of Piacenza, Piacenza, Italy

Giovanna Casella

Alice fanfani.

5 University of Florence, School of Specialization in Hygiene and Preventive Medicine, Florence, Italy

Claudia Cosma

Paula mindrican, guglielmo bonaccorsi, elena corradini, giovanna artioli, leopoldo sarli, glenn laverack.

6 University of Trento, Department of Sociology and Social Research, Italy

Ermanno Rondini

7 Italian League Against Cancer (LILT) Local Association of Reggio Emilia, Reggio Emilia, Italy.

Gianfranco Martucci

Background and aim:.

Due to the COVID-19 pandemics, The Italian League Against Cancer (LILT), a national federation of local associations promoting cancer prevention, had to face the challenge to find new ways and technologies to promote health in their territories. This study aims to explore how LILT associations led their health promotion interventions during the COVID-19 pandemic and to understand which interventions had a greater impact, for which population group, and why.

In this descriptive multiple embedded case study, each case will focus on the activities of a local LILT association and their collaborators on the perception and experience of the use of digital technology for health promotion and prevention, through interviews, observations, and a study of products and artifacts. A general overview of each case study will be provided, along with an introduction of the unit(s) of more in-depth analysis. The logical models that emerge from the analysis of each case will be described by using realist analysis, producing a list of possible CMO configurations (Context; Mechanisms; Outcomes). The final report will consist of a cross-case analysis (a comparison between the different case studies).

Discussion:

This multiple case study will help generate a first theory of the use of digital technology in health promotion in local LILT communities. The observation of what local LILT associations in Italy have done during COVID-19 will help identify new and useful health promotion strategies based on these technologies. ( www.actabiomedica.it )

Introduction

Since early 2020, the COVID-19 pandemic has had a massive impact on people’s health and wellbeing and has posed crucial challenges to national health services ( 1 ). One of the fields that has been affected the most is cancer screening, especially in the early phases of the pandemic. Although it is widely known that cancer screening programs can decrease specific-cause mortality and all-cause mortality (up to 20% and 3%, respectively) ( 2 ) and may have an important role in helping to identify early-stage cancers, the proportion of eligible individuals screened for different types of cancer dropped 62–96% in April-May of 2020 compared to April-September 2019, in the USA ( 3 ). In fact, due to the disruption of health services caused by COVID-19, an estimated 9.4 million screening tests that normally would have taken place in the United States in 2020 didn’t happen ( 4 ). A similar situation was observed in Italy, where the COVID-19 pandemic led to a national lockdown in March 2020 and the temporary interruption of several non-urgent healthcare activities, including cancer screening ( 5 , 6 ).

The Italian Cancer League (LILT), a national federation of local associations promoting cancer prevention through awareness campaigns, educational programs for schools and early diagnosis programs, had to face the challenge to find new ways including new technologies to promote health in their territories. Digital health promotion was already a growing field, but the nature of services as non-profits makes it difficult to produce a strict guide line on this field, while a “case study” approach might help in define good practices that can be adapted to each individual specific situation ( 7 ).

In this project, whose short name is “5x1000 Community” we will collect the experiences of health promotion through digital technologies had by local LILTs (in the form of “case studies”) and, through a community of practices between LILT and other partners in this project. We will examine the approaches to implement or improve new practices (in the form of “action research”), in particular on the subject of smoking and the promotion of screening. As a community intervention, the partners and privileged targets are the local units, involved through training courses and community micro-projects. This protocol covers the study of the multiple case studies, while the action-research will be described in forthcoming papers.

This study aims to explore how LILT associations led their health promotion interventions, especially in times of in-person meetings restrictions, with a specific focus on how they used digital instruments, and to understand which interventions had a greater impact, for which population group, and why.

The study objectives of this project will be to:

  • - understand how the LILTs of the local sections participating in the project used digital technology in health promotion, starting from the onset of the COVID-19 pandemic;
  • - explore the tools and the new practices adopted by the local LILTs to conduct their work during the pandemic;
  • - assess the results achieved by the local LILTs by using digital technology, especially those which were not able to be achieved before the pandemic and those that would likely be adopted in the work practices

This descriptive multiple embedded case study is designed according to the principles described by Yin ( 8 ). A case study design is appropriate to investigate ‘how, what and why’ a phenomenon takes place, and specifically suits contemporary, real-world events that are influenced by context ( 8 ). In particular, a multiple case study can increase reliability by contemporarily examining different cases instead of analysing one single unit ( 9 ). In this study different local LILT associations will be directly involved, each representing a different study case; this will allow reporting and analysing several interventions, and every unit of analysis will be embedded in the case study. In Italy, there are 106 Local LILT Associations, that vary depending on the geographical area, the population, and its needs: this is why we have chosen this research design, which seems appropriate according to the contextual variations. Analysing the differences between the study cases and their units will help to explain why some cases have certain results and to compare and contrast results between units.

Definition of the case

Case studies have been defined in many ways ( 10 ) but generally share some common features. A case can be represented either by individuals, roles, or communities; it always occurs in a specific social and physical context, and it is therefore defined as the unit of focus being analysed and limited by specified boundaries ( 9 , 11 ).

For this study, a case is defined as the set of health promotion operators and primary care professionals involved in digital health promotion programmes led by each of the seven local LILT associations involved in the project.

LILTs are local associations with the aim of fighting cancer in terms of research and primary, secondary, and tertiary prevention: within each local LILT association primary health care professionals (medical doctors, nurses, psychologists, etc.) and volunteers are involved in the activities. Each LILT association carries on many different activities such as cancer screening promotion, online/in presence education (e.g., in schools or working environment), diagnostic exams and rehabilitation programmes.

Our cases will be bound by time and setting, as suggested by Creswell and colleagues ( 12 ) as case boundaries help focus the study questions and differentiate between the phenomenon under study and its context ( 8 ).

The time period for this study is represented by the period that followed the onset of the COVID-19 pandemic, as the main aims focus on the tools, the strategies, and the solutions adopted by the local LILT associations to carry on their activities (and to assess and monitor their results) during the COVID-19 pandemic (January 2020), to a conventionally decided July 2022. The project is articulated in four semesters: the case studies data collection will be conducted during the first semester, while the second and third semesters will be used for the evaluation of the processes analysed and during the first phase. The last semester will involve final evaluations and final reports writing, as well as scientific dissemination.

With respect to the setting, this study will be bound by the LILT national network, and in particular the project will be conducted together with the participating seven local LILT associations (LILT of Reggio Emilia, Piacenza, Ferrara, Firenze, Oristano, Campobasso, BAT).

Participants

The project management staff (principal investigator and project manager) will organize the data collection and perform the analyses. Each case (within a local LILT association) will be managed by a dedicated research team, formed by some members of the other LILTs and other project partners, in particular medical residents and master’s students that will have hours formally assigned to the project through institutional agreements.

Participation will involve many different health promotion and disease prevention actors with local LILT associations, each having a specific role, however the LILT operators will both act as part of case studies and researchers, their activities being the main focus of the project

Data collection instruments

Data collection will be based on triangulation of:

  • - semi-structured interviews (see additional file 1 for an interview guide provided to researchers),
  • - participant/non-participant observation,
  • - analysis of materials such as website and social media contents produced during their activities.

An operational guide, including a description of what a “case study research” is, how to gather data and how to analyse them has been provided to all the professionals involved in the project. A possible outline for semi-structured interviews and a list of relevant points on how to conduct an observation were included. The interview outline is reported in a translated version in the additional file n.1.

The subjects involved in the units of analysis of the case studies will be observed directly (when possible) or indirectly (by watching and analysing the records of their activities) while conducting those activities that involve the use of digital technology for health promotion and prevention, and then interviewed by the project coordination staff members or by other LILT operators, previously trained. LILT operators and coordination staff members will be helped by primary care professionals (e.g. nurses) and university partners (medical residents in preventive medicine and public health, medical residents in community care, master students) in conducting the interviews, recording the activities conducted by each participating local LILT association, producing reports, minutes, manuals, and assessing whether there are new practices involving digital technologies that have been put in place and could be considered “good practices” to be exported in other settings (e.g. in other local LILTs or promoted as a national model).

Reports, recordings, videos, and other materials that were produced in the pandemic period by any of the participating local LILT associations will be analysed.

To optimize resources, it was decided to investigate only a few “units of analysis” (subunits that form a case study) for each LILT. Each LILT has independently indicated (via email and/or interviews with the management staff) which aspects of their activities they wanted to describe in detail.

As previously reported, the operators of a LILT will also collaborate in the data collection of the other local LILTs’ units of analysis (“peer-research”): in this way they will develop research skills in a protected environment and also have the opportunity to learn more in depth the work of the other sections on the area of interest of the project. This exchange of working practices aims to support the creation of a real “community of practices”, an expected product of the project as a whole.

Data analysis and reporting

Analysis and reporting will be likely divided into two main parts:

  • - individual case studies: a general overview of the case study will be provided, along with an introduction of the unit or units of more in-depth analysis (e.g. “LILT-Reggio Emilia identified five areas in which the role of digital tools had a relevant role in conducting different activities [...]; of these, we analyse in detail three of them [...]”). A visual summary (e.g. a short video) will be provided, in order to allow a first immediate understanding of the outlines of the results of each case. Furthermore, according to the realist theory, the logical models emerged from the analysis of the case will be reported and will describe the case by using the CMO configurations (Context; Mechanisms; Outcomes) ( 13 ). These configurations will be presented with tables and summarised using a diagram.
  • - integrated multiple case study report: an overall report of the entire multiple case study will be prepared as a result of a cross-case analysis (e.g. comparison between the different case studies). In particular, this report will include a brief visual summary of the main “lessons learned” and will be based on one side on the comparison between the different “CMO” configurations found in each case, on the other side on the identification of more or less repeated patterns and the formulation of possible theories behind them. The multiple case study report will help generate a first “theory of the use of digital in health promotion in local LILT communities”, which will then be deepened and refined through the second phase of the research (research-action).

In order to increase credibility, consistency, and confirmability of our findings ( 14 ) we will apply the following strategies: semi-structured interview models will be created that will include a form of member-checking through restating or summarizing participant responses to ensure accurate understanding ( 14 ). Two or more strategies will be applied in every unit of analysis in order to triangulate the findings ( 15 ). As this multiple case includes different local case teams, triangulation will also be achieved with cross-case interviews that teams will conduct interviewing the other teams. As the multiple case involve operators interviewing colleagues, this peer examination will also increase credibility and dependability, also considering that the research team is also formed by both operators and researchers.

This study aims to provide an in-depth analysis of the rapidly evolving world of the digital tools and strategies used by local associations involved in health promotion and prevention.

Digital interventions are now often considered as an essential part of a community health promotion campaigns: they are used to better understand factors influencing compliance, to improve people’s knowledge and education about cancer and cancer screenings, to reduce barriers that limit participation and reach different communities. This project will help understand which tools have been used by seven local LILT associations in Italy, assess their impact and results, and understand whether some tools or strategies can be helpful and therefore adopted in other settings in the future.

Different strategies to collect the data (interviews, direct and indirect observations of the activities) will then support thorough and diversified discussions of the observed phenomena.

This study will present several limitations, mainly due to its design. First, it will have specific boundaries in terms of context (time and setting): while this helps better define the cases and identify a logical framework of research and action, it will probably limit the generalizability of the results (as typical of case studies). Moreover, the inductive approach that will be used to analyse the data and draw conclusions (or build new hypotheses) is sometimes considered less rigorous and therefore producing less solid results; however, it must be said that this approach does not limit the researcher from using existing theories, and this may help formulate new hypotheses that can act as guide.

Conclusions

The COVID-19 pandemic forced many different healthcare organizations and professionals to reorganize their activities and posed new challenges and barriers that made it harder to achieve good especially in terms of cancer screening compliance. Therefore, the observation of what local LILT associations in Italy have done to overcome these barriers and the contextual analysis of which digital intervention was effective (and why) could help identify new strategies to support health promotion and public health in the future. This knowledge will indeed support decision-making in relation to funding, resource allocation and organization not only for local associations but possibly at a regional/national level.

Acknowledgements:

Thanks to Maria Teresa Martucci for her organizational support.

Supplementary file

Interview outline:.

the interview outline has been developed according to the suggestions provided by the “RAMESES project”:

https://www.ramesesproject.org/media/RAMESES_II_Realist_interviewing.pdf

https://www.ramesesproject.org/media/RAMESES_II_Realist_interviewing_starter_questions.pdf

(links retrieved December 19, 2022).

Introduction:

the interviewer introduces himself.

He clarifies that the interview will be recorded for research purposes and obtains oral consent to record and report what emerged from the interview as aggregated data, combined with what emerges from the other interviews.

He briefly explains the meaning of the research: “the ongoing research is linked to the “5x1000 Community Project” and intends to collect, in this first phase, the experiences made by the LILTs (Italian Leagues Against Cancer) with the use of digital technology in health promotion. In particular, today I would like to collect your experience on the topic [... declare the topic of the unit of analysis you are investigating, for example: LILT’s experience with the schools of Reggio Emilia]”.

Icebreaker:

Could you say three words that you would connect to your experience, as LILT, with the use of digital instruments?

Opening questions:

How would you define your role in the experience we are talking about?

What other people were involved?

Can you describe to me how this experience was organized, in general?

Central questions:

How would you define the results achieved from your point of view?

Were some of the results measured in some way? How? Was a formal assessment done?

How was this experience perceived by users?

Were there any types of users for which it worked more or less?

What aspects worked the most in this experience?

What allowed or helped the achievement of these aspects that worked better (“facilitating” elements)?

What worked less?

What obstacles you found that led to these aspects that have worked less (“barriers”)?

In which contexts do you think this intervention could work better?

What lessons do you think this experience has taught you?

What materials were produced in this experience?

Is it possible to view them?

Which other people do you find useful or relevant to be interviewed to get a complete picture of this experience?

Conclusion:

I try to make a very quick summary of what has been said: […]. Can you think of anything else to add?

“Thanks for your kind participation, you and your staff will be involved in future progress of this project, as we’ll be asking you feedbacks as we progress in collecting the case studies and writing them down.”

This study was supported by the research program of the Italian League Against Cancer (‘5x1000 2020-2021’; registration number: C25F21000590008).

Conflict of interest:

Each author declares that he or she has no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangement etc.) that might pose a conflict of interest in connection with the submitted article.

Authors contribution:

Conceptualization: GM, LC, MDR, MG. Design and methodology: GM, LC. Supervision and validation: GM. Writing the draft: MDR, LC. Supervision and proofreading: GL. Commenting and editing: all authors.

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