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28 Case Selection for Case‐Study Analysis: Qualitative and Quantitative Techniques

John Gerring is Professor of Political Science, Boston University.

• Published: 02 September 2009
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This article presents some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. It also indicates that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. In particular, the article clarifies the general principles that might guide the process of case selection in case-study research. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. The article then draws attention to two ambiguities in case-selection strategies in case-study research. The first concerns the admixture of several case-selection strategies. The second concerns the changing status of a case as a study proceeds. Some case studies follow only one strategy of case selection.

Case ‐study analysis focuses on one or several cases that are expected to provide insight into a larger population. This presents the researcher with a formidable problem of case selection: Which cases should she or he choose?

In large‐sample research, the task of case selection is usually handled by some version of randomization. However, in case‐study research the sample is small (by definition) and this makes random sampling problematic, for any given sample may be wildly unrepresentative. Moreover, there is no guarantee that a few cases, chosen randomly, will provide leverage into the research question of interest.

In order to isolate a sample of cases that both reproduces the relevant causal features of a larger universe (representativeness) and provides variation along the dimensions of theoretical interest (causal leverage), case selection for very small samples must employ purposive (nonrandom) selection procedures. Nine such methods are discussed in this chapter, each of which may be identified with a distinct case‐study “type:” typical, diverse, extreme, deviant, influential, crucial, pathway, most‐similar , and most‐different . Table 28.1 summarizes each type, including its general definition, a technique for locating it within a population of potential cases, its uses, and its probable representativeness.

While each of these techniques is normally practiced on one or several cases (the diverse, most‐similar, and most‐different methods require at least two), all may employ additional cases—with the proviso that, at some point, they will no longer offer an opportunity for in‐depth analysis and will thus no longer be “case studies” in the usual sense ( Gerring 2007 , ch. 2 ). It will also be seen that small‐ N case‐selection procedures rest, at least implicitly, upon an analysis of a larger population of potential cases (as does randomization). The case(s) identified for intensive study is chosen from a population and the reasons for this choice hinge upon the way in which it is situated within that population. This is the origin of the terminology—typical, diverse, extreme, et al. It follows that case‐selection procedures in case‐study research may build upon prior cross‐case analysis and that they depend, at the very least, upon certain assumptions about the broader population.

In certain circumstances, the case‐selection procedure may be structured by a quantitative analysis of the larger population. Here, several caveats must be satisfied. First, the inference must pertain to more than a few dozen cases; otherwise, statistical analysis is problematic. Second, relevant data must be available for that population, or a significant sample of that population, on key variables, and the researcher must feel reasonably confident in the accuracy and conceptual validity of these variables. Third, all the standard assumptions of statistical research (e.g. identification, specification, robustness) must be carefully considered, and wherever possible, tested. I shall not dilate further on these familiar issues except to warn the researcher against the unreflective use of statistical techniques. 1 When these requirements are not met, the researcher must employ a qualitative approach to case selection.

The point of this chapter is to elucidate general principles that might guide the process of case selection in case‐study research, building upon earlier work by Harry Eckstein, Arend Lijphart, and others. Sometimes, these principles can be applied in a quantitative framework and sometimes they are limited to a qualitative framework. In either case, the logic of case selection remains quite similar, whether practiced in small‐ N or large‐ N contexts.

Before we begin, a bit of notation is necessary. In this chapter “ N ” refers to cases, not observations. Here, I am concerned primarily with causal inference, rather than inferences that are descriptive or predictive in nature. Thus, all hypotheses involve at least one independent variable ( X ) and one dependent variable ( Y ). For convenience, I shall label the causal factor of special theoretical interest X   1 , and the control variable, or vector of controls (if there are any), X   2 . If the writer is concerned to explain a puzzling outcome, but has no preconceptions about its causes, then the research will be described as Y‐centered . If a researcher is concerned to investigate the effects of a particular cause, with no preconceptions about what these effects might be, the research will be described as X‐centered . If a researcher is concerned to investigate a particular causal relationship, the research will be described as X   1 / Y‐centered , for it connects a particular cause with a particular outcome. 2   X ‐ or Y ‐centered research is exploratory; its purpose is to generate new hypotheses. X   1 / Y‐centered research, by contrast, is confirmatory/disconfirmatory; its purpose is to test an existing hypothesis.

1 Typical Case

In order for a focused case study to provide insight into a broader phenomenon it must be representative of a broader set of cases. It is in this context that one may speak of a typical‐case approach to case selection. The typical case exemplifies what is considered to be a typical set of values, given some general understanding of a phenomenon. By construction, the typical case is also a representative case.

Some typical cases serve an exploratory role. Here, the author chooses a case based upon a set of descriptive characteristics and then probes for causal relationships. Robert and Helen Lynd (1929/1956) selected a single city “to be as representative as possible of contemporary American life.” Specifically, they were looking for a city with

1) a temperate climate; 2) a sufficiently rapid rate of growth to ensure the presence of a plentiful assortment of the growing pains accompanying contemporary social change; 3) an industrial culture with modern, high‐speed machine production; 4) the absence of dominance of the city's industry by a single plant (i.e., not a one‐industry town); 5) a substantial local artistic life to balance its industrial activity …; and 6) the absence of any outstanding peculiarities or acute local problems which would mark the city off from the midchannel sort of American community. ( Lynd and Lynd 1929/1956 , quoted in Yin 2004 , 29–30)

After examining a number of options the Lynds decided that Muncie, Indiana, was more representative than, or at least as representative as, other midsized cities in America, thus qualifying as a typical case.

This is an inductive approach to case selection. Note that typicality may be understood according to the mean, median, or mode on a particular dimension; there may be multiple dimensions (as in the foregoing example); and each may be differently weighted (some dimensions may be more important than others). Where the selection criteria are multidimensional and a large sample of potential cases is in play, some form of factor analysis may be useful in identifying the most‐typical case(s).

However, the more common employment of the typical‐case method involves a causal model of some phenomenon of theoretical interest. Here, the researcher has identified a particular outcome ( Y ), and perhaps a specific X   1 / Y hypothesis, which she wishes to investigate. In order to do so, she looks for a typical example of that causal relationship. Intuitively, one imagines that a case selected according to the mean values of all parameters must be a typical case relative to some causal relationship. However, this is by no means assured.

Suppose that the Lynds were primarily interested in explaining feelings of trust/distrust among members of different social classes (one of the implicit research goals of the Middletown study). This outcome is likely to be affected by many factors, only some of which are included in their six selection criteria. So choosing cases with respect to a causal hypothesis involves, first of all, identifying the relevant parameters. It involves, secondly, the selection of a case that has a “typical” value relative to the overall causal model; it is well explained. Cases with untypical scores on a particular dimension (e.g. very high or very low) may still be typical examples of a causal relationship. Indeed, they may be more typical than cases whose values lie close to the mean. Thus, a descriptive understanding of typicality is quite different from a causal understanding of typicality. Since it is the latter version that is more common, I shall adopt this understanding of typicality in the remainder of the discussion.

From a qualitative perspective, causal typicality involves the selection of a case that conforms to expectations about some general causal relationship. It performs as expected. In a quantitative setting, this notion is measured by the size of a case's residual in a large‐ N cross‐case model. Typical cases lie on or near the regression line; their residuals are small. Insofar as the model is correctly specified, the size of a case's residual (i.e. the number of standard deviations that separate the actual value from the fitted value) provides a helpful clue to how representative that case is likely to be. “Outliers” are unlikely to be representative of the target population.

Of course, just because a case has a low residual does not necessarily mean that it is a representative case (with respect to the causal relationship of interest). Indeed, the issue of case representativeness is an issue that can never be definitively settled. When one refers to a “typical case” one is saying, in effect, that the probability of a case's representativeness is high, relative to other cases. This test of typicality is misleading if the statistical model is mis‐specified. And it provides little insurance against errors that are purely stochastic. A case may lie directly on the regression line but still be, in some important respect, atypical. For example, it might have an odd combination of values; the interaction of variables might be different from other cases; or additional causal mechanisms might be at work. For this reason, it is important to supplement a statistical analysis of cases with evidence drawn from the case in question (the case study itself) and with our deductive knowledge of the world. One should never judge a case solely by its residual. Yet, all other things being equal, a case with a low residual is less likely to be unusual than a case with a high residual, and to this extent the method of case selection outlined here may be a helpful guide to case‐study researchers faced with a large number of potential cases.

By way of conclusion, it should be noted that because the typical case embodies a typical value on some set of causally relevant dimensions, the variance of interest to the researcher must lie within that case. Specifically, the typical case of some phenomenon may be helpful in exploring causal mechanisms and in solving identification problems (e.g. endogeneity between X   1 and Y , an omitted variable that may account for X   1   and Y , or some other spurious causal association). Depending upon the results of the case study, the author may confirm an existing hypothesis, disconfirm that hypothesis, or reframe it in a way that is consistent with the findings of the case study. These are the uses of the typical‐case study.

2 Diverse Cases

A second case‐selection strategy has as its primary objective the achievement of maximum variance along relevant dimensions. I refer to this as a diverse‐case method. For obvious reasons, this method requires the selection of a set of cases—at minimum, two—which are intended to represent the full range of values characterizing X   1 , Y , or some particular X   1 / Y relationship. 3

Where the individual variable of interest is categorical (on/off, red/black/blue, Jewish/Protestant/Catholic), the identification of diversity is readily apparent. The investigator simply chooses one case from each category. For a continuous variable, the choices are not so obvious. However, the researcher usually chooses both extreme values (high and low), and perhaps the mean or median as well. The researcher may also look for break‐points in the distribution that seem to correspond to categorical differences among cases. Or she may follow a theoretical hunch about which threshold values count, i.e. which are likely to produce different values on Y .

Another sort of diverse case takes account of the values of multiple variables (i.e. a vector), rather than a single variable. If these variables are categorical, the identification of causal types rests upon the intersection of each category. Two dichotomous variables produce a matrix with four cells. Three trichotomous variables produce a matrix of eight cells. And so forth. If all variables are deemed relevant to the analysis, the selection of diverse cases mandates the selection of one case drawn from within each cell. Let us say that an outcome is thought to be affected by sex, race (black/white), and marital status. Here, a diverse‐case strategy of case selection would identify one case within each of these intersecting cells—a total of eight cases. Things become slightly more complicated when one or more of the factors is continuous, rather than categorical. Here, the diversity of case values do not fall neatly into cells. Rather, these cells must be created by fiat—e.g. high, medium, low.

It will be seen that where multiple variables are under consideration, the logic of diverse‐case analysis rests upon the logic of typological theorizing—where different combinations of variables are assumed to have effects on an outcome that vary across types ( Elman 2005 ; George and Bennett 2005 , 235; Lazarsfeld and Barton 1951 ). George and Smoke, for example, wish to explore different types of deterrence failure—by “fait accompli,” by “limited probe,” and by “controlled pressure.” Consequently, they wish to find cases that exemplify each type of causal mechanism. 4

Diversity may thus refer to a range of variation on X or Y , or to a particular combination of causal factors (with or without a consideration of the outcome). In each instance, the goal of case selection is to capture the full range of variation along the dimension(s) of interest.

Since diversity can mean many things, its employment in a large‐ N setting is necessarily dependent upon how this key term is defined. If it is understood to pertain only to a single variable ( X   1 or Y ), then the task is fairly simple. A categorical variable mandates the choice of at least one case from each category—two if dichotomous, three if trichotomous, and so forth. A continuous variable suggests the choice of at least one “high” and “low” value, and perhaps one drawn from the mean or median. But other choices might also be justified, according to one's hunch about the underlying causal relationship or according to natural thresholds found in the data, which may be grouped into discrete categories. Single‐variable traits are usually easy to discover in a large‐ N setting through descriptive statistics or through visual inspection of the data.

Where diversity refers to particular combinations of variables, the relevant cross‐ case technique is some version of stratified random sampling (in a probabilistic setting) or Qualitative Comparative Analysis (in a deterministic setting) ( Ragin 2000 ). If the researcher suspects that a causal relationship is affected not only by combinations of factors but also by their sequencing , then the technique of analysis must incorporate temporal elements ( Abbott 2001 ; Abbott and Forrest 1986 ; Abbott and Tsay 2000 ). Thus, the method of identifying causal types rests upon whatever method of identifying causal relationships is employed in the large‐ N sample.

Note that the identification of distinct case types is intended to identify groups of cases that are internally homogeneous (in all respects that might affect the causal relationship of interest). Thus, the choice of cases within each group should not be problematic, and may be accomplished through random sampling or purposive case selection. However, if there is suspected diversity within each category, then measures should be taken to assure that the chosen cases are typical of each category. A case study should not focus on an atypical member of a subgroup.

Indeed, considerations of diversity and typicality often go together. Thus, in a study of globalization and social welfare systems, Duane Swank (2002) first identifies three distinctive groups of welfare states: “universalistic” (social democratic), “corporatist conservative,” and “liberal.” Next, he looks within each group to find the most‐typical cases. He decides that the Nordic countries are more typical of the universalistic model than the Netherlands since the latter has “some characteristics of the occupationally based program structure and a political context of Christian Democratic‐led governments typical of the corporatist conservative nations” ( Swank 2002 , 11; see also Esping‐Andersen 1990 ). Thus, the Nordic countries are chosen as representative cases within the universalistic case type, and are accompanied in the case‐study portion of his analysis by other cases chosen to represent the other welfare state types (corporatist conservative and liberal).

Evidently, when a sample encompasses a full range of variation on relevant parameters one is likely to enhance the representativeness of that sample (relative to some population). This is a distinct advantage. Of course, the inclusion of a full range of variation may distort the actual distribution of cases across this spectrum. If there are more “high” cases than “low” cases in a population and the researcher chooses only one high case and one low case, the resulting sample of two is not perfectly representative. Even so, the diverse‐case method probably has stronger claims to representativeness than any other small‐ N sample (including the standalone typical case). The selection of diverse cases has the additional advantage of introducing variation on the key variables of interest. A set of diverse cases is, by definition, a set of cases that encompasses a range of high and low values on relevant dimensions. There is, therefore, much to recommend this method of case selection. I suspect that these advantages are commonly understood and are applied on an intuitive level by case‐study researchers. However, the lack of a recognizable name—and an explicit methodological defense—has made it difficult for case‐study researchers to utilize this method of case selection, and to do so in an explicit and self‐conscious fashion. Neologism has its uses.

3 Extreme Case

The extreme‐case method selects a case because of its extreme value on an independent ( X   1 ) or dependent ( Y ) variable of interest. Thus, studies of domestic violence may choose to focus on extreme instances of abuse ( Browne 1987 ). Studies of altruism may focus on those rare individuals who risked their lives to help others (e.g. Holocaust resisters) ( Monroe 1996 ). Studies of ethnic politics may focus on the most heterogeneous societies (e.g. Papua New Guinea) in order to better understand the role of ethnicity in a democratic setting ( Reilly 2000–1 ). Studies of industrial policy often focus on the most successful countries (i.e. the NICS) ( Deyo 1987 ). And so forth. 5

Often an extreme case corresponds to a case that is considered to be prototypical or paradigmatic of some phenomena of interest. This is because concepts are often defined by their extremes, i.e. their ideal types. Italian Fascism defines the concept of Fascism, in part, because it offered the most extreme example of that phenomenon. However, the methodological value of this case, and others like it, derives from its extremity (along some dimension of interest), not its theoretical status or its status in the literature on a subject.

The notion of “extreme” may now be defined more precisely. An extreme value is an observation that lies far away from the mean of a given distribution. This may be measured (if there are sufficient observations) by a case's “Z score”—the number of standard deviations between a case and the mean value for that sample. Extreme cases have high Z scores, and for this reason may serve as useful subjects for intensive analysis.

For a continuous variable, the distance from the mean may be in either direction (positive or negative). For a dichotomous variable (present/absent), extremeness may be interpreted as unusual . If most cases are positive along a given dimension, then a negative case constitutes an extreme case. If most cases are negative, then a positive case constitutes an extreme case. It should be clear that researchers are not simply concerned with cases where something “happened,” but also with cases where something did not. It is the rareness of the value that makes a case valuable, in this context, not its positive or negative value. 6 Thus, if one is studying state capacity, a case of state failure is probably more informative than a case of state endurance simply because the former is more unusual. Similarly, if one is interested in incest taboos a culture where the incest taboo is absent or weak is probably more useful than a culture where it is present or strong. Fascism is more important than nonfascism. And so forth. There is a good reason, therefore, why case studies of revolution tend to focus on “revolutionary” cases. Theda Skocpol (1979) had much more to learn from France than from Austro‐Hungary since France was more unusual than Austro‐Hungary within the population of nation states that Skocpol was concerned to explain. The reason is quite simple: There are fewer revolutionary cases than nonrevolutionary cases; thus, the variation that we explore as a clue to causal relationships is encapsulated in these cases, against a background of nonrevolutionary cases.

Note that the extreme‐case method of case selection appears to violate the social science folk wisdom warning us not to “select on the dependent variable.” 7 Selecting cases on the dependent variable is indeed problematic if a number of cases are chosen, all of which lie on one end of a variable's spectrum (they are all positive or negative), and if the researcher then subjects this sample to cross‐case analysis as if it were representative of a population. 8 Results for this sort of analysis would almost assuredly be biased. Moreover, there will be little variation to explain since the values of each case are explicitly constrained.

However, this is not the proper employment of the extreme‐case method. (It is more appropriately labeled an extreme‐ sample method.) The extreme‐case method actually refers back to a larger sample of cases that lie in the background of the analysis and provide a full range of variation as well as a more representative picture of the population. It is a self‐conscious attempt to maximize variance on the dimension of interest, not to minimize it. If this population of cases is well understood— either through the author's own cross‐case analysis, through the work of others, or through common sense—then a researcher may justify the selection of a single case exemplifying an extreme value for within‐case analysis. If not, the researcher may be well advised to follow a diverse‐case method, as discussed above.

By way of conclusion, let us return to the problem of representativeness. It will be seen that an extreme case may be typical or deviant. There is simply no way to tell because the researcher has not yet specified an X   1 / Y causal proposition. Once such a causal proposition has been specified one may then ask whether the case in question is similar to some population of cases in all respects that might affect the X   1 / Y relationship of interest (i.e. unit homogeneous). It is at this point that it becomes possible to say, within the context of a cross‐case statistical model, whether a case lies near to, or far from, the regression line. However, this sort of analysis means that the researcher is no longer pursuing an extreme‐case method. The extreme‐case method is purely exploratory—a way of probing possible causes of Y , or possible effects of X , in an open‐ended fashion. If the researcher has some notion of what additional factors might affect the outcome of interest, or of what relationship the causal factor of interest might have with Y , then she ought to pursue one of the other methods explored in this chapter. This also implies that an extreme‐case method may transform into a different kind of approach as a study evolves; that is, as a more specific hypothesis comes to light. Useful extreme cases at the outset of a study may prove less useful at a later stage of analysis.

4 Deviant Case

The deviant‐case method selects that case(s) which, by reference to some general understanding of a topic (either a specific theory or common sense), demonstrates a surprising value. It is thus the contrary of the typical case. Barbara Geddes (2003) notes the importance of deviant cases in medical science, where researchers are habitually focused on that which is “pathological” (according to standard theory and practice). The New England Journal of Medicine , one of the premier journals of the field, carries a regular feature entitled Case Records of the Massachusetts General Hospital. These articles bear titles like the following: “An 80‐Year‐Old Woman with Sudden Unilateral Blindness” or “A 76‐Year‐Old Man with Fever, Dyspnea, Pulmonary Infiltrates, Pleural Effusions, and Confusion.” 9 Another interesting example drawn from the field of medicine concerns the extensive study now devoted to a small number of persons who seem resistant to the AIDS virus ( Buchbinder and Vittinghoff 1999 ; Haynes, Pantaleo, and Fauci 1996 ). Why are they resistant? What is different about these people? What can we learn about AIDS in other patients by observing people who have built‐in resistance to this disease?

Likewise, in psychology and sociology case studies may be comprised of deviant (in the social sense) persons or groups. In economics, case studies may consist of countries or businesses that overperform (e.g. Botswana; Microsoft) or underperform (e.g. Britain through most of the twentieth century; Sears in recent decades) relative to some set of expectations. In political science, case studies may focus on countries where the welfare state is more developed (e.g. Sweden) or less developed (e.g. the United States) than one would expect, given a set of general expectations about welfare state development. The deviant case is closely linked to the investigation of theoretical anomalies. Indeed, to say deviant is to imply “anomalous.” 10

Note that while extreme cases are judged relative to the mean of a single distribution (the distribution of values along a single variable), deviant cases are judged relative to some general model of causal relations. The deviant‐case method selects cases which, by reference to some (presumably) general relationship, demonstrate a surprising value. They are “deviant” in that they are poorly explained by the multivariate model. The important point is that deviant‐ness can only be assessed relative to the general (quantitative or qualitative) model. This means that the relative deviant‐ness of a case is likely to change whenever the general model is altered. For example, the United States is a deviant welfare state when this outcome is gauged relative to societal wealth. But it is less deviant—and perhaps not deviant at all—when certain additional (political and societal) factors are included in the model, as discussed in the epilogue. Deviance is model dependent. Thus, when discussing the concept of the deviant case it is helpful to ask the following question: Relative to what general model (or set of background factors) is Case A deviant?

Conceptually, we have said that the deviant case is the logical contrary of the typical case. This translates into a directly contrasting statistical measurement. While the typical case is one with a low residual (in some general model of causal relations), a deviant case is one with a high residual. This means, following our previous discussion, that the deviant case is likely to be an un representative case, and in this respect appears to violate the supposition that case‐study samples should seek to reproduce features of a larger population.

However, it must be borne in mind that the primary purpose of a deviant‐case analysis is to probe for new—but as yet unspecified—explanations. (If the purpose is to disprove an extant theory I shall refer to the study as crucial‐case, as discussed below.) The researcher hopes that causal processes identified within the deviant case will illustrate some causal factor that is applicable to other (more or less deviant) cases. This means that a deviant‐case study usually culminates in a general proposition, one that may be applied to other cases in the population. Once this general proposition has been introduced into the overall model, the expectation is that the chosen case will no longer be an outlier. Indeed, the hope is that it will now be typical , as judged by its small residual in the adjusted model. (The exception would be a circumstance in which a case's outcome is deemed to be “accidental,” and therefore inexplicable by any general model.)

This feature of the deviant‐case study should help to resolve questions about its representativeness. Even if it is not possible to measure the new causal factor (and thus to introduce it into a large‐ N cross‐case model), it may still be plausible to assert (based on general knowledge of the phenomenon) that the chosen case is representative of a broader population.

5 Influential Case

Sometimes, the choice of a case is motivated solely by the need to verify the assumptions behind a general model of causal relations. Here, the analyst attempts to provide a rationale for disregarding a problematic case or a set of problematic cases. That is to say, she attempts to show why apparent deviations from the norm are not really deviant, or do not challenge the core of the theory, once the circumstances of the special case or cases are fully understood. A cross‐case analysis may, after all, be marred by several classes of problems including measurement error, specification error, errors in establishing proper boundaries for the inference (the scope of the argument), and stochastic error (fluctuations in the phenomenon under study that are treated as random, given available theoretical resources). If poorly fitting cases can be explained away by reference to these kinds of problems, then the theory of interest is that much stronger. This sort of deviant‐case analysis answers the question, “What about Case A (or cases of type A)? How does that, seemingly disconfirming, case fit the model?”

Because its underlying purpose is different from the usual deviant‐case study, I offer a new term for this method. The influential case is a case that casts doubt upon a theory, and for that reason warrants close inspection. This investigation may reveal, after all, that the theory is validated—perhaps in some slightly altered form. In this guise, the influential case is the “case that proves the rule.” In other instances, the influential‐case analysis may contribute to disconfirming, or reconceptualizing, a theory. The key point is that the value of the case is judged relative to some extant cross‐case model.

A simple version of influential‐case analysis involves the confirmation of a key case's score on some critical dimension. This is essentially a question of measurement. Sometimes cases are poorly explained simply because they are poorly understood. A close examination of a particular context may reveal that an apparently falsifying case has been miscoded. If so, the initial challenge presented by that case to some general theory has been obviated.

However, the more usual employment of the influential‐case method culminates in a substantive reinterpretation of the case—perhaps even of the general model. It is not just a question of measurement. Consider Thomas Ertman's (1997) study of state building in Western Europe, as summarized by Gerardo Munck. This study argues

that the interaction of a) the type of local government during the first period of statebuilding, with b) the timing of increases in geopolitical competition, strongly influences the kind of regime and state that emerge. [Ertman] tests this hypothesis against the historical experience of Europe and finds that most countries fit his predictions. Denmark, however, is a major exception. In Denmark, sustained geopolitical competition began relatively late and local government at the beginning of the statebuilding period was generally participatory, which should have led the country to develop “patrimonial constitutionalism.” But in fact, it developed “bureaucratic absolutism.” Ertman carefully explores the process through which Denmark came to have a bureaucratic absolutist state and finds that Denmark had the early marks of a patrimonial constitutionalist state. However, the country was pushed off this developmental path by the influence of German knights, who entered Denmark and brought with them German institutions of local government. Ertman then traces the causal process through which these imported institutions pushed Denmark to develop bureaucratic absolutism, concluding that this development was caused by a factor well outside his explanatory framework. ( Munck 2004 , 118)

Ertman's overall framework is confirmed insofar as he has been able to show, by an in‐depth discussion of Denmark, that the causal processes stipulated by the general theory hold even in this apparently disconfirming case. Denmark is still deviant, but it is so because of “contingent historical circumstances” that are exogenous to the theory ( Ertman 1997 , 316).

Evidently, the influential‐case analysis is similar to the deviant‐case analysis. Both focus on outliers. However, as we shall see, they focus on different kinds of outliers. Moreover, the animating goals of these two research designs are quite different. The influential‐case study begins with the aim of confirming a general model, while the deviant‐case study has the aim of generating a new hypothesis that modifies an existing general model. The confusion stems from the fact that the same case study may fulfill both objectives—qualifying a general model and, at the same time, confirming its core hypothesis.

Thus, in their study of Roberto Michels's “iron law of oligarchy,” Lipset, Trow, and Coleman (1956) choose to focus on an organization—the International Typographical Union—that appears to violate the central presupposition. The ITU, as noted by one of the authors, has “a long‐term two‐party system with free elections and frequent turnover in office” and is thus anything but oligarchic ( Lipset 1959 , 70). As such, it calls into question Michels's grand generalization about organizational behavior. The authors explain this curious result by the extraordinarily high level of education among the members of this union. Michels's law is shown to be true for most organizations, but not all. It is true, with qualifications. Note that the respecification of the original model (in effect, Lipset, Trow, and Coleman introduce a new control variable or boundary condition) involves the exploration of a new hypothesis. In this instance, therefore, the use of an influential case to confirm an existing theory is quite similar to the use of a deviant case to explore a new theory.

In a quantitative idiom, influential cases are those that, if counterfactually assigned a different value on the dependent variable, would most substantially change the resulting estimates. They may or may not be outliers (high‐residual cases). Two quantitative measures of influence are commonly applied in regression diagnostics ( Belsey, Kuh, and Welsch 2004 ). The first, often referred to as the leverage of a case, derives from what is called the hat matrix . Based solely on each case's scores on the independent variables, the hat matrix tells us how much a change in (or a measurement error on) the dependent variable for that case would affect the overall regression line. The second is Cook's distance , a measure of the extent to which the estimates of all the parameters would change if a given case were omitted from the analysis. Cases with a large leverage or Cook's distance contribute quite a lot to the inferences drawn from a cross‐case analysis. In this sense, such cases are vital for maintaining analytic conclusions. Discovering a significant measurement error on the dependent variable or an important omitted variable for such a case may dramatically revise estimates of the overall relationships. Hence, it may be quite sensible to select influential cases for in‐depth study.

Note that the use of an influential‐case strategy of case selection is limited to instances in which a researcher has reason to be concerned that her results are being driven by one or a few cases. This is most likely to be true in small to moderate‐sized samples. Where N is very large—greater than 1,000, let us say—it is extremely unlikely that a small set of cases (much less an individual case) will play an “influential” role. Of course, there may be influential sets of cases, e.g. countries within a particular continent or cultural region, or persons of Irish extraction. Sets of influential observations are often problematic in a time‐series cross‐section data‐set where each unit (e.g. country) contains multiple observations (through time), and hence may have a strong influence on aggregate results. Still, the general rule is: the larger the sample, the less important individual cases are likely to be and, hence, the less likely a researcher is to use an influential‐case approach to case selection.

6 Crucial Case

Of all the extant methods of case selection perhaps the most storied—and certainly the most controversial—is the crucial‐case method, introduced to the social science world several decades ago by Harry Eckstein. In his seminal essay, Eckstein (1975 , 118) describes the crucial case as one “that must closely fit a theory if one is to have confidence in the theory's validity, or, conversely, must not fit equally well any rule contrary to that proposed.” A case is crucial in a somewhat weaker—but much more common—sense when it is most, or least, likely to fulfill a theoretical prediction. A “most‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted to achieve a certain outcome, and yet does not. It is therefore used to disconfirm a theory. A “least‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted not to achieve a certain outcome, and yet does so. It is therefore used to confirm a theory. In all formulations, the crucial‐case offers a most‐difficult test for an argument, and hence provides what is perhaps the strongest sort of evidence possible in a nonexperimental, single‐case setting.

Since the publication of Eckstein's influential essay, the crucial‐case approach has been claimed in a multitude of studies across several social science disciplines and has come to be recognized as a staple of the case‐study method. 11 Yet the idea of any single case playing a crucial (or “critical”) role is not widely accepted among most methodologists (e.g. Sekhon 2004 ). (Even its progenitor seems to have had doubts.)

Let us begin with the confirmatory (a.k.a. least‐likely) crucial case. The implicit logic of this research design may be summarized as follows. Given a set of facts, we are asked to contemplate the probability that a given theory is true. While the facts matter, to be sure, the effectiveness of this sort of research also rests upon the formal properties of the theory in question. Specifically, the degree to which a theory is amenable to confirmation is contingent upon how many predictions can be derived from the theory and on how “risky” each individual prediction is. In Popper's (1963 , 36) words, “Confirmations should count only if they are the result of risky predictions ; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory—and event which would have refuted the theory. Every ‘good’ scientific theory is a prohibition; it forbids certain things to happen. The more a theory forbids, the better it is” (see also Popper 1934/1968 ). A risky prediction is therefore one that is highly precise and determinate, and therefore unlikely to be achieved by the product of other causal factors (external to the theory of interest) or through stochastic processes. A theory produces many such predictions if it is fully elaborated, issuing predictions not only on the central outcome of interest but also on specific causal mechanisms, and if it is broad in purview. (The notion of riskiness may also be conceptualized within the Popperian lexicon as degrees of falsifiability .)

These points can also be articulated in Bayesian terms. Colin Howson and Peter Urbach explain: “The degree to which h [a hypothesis] is confirmed by e [a set of evidence] depends … on the extent to which P(eČh) exceeds P (e) , that is, on how much more probable e is relative to the hypothesis and background assumptions than it is relative just to background assumptions.” Again, “confirmation is correlated with how much more probable the evidence is if the hypothesis is true than if it is false” ( Howson and Urlbach 1989 , 86). Thus, the stranger the prediction offered by a theory—relative to what we would normally expect—the greater the degree of confirmation that will be afforded by the evidence. As an intuitive example, Howson and Urbach (1989 , 86) offer the following:

If a soothsayer predicts that you will meet a dark stranger sometime and you do in fact, your faith in his powers of precognition would not be much enhanced: you would probably continue to think his predictions were just the result of guesswork. However, if the prediction also gave the correct number of hairs on the head of that stranger, your previous scepticism would no doubt be severely shaken.

While these Popperian/Bayesian notions 12 are relevant to all empirical research designs, they are especially relevant to case‐study research designs, for in these settings a single case (or, at most, a small number of cases) is required to bear a heavy burden of proof. It should be no surprise, therefore, that Popper's idea of “riskiness” was to be appropriated by case‐study researchers like Harry Eckstein to validate the enterprise of single‐case analysis. (Although Eckstein does not cite Popper the intellectual lineage is clear.) Riskiness, here, is analogous to what is usually referred to as a “most‐ difficult” research design, which in a case‐study research design would be understood as a “least‐likely” case. Note also that the distinction between a “must‐fit” case and a least‐likely case—that, in the event, actually does fit the terms of a theory—is a matter of degree. Cases are more or less crucial for confirming theories. The point is that, in some circumstances, a paucity of empirical evidence may be compensated by the riskiness of the theory.

The crucial‐case research design is, perforce, a highly deductive enterprise; much depends on the quality of the theory under investigation. It follows that the theories most amenable to crucial‐case analysis are those which are lawlike in their precision, degree of elaboration, consistency, and scope. The more a theory attains the status of a causal law, the easier it will be to confirm, or to disconfirm, with a single case. Indeed, risky predictions are common in natural science fields such as physics, which in turn served as the template for the deductive‐nomological (“covering‐law”) model of science that influenced Eckstein and others in the postwar decades (e.g. Hempel 1942 ).

A frequently cited example is the first important empirical demonstration of the theory of relativity, which took the form of a single‐event prediction on the occasion of the May 29, 1919, solar eclipse ( Eckstein 1975 ; Popper 1963 ). Stephen Van Evera (1997 , 66–7) describes the impact of this prediction on the validation of Einstein's theory.

Einstein's theory predicted that gravity would bend the path of light toward a gravity source by a specific amount. Hence it predicted that during a solar eclipse stars near the sun would appear displaced—stars actually behind the sun would appear next to it, and stars lying next to the sun would appear farther from it—and it predicted the amount of apparent displacement. No other theory made these predictions. The passage of this one single‐case‐study test brought the theory wide acceptance because the tested predictions were unique—there was no plausible competing explanation for the predicted result—hence the passed test was very strong.

The strength of this test is the extraordinary fit between the theory and a set of facts found in a single case, and the corresponding lack of fit between all other theories and this set of facts. Einstein offered an explanation of a particular set of anomalous findings that no other existing theory could make sense of. Of course, one must assume that there was no—or limited—measurement error. And one must assume that the phenomenon of interest is largely invariant; light does not bend differently at different times and places (except in ways that can be understood through the theory of relativity). And one must assume, finally, that the theory itself makes sense on other grounds (other than the case of special interest); it is a plausible general theory. If one is willing to accept these a priori assumptions, then the 1919 “case study” provides a very strong confirmation of the theory. It is difficult to imagine a stronger proof of the theory from within an observational (nonexperimental) setting.

In social science settings, by contrast, one does not commonly find single‐case studies offering knockout evidence for a theory. This is, in my view, largely a product of the looseness (the underspecification) of most social science theories. George and Bennett point out that while the thesis of the democratic peace is as close to a “law” as social science has yet seen, it cannot be confirmed (or refuted) by looking at specific causal mechanisms because the causal pathways mandated by the theory are multiple and diverse. Under the circumstances, no single‐case test can offer strong confirmation of the theory ( George and Bennett 2005 , 209).

However, if one adopts a softer version of the crucial‐case method—the least‐likely (most difficult) case—then possibilities abound. Indeed, I suspect that, implicitly , most case‐study work that makes a positive argument focusing on a single case (without a corresponding cross‐case analysis) relies largely on the logic of the least‐ likely case. Rarely is this logic made explicit, except perhaps in a passing phrase or two. Yet the deductive logic of the “risky” prediction is central to the case‐study enterprise. Whether a case study is convincing or not often rests on the reader's evaluation of how strong the evidence for an argument might be, and this in turn—wherever cross‐ case evidence is limited and no manipulated treatment can be devised—rests upon an estimation of the degree of “fit” between a theory and the evidence at hand, as discussed.

Lily Tsai's (2007) investigation of governance at the village level in China employs several in‐depth case studies of villages which are chosen (in part) because of their least‐likely status relative to the theory of interest. Tsai's hypothesis is that villages with greater social solidarity (based on preexisting religious or familial networks) will develop a higher level of social trust and mutual obligation and, as a result, will experience better governance. Crucial cases, therefore, are villages that evidence a high level of social solidarity but which, along other dimensions, would be judged least likely to develop good governance, e.g. they are poor, isolated, and lack democratic institutions or accountability mechanisms from above. “Li Settlement,” in Fujian province, is such a case. The fact that this impoverished village nonetheless boasts an impressive set of infrastructural accomplishments such as paved roads with drainage ditches (a rarity in rural China) suggests that something rather unusual is going on here. Because her case is carefully chosen to eliminate rival explanations, Tsai's conclusions about the special role of social solidarity are difficult to gainsay. How else is one to explain this otherwise anomalous result? This is the strength of the least‐likely case, where all other plausible causal factors for an outcome have been minimized. 13

Jack Levy (2002 , 144) refers to this, evocatively, as a “Sinatra inference:” if it can make it here, it can make it anywhere (see also Khong 1992 , 49; Sagan 1995 , 49; Shafer 1988 , 14–6). Thus, if social solidarity has the hypothesized effect in Li Settlement it should have the same effect in more propitious settings (e.g. where there is greater economic surplus). The same implicit logic informs many case‐study analyses where the intent of the study is to confirm a hypothesis on the basis of a single case.

Another sort of crucial case is employed for the purpose of dis confirming a causal hypothesis. A central Popperian insight is that it is easier to disconfirm an inference than to confirm that same inference. (Indeed, Popper doubted that any inference could be fully confirmed, and for this reason preferred the term “corroborate.”) This is particularly true of case‐study research designs, where evidence is limited to one or several cases. The key proviso is that the theory under investigation must take a consistent (a.k.a. invariant, deterministic) form, even if its predictions are not terrifically precise, well elaborated, or broad.

As it happens, there are a fair number of invariant propositions floating around the social science disciplines (Goertz and Levy forthcoming; Goertz and Starr 2003 ). It used to be argued, for example, that political stability would occur only in countries that are relatively homogeneous, or where existing heterogeneities are mitigated by cross‐cutting cleavages ( Almond 1956 ; Bentley 1908/1967 ; Lipset 1960/1963 ; Truman 1951 ). Arend Lijphart's (1968) study of the Netherlands, a peaceful country with reinforcing social cleavages, is commonly viewed as refuting this theory on the basis of a single in‐depth case analysis. 14

Granted, it may be questioned whether presumed invariant theories are really invariant; perhaps they are better understood as probabilistic. Perhaps, that is, the theory of cross‐cutting cleavages is still true, probabilistically, despite the apparent Dutch exception. Or perhaps the theory is still true, deterministically, within a subset of cases that does not include the Netherlands. (This sort of claim seems unlikely in this particular instance, but it is quite plausible in many others.) Or perhaps the theory is in need of reframing; it is true, deterministically, but applies only to cross‐ cutting ethnic/racial cleavages, not to cleavages that are primarily religious. One can quibble over what it means to “disconfirm” a theory. The point is that the crucial case has, in all these circumstances, provided important updating of a theoretical prior.

Heretofore, I have treated causal factors as dichotomous. Countries have either reinforcing or cross‐cutting cleavages and they have regimes that are either peaceful or conflictual. Evidently, these sorts of parameters are often matters of degree. In this reading of the theory, cases are more or less crucial. Accordingly, the most useful—i.e. most crucial—case for Lijphart's purpose is one that has the most segregated social groups and the most peaceful and democratic track record. In these respects, the Netherlands was a very good choice. Indeed, the degree of disconfirmation offered by this case study is probably greater than the degree of disconfirmation that might have been provided by other cases such as India or Papua New Guinea—countries where social peace has not always been secure. The point is that where variables are continuous rather than dichotomous it is possible to evaluate potential cases in terms of their degree of crucialness .

Note that the crucial‐case method of case‐selection, whether employed in a confirmatory or disconfirmatory mode, cannot be employed in a large‐ N context. This is because an explicit cross‐case model would render the crucial‐case study redundant. Once one identifies the relevant parameters and the scores of all cases on those parameters, one has in effect constructed a cross‐case model that confirms or disconfirms the theory in question. The case study is thenceforth irrelevant, at least as a means of decisive confirmation or disconfirmation. 15 It remains highly relevant as a means of exploring causal mechanisms, of course. Yet, because this objective is quite different from that which is usually associated with the term, I enlist a new term for this technique.

7 Pathway Case

One of the most important functions of case‐study research is the elucidation of causal mechanisms. But which sort of case is most useful for this purpose? Although all case studies presumably shed light on causal mechanisms, not all cases are equally transparent. In situations where a causal hypothesis is clear and has already been confirmed by cross‐case analysis, researchers are well advised to focus on a case where the causal effect of X   1 on Y can be isolated from other potentially confounding factors ( X   2 ). I shall call this a pathway case to indicate its uniquely penetrating insight into causal mechanisms. In contrast to the crucial case, this sort of method is practicable only in circumstances where cross‐case covariational patterns are well studied and where the mechanism linking X   1 and Y remains dim. Because the pathway case builds on prior cross‐case analysis, the problem of case selection must be situated within that sample. There is no standalone pathway case.

The logic of the pathway case is clearest in situations of causal sufficiency—where a causal factor of interest, X   1 , is sufficient by itself (though perhaps not necessary) to account for Y 's value (0 or 1). The other causes of Y , about which we need make no assumptions, are designated as a vector, X   2 .

Note that wherever various causal factors are substitutable for one another, each factor is conceptualized (individually) as sufficient ( Braumoeller 2003 ). Thus, situations of causal equifinality presume causal sufficiency on the part of each factor or set of conjoint factors. An example is provided by the literature on democratization, which stipulates three main avenues of regime change: leadership‐initiated reform, a controlled opening to opposition, or the collapse of an authoritarian regime ( Colomer 1991 ). The case‐study format constrains us to analyze one at a time, so let us limit our scope to the first one—leadership‐initiated reform. So considered, a causal‐pathway case would be one with the following features: (a) democratization, (b) leadership‐initiated reform, (c) no controlled opening to the opposition, (d) no collapse of the previous authoritarian regime, and (e) no other extraneous factors that might affect the process of democratization. In a case of this type, the causal mechanisms by which leadership‐initiated reform may lead to democratization will be easiest to study. Note that it is not necessary to assume that leadership‐initiated reform always leads to democratization; it may or may not be a deterministic cause. But it is necessary to assume that leadership‐initiated reform can sometimes lead to democratization on its own (given certain background features).

Now let us move from these examples to a general‐purpose model. For heuristic purposes, let us presume that all variables in that model are dichotomous (coded as 0 or 1) and that the model is complete (all causes of Y are included). All causal relationships will be coded so as to be positive: X   1 and Y covary as do X   2 and Y . This allows us to visualize a range of possible combinations at a glance.

Recall that the pathway case is always focused, by definition, on a single causal factor, denoted X   1 . (The researcher's focus may shift to other causal factors, but may only focus on one causal factor at a time.) In this scenario, and regardless of how many additional causes of Y there might be (denoted X   2 , a vector of controls), there are only eight relevant case types, as illustrated in Table 28.2 . Identifying these case types is a relatively simple matter, and can be accomplished in a small‐ N sample by the construction of a truth‐table (modeled after Table 28.2 ) or in a large‐ N sample by the use of cross‐tabs.

Notes : X   1 = the variable of theoretical interest. X   2 = a vector of controls (a score of 0 indicates that all control variables have a score of 0, while a score of 1 indicates that all control variables have a score of 1). Y = the outcome of interest. A–H = case types (the N for each case type is indeterminate). G, H = possible pathway cases. Sample size = indeterminate.

Assumptions : (a) all variables can be coded dichotomously (a binary coding of the concept is valid); (b) all independent variables are positively correlated with Y in the general case; ( c ) X   1 is (at least sometimes) a sufficient cause of Y .

Note that the total number of combinations of values depends on the number of control variables, which we have represented with a single vector, X   2 . If this vector consists of a single variable then there are only eight case types. If this vector consists of two variables ( X   2a , X   2b ) then the total number of possible combinations increases from eight (2 3 ) to sixteen (2 4 ). And so forth. However, none of these combinations is relevant for present purposes except those where X   2a and X   2b have the same value (0 or 1). “Mixed” cases are not causal pathway cases, for reasons that should become clear.

The pathway case, following the logic of the crucial case, is one where the causal factor of interest, X   1 , correctly predicts Y while all other possible causes of Y (represented by the vector, X   2 ) make “wrong” predictions. If X   1 is—at least in some circumstances—a sufficient cause of Y , then it is these sorts of cases that should be most useful for tracing causal mechanisms. There are only two such cases in Ta b l e 28.2—G and H. In all other cases, the mechanism running from X   1 to Y would be difficult to discern either because X   1 and Y are not correlated in the usual way (constituting an unusual case, in the terms of our hypothesis) or because other confounding factors ( X   2 ) intrude. In case A, for example, the positive value on Y could be a product of X   1 or X   2 . An in‐depth examination of this case is not likely to be very revealing.

Keep in mind that because the researcher already knows from her cross‐case examination what the general causal relationships are, she knows (prior to the case‐ study investigation) what constitutes a correct or incorrect prediction. In the crucial‐ case method, by contrast, these expectations are deductive rather than empirical. This is what differentiates the two methods. And this is why the causal pathway case is useful principally for elucidating causal mechanisms rather than verifying or falsifying general propositions (which are already more or less apparent from the cross‐case evidence). Of course, we must leave open the possibility that the investigation of causal mechanisms would invalidate a general claim, if that claim is utterly contingent upon a specific set of causal mechanisms and the case study shows that no such mechanisms are present. However, this is rather unlikely in most social science settings. Usually, the result of such a finding will be a reformulation of the causal processes by which X   1 causes Y —or, alternatively, a realization that the case under investigation is aberrant (atypical of the general population of cases).

Sometimes, the research question is framed as a unidirectional cause: one is interested in why 0 becomes 1 (or vice versa) but not in why 1 becomes 0. In our previous example, we asked why democracies fail, not why countries become democratic or authoritarian. So framed, there can be only one type of causal‐pathway case. (Whether regime failure is coded as 0 or 1 is a matter of taste.) Where researchers are interested in bidirectional causality—a movement from 0 to 1 as well as from 1 to 0—there are two possible causal‐pathway cases, G and H. In practice, however, one of these case types is almost always more useful than the other. Thus, it seems reasonable to employ the term “pathway case” in the singular. In order to determine which of these two case types will be more useful for intensive analysis the researcher should look to see whether each case type exhibits desirable features such as: (a) a rare (unusual) value on X   1 or Y (designated “extreme” in our previous discussion), (b) observable temporal variation in X   1 , ( c ) an X   1 / Y relationship that is easier to study (it has more visible features; it is more transparent), or (d) a lower residual (thus indicating a more typical case, within the terms of the general model). Usually, the choice between G and H is intuitively obvious.

Now, let us consider a scenario in which all (or most) variables of concern to the model are continuous, rather than dichotomous. Here, the job of case selection is considerably more complex, for causal “sufficiency” (in the usual sense) cannot be invoked. It is no longer plausible to assume that a given cause can be entirely partitioned, i.e. rival factors eliminated. However, the search for a pathway case may still be viable. What we are looking for in this scenario is a case that satisfies two criteria: (1) it is not an outlier (or at least not an extreme outlier) in the general model and (2) its score on the outcome ( Y ) is strongly influenced by the theoretical variable of interest ( X   1 ), taking all other factors into account ( X   2 ). In this sort of case it should be easiest to “see” the causal mechanisms that lie between X   1 and Y .

Achieving the second desiderata requires a bit of manipulation. In order to determine which (nonoutlier) cases are most strongly affected by X   1 , given all the other parameters in the model, one must compare the size of the residuals for each case in a reduced form model, Y = Constant + X   2 + Res reduced , with the size of the residuals for each case in a full model, Y = Constant + X   2 + X   1 + Res full . The pathway case is that case, or set of cases, which shows the greatest difference between the residual for the reduced‐form model and the full model (ΔResidual). Thus,

Note that the residual for a case must be smaller in the full model than in the reduced‐ form model; otherwise, the addition of the variable of interest ( X   1 ) pulls the case away from the regression line. We want to find a case where the addition of X   1 pushes the case towards the regression line, i.e. it helps to “explain” that case.

As an example, let us suppose that we are interested in exploring the effect of mineral wealth on the prospects for democracy in a society. According to a good deal of work on this subject, countries with a bounty of natural resources—particularly oil—are less likely to democratize (or once having undergone a democratic transition, are more likely to revert to authoritarian rule) ( Barro 1999 ; Humphreys 2005 ; Ross 2001 ). The cross‐country evidence is robust. Yet as is often the case, the causal mechanisms remain rather obscure. In order to better understand this phenomenon it may be worthwhile to exploit the findings of cross‐country regression models in order to identify a country whose regime type (i.e. its democracy “score” on some general index) is strongly affected by its natural‐research wealth, all other things held constant. An analysis of this sort identifies two countries— the United Arab Emirates and Kuwait—with high Δ Residual values and modest residuals in the full model (signifying that these cases are not outliers). Researchers seeking to explore the effect of oil wealth on regime type might do well to focus on these two cases since their patterns of democracy cannot be well explained by other factors—e.g. economic development, religion, European influence, or ethnic fractionalization. The presence of oil wealth in these countries would appear to have a strong independent effect on the prospects for democratization in these cases, an effect that is well modeled by general theory and by the available cross‐case evidence.

To reiterate, the logic of causal “elimination” is much more compelling where variables are dichotomous and where causal sufficiency can be assumed ( X   1 is sufficient by itself, at least in some circumstances, to cause Y ). Where variables are continuous, the strategy of the pathway case is more dubious, for potentially confounding causal factors ( X   2 ) cannot be neatly partitioned. Even so, we have indicated why the selection of a pathway case may be a logical approach to case‐study analysis in many circumstances.

The exceptions may be briefly noted. Sometimes, where all variables in a model are dichotomous, there are no pathway cases, i.e. no cases of type G or H (in Table 28.2 ). This is known as the “empty cell” problem, or a problem of severe causal multicollinearity. The universe of observational data does not always oblige us with cases that allow us to independently test a given hypothesis. Where variables are continuous, the analogous problem is that of a causal variable of interest ( X   1 ) that has only minimal effects on the outcome of interest. That is, its role in the general model is quite minor. In these situations, the only cases that are strongly affected by X   1 —if there are any at all—may be extreme outliers, and these sorts of cases are not properly regarded as providing confirmatory evidence for a proposition, for reasons that are abundantly clear by now.

Finally, it should be clarified that the identification of a causal pathway case does not obviate the utility of exploring other cases. One might, for example, want to compare both sorts of potential pathway cases—G and H—with each other. Many other combinations suggest themselves. However, this sort of multi‐case investigation moves beyond the logic of the causal‐pathway case.

8 Most‐similar Cases

The most‐similar method employs a minimum of two cases. 16 In its purest form, the chosen pair of cases is similar in all respects except the variable(s) of interest. If the study is exploratory (i.e. hypothesis generating), the researcher looks for cases that differ on the outcome of theoretical interest but are similar on various factors that might have contributed to that outcome, as illustrated in Table 28.3 (A) . This is a common form of case selection at the initial stage of research. Often, fruitful analysis begins with an apparent anomaly: two cases are apparently quite similar, and yet demonstrate surprisingly different outcomes. The hope is that intensive study of these cases will reveal one—or at most several—factors that differ across these cases. These differing factors ( X   1 ) are looked upon as putative causes. At this stage, the research may be described by the second diagram in Table 28.3 (B) . Sometimes, a researcher begins with a strong hypothesis, in which case her research design is confirmatory (hypothesis testing) from the get‐go. That is, she strives to identify cases that exhibit different outcomes, different scores on the factor of interest, and similar scores on all other possible causal factors, as illustrated in the second (hypothesis‐testing) diagram in Table 28.3 (B) .

The point is that the purpose of a most‐similar research design, and hence its basic setup, often changes as a researcher moves from an exploratory to a confirmatory mode of analysis. However, regardless of where one begins, the results, when published, look like a hypothesis‐testing research design. Question marks have been removed: (A) becomes (B) in Table 28.3 .

As an example, let us consider Leon Epstein's classic study of party cohesion, which focuses on two “most‐similar” countries, the United States and Canada. Canada has highly disciplined parties whose members vote together on the floor of the House of Commons while the United States has weak, undisciplined parties, whose members often defect on floor votes in Congress. In explaining these divergent outcomes, persistent over many years, Epstein first discusses possible causal factors that are held more or less constant across the two cases. Both the United States and Canada inherited English political cultures, both have large territories and heterogeneous populations, both are federal, and both have fairly loose party structures with strong regional bases and a weak center. These are the “control” variables. Where they differ is in one constitutional feature: Canada is parliamentary while the United States is presidential. And it is this institutional difference that Epstein identifies as the crucial (differentiating) cause. (For further examples of the most‐similar method see Brenner 1976 ; Hamilton 1977 ; Lipset 1968 ; Miguel 2004 ; Moulder 1977 ; Posner 2004 .)

X   1 = the variable of theoretical interest. X   2 = a vector of controls. Y = the outcome of interest.

Several caveats apply to any most‐similar analysis (in addition to the usual set of assumptions applying to all case‐study analysis). First, each causal factor is understood as having an independent and additive effect on the outcome; there are no “interaction” effects. Second, one must code cases dichotomously (high/low, present/absent). This is straightforward if the underlying variables are also dichotomous (e.g. federal/unitary). However, it is often the case that variables of concern in the model are continuous (e.g. party cohesion). In this setting, the researcher must “dichotomize” the scoring of cases so as to simplify the two‐case analysis. (Some flexibility is admissible on the vector of controls ( X   2 ) that are “held constant” across the cases. Nonidentity is tolerable if the deviation runs counter to the predicted hypothesis. For example, Epstein describes both the United States and Canada as having strong regional bases of power, a factor that is probably more significant in recent Canadian history than in recent American history. However, because regional bases of power should lead to weaker parties, rather than stronger parties, this element of nonidentity does not challenge Epstein's conclusions. Indeed, it sets up a most‐difficult research scenario, as discussed above.)

In one respect the requirements for case control are not so stringent. Specifically, it is not usually necessary to measure control variables (at least not with a high degree of precision) in order to control for them. If two countries can be assumed to have similar cultural heritages one needn't worry about constructing variables to measure that heritage. One can simply assert that, whatever they are, they are more or less constant across the two cases. This is similar to the technique employed in a randomized experiment, where the researcher typically does not attempt to measure all the factors that might affect the causal relationship of interest. She assumes, rather, that these unknown factors have been neutralized across the treatment and control groups by randomization or by the choice of a sample that is internally homogeneous.

The most useful statistical tool for identifying cases for in‐depth analysis in a most‐ similar setting is probably some variety of matching strategy—e.g. exact matching, approximate matching, or propensity‐score matching. 17 The product of this procedure is a set of matched cases that can be compared in whatever way the researcher deems appropriate. These are the “most‐similar” cases. Rosenbaum and Silber (2001 , 223) summarize:

Unlike model‐based adjustments, where [individuals] vanish and are replaced by the coefficients of a model, in matching, ostensibly comparable patterns are compared directly, one by one. Modern matching methods involve statistical modeling and combinatorial algorithms, but the end result is a collection of pairs or sets of people who look comparable, at least on average. In matching, people retain their integrity as people, so they can be examined and their stories can be told individually.

Matching, conclude the authors, “facilitates, rather than inhibits, thick description” ( Rosenbaum and Silber 2001 , 223).

In principle, the same matching techniques that have been used successfully in observational studies of medical treatments might also be adapted to the study of nation states, political parties, cities, or indeed any traditional paired cases in the social sciences. Indeed, the current popularity of matching among statisticians—relative, that is, to garden‐variety regression models—rests upon what qualitative researchers would recognize as a “case‐based” approach to causal analysis. If Rosenbaum and Silber are correct, it may be perfectly reasonable to appropriate this large‐ N method of analysis for case‐study purposes.

As with other methods of case selection, the most‐similar method is prone to problems of nonrepresentativeness. If employed in a qualitative fashion (without a systematic cross‐case selection strategy), potential biases in the chosen case must be addressed in a speculative way. If the researcher employs a matching technique of case selection within a large‐ N sample, the problem of potential bias can be addressed by assuring the choice of cases that are not extreme outliers, as judged by their residuals in the full model. Most‐similar cases should also be “typical” cases, though some scope for deviance around the regression line may be acceptable for purposes of finding a good fit among cases.

X   1 = the variable of theoretical interest. X   2a–d = a vector of controls. Y = the outcome of interest.

9 Most‐different Cases

A final case‐selection method is the reverse image of the previous method. Here, variation on independent variables is prized, while variation on the outcome is eschewed. Rather than looking for cases that are most‐similar, one looks for cases that are most‐ different . Specifically, the researcher tries to identify cases where just one independent variable ( X   1 ), as well as the dependent variable ( Y ), covary, while all other plausible factors ( X   2a–d ) show different values. 18

The simplest form of this two‐case comparison is illustrated in Table 28.4 . Cases A and B are deemed “most different,” though they are similar in two essential respects— the causal variable of interest and the outcome.

As an example, I follow Marc Howard's (2003) recent work, which explores the enduring impact of Communism on civil society. 19 Cross‐national surveys show a strong correlation between former Communist regimes and low social capital, controlling for a variety of possible confounders. It is a strong result. Howard wonders why this relationship is so strong and why it persists, and perhaps even strengthens, in countries that are no longer socialist or authoritarian. In order to answer this question, he focuses on two most‐different cases, Russia and East Germany. These two countries were quite different—in all ways other than their Communist experience— prior to the Soviet era, during the Soviet era (since East Germany received substantial subsidies from West Germany), and in the post‐Soviet era, as East Germany was absorbed into West Germany. Yet, they both score near the bottom of various cross‐ national indices intended to measure the prevalence of civic engagement in the current era. Thus, Howard's (2003 , 6–9) case selection procedure meets the requirements of the most‐different research design: Variance is found on all (or most) dimensions aside from the key factor of interest (Communism) and the outcome (civic engagement).

What leverage is brought to the analysis from this approach? Howard's case studies combine evidence drawn from mass surveys and from in‐depth interviews of small, stratified samples of Russians and East Germans. (This is a good illustration, incidentally, of how quantitative and qualitative evidence can be fruitfully combined in the intensive study of several cases.) The product of this analysis is the identification of three causal pathways that, Howard (2003 , 122) claims, help to explain the laggard status of civil society in post‐Communist polities: “the mistrust of communist organizations, the persistence of friendship networks, and the disappointment with post‐communism.” Simply put, Howard (2003 , 145) concludes, “a great number of citizens in Russia and Eastern Germany feel a strong and lingering sense of distrust of any kind of public organization, a general satisfaction with their own personal networks (accompanied by a sense of deteriorating relations within society overall), and disappointment in the developments of post‐communism.”

The strength of this most‐different case analysis is that the results obtained in East Germany and Russia should also apply in other post‐Communist polities (e.g. Lithuania, Poland, Bulgaria, Albania). By choosing a heterogeneous sample, Howard solves the problem of representativeness in his restricted sample. However, this sample is demonstrably not representative across the population of the inference, which is intended to cover all countries of the world.

More problematic is the lack of variation on key causal factors of interest— Communism and its putative causal pathways. For this reason, it is difficult to reach conclusions about the causal status of these factors on the basis of the most‐different analysis alone. It is possible, that is, that the three causal pathways identified by Howard also operate within polities that never experienced Communist rule.

Nor does it seem possible to conclusively eliminate rival hypotheses on the basis of this most‐different analysis. Indeed, this is not Howard's intention. He wishes merely to show that whatever influence on civil society might be attributed to economic, cultural, and other factors does not exhaust this subject.

My considered judgment is that the most‐different research design provides minimal leverage into the problem of why Communist systems appear to suppress civic engagement, years after their disappearance. Fortunately, this is not the only research design employed by Howard in his admirable study. Indeed, the author employs two other small‐ N cross‐case methods, as well as a large‐ N cross‐country statistical analysis. These methods do most of the analytic work. East Germany may be regarded as a causal pathway case (see above). It has all the attributes normally assumed to foster civic engagement (e.g. a growing economy, multiparty competition, civil liberties, a free press, close association with Western European culture and politics), but nonetheless shows little or no improvement on this dimension during the post‐ transition era ( Howard 2003 , 8). It is plausible to attribute this lack of change to its Communist past, as Howard does, in which case East Germany should be a fruitful case for the investigation of causal mechanisms. The contrast between East and West Germany provides a most‐similar analysis since the two polities share virtually everything except a Communist past. This variation is also deftly exploited by Howard.

I do not wish to dismiss the most‐different research method entirely. Surely, Howard's findings are stronger with the intensive analysis of Russia than they would be without. Yet his book would not stand securely on the empirical foundation provided by most‐different analysis alone. If one strips away the pathway‐case (East Germany) and the most‐similar analysis (East/West Germany) there is little left upon which to base an analysis of causal relations (aside from the large‐ N cross‐national analysis). Indeed, most scholars who employ the most‐different method do so in conjunction with other methods. 20 It is rarely, if ever, a standalone method. 21

Generalizing from this discussion of Marc Howard's work, I offer the following summary remarks on the most‐different method of case analysis. (I leave aside issues faced by all case‐study analyses, issues that are explored in Gerring 2007 .)

Let us begin with a methodological obstacle that is faced by both Millean styles of analysis—the necessity of dichotomizing every variable in the analysis. Recall that, as with most‐similar analysis, differences across cases must generally be sizeable enough to be interpretable in an essentially dichotomous fashion (e.g. high/low, present/absent) and similarities must be close enough to be understood as essentially identical (e.g. high/high, present/present). Otherwise the results of a Millean style analysis are not interpretable. The problem of “degrees” is deadly if the variables under consideration are, by nature, continuous (e.g. GDP). This is a particular concern in Howard's analysis, where East Germany scores somewhat higher than Russia in civic engagement; they are both low, but Russia is quite a bit lower. Howard assumes that this divergence is minimal enough to be understood as a difference of degrees rather than of kinds, a judgment that might be questioned. In these respects, most‐different analysis is no more secure—but also no less—than most‐similar analysis.

In one respect, most‐different analysis is superior to most‐similar analysis. If the coding assumptions are sound, the most‐different research design may be quite useful for eliminating necessary causes . Causal factors that do not appear across the chosen cases—e.g. X   2a–d in Table 28.4 —are evidently unnecessary for the production of Y . However, it does not follow that the most‐different method is the best method for eliminating necessary causes. Note that the defining feature of this method is the shared element across cases— X   1 in Table 28.4 . This feature does not help one to eliminate necessary causes. Indeed, if one were focused solely on eliminating necessary causes one would presumably seek out cases that register the same outcomes and have maximum diversity on other attributes. In Table 28.4 , this would be a set of cases that satisfy conditions X   2a–d , but not X   1 . Thus, even the presumed strength of the most‐different analysis is not so strong.

Usually, case‐study analysis is focused on the identification (or clarification) of causal relations, not the elimination of possible causes. In this setting, the most‐ different technique is useful, but only if assumptions of causal uniqueness hold. By “causal uniqueness,” I mean a situation in which a given outcome is the product of only one cause: Y cannot occur except in the presence of X . X is necessary, and in some situations (given certain background conditions) sufficient, to cause Y . 22

Consider the following hypothetical example. Suppose that a new disease, about which little is known, has appeared in Country A. There are hundreds of infected persons across dozens of affected communities in that country. In Country B, located at the other end of the world, several new cases of the disease surface in a single community. In this setting, we can imagine two sorts of Millean analyses. The first examines two similar communities within Country A, one of which has developed the disease and the other of which has not. This is the most‐similar style of case comparison, and focuses accordingly on the identification of a difference between the two cases that might account for variation across the sample. A second approach focuses on communities where the disease has appeared across the two countries and searches for any similarities that might account for these similar outcomes. This is the most‐different research design.

Both are plausible approaches to this particular problem, and we can imagine epidemiologists employing them simultaneously. However, the most‐different design demands stronger assumptions about the underlying factors at work. It supposes that the disease arises from the same cause in any setting. This is often a reasonable operating assumption when one is dealing with natural phenomena, though there are certainly many exceptions. Death, for example, has many causes. For this reason, it would not occur to us to look for most‐different cases of high mortality around the world. In order for the most‐different research design to effectively identify a causal factor at work in a given outcome, the researcher must assume that X   1 —the factor held constant across the diverse cases—is the only possible cause of Y (see Table 28.4 ). This assumption rarely holds in social‐scientific settings. Most outcomes of interest to anthropologists, economists, political scientists, and sociologists have multiple causes. There are many ways to win an election, to build a welfare state, to get into a war, to overthrow a government, or—returning to Marc Howard's work—to build a strong civil society. And it is for this reason that most‐different analysis is rarely applied in social science work and, where applied, is rarely convincing.

If this seems a tad severe, there is a more charitable way of approaching the most‐different method. Arguably, this is not a pure “method” at all but merely a supplement, a way of incorporating diversity in the sub‐sample of cases that provide the unusual outcome of interest. If the unusual outcome is revolutions, one might wish to encompass a wide variety of revolutions in one's analysis. If the unusual outcome is post‐Communist civil society, it seems appropriate to include a diverse set of post‐Communist polities in one's sample of case studies, as Marc Howard does. From this perspective, the most‐different method (so‐called) might be better labeled a diverse‐case method, as explored above.

10 Conclusions

In order to be a case of something broader than itself, the chosen case must be representative (in some respects) of a larger population. Otherwise—if it is purely idiosyncratic (“unique”)—it is uninformative about anything lying outside the borders of the case itself. A study based on a nonrepresentative sample has no (or very little) external validity. To be sure, no phenomenon is purely idiosyncratic; the notion of a unique case is a matter that would be difficult to define. One is concerned, as always, with matters of degree. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. (The one exception, as noted, is the influential case.)

Of all the problems besetting case‐study analysis, perhaps the most persistent— and the most persistently bemoaned—is the problem of sample bias ( Achen and Snidal 1989 ; Collier and Mahoney 1996 ; Geddes 1990 ; King, Keohane, and Verba 1994 ; Rohlfing 2004 ; Sekhon 2004 ). Lisa Martin (1992 , 5) finds that the overemphasis of international relations scholars on a few well‐known cases of economic sanctions— most of which failed to elicit any change in the sanctioned country—“has distorted analysts view of the dynamics and characteristics of economic sanctions.” Barbara Geddes (1990) charges that many analyses of industrial policy have focused exclusively on the most successful cases—primarily the East Asian NICs—leading to biased inferences. Anna Breman and Carolyn Shelton (2001) show that case‐study work on the question of structural adjustment is systematically biased insofar as researchers tend to focus on disaster cases—those where structural adjustment is associated with very poor health and human development outcomes. These cases, often located in sub‐Saharan Africa, are by no means representative of the entire population. Consequently, scholarship on the question of structural adjustment is highly skewed in a particular ideological direction (against neoliberalism) (see also Gerring, Thacker, and Moreno 2005) .

These examples might be multiplied many times. Indeed, for many topics the most‐studied cases are acknowledged to be less than representative. It is worth reflecting upon the fact that our knowledge of the world is heavily colored by a few “big” (populous, rich, powerful) countries, and that a good portion of the disciplines of economics, political science, and sociology are built upon scholars' familiarity with the economics, political science, and sociology of one country, the United States. 23 Case‐study work is particularly prone to problems of investigator bias since so much rides on the researcher's selection of one (or a few) cases. Even if the investigator is unbiased, her sample may still be biased simply by virtue of “random” error (which may be understood as measurement error, error in the data‐generation process, or as an underlying causal feature of the universe).

There are only two situations in which a case‐study researcher need not be concerned with the representativeness of her chosen case. The first is the influential case research design, where a case is chosen because of its possible influence on a cross‐case model, and hence is not expected to be representative of a larger sample. The second is the deviant‐case method, where the chosen case is employed to confirm a broader cross‐case argument to which the case stands as an apparent exception. Yet even here the chosen case is expected to be representative of a broader set of cases—those, in particular, that are poorly explained by the extant model.

In all other circumstances, cases must be representative of the population of interest in whatever ways might be relevant to the proposition in question. Note that where a researcher is attempting to disconfirm a deterministic proposition the question of representativeness is perhaps more appropriately understood as a question of classification: Is the chosen case appropriately classified as a member of the designated population? If so, then it is fodder for a disconfirming case study.

If the researcher is attempting to confirm a deterministic proposition, or to make probabilistic arguments about a causal relationship, then the problem of representativeness is of the more usual sort: Is case A unit‐homogeneous relative to other cases in the population? This is not an easy matter to test. However, in a large‐ N context the residual for that case (in whatever model the researcher has greatest confidence in) is a reasonable place to start. Of course, this test is only as good as the model at hand. Any incorrect specifications or incorrect modeling procedures will likely bias the results and give an incorrect assessment of each case's “typicality.” In addition, there is the possibility of stochastic error, errors that cannot be modeled in a general framework. Given the explanatory weight that individual cases are asked to bear in a case‐study analysis, it is wise to consider more than just the residual test of representativeness. Deductive logic and an in‐depth knowledge of the case in question are often more reliable tools than the results of a cross‐case model.

In any case, there is no dispensing with the question. Case studies (with the two exceptions already noted) rest upon an assumed synecdoche: The case should stand for a population. If this is not true, or if there is reason to doubt this assumption, then the utility of the case study is brought severely into question.

Fortunately, there is some safety in numbers. Insofar as case‐study evidence is combined with cross‐case evidence the issue of sample bias is mitigated. Indeed, the suspicion of case‐study work that one finds in the social sciences today is, in my view, a product of a too‐literal interpretation of the case‐study method. A case study tout court is thought to mean a case study tout seul . Insofar as case studies and cross‐case studies can be enlisted within the same investigation (either in the same study or by reference to other studies in the same subfield), problems of representativeness are less worrisome. This is the virtue of cross‐level work, a.k.a. “triangulation.”

11 Ambiguities

Before concluding, I wish to draw attention to two ambiguities in case‐selection strategies in case‐study research. The first concerns the admixture of several case‐ selection strategies. The second concerns the changing status of a case as a study proceeds.

Some case studies follow only one strategy of case selection. They are typical , diverse , extreme , deviant , influential , crucial , pathway , most‐similar , or most‐different research designs, as discussed. However, many case studies mix and match among these case‐selection strategies. Indeed, insofar as all case studies seek representative samples, they are always in search of “typical” cases. Thus, it is common for writers to declare that their case is, for example, both extreme and typical; it has an extreme value on X   1 or Y but is not, in other respects, idiosyncratic. There is not much that one can say about these combinations of strategies except that, where the cases allow for a variety of empirical strategies, there is no reason not to pursue them. And where the same cases can serve several functions at once (without further effort on the researcher's part), there is little cost to a multi‐pronged approach to case analysis.

The second issue that deserves emphasis is the changing status of a case during the course of a researcher's investigation—which may last for years, if not decades. The problem is acute wherever a researcher begins in an exploratory mode and proceeds to hypothesis‐testing (that is, she develops a specific X   1 / Y proposition) or where the operative hypothesis or key control variable changes (a new causal factor is discovered or another outcome becomes the focus of analysis). Things change. And it is the mark of a good researcher to keep her mind open to new evidence and new insights. Too often, methodological discussions give the misleading impression that hypotheses are clear and remain fixed over the course of a study's development. Nothing could be further from the truth. The unofficial transcripts of academia— accessible in informal settings, where researchers let their guards down (particularly if inebriated)—are filled with stories about dead‐ends, unexpected findings, and drastically revised theory chapters. It would be interesting, in this vein, to compare published work with dissertation prospectuses and fellowship applications. I doubt if the correlation between these two stages of research is particularly strong.

Research, after all, is about discovery, not simply the verification or falsification of static hypotheses. That said, it is also true that research on a particular topic should move from hypothesis generating to hypothesis‐testing. This marks the progress of a field, and of a scholar's own work. As a rule, research that begins with an open‐ended ( X ‐ or Y ‐centered) analysis should conclude with a determinate X   1 / Y hypothesis.

The problem is that research strategies that are ideal for exploration are not always ideal for confirmation. The extreme‐case method is inherently exploratory since there is no clear causal hypothesis; the researcher is concerned merely to explore variation on a single dimension ( X or Y ). Other methods can be employed in either an open‐ ended (exploratory) or a hypothesis‐testing (confirmatory/disconfirmatory) mode. The difficulty is that once the researcher has arrived at a determinate hypothesis the originally chosen research design may no longer appear to be so well designed.

This is unfortunate, but inevitable. One cannot construct the perfect research design until (a) one has a specific hypothesis and (b) one is reasonably certain about what one is going to find “out there” in the empirical world. This is particularly true of observational research designs, but it also applies to many experimental research designs: Usually, there is a “good” (informative) finding, and a finding that is less insightful. In short, the perfect case‐study research design is usually apparent only ex post facto .

There are three ways to handle this. One can explain, straightforwardly, that the initial research was undertaken in an exploratory fashion, and therefore not constructed to test the specific hypothesis that is—now—the primary argument. Alternatively, one can try to redesign the study after the new (or revised) hypothesis has been formulated. This may require additional field research or perhaps the integration of additional cases or variables that can be obtained through secondary sources or through consultation of experts. A final approach is to simply jettison, or de‐emphasize, the portion of research that no longer addresses the (revised) key hypothesis. A three‐case study may become a two‐case study, and so forth. Lost time and effort are the costs of this downsizing.

In the event, practical considerations will probably determine which of these three strategies, or combinations of strategies, is to be followed. (They are not mutually exclusive.) The point to remember is that revision of one's cross‐case research design is normal and perhaps to be expected. Not all twists and turns on the meandering trail of truth can be anticipated.

12 Are There Other Methods of Case Selection?

At the outset of this chapter I summarized the task of case selection as a matter of achieving two objectives: representativeness (typicality) and variation (causal leverage). Evidently, there are other objectives as well. For example, one wishes to identify cases that are independent of each other. If chosen cases are affected by each other (sometimes known as Galton's problem or a problem of diffusion), this problem must be corrected before analysis can take place. I have neglected this issue because it is usually apparent to the researcher and, in any case, there are no simple techniques that might be utilized to correct for such biases. (For further discussion of this and other factors impinging upon case selection see Gerring 2001 , 178–81.)

I have also disregarded pragmatic/logistical issues that might affect case selection. Evidently, case selection is often influenced by a researcher's familiarity with the language of a country, a personal entrée into that locale, special access to important data, or funding that covers one archive rather than another. Pragmatic considerations are often—and quite rightly—decisive in the case‐selection process.

A final consideration concerns the theoretical prominence of a particular case within the literature on a subject. Researchers are sometimes obliged to study cases that have received extensive attention in previous studies. These are sometimes referred to as “paradigmatic” cases or “exemplars” ( Flyvbjerg 2004 , 427).

However, neither pragmatic/logistical utility nor theoretical prominence qualifies as a methodological factor in case selection. That is, these features of a case have no bearing on the validity of the findings stemming from a study. As such, it is appropriate to grant these issues a peripheral status in this chapter.

One final caveat must be issued. While it is traditional to distinguish among the tasks of case selection and case analysis, a close look at these processes shows them to be indistinct and overlapping. One cannot choose a case without considering the sort of analysis that it might be subjected to, and vice versa. Thus, the reader should consider choosing cases by employing the nine techniques laid out in this chapter along with any considerations that might be introduced by virtue of a case's quasi‐experimental qualities, a topic taken up elsewhere ( Gerring 2007 , ch. 6 ).

Abadie, A. , Drukker, D. , Herr, J. L. , and Imbens, G. W.   2001 . Implementing matching estimators for average treatment effects in Stata.   Stata Journal , 1: 1–18.

Google Scholar

Abbott, A.   2001 . Time Matters: On Theory and Method . Chicago: University of Chicago Press.

Google Preview

——  and Tsay, A.   2000 . Sequence analysis and optimal matching methods in sociology.   Sociological Methods and Research , 29: 3–33. 10.1177/0049124100029001001

——  and Forrest, J.   1986 . Optimal matching methods for historical sequences.   Journal of Interdisciplinary History , 16: 471–94. 10.2307/204500

Achen, C. H. , and Snidal, D.   1989 . Rational deterrence theory and comparative case studies.   World Politics , 41: 143–69. 10.2307/2010405

Allen, W. S.   1965 . The Nazi Seizure of Power: The Experience of a Single German Town, 1930–1935 . New York: Watts.

Almond, G. A.   1956 . Comparative political systems.   Journal of Politics , 18: 391–409.

Amenta, E.   1991 . Making the most of a case study: theories of the welfare state and the American experience. Pp. 172–94 in Issues and Alternatives in Comparative Social Research ed. C. C. Ragin . Leiden: E. J. Brill.

Barro, R. J.   1999 . Determinants of democracy.   Journal of Political Economy , 107: 158–83. 10.1086/250107

Belsey, D. A. , Kuh, E. , and Welsch, R. E.   2004 . Regression Diagnostics: Identifying Influential Data and Sources of Collinearity . New York: Wiley.

Bennett, A. , Lepgold, J. , and Unger, D.   1994 . Burden‐sharing in the Persian Gulf War.   International Organization , 48: 39–75. 10.1017/S0020818300000813

Bentley, A. 1908/ 1967 . The Process of Government . Cambridge, Mass.: Harvard University Press.

Brady, H. E. , and Collier, D. (eds.) 2004 . Rethinking Social Inquiry: Diverse Tools, Shared Standards . Lanham, Md.: Rowman and Littlefield.

Braumoeller, B. F.   2003 . Causal complexity and the study of politics.   Political Analysis , 11: 209–33. 10.1093/pan/mpg012

Breman, A. , and Shelton, C. 2001. Structural adjustment and health: a literature review of the debate, its role‐players and presented empirical evidence. CMH Working Paper Series, Paper No. WG6: 6. WHO, Commission on Macroeconomics and Health.

Brenner, R.   1976 . Agrarian class structure and economic development in pre‐industrial Europe.   Past and Present , 70: 30–75. 10.1093/past/70.1.30

Browne, A.   1987 . When Battered Women Kill . New York: Free Press.

Buchbinder, S. , and Vittinghoff, E.   1999 . HIV‐infected long‐term nonprogressors: epidemiology, mechanisms of delayed progression, and clinical and research implications.   Microbes Infect , 1: 1113–20. 10.1016/S1286-4579(99)00204-X

Cohen, M. R. , and Nagel, E.   1934 . An Introduction to Logic and Scientific Method . New York: Harcourt, Brace and Company.

Collier, D. , and Mahoney, J.   1996 . Insights and pitfalls: selection bias in qualitative research.   World Politics , 49: 56–91. 10.1353/wp.1996.0023

Collier, R. B. , and Collier, D. 1991/ 2002 . Shaping the Political Arena: Critical Junctures, the Labor Movement, and Regime Dynamics in Latin America . Notre Dame, Ind.: University of Notre Dame Press.

Colomer, J. M.   1991 . Transitions by agreement: modeling the Spanish way.   American Political Science Review , 85: 1283–302. 10.2307/1963946

Converse, P. E. , and Dupeux, G.   1962 . Politicization of the electorate in France and the United States.   Public Opinion Quarterly , 16: 1–23. 10.1086/267067

Coppedge, M. J. 2004. The conditional impact of the economy on democracy in Latin America. Presented at the conference “Democratic Advancements and Setbacks: What Have We Learnt?”, Uppsala University, June 11–13.

De Felice, E. G.   1986 . Causal inference and comparative methods.   Comparative Political Studies , 19: 415–37. 10.1177/0010414086019003005

Desch, M. C.   2002 . Democracy and victory: why regime type hardly matters.   International Security , 27: 5–47. 10.1162/016228802760987815

Deyo, F. (ed.) 1987 . The Political Economy of the New Asian Industrialism . Ithaca, NY: Cornell University Press.

Dion, D.   1998 . Evidence and inference in the comparative case study.   Comparative Politics , 30: 127–45. 10.2307/422284

Eckstein, H.   1975 . Case studies and theory in political science. In Handbook of Political Science , vii: Political Science: Scope and Theory , ed. F. I. Greenstein and N. W. Polsby . Reading, Mass.: Addison‐Wesley.

Eggan, F.   1954 . Social anthropology and the method of controlled comparison.   American Anthropologist , 56: 743–63. 10.1525/aa.1954.56.5.02a00020

Elman, C.   2003 . Lessons from Lakatos. In Progress in International Relations Theory: Appraising the Field , ed. C. Elman and M. F. Elman . Cambridge, Mass.: MIT Press.

——  2005 . Explanatory typologies in qualitative studies of international politics.   International Organization , 59: 293–326.

Emigh, R.   1997 . The power of negative thinking: the use of negative case methodology in the development of sociological theory.   Theory and Society , 26: 649–84. 10.1023/A:1006896217647

Epstein, L. D.   1964 . A comparative study of Canadian parties.   American Political Science Review , 58: 46–59. 10.2307/1952754

Ertman, T.   1997 . Birth of the Leviathan: Building States and Regimes in Medieval and Early Modern Europe . Cambridge: Cambridge University Press.

Esping‐Andersen, G.   1990 . The Three Worlds of Welfare Capitalism . Princeton, NJ: Princeton University Press.

Flyvbjerg, B.   2004 . Five misunderstandings about case‐study research. Pp. 420–34 in Qualitative Research Practice , ed. C. Seale , G. Gobo , J. F. Gubrium , and D. Silverman . London: Sage.

Geddes, B.   1990 . How the cases you choose affect the answers you get: selection bias in comparative politics. In Political Analysis , vol. ii, ed. J. A. Stimson . Ann Arbor: University of Michigan Press.

——  2003 . Paradigms and Sand Castles: Theory Building and Research Design in Comparative Politics . Ann Arbor: University of Michigan Press.

George, A. L. , and Bennett, A.   2005 . Case Studies and Theory Development . Cambridge, Mass.: MIT Press.

——  and Smoke, R.   1974 . Deterrence in American Foreign Policy: Theory and Practice . New York: Columbia University Press.

Gerring, J.   2001 . Social Science Methodology: A Criterial Framework . Cambridge: Cambridge University Press.

——  2007 . Case Study Research: Principles and Practices . Cambridge: Cambridge University Press.

——  Thacker, S. and Moreno, C. 2005. Do neoliberal policies save lives? Unpublished manuscript.

Goertz, G. and Starr, H. (eds.) 2003 . Necessary Conditions: Theory, Methodology and Applications . New York: Rowman and Littlefield.

——  and Levy, J. (eds.) forthcoming. Causal explanations, necessary conditions, and case studies: World War I and the end of the Cold War. Manuscript.

Goodin, R. E. and Smitsman, A.   2000 . Placing welfare states: the Netherlands as a crucial test case.   Journal of Comparative Policy Analysis , 2: 39–64. 10.1080/13876980008412635

Gujarati, D. N.   2003 . Basic Econometrics , 4th edn. New York: McGraw‐Hill.

Hamilton, G. G.   1977 . Chinese consumption of foreign commodities: a comparative perspective.   American Sociological Review , 42: 877–91. 10.2307/2094574

Haynes, B. F.   Pantaleo, G. and Fauci, A. S.   1996 . Toward an understanding of the correlates of protective immunity to HIV infection.   Science , 271: 324–8. 10.1126/science.271.5247.324

Hempel, C. G.   1942 . The function of general laws in history.   Journal of Philosophy , 39: 35–48. 10.2307/2017635

Ho, D. E.   Imai, K.   King, G. and Stuart, E. A. 2004. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Manuscript.

Howard, M. M.   2003 . The Weakness of Civil Society in Post‐Communist Europe . Cambridge: Cambridge University Press.

Howson, C. and Urbach, P.   1989 . Scientific Reasoning: The Bayesian Approach . La Salle, Ill.: Open Court.

Humphreys, M.   2005 . Natural resources, conflict, and conflict resolution: uncovering the mechanisms.   Journal of Conflict Resolution , 49: 508–37. 10.1177/0022002705277545

Jenicek, M.   2001 . Clinical Case Reporting in Evidence‐Based Medicine , 2nd edn. Oxford: Oxford University Press.

Karl, T. L.   1997 . The Paradox of Plenty: Oil Booms and Petro‐states . Berkeley: University of California Press.

Kazancigil, A.   1994 . The deviant case in comparative analysis: high stateness in comparative analysis. Pp. 213–38 in Comparing Nations: Concepts, Strategies, Substance , ed. M. Dogan and A. Kazancigil . Cambridge: Blackwell.

Kemp, K. A.   1986 . Race, ethnicity, class and urban spatial conflict: Chicago as a crucial case   Urban Studies , 23: 197–208. 10.1080/00420988620080231

Kendall, P. L. and Wolf, K. M. 1949/ 1955 . The analysis of deviant cases in communications research. In Communications Research, 1948–1949 , ed. P. F. Lazarsfeld and F. N. Stanton. New York: Harper and Brothers. Reprinted as pp. 167–70 in The Language of Social Research , ed. P. F. Lazarsfeld and M. Rosenberg . New York: Free Press.

Kennedy, C. H.   2005 . Single‐case Designs for Educational Research . Boston: Allyn and Bacon.

Kennedy, P.   2003 . A Guide to Econometrics , 5th edn. Cambridge, Mass.: MIT Press.

Khong, Y. F.   1992 . Analogies at War: Korea, Munich, Dien Bien Phu, and the Vietnam Decisions of 1965 . Princeton, NJ: Princeton University Press.

King, G.   Keohane, R. O. and Verba, S.   1994 . Designing Social Inquiry: Scientific Inference in Qualitative Research . Princeton, NJ: Princeton University Press.

Lakatos, I.   1978 . The Methodology of Scientific Research Programmes . Cambridge: Cambridge University Press.

Lazarsfeld, P. F. and Barton, A. H.   1951 . Qualitative measurement in the social sciences: classification, typologies, and indices. In The Policy Sciences , ed. D. Lerner and H. D. Lass‐ well. Stanford, Calif.: Stanford University Press.

Levy, J. S.   2002 . Qualitative methods in international relations. In Evaluating Methodology in International Studies , ed. F. P. Harvey and M. Brecher. Ann Arbor: University of Michigan Press.

Lijphart, A.   1968 . The Politics of Accommodation: Pluralism and Democracy in the Netherlands . Berkeley: University of California Press.

——  1969 . Consociational democracy.   World Politics , 21: 207–25. 10.2307/2009820

——  1971 . Comparative politics and the comparative method. American Political Science Review , 65: 682–93.

——  1975 . The comparable cases strategy in comparative research.   Comparative Political Studies , 8: 158–77.

Lipset, S. M.   1959 . Some social requisites of democracy: economic development and political development.   American Political Science Review , 53: 69–105. 10.2307/1951731

——  1960/ 1963 . Political Man: The Social Bases of Politics . Garden City, NY: Anchor.

——  1968 . Agrarian Socialism: The Cooperative Commonwealth Federation in Saskatchewan. A Study in Political Sociology . Garden City, NY: Doubleday.

——  Trow, M. A. and Coleman, J. S.   1956 . Union Democracy: The Internal Politics of the International Typographical Union . New York: Free Press.

Lynd, R. S. and Lynd, H. M. 1929/ 1956 . Middletown: A Study in American Culture . New York: Harcourt, Brace.

Mahoney, J. and Goertz, G.   2004 . The possibility principle: choosing negative cases in comparative research.   American Political Science Review , 98: 653–69.

Martin, L. L.   1992 . Coercive Cooperation: Explaining Multilateral Economic Sanctions .Princeton, NJ: Princeton University Press.

Mayo, D. G.   1996 . Error and the Growth of Experimental Knowledge . Chicago: University of Chicago Press.

Meckstroth, T.   1975 . “Most different systems” and “most similar systems:” a study in the logic of comparative inquiry.   Comparative Political Studies , 8: 133–77.

Miguel, E.   2004 . Tribe or nation: nation‐building and public goods in Kenya versus Tanzania.   World Politics , 56: 327–62. 10.1353/wp.2004.0018

Mill, J. S. 1843/ 1872 . The System of Logic , 8th edn. London: Longmans, Green.

Monroe, K. R.   1996 . The Heart of Altruism: Perceptions of a Common Humanity . Princeton, NJ: Princeton University Press.

Moore, B., Jr.   1966 . Social Origins of Dictatorship and Democracy: Lord and Peasant in the Making of the Modern World . Boston: Beacon Press.

Morgan, S. L. and Harding, D. J. 2005. Matching estimators of causal effects: from stratification and weighting to practical data analysis routines. Manuscript.

Moulder, F. V.   1977 . Japan, China and the Modern World Economy: Toward a Reinterpretation of East Asian Development ca. 1600 to ca. 1918 . Cambridge: Cambridge University Press.

Munck, G. L.   2004 . Tools for qualitative research. Pp. 105–21 in Rethinking Social Inquiry: Diverse Tools, Shared Standards , ed. H. E. Brady and D. Collier . Lanham, Md. : Rowman and Littlefield.

Njolstad, O.   1990 . Learning from history? Case studies and the limits to theory‐building. Pp. 220–46 in Arms Races: Technological and Political Dynamics , ed. O. Njolstad . Thousand Oaks, Calif.: Sage.

Patton, M. Q.   2002 . Qualitative Evaluation and Research Methods . Newbury Park, Calif.: Sage.

Popper, K. 1934/ 1968 . The Logic of Scientific Discovery . New York: Harper and Row.

——  1963 . Conjectures and Refutations . London: Routledge and Kegan Paul.

Posner, D.   2004 . The political salience of cultural difference: why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi.   American Political Science Review , 98: 529–46.

Przeworski, A. and Teune, H.   1970 . The Logic of Comparative Social Inquiry . New York: John Wiley.

Queen, S.   1928 . Round table on the case study in sociological research.   Publications of the American Sociological Society, Papers and Proceedings , 22: 225–7.

Ragin, C. C.   2000 . Fuzzy‐set Social Science . Chicago: University of Chicago Press.

——  2004 . Turning the tables. Pp. 123–38 in Rethinking Social Inquiry: Diverse Tools, Shared Standards , ed. H. E. Brady and D. Collier.   Lanham, Md. : Rowman and Littlefield.

Reilly, B.   2000 –1. Democracy, ethnic fragmentation, and internal conflict: confused theories, faulty data, and the “crucial case” of Papua New Guinea.   International Security , 25: 162–85. 10.1162/016228800560552

——  and Phillpot, R.   2003 . “Making democracy work” in Papua New Guinea: social capital and provincial development in an ethnically fragmented society.   Asian Survey , 42: 906–27. 10.1525/as.2002.42.6.906

Rogowski, R.   1995 . The role of theory and anomaly in social‐scientific inference.   American Political Science Review , 89: 467–70. 10.2307/2082443

Rohlfing, I. 2004. Have you chosen the right case? Uncertainty in case selection for single case studies. Working Paper, International University, Bremen.

Rosenbaum, P. R.   2004 . Matching in observational studies. In Applied Bayesian Modeling and Causal Inference from an Incomplete‐data Perspective , ed. A. Gelman and X.‐L. Meng . New York: John Wiley.

——  and Silber, J. H.   2001 . Matching and thick description in an observational study of mortality after surgery.   Biostatistics , 2: 217–32. 10.1093/biostatistics/2.2.217

Ross, M.   2001 . Does oil hinder democracy?   World Politics , 53: 325–61. 10.1353/wp.2001.0011

Sagan, S. D.   1995 . Limits of Safety: Organizations, Accidents, and Nuclear Weapons . Princeton, NJ: Princeton University Press.

Sekhon, J. S.   2004 . Quality meets quantity: case studies, conditional probability and counter‐ factuals.   Perspectives in Politics , 2: 281–93.

Shafer, M. D.   1988 . Deadly Paradigms: The Failure of U.S. Counterinsurgency Policy . Princeton, NJ: Princeton University Press.

Skocpol, T.   1979 . States and Social Revolutions: A Comparative Analysis of France, Russia, and China . Cambridge: Cambridge University Press.

——  and Somers, M.   1980 . The uses of comparative history in macrosocial inquiry.   Comparative Studies in Society and History , 22: 147–97.

Stinchcombe, A. L.   1968 . Constructing Social Theories . New York: Harcourt, Brace.

Swank, D. H.   2002 . Global Capital, Political Institutions, and Policy Change in Developed Welfare States . Cambridge: Cambridge University Press.

Tendler, J.   1997 . Good Government in the Tropics . Baltimore: Johns Hopkins University Press.

Truman, D. B.   1951 . The Governmental Process . New York: Alfred A. Knopf.

Tsai, L.   2007 . Accountability without Democracy: How Solidary Groups Provide Public Goods in Rural China . Cambridge: Cambridge University Press.

Van Evera, S.   1997 . Guide to Methods for Students of Political Science . Ithaca, NY: Cornell University Press.

Wahlke, J. C.   1979 . Pre‐behavioralism in political science. American Political Science Review , 73: 9–31. 10.2307/1954728

Yashar, D. J.   2005 . Contesting Citizenship in Latin America: The Rise of Indigenous Movements and the Postliberal Challenge . Cambridge: Cambridge University Press.

Yin, R. K.   2004 . Case Study Anthology . Thousand Oaks, Calif.: Sage.

Gujarati (2003) ; Kennedy (2003) . Interestingly, the potential of cross‐case statistics in helping to choose cases for in‐depth analysis is recognized in some of the earliest discussions of the case‐study method (e.g. Queen 1928 , 226).

This expands on Mill (1843/1872 , 253), who wrote of scientific enquiry as twofold: “either inquiries into the cause of a given effect or into the effects or properties of a given cause.”

This method has not received much attention on the part of qualitative methodologists; hence, the absence of a generally recognized name. It bears some resemblance to J. S. Mill's Joint Method of Agreement and Difference ( Mill 1843/1872 ), which is to say a mixture of most‐similar and most‐different analysis, as discussed below. Patton (2002 , 234) employs the concept of “maximum variation (heterogeneity) sampling.”

More precisely, George and Smoke (1974 , 534, 522–36, ch. 18 ; see also discussion in Collier and Mahoney 1996 , 78) set out to investigate causal pathways and discovered, through the course of their investigation of many cases, these three causal types. Yet, for our purposes what is important is that the final sample includes at least one representative of each “type.”

For further examples see Collier and Mahoney (1996) ; Geddes (1990) ; Tendler (1997) .

Traditionally, methodologists have conceptualized cases as having “positive” or “negative” values (e.g. Emigh 1997 ; Mahoney and Goertz 2004 ; Ragin 2000 , 60; 2004 , 126).

Geddes (1990) ; King, Keohane, and Verba (1994) . See also discussion in Brady and Collier (2004) ; Collier and Mahoney (1996) ; Rogowski (1995) .

The exception would be a circumstance in which the researcher intends to disprove a deterministic argument ( Dion 1998 ).

Geddes (2003 , 131). For other examples of casework from the annals of medicine see “Clinical reports” in the Lancet , “Case studies” in Canadian Medical Association Journal , and various issues of the Journal of Obstetrics and Gynecology , often devoted to clinical cases (discussed in Jenicek 2001 , 7). For examples from the subfield of comparative politics see Kazancigil (1994) .

For a discussion of the important role of anomalies in the development of scientific theorizing see Elman (2003) ; Lakatos (1978) . For examples of deviant‐case research designs in the social sciences see Amenta (1991) ; Coppedge (2004) ; Eckstein (1975) ; Emigh (1997) ; Kendall and Wolf (1949/1955) .

For examples of the crucial‐case method see Bennett, Lepgold, and Unger (1994) ; Desch (2002) ; Goodin and Smitsman (2000) ; Kemp (1986) ; Reilly and Phillpot (2003) . For general discussion see George and Bennett (2005) ; Levy (2002) ; Stinchcombe (1968 , 24–8).

A third position, which purports to be neither Popperian or Bayesian, has been articulated by Mayo (1996 , ch. 6 ). From this perspective, the same idea is articulated as a matter of “severe tests.”

It should be noted that Tsai's conclusions do not rest solely on this crucial case. Indeed, she employs a broad range of methodological tools, encompassing case‐study and cross‐case methods.

See also the discussion in Eckstein (1975) and Lijphart (1969) . For additional examples of case studies disconfirming general propositions of a deterministic nature see Allen (1965); Lipset, Trow, and Coleman (1956) ; Njolstad (1990) ; Reilly (2000–1) ; and discussion in Dion (1998) ; Rogowski (1995) .

Granted, insofar as case‐study analysis provides a window into causal mechanisms, and causal mechanisms are integral to a given theory, a single case may be enlisted to confirm or disconfirm a proposition. However, if the case study upholds a posited pattern of X/Y covariation, and finds fault only with the stipulated causal mechanism, it would be more accurate to say that the study forces the reformulation of a given theory, rather than its confirmation or disconfirmation. See further discussion in the following section.

Sometimes, the most‐similar method is known as the “method of difference,” after its inventor ( Mill 1843/1872 ). For later treatments see Cohen and Nagel (1934) ; Eggan (1954) ; Gerring (2001 , ch. 9 ); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) .

For good introductions see Ho et al. (2004) ; Morgan and Harding (2005) ; Rosenbaum (2004) ; Rosenbaum and Silber (2001) . For a discussion of matching procedures in Stata see Abadie et al. (2001) .

The most‐different method is also sometimes referred to as the “method of agreement,” following its inventor, J. S. Mill (1843/1872) . See also De Felice (1986) ; Gerring (2001 , 212–14); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) . For examples of this method see Collier and Collier (1991/2002) ; Converse and Dupeux (1962) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). However, most of these studies are described as combining most‐similar and most‐different methods.

In the following discussion I treat the terms social capital, civil society, and civic engagement interchangeably.

E.g. Collier and Collier (1991/2002) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). Karl (1997) , which affects to be a most‐different system analysis (20), is a particularly clear example of this. Her study, focused ostensibly on petro‐states (states with large oil reserves), makes two sorts of inferences. The first concerns the (usually) obstructive role of oil in political and economic development. The second sort of inference concerns variation within the population of petro‐states, showing that some countries (e.g. Norway, Indonesia) manage to avoid the pathologies brought on elsewhere by oil resources. When attempting to explain the constraining role of oil on petro‐states, Karl usually relies on contrasts between petro‐states and nonpetro‐states (e.g. ch. 10 ). Only when attempting to explain differences among petro‐states does she restrict her sample to petro‐states. In my opinion, very little use is made of the most‐different research design.

This was recognized, at least implicitly, by Mill (1843/1872 , 258–9). Skepticism has been echoed by methodologists in the intervening years (e.g. Cohen and Nagel 1934 , 251–6; Gerring 2001 ; Skocpol and Somers 1980 ). Indeed, explicit defenses of the most‐different method are rare (but see De Felice 1986 ).

Another way of stating this is to say that X is a “nontrivial necessary condition” of Y .

Wahlke (1979 , 13) writes of the failings of the “behavioralist” mode of political science analysis: “It rarely aims at generalization; research efforts have been confined essentially to case studies of single political systems, most of them dealing …with the American system.”

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What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

Partner Center

How to write a case study — examples, templates, and tools

It’s a marketer’s job to communicate the effectiveness of a product or service to potential and current customers to convince them to buy and keep business moving. One of the best methods for doing this is to share success stories that are relatable to prospects and customers based on their pain points, experiences, and overall needs.

That’s where case studies come in. Case studies are an essential part of a content marketing plan. These in-depth stories of customer experiences are some of the most effective at demonstrating the value of a product or service. Yet many marketers don’t use them, whether because of their regimented formats or the process of customer involvement and approval.

A case study is a powerful tool for showcasing your hard work and the success your customer achieved. But writing a great case study can be difficult if you’ve never done it before or if it’s been a while. This guide will show you how to write an effective case study and provide real-world examples and templates that will keep readers engaged and support your business.

In this article, you’ll learn:

What is a case study?

How to write a case study, case study templates, case study examples, case study tools.

A case study is the detailed story of a customer’s experience with a product or service that demonstrates their success and often includes measurable outcomes. Case studies are used in a range of fields and for various reasons, from business to academic research. They’re especially impactful in marketing as brands work to convince and convert consumers with relatable, real-world stories of actual customer experiences.

The best case studies tell the story of a customer’s success, including the steps they took, the results they achieved, and the support they received from a brand along the way. To write a great case study, you need to:

• Celebrate the customer and make them — not a product or service — the star of the story.
• Craft the story with specific audiences or target segments in mind so that the story of one customer will be viewed as relatable and actionable for another customer.
• Write copy that is easy to read and engaging so that readers will gain the insights and messages intended.
• Follow a standardized format that includes all of the essentials a potential customer would find interesting and useful.
• Support all of the claims for success made in the story with data in the forms of hard numbers and customer statements.

Case studies are a type of review but more in depth, aiming to show — rather than just tell — the positive experiences that customers have with a brand. Notably, 89% of consumers read reviews before deciding to buy, and 79% view case study content as part of their purchasing process. When it comes to B2B sales, 52% of buyers rank case studies as an important part of their evaluation process.

Telling a brand story through the experience of a tried-and-true customer matters. The story is relatable to potential new customers as they imagine themselves in the shoes of the company or individual featured in the case study. Showcasing previous customers can help new ones see themselves engaging with your brand in the ways that are most meaningful to them.

Besides sharing the perspective of another customer, case studies stand out from other content marketing forms because they are based on evidence. Whether pulling from client testimonials or data-driven results, case studies tend to have more impact on new business because the story contains information that is both objective (data) and subjective (customer experience) — and the brand doesn’t sound too self-promotional.

Case studies are unique in that there’s a fairly standardized format for telling a customer’s story. But that doesn’t mean there isn’t room for creativity. It’s all about making sure that teams are clear on the goals for the case study — along with strategies for supporting content and channels — and understanding how the story fits within the framework of the company’s overall marketing goals.

Here are the basic steps to writing a good case study.

1. Identify your goal

Start by defining exactly who your case study will be designed to help. Case studies are about specific instances where a company works with a customer to achieve a goal. Identify which customers are likely to have these goals, as well as other needs the story should cover to appeal to them.

The answer is often found in one of the buyer personas that have been constructed as part of your larger marketing strategy. This can include anything from new leads generated by the marketing team to long-term customers that are being pressed for cross-sell opportunities. In all of these cases, demonstrating value through a relatable customer success story can be part of the solution to conversion.

2. Choose your client or subject

Who you highlight matters. Case studies tie brands together that might otherwise not cross paths. A writer will want to ensure that the highlighted customer aligns with their own company’s brand identity and offerings. Look for a customer with positive name recognition who has had great success with a product or service and is willing to be an advocate.

The client should also match up with the identified target audience. Whichever company or individual is selected should be a reflection of other potential customers who can see themselves in similar circumstances, having the same problems and possible solutions.

Some of the most compelling case studies feature customers who:

• Switch from one product or service to another while naming competitors that missed the mark.
• Experience measurable results that are relatable to others in a specific industry.
• Represent well-known brands and recognizable names that are likely to compel action.
• Advocate for a product or service as a champion and are well-versed in its advantages.

Whoever or whatever customer is selected, marketers must ensure they have the permission of the company involved before getting started. Some brands have strict review and approval procedures for any official marketing or promotional materials that include their name. Acquiring those approvals in advance will prevent any miscommunication or wasted effort if there is an issue with their legal or compliance teams.

3. Conduct research and compile data

Substantiating the claims made in a case study — either by the marketing team or customers themselves — adds validity to the story. To do this, include data and feedback from the client that defines what success looks like. This can be anything from demonstrating return on investment (ROI) to a specific metric the customer was striving to improve. Case studies should prove how an outcome was achieved and show tangible results that indicate to the customer that your solution is the right one.

This step could also include customer interviews. Make sure that the people being interviewed are key stakeholders in the purchase decision or deployment and use of the product or service that is being highlighted. Content writers should work off a set list of questions prepared in advance. It can be helpful to share these with the interviewees beforehand so they have time to consider and craft their responses. One of the best interview tactics to keep in mind is to ask questions where yes and no are not natural answers. This way, your subject will provide more open-ended responses that produce more meaningful content.

4. Choose the right format

There are a number of different ways to format a case study. Depending on what you hope to achieve, one style will be better than another. However, there are some common elements to include, such as:

• An engaging headline
• A subject and customer introduction
• The unique challenge or challenges the customer faced
• The solution the customer used to solve the problem
• The results achieved
• Data and statistics to back up claims of success
• A strong call to action (CTA) to engage with the vendor

It’s also important to note that while case studies are traditionally written as stories, they don’t have to be in a written format. Some companies choose to get more creative with their case studies and produce multimedia content, depending on their audience and objectives. Case study formats can include traditional print stories, interactive web or social content, data-heavy infographics, professionally shot videos, podcasts, and more.

5. Write your case study

We’ll go into more detail later about how exactly to write a case study, including templates and examples. Generally speaking, though, there are a few things to keep in mind when writing your case study.

• Be clear and concise. Readers want to get to the point of the story quickly and easily, and they’ll be looking to see themselves reflected in the story right from the start.
• Provide a big picture. Always make sure to explain who the client is, their goals, and how they achieved success in a short introduction to engage the reader.
• Construct a clear narrative. Stick to the story from the perspective of the customer and what they needed to solve instead of just listing product features or benefits.
• Leverage graphics. Incorporating infographics, charts, and sidebars can be a more engaging and eye-catching way to share key statistics and data in readable ways.
• Offer the right amount of detail. Most case studies are one or two pages with clear sections that a reader can skim to find the information most important to them.
• Include data to support claims. Show real results — both facts and figures and customer quotes — to demonstrate credibility and prove the solution works.

6. Promote your story

Marketers have a number of options for distribution of a freshly minted case study. Many brands choose to publish case studies on their website and post them on social media. This can help support SEO and organic content strategies while also boosting company credibility and trust as visitors see that other businesses have used the product or service.

Marketers are always looking for quality content they can use for lead generation. Consider offering a case study as gated content behind a form on a landing page or as an offer in an email message. One great way to do this is to summarize the content and tease the full story available for download after the user takes an action.

Sales teams can also leverage case studies, so be sure they are aware that the assets exist once they’re published. Especially when it comes to larger B2B sales, companies often ask for examples of similar customer challenges that have been solved.

Now that you’ve learned a bit about case studies and what they should include, you may be wondering how to start creating great customer story content. Here are a couple of templates you can use to structure your case study.

Template 1 — Challenge-solution-result format

• Start with an engaging title. This should be fewer than 70 characters long for SEO best practices. One of the best ways to approach the title is to include the customer’s name and a hint at the challenge they overcame in the end.
• Create an introduction. Lead with an explanation as to who the customer is, the need they had, and the opportunity they found with a specific product or solution. Writers can also suggest the success the customer experienced with the solution they chose.
• Present the challenge. This should be several paragraphs long and explain the problem the customer faced and the issues they were trying to solve. Details should tie into the company’s products and services naturally. This section needs to be the most relatable to the reader so they can picture themselves in a similar situation.
• Share the solution. Explain which product or service offered was the ideal fit for the customer and why. Feel free to delve into their experience setting up, purchasing, and onboarding the solution.
• Explain the results. Demonstrate the impact of the solution they chose by backing up their positive experience with data. Fill in with customer quotes and tangible, measurable results that show the effect of their choice.
• Ask for action. Include a CTA at the end of the case study that invites readers to reach out for more information, try a demo, or learn more — to nurture them further in the marketing pipeline. What you ask of the reader should tie directly into the goals that were established for the case study in the first place.

Template 2 — Data-driven format

• Start with an engaging title. Be sure to include a statistic or data point in the first 70 characters. Again, it’s best to include the customer’s name as part of the title.
• Create an overview. Share the customer’s background and a short version of the challenge they faced. Present the reason a particular product or service was chosen, and feel free to include quotes from the customer about their selection process.
• Present data point 1. Isolate the first metric that the customer used to define success and explain how the product or solution helped to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Present data point 2. Isolate the second metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Present data point 3. Isolate the final metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
• Summarize the results. Reiterate the fact that the customer was able to achieve success thanks to a specific product or service. Include quotes and statements that reflect customer satisfaction and suggest they plan to continue using the solution.
• Ask for action. Include a CTA at the end of the case study that asks readers to reach out for more information, try a demo, or learn more — to further nurture them in the marketing pipeline. Again, remember that this is where marketers can look to convert their content into action with the customer.

While templates are helpful, seeing a case study in action can also be a great way to learn. Here are some examples of how Adobe customers have experienced success.

Juniper Networks

One example is the Adobe and Juniper Networks case study , which puts the reader in the customer’s shoes. The beginning of the story quickly orients the reader so that they know exactly who the article is about and what they were trying to achieve. Solutions are outlined in a way that shows Adobe Experience Manager is the best choice and a natural fit for the customer. Along the way, quotes from the client are incorporated to help add validity to the statements. The results in the case study are conveyed with clear evidence of scale and volume using tangible data.

The story of Lenovo’s journey with Adobe is one that spans years of planning, implementation, and rollout. The Lenovo case study does a great job of consolidating all of this into a relatable journey that other enterprise organizations can see themselves taking, despite the project size. This case study also features descriptive headers and compelling visual elements that engage the reader and strengthen the content.

Tata Consulting

When it comes to using data to show customer results, this case study does an excellent job of conveying details and numbers in an easy-to-digest manner. Bullet points at the start break up the content while also helping the reader understand exactly what the case study will be about. Tata Consulting used Adobe to deliver elevated, engaging content experiences for a large telecommunications client of its own — an objective that’s relatable for a lot of companies.

Case studies are a vital tool for any marketing team as they enable you to demonstrate the value of your company’s products and services to others. They help marketers do their job and add credibility to a brand trying to promote its solutions by using the experiences and stories of real customers.

When you’re ready to get started with a case study:

• Think about a few goals you’d like to accomplish with your content.
• Make a list of successful clients that would be strong candidates for a case study.
• Reach out to the client to get their approval and conduct an interview.
• Gather the data to present an engaging and effective customer story.

Adobe can help

There are several Adobe products that can help you craft compelling case studies. Adobe Experience Platform helps you collect data and deliver great customer experiences across every channel. Once you’ve created your case studies, Experience Platform will help you deliver the right information to the right customer at the right time for maximum impact.

To learn more, watch the Adobe Experience Platform story .

Keep in mind that the best case studies are backed by data. That’s where Adobe Real-Time Customer Data Platform and Adobe Analytics come into play. With Real-Time CDP, you can gather the data you need to build a great case study and target specific customers to deliver the content to the right audience at the perfect moment.

Watch the Real-Time CDP overview video to learn more.

Finally, Adobe Analytics turns real-time data into real-time insights. It helps your business collect and synthesize data from multiple platforms to make more informed decisions and create the best case study possible.

Request a demo to learn more about Adobe Analytics.

https://business.adobe.com/blog/perspectives/b2b-ecommerce-10-case-studies-inspire-you

https://business.adobe.com/blog/basics/business-case

https://business.adobe.com/blog/basics/what-is-real-time-analytics

The case for behavioral strategy

Once heretical, behavioral economics is now mainstream. Money managers employ its insights about the limits of rationality in understanding investor behavior and exploiting stock-pricing anomalies. Policy makers use behavioral principles to boost participation in retirement-savings plans. Marketers now understand why some promotions entice consumers and others don’t.

Yet very few corporate strategists making important decisions consciously take into account the cognitive biases—systematic tendencies to deviate from rational calculations—revealed by behavioral economics. It’s easy to see why: unlike in fields such as finance and marketing, where executives can use psychology to make the most of the biases residing in others , in strategic decision making leaders need to recognize their own biases. So despite growing awareness of behavioral economics and numerous efforts by management writers, including ourselves, to make the case for its application, most executives have a justifiably difficult time knowing how to harness its power. 1 1. See Charles Roxburgh, “ Hidden flaws in strategy ,” McKinsey Quarterly , May 2003; and Dan P. Lovallo and Olivier Sibony, “ Distortions and deceptions in strategic decisions ,” McKinsey Quarterly , February 2006.

This is not to say that executives think their strategic decisions are perfect. In a recent McKinsey Quarterly survey of 2,207 executives, only 28 percent said that the quality of strategic decisions in their companies was generally good, 60 percent thought that bad decisions were about as frequent as good ones, and the remaining 12 percent thought good decisions were altogether infrequent. 2 2. See “ Flaws in strategic decision making: McKinsey Global Survey Results ,” January 2009. Our candid conversations with senior executives behind closed doors reveal a similar unease with the quality of decision making and confirm the significant body of research indicating that cognitive biases affect the most important strategic decisions made by the smartest managers in the best companies. Mergers routinely fail to deliver the expected synergies. 3 3. See Dan Lovallo, Patrick Viguerie, Robert Uhlaner, and John Horn, “Deals without delusions,” Harvard Business Review , December 2007, Volume 85, Number 12, pp. 92–99. Strategic plans often ignore competitive responses. 4 4. See John T. Horn, Dan P. Lovallo, and S. Patrick Viguerie, “ Beating the odds in market entry ,” McKinsey Quarterly , November 2005. And large investment projects are over budget and over time—over and over again. 5 5. See Bent Flyvbjerg, Dan Lovallo, and Massimo Garbuio, “Delusion and deception in large infrastructure projects,” California Management Review , 2009, Volume 52, Number 1, pp. 170–93.

In this article, we share the results of new research quantifying the financial benefits of processes that “debias” strategic decisions. The size of this prize makes a strong case for practicing behavioral strategy—a style of strategic decision making that incorporates the lessons of psychology. It starts with the recognition that even if we try, like Baron Münchhausen, to escape the swamp of biases by pulling ourselves up by our own hair, we are unlikely to succeed. Instead, we need new norms for activities such as managing meetings (for more on running unbiased meetings, see “ Taking the bias out of meetings ”), gathering data, discussing analogies, and stimulating debate that together can diminish the impact of cognitive biases on critical decisions. To support those new norms, we also need a simple language for recognizing and discussing biases, one that is grounded in the reality of corporate life, as opposed to the sometimes-arcane language of academia. All this represents a significant commitment and, in some organizations, a profound cultural change.

The value of good decision processes

Think of a large business decision your company made recently: a major acquisition, a large capital expenditure, a key technological choice, or a new-product launch. Three things went into it. The decision almost certainly involved some fact gathering and analysis. It relied on the insights and judgment of a number of executives (a number sometimes as small as one). And it was reached after a process—sometimes very formal, sometimes completely informal—turned the data and judgment into a decision.

Our research indicates that, contrary to what one might assume, good analysis in the hands of managers who have good judgment won’t naturally yield good decisions. The third ingredient—the process—is also crucial. We discovered this by asking managers to report on both the nature of an important decision and the process through which it was reached. In all, we studied 1,048 major decisions made over the past five years, including investments in new products, M&A decisions, and large capital expenditures (Exhibit 1).

The research analyzed a variety of decisions.

We asked managers to report on the extent to which they had applied 17 practices in making that decision. Eight of these practices had to do with the quantity and detail of the analysis: did you, for example, build a detailed financial model or run sensitivity analyses? The others described the decision-making process: for instance, did you explicitly explore and discuss major uncertainties or discuss viewpoints that contradicted the senior leader’s? We chose these process characteristics because in academic research and in our experience, they have proved effective at overcoming biases. 6 6. Research like this is challenging because of what International Institute for Management Development (IMD) professor Phil Rosenzweig calls the “halo effect”: the tendency of people to believe that when their companies are successful or a decision turns out well, their actions were important contributors (see Phil Rosenzweig, “ The halo effect, and other managerial delusions ,” McKinsey Quarterly , February 2007). We sought to mitigate the halo effect by asking respondents to focus on a typical decision process in their companies and to list several decisions before landing on one for detailed questioning. Next, we asked analytical and process questions about the specific decision for the bulk of the survey. Finally, at the very end of it, we asked about performance metrics.

After controlling for factors like industry, geography, and company size, we used regression analysis to calculate how much of the variance in decision outcomes 7 7. We asked respondents to assess outcomes along four dimensions: revenue, profitability, market share, and productivity. was explained by the quality of the process and how much by the quantity and detail of the analysis. The answer: process mattered more than analysis—by a factor of six (Exhibit 2). This finding does not mean that analysis is unimportant, as a closer look at the data reveals: almost no decisions in our sample made through a very strong process were backed by very poor analysis. Why? Because one of the things an unbiased decision-making process will do is ferret out poor analysis. The reverse is not true; superb analysis is useless unless the decision process gives it a fair hearing.

Process, analysis, and industry variables explain decision-making effectiveness.

To get a sense of the value at stake, we also assessed the return on investment (ROI) of decisions characterized by a superior process. 8 8. This analysis covers the subset of 673 (out of all 1,048) decisions for which ROI data were available. The analysis revealed that raising a company’s game from the bottom to the top quartile on the decision-making process improved its ROI by 6.9 percentage points. The ROI advantage for top-quartile versus bottom-quartile analytics was 5.3 percentage points, further underscoring the tight relationship between process and analysis. Good process, in short, isn’t just good hygiene; it’s good business.

The building blocks of behavioral strategy

Any seasoned executive will of course recognize some biases and take them into account. That is what we do when we apply a discount factor to a plan from a direct report (correcting for that person’s overoptimism). That is also what we do when we fear that one person’s recommendation may be colored by self-interest and ask a neutral third party for an independent opinion.

However, academic research and empirical observation suggest that these corrections are too inexact and limited to be helpful. The prevalence of biases in corporate decisions is partly a function of habit, training, executive selection, and corporate culture. But most fundamentally, biases are pervasive because they are a product of human nature—hardwired and highly resistant to feedback, however brutal. For example, drivers laid up in hospitals for traffic accidents they themselves caused overestimate their driving abilities just as much as the rest of us do. 9 9. Caroline E. Preston and Stanley Harris, “Psychology of drivers in traffic accidents,” Journal of Applied Psychology , 1965, Volume 49, Number 4, pp. 284–88.

Improving strategic decision making therefore requires not only trying to limit our own (and others’) biases but also orchestrating a decision-making process that will confront different biases and limit their impact. To use a judicial analogy, we cannot trust the judges or the jurors to be infallible; they are, after all, human. But as citizens, we can expect verdicts to be rendered by juries and trials to follow the rules of due process. It is through teamwork, and the process that organizes it, that we seek a high-quality outcome.

Building such a process for strategic decision making requires an understanding of the biases the process needs to address. In the discussion that follows, we focus on the subset of biases we have found to be most relevant for executives and classify those biases into five simple, business-oriented groupings. (You can download a PDF of the groupings of biases that occur most frequently in business.) A familiarity with this classification is useful in itself because, as the psychologist and Nobel laureate in economics Daniel Kahneman has pointed out, the odds of defeating biases in a group setting rise when discussion of them is widespread. But familiarity alone isn’t enough to ensure unbiased decision making, so as we discuss each family of bias, we also provide some general principles and specific examples of practices that can help counteract it.

Counter pattern-recognition biases by changing the angle of vision

The ability to identify patterns helps set humans apart but also carries with it a risk of misinterpreting conceptual relationships. Common pattern-recognition biases include saliency biases (which lead us to overweight recent or highly memorable events) and the confirmation bias (the tendency, once a hypothesis has been formed, to ignore evidence that would disprove it). Particularly imperiled are senior executives, whose deep experience boosts the odds that they will rely on analogies, from their own experience, that may turn out to be misleading. 10 10. For more on misleading experiences, see Sydney Finkelstein, Jo Whitehead, and Andrew Campbell, Think Again: Why Good Leaders Make Bad Decisions and How to Keep It from Happening to You , Boston: Harvard Business Press, 2008. Whenever analogies, comparisons, or salient examples are used to justify a decision, and whenever convincing champions use their powers of persuasion to tell a compelling story, pattern-recognition biases may be at work.

Pattern recognition is second nature to all of us—and often quite valuable—so fighting biases associated with it is challenging. The best we can do is to change the angle of vision by encouraging participants to see facts in a different light and to test alternative hypotheses to explain those facts. This practice starts with things as simple as field and customer visits. It continues with meeting-management techniques such as reframing or role reversal, which encourage participants to formulate alternative explanations for the evidence with which they are presented. It can also leverage tools, such as competitive war games, that promote out-of-the-box thinking.

Sometimes, simply coaxing managers to articulate the experiences influencing them is valuable. According to Kleiner Perkins partner Randy Komisar, for example, a contentious discussion over manufacturing strategy at the start-up WebTV 11 11. WebTV is now MSN TV. suddenly became much more manageable once it was clear that the preferences of executives about which strategy to pursue stemmed from their previous career experience. When that realization came, he told us, there was immediately a “sense of exhaling in the room.” Managers with software experience were frightened about building hardware; managers with hardware experience were afraid of ceding control to contract manufacturers.

Getting these experiences into the open helped WebTV’s management team become aware of the pattern recognition they triggered and see more clearly the pros and cons of both options. Ultimately, WebTV’s executives decided both to outsource hardware production to large electronics makers and, heeding the worries of executives with hardware experience, to establish a manufacturing line in Mexico as a backup, in case the contractors did not deliver in time for the Christmas season. That in fact happened, and the backup plan, which would not have existed without a decision process that changed the angle of vision, “saved the company.”

Another useful means of changing the angle of vision is to make it wider by creating a reasonably large—in our experience at least six—set of similar endeavors for comparative analysis. For example, in an effort to improve US military effectiveness in Iraq in 2004, Colonel Kalev Sepp—by himself, in 36 hours—developed a reference class of 53 similar counterinsurgency conflicts, complete with strategies and outcomes. This effort informed subsequent policy changes. 12 12. Thomas E. Ricks, Fiasco: The American Military Adventure in Iraq , New York: Penguin Press, 2006, pp. 393–94.

Counter action-oriented biases by recognizing uncertainty

Most executives rightly feel a need to take action. However, the actions we take are often prompted by excessive optimism about the future and especially about our own ability to influence it. Ask yourself how many plans you have reviewed that turned out to be based on overly optimistic forecasts of market potential or underestimated competitive responses. When you or your people feel—especially under pressure—an urge to take action and an attractive plan presents itself, chances are good that some elements of overconfidence have tainted it.

To make matters worse, the culture of many organizations suppresses uncertainty and rewards behavior that ignores it. For instance, in most organizations, an executive who projects great confidence in a plan is more likely to get it approved than one who lays out all the risks and uncertainties surrounding it. Seldom do we see confidence as a warning sign—a hint that overconfidence, overoptimism, and other action-oriented biases may be at work.

Superior decision-making processes counteract action-oriented biases by promoting the recognition of uncertainty. For example, it often helps to make a clear and explicit distinction between decision meetings, where leaders should embrace uncertainty while encouraging dissent, and implementation meetings, where it’s time for executives to move forward together. Also valuable are tools—such as scenario planning, decision trees, and the “premortem” championed by research psychologist Gary Klein (for more on the premortem, see “ Strategic decisions: When can you trust your gut? ”)—that force consideration of many potential outcomes. And at the time of a major decision, it’s critical to discuss which metrics need to be monitored to highlight necessary course corrections quickly.

Counter stability biases by shaking things up

In contrast to action biases, stability biases make us less prone to depart from the status quo than we should be. This category includes anchoring—the powerful impact an initial idea or number has on the subsequent strategic conversation. (For instance, last year’s numbers are an implicit but extremely powerful anchor in any budget review.) Stability biases also include loss aversion—the well-documented tendency to feel losses more acutely than equivalent gains—and the sunk-cost fallacy, which can lead companies to hold on to businesses they should divest. 13 13. See John T. Horn, Dan P. Lovallo, and S. Patrick Viguerie, “ Learning to let go: Making better exit decisions ,” McKinsey Quarterly , May 2006.

One way of diagnosing your company’s susceptibility to stability biases is to compare decisions over time. For example, try mapping the percentage of total new investment each division of the company receives year after year. If that percentage is stable but the divisions’ growth opportunities are not, this finding is cause for concern—and quite a common one. Our research indicates, for example, that in multibusiness corporations over a 15-year time horizon, there is a near-perfect correlation between a business unit’s current share of the capital expenditure budget and its budget share in the previous year. A similar inertia often bedevils advertising budgets and R&D project pipelines.

One way to help managers shake things up is to establish stretch targets that are impossible to achieve through “business as usual.” Zero-based (or clean-sheet) budgeting sounds promising, but in our experience companies use this approach only when they are in dire straits. An alternative is to start by reducing each reporting unit’s budget by a fixed percentage (for instance, 10 percent). The resulting tough choices facilitate the redeployment of resources to more valuable opportunities. Finally, challenging budget allocations at a more granular level can help companies reprioritize their investments. 14 14. For more on reviewing the growth opportunities available across different micromarkets ranging in size from $50 million to $200 million, rather than across business units as a whole, see Mehrdad Baghai, Sven Smit, and Patrick Viguerie, “Is your growth strategy flying blind?” Harvard Business Review , May 2009, Volume 87, Number 5, pp. 86–96.

Counter interest biases by making them explicit

Misaligned incentives are a major source of bias. “Silo thinking,” in which organizational units defend their own interests, is its most easily detectable manifestation. Furthermore, senior executives sometimes honestly view the goals of a company differently because of their different roles or functional expertise. Heated discussions in which participants seem to see issues from completely different perspectives often reflect the presence of different (and generally unspoken) interest biases.

The truth is that adopting a sufficiently broad (and realistic) definition of “interests,” including reputation, career options, and individual preferences, leads to the inescapable conclusion that there will always be conflicts between one manager and another and between individual managers and the company as a whole. Strong decision-making processes explicitly account for diverging interests. For example, if before the time of a decision, strategists formulate precisely the criteria that will and won’t be used to evaluate it, they make it more difficult for individual managers to change the terms of the debate to make their preferred actions seem more attractive. Similarly, populating meetings or teams with participants whose interests clash can reduce the likelihood that one set of interests will undermine thoughtful decision making.

Counter social biases by depersonalizing debate

Social biases are sometimes interpreted as corporate politics but in fact are deep-rooted human tendencies. Even when nothing is at stake, we tend to conform to the dominant views of the group we belong to (and of its leader). 15 15. The Asch conformity experiments, conducted during the 1950s, are a classic example of this dynamic. In the experiments, individuals gave clearly incorrect answers to simple questions after confederates of the experimenter gave the same incorrect answers aloud. See Solomon E. Asch, “Opinions and social pressure,” Scientific American , 1955, Volume 193, Number 5, pp. 31–35. Many organizations compound these tendencies because of both strong corporate cultures and incentives to conform. An absence of dissent is a strong warning sign. Social biases also are likely to prevail in discussions where everyone in the room knows the views of the ultimate decision maker (and assumes that the leader is unlikely to change her mind).

Countless techniques exist to stimulate debate among executive teams, and many are simple to learn and practice. (For more on promoting debate, see suggestions from Kleiner Perkins’ Randy Komisar and Xerox’s Anne Mulcahy in “ How we do it: Three executives reflect on strategic decision making .”) But tools per se won’t create debate: that is a matter of behavior. Genuine debate requires diversity in the backgrounds and personalities of the decision makers, a climate of trust, and a culture in which discussions are depersonalized.

Most crucially, debate calls for senior leaders who genuinely believe in the collective intelligence of a high-caliber management team. Such executives see themselves serving not only as the ultimate decision makers but also as the orchestrators of disciplined decision processes. They shape management teams with the humility to encourage dissent and the self-confidence and mutual trust to practice vigorous debate without damaging personal relationships. We do not suggest that CEOs should become humble listeners who rely solely on the consensus of their teams—that would substitute one simplistic stereotype for another. But we do believe that behavioral strategy will founder without their leadership and role modeling.

Four steps to adopting behavioral strategy

Our readers will probably recognize some of these ideas and tools as techniques they have used in the past. But techniques by themselves will not improve the quality of decisions. Nothing is easier, after all, than orchestrating a perfunctory debate to justify a decision already made (or thought to be made) by the CEO. Leaders who want to shape the decision-making style of their companies must commit themselves to a new path.

1. Decide which decisions warrant the effort

Some executives fear that applying the principles we describe here could be divisive, counterproductive, or simply too time consuming (for more on the dangers of decision paralysis, see the commentary by WPP’s Sir Martin Sorrell in “ How we do it: Three executives reflect on strategic decision making ”). We share this concern and do not suggest applying these principles to all decisions. Here again, the judicial analogy is instructive. Just as higher standards of process apply in a capital case than in a proceeding before a small-claims court, companies can and should pay special attention to two types of decisions.

The first set consists of rare, one-of-a-kind strategic decisions. Major mergers and acquisitions, “bet the company” investments, and crucial technological choices fall in this category. In most companies, these decisions are made by a small subgroup of the executive team, using an ad hoc, informal, and often iterative process. The second set includes repetitive but high-stakes decisions that shape a company’s strategy over time. In most companies, there are generally no more than one or two such crucial processes, such as R&D allocations in a pharmaceutical company, investment decisions in a private-equity firm, or capital expenditure decisions in a utility. Formal processes—often affected by biases—are typically in place to make these decisions.

2. Identify the biases most likely to affect critical decisions

Open discussion of the biases that may be undermining decision making is invaluable. It can be stimulated both by conducting postmortems of past decisions and by observing current decision processes. Are we at risk, in this meeting, of being too action oriented? Do I see someone who thinks he recognizes a pattern but whose choice of analogies seems misleading to me? Are we seeing biases combine to create dysfunctional patterns that, when repeated in an organization, can become cultural traits? For example, is the combination of social and status quo biases creating a culture of consensus-based inertia? This discussion will help surface the biases to which the decision process under review is particularly prone.

3. Select practices and tools to counter the most relevant biases

Companies should select mechanisms that are appropriate to the type of decision at hand, to their culture, and to the decision-making styles of their leaders. For instance, one company we know counters social biases by organizing, as part of its annual planning cycle, a systematic challenge by outsiders to its business units’ plans. Another fights pattern-recognition biases by asking managers who present a recommendation to share the raw data supporting it, so other executives in this analytically minded company can try to discern alternative patterns.

If, as you read these lines, you have already thought of three reasons these techniques won’t work in your own company’s culture, you are probably right. The question is which ones will. Adopting behavioral strategy means not only embracing the broad principles set forth above but also selecting and tailoring specific debiasing practices to turn the principles into action.

4. Embed practices in formal processes

By embedding these practices in formal corporate operating procedures (such as capital-investment approval processes or R&D reviews), executives can ensure that such techniques are used with some regularity and not just when the ultimate decision maker feels unusually uncertain about which call to make. One reason it’s important to embed these practices in recurring procedures is that everything we know about the tendency toward overconfidence suggests that it is unwise to rely on one’s instincts to decide when to rely on one’s instincts! Another is that good decision making requires practice as a management team: without regular opportunities, the team will agree in principle on the techniques it should use but lack the experience (and the mutual trust) to use them effectively.

The behavioral-strategy journey requires effort and the commitment of senior leadership, but the payoff—better decisions, not to mention more engaged managers—makes it one of the most valuable strategic investments organizations can make.

Dan Lovallo is a professor at the University of Sydney, a senior research fellow at the Institute for Business Innovation at the University of California, Berkeley, and an adviser to McKinsey; Olivier Sibony is a director in McKinsey’s Brussels office.

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Business Strategy Examples In 2024: Examples, Case Studies, And Tools

A business strategy is a deliberate plan that helps a business to achieve a long-term vision and mission by drafting a business model to execute that business strategy. A business strategy, in most cases, doesn’t follow a linear path, and execution will help shape it along the way.

Table of Contents

What is a business strategy?

At this stage, it is important to clarify a few critical aspects.

As an HBR working paper entitled “From Strategy to Business Models and to Tactics” pointed out:

Put succinctly, business model refers to the logic of the firm, the way it operates and how it creates value for its stakeholders. Strategy refers to the choice of business model through which the firm will compete in the marketplace. Tactics refers to the residual choices open to a firm by virtue of the business model that it employs.

Personally, I have a controversial relationship with the concept of “strategy.” I feel it’s too easy to make it foggy and empty of practical meaning.

Yet strategy and vision matter in business.

A strategy isn’t just a calculated path, but often a philosophical choice about how the world works.

Usually, it takes years and, at times, also decades for a strategy to become viable. And once it does become viable, it seems obvious only in hindsight.

In this guide, we see what that means.  

In the real world, the difficult part is understanding the problem

In the real world, a lot of time and resources are spent on defining the problem.

Classic case studies at business school assume in most scenarios that the problem is known and the solution needs to be found.

In the real world, the problem is unknown, the situation is highly ambiguous, and the most difficult part is making the decision that might solve that same problem you’re trying to figure out. 

How do you execute a strategy in that context? Business modeling can help!

Is a business strategy the same thing as a business model?

As the business world started to change dramatically, again, by the early 2000s, also the concept of strategy changed with it. 

In the previous era, the strategy was primarily made of locking in the supply chain to guarantee a strong distribution toward the marketplace. 

And yet, the web enabled new companies to form with a bottom-up approach.

In short, product development cycles shortened, and frameworks like lean , agile , and continuous innovation became integrated into a world where software took over. 

Where most of the processes before the digital age, were physical in nature. As the web took off, most of the processes became digital.

In short, the software would become the core enhancer of hardware. 

We’ve seen how in cases like Apple’s iPhone , it wasn’t just the hardware that made the difference.

But it was the development ecosystem and the applications that enhanced the capabilities of the device. 

Thus, from a product standpoint, hardware has been enhanced more and more with the software side.

At the same time, the way companies developed products in the first place changed. 

Software and digits-based companies could gather feedback early on, thus enabling the customers’ feedback as a key element of the whole product development cycle. 

Therefore, wherein the previous era, companies spent billions of budgets to release markets, and products, with little customer feedback.

In the digital era, customer feedback became built into the product development loop. 

That led to frameworks with faster and faster product releases, which also changed the way we do marketing . 

In a classic MVP approach, the loop (build, measure, learn) has to be very quick, and it has to lead to the so-called product/market fit .

As the web made the ability to gather customers’ feedback early on, and as the whole process becomes less and less expensive, also lean approaches evolved, to gain feedback from customers as early as possible. 

From build > demo > sell, to demo > sell > build , lean approaches got leaner. 

And the era of customer-centrism and customer obsession developed:

This whole change flipped the strategy world upside down.

And from elaborate business plans , we moved to business modeling , as an experimental tool, that enabled entrepreneurs to gather feedback continuously.

In a customer-centered business world, business models have become effective thinking tools, to represent a business and a business strategy on a single page, which helped the whole execution process. 

The key building blocks of a classic business model approach, like a business model canvas or lean startup canvas  move around the concept of value proposition , that glue them together. 

And from the supply chain , we moved to customer value chains .

Where most digital business models  learned to gather customers’ feedback in multiple ways. 

The business strategy formed in the digital era, therefore, developed its own customer-centered view of the world, and the business theory world followed.

Academics, following practitioners, moved away from traditional models (like Porter’s Five Forces ) to more customer-centered approaches ( business model canvas , lean canvas).  

The mindset shift flipped from distribution and optimization on the supply side.

To optimize on the demand side, or how to build products that people want, in the first place. This is the new mantra.

No more grandiose business plans, just substantial testing, iteration, and experimentation. 

In this new context, we can understand the strategy developed by several players and how business modeling has become the most important strategy tool. 

And the interesting part is, whether you want to scale to become a tech giant, or you just want to build a small, viable business, it all starts from the same place!

Is business strategy a science?

Business strategy is more of an art than a science.

In short, a business strategy starts with a series of assumptions about how the business world looks in a certain period of time and for a certain target of people.

Whether those assumptions will turn out to be successful will highly depend on several factors.

For instance, back in the late 1990s when the web took over, new startups came up with the idea of revolutionizing many services.

While those ideas seemed to make sense, they turned out to be completely off, and many of those startups failed in what would be recognized as a dot-com bubble.

While in hindsight certain aspects of that bubble came up (like frauds, or schemes).

In general, some of the ideas for which startups got financed seemed to be visionary and turned out to work a decade later (see DoorDash , or Instacart , in relation to Webvan’s bankruptcy). 

For instance, some startups tried to bring on-demand streaming to the web (which today we call Netflix ). Those ideas proved to be too early.

They made sense but from the commercial standpoint, they didn’t.

Thus, if we were to use the scientific method, once those assumptions would have proved wrong in the real world, we would have discarded them.

However, those assumptions proved to be wrong, in that time period, given the current circumstances.

While we can use the scientific inquiry process in business strategy, it’s hard to say that it is a scientific discipline.

So what’s the use of business strategy?

In my opinion, business strategy is useful for three main reasons:

• Focus : chose one path over another.
• Vision : have a long-term strategic goal.
• Commercial viability : create a self-sustainable business.

As a practitioner, someone who tries to build successful businesses, I don’t need to be “scientific.”

I need to make sure not to be completely off track. For that matter, I aim at creating businesses.

Thus, I need to understand where to focus my attention in a relatively long period of time (3-5 years at least) and make sure that those ideas I pursue are able to generate profits, which – in my opinion – might be a valid indicator that those ideas are correct for the time being.

If those conditions are met, I’ll call it a “successful business.”

Those ideas will become a business model , that executes a business strategy.

This doesn’t mean those ideas, turned into a business model , pushed into the world will always be successful (profitable).

As the marketplace evolves I will need to adjust, and tweak a business model to fit with the new evolving scenarios, and I’ll need to be able to “bet” on new possible business models .

Survivorship bias

Survivorship bias is a phenomenon where what’s not visible (because extinct) isn’t taken into account when analyzing the past.

In short, we analyze the past based on what’s visible.

This error happens in any field, and in business, we might get fooled by that as well.

In short, when we analyze the past we do that in hindsight.

That makes us cherry-pick the things that survived and assume that those carry the successful characteristics we’re looking for.

For instance, for each Amazon or Google that survived there were hundreds if not thousands of companies that failed, with the same kind of “successful features” as Amazon or Google.  

So why do we analyze successful companies in the first place? In my opinion, there are several reasons: 

• Those successful companies have turned into Super Gatekeepers to billions of people : as I showed in the gatekeeping hypothesis , and in the surfer’s model , a go-to-market strategy for startups will need to be able to leverage existing digital pipelines to reach key customers.

• Modeling and experimentation : another key point is about modeling what’s working for other businesses and borrowing parts of those models, to see what works for our business. By borrowing parts you can build your own business model, yet that requires a lot of testing. 

• Skin in the game testing : therefore business models become key tools for experimentation, where we can use real customers’ feedback (not a survey, or opinions but actions) and test our hypotheses and assumptions. When we’re able to sell our products, when people keep getting back to our platform, or service, there is no best way to test our assumptions that measure those actions. 

Lindy effect and aging in reverse

Nicholas Nassim Taleb , in his book Antifragile , popularized a concept called Lindy Effect .

In very simple terms the Lindy Effect states that in technology (like any other field where the object of discussion is  non-perishable)  things age in reverse.

Thus, life expectancy, rather than diminishing with age, has a longer life expectancy.

Therefore, a technology that has lived for two thousand years, has a life expectancy of another thousand years.

That is a probabilistic rule of thumb that works on averages.

Thus, if a technology (say the Internet) has stayed with us for twenty years, it doesn’t mean we can expect only to live for another twenty years at least.

But as the Internet has proved successful already, the Lindy Effect might not apply.

In short, as we have additional information about a phenomenon the Lindy Effect might lose relevance.

For instance, if I know a person is twenty, yet sick of a terminal disease, I can’t expect to use normal life expectancy tables.

So I’ll have to apply that information to understand the future.

Strategies take years to fully roll out

It was 2006, when Tesla, with his co-founder   Martin Eberhard , launched a sports car that broke down the trade-off between high performance and fuel efficiency.

Tesla, which for a few years had been building up an electric sports car ready to be marketed, finally pulled it off.

As Elon Musk would   explain   Back in 2012:  

In 2006 our plan was to build an electric sports car followed by an affordable electric sedan, and reduce our dependence on oil…delivering Model S is a key part of that plan and represents Tesla’s transition to a mass-production automaker and the most compelling car company of the 21st century.

The beauty of a strategy that turns into a successful company, is that it might take years to roll out and seem obvious only in hindsight. 

This connects to what I like to call the transitional business model.

Or the idea, that many companies, before getting into a fully rolled out business strategy, transition through a period of low scalability and low market size, which will help them gain initial traction. 

As a transitional business model proves viable, it helps the company shape its long-term vision, while its built-in strategy is different from the long-term strategy.

The transitional business model will guarantee survival. It will help further refine the long-term strategy and it will also work as a reality check. 

As the transitional business model proves viable, the company moves to its long-term strategy execution. 

As the business strategy gets rolled out, over the years, it becomes evident and obvious, and yet none managed to pull it off.

When Netflix moved from DVD rental to streaming. DVD rental was the transitional business model that helped Netflix stay in business in the first place.

And yet, when Netflix moved from DVD to streaming it had to apparently change its strategy.

When, in reality, it was rolling out its long-term strategy, shaped by the transitional business model. 

Caveat: Frameworks work until suddenly they don’t

When you stumbled upon a “business formula,” you can’t stop there.

That business formula, if you’re lucky, will allow you to succeed in the long term. Yet as more and more people will find that out, that will lose relevance.

And the matter is, the reality is a villain. Things work for years until they suddenly don’t work anymore.

We’ll see some frameworks, but the real deal is not a framework but the inquiry process that makes us discover those frameworks.

In short, the value is in the repeatable process of discovery and not in the discovery itself. A discovery, once spread, loses value.

Master a business strategy process

There isn’t a size-fits-all business playbook that you can apply to all the scenarios.

Some of the business case studies we’ll see throughout this article will show companies that have dominated the tech space in the last decade and more.

While the playbook executed by those companies worked for the time being.

That doesn’t mean you should play according to their playbook. If at all you’ll need to figure out your own.

Thus, what matters is the process behind finding your business playbook and my hope is that this guide will inspire you and give you some good ideas on how to develop your own business strategy process!

Business strategy case studies

We’ll look now at a few case studies of companies that, at the time of this writing, are playing an important role in the business world.

• Alibaba Business Strategy.
• Amazon Business Strategy.
• Apple Business Strategy.
• Airbnb Business Strategy.
• Baidu Business Strategy.
• Booking Business Strategy.
• DuckDuckGo Business Strategy.
• Google (Alphabet) Business Strategy.

What is a business model’s essence?

Keeping in mind the distinction between business strategy and business models is critical.

The other element used in this guide is a business model essence.

Shortly, I’ve been looking for a way to summarize the key elements of any business in a couple of lines of text:

Therefore, for the sake of this discussion, you’ll find each company’s business strategy, a business model essence that will help us navigate through the noisy business world.

From there, we’ll see the business strategy of a company.

Alibaba Business Strategy

Business Model Essence : Online Stores Leveraging On An E-Commerce/Marketplace Distribution And Monetization Strategy  

As pointed out in Alibaba’s annual report for 2017:

We derive revenue from our four business segments: core commerce, cloud computing, digital media and entertainment, and innovation initiatives and others. We derive most of our revenue from our core commerce segment, which accounted for 85% of our total revenue in fiscal year 2017, while cloud computing, digital media and entertainment, and innovation initiatives and others contributed 4%, 9% and 2%, respectively. We derive a substantial majority of our core commerce revenue from online marketing services. 

Alibaba, like Amazon , became an “everything store” in China.

It leveraged its success to build also other media platforms ( Youku Todou and UCWeb). The e-commerce, marketplace business model has become quite common since the dawn of the web.

From that business model tech giants like Amazon , eBay and Alibaba have raised.

Alibaba’s vision, mission, and core principles

Alibaba’s Business Strategy starts from its core values defined in its annual report:

• Customer First : “The interests of our community of consumers, merchants, and enterprises must be our first”
• Teamwork: “ We believe teamwork enables ordinary people to achieve extraordinary things.”
• Embrace Change   I”n this fast-changing world, we must be flexible, innovative, and ready to adapt to new business conditions in order to maintain sustainability and vitality in our business.”
• Integrity “We expect our people to uphold the highest standards of honesty and to deliver on their commitments.”
• Passion “We expect our people to approach everything with fire in their belly and never give up on doing what they believe is right.”
• Commitment  “Employees who demonstrate perseverance and excellence are richly rewarded. Nothing should be taken for granted as we encourage our people to “work happily and live seriously.”

Alibaba’s mission is “ to make it easy to do business anywhere, ” and its vision is “to build the future infrastructure of commerce… a company that would last at least 102 years.”

For that vision to be executed it has three major stakeholders: users, consumers, and merchants.

The focus on the “at least 102 years” might seem fluffy words, yet those are important as this kind of goal helps you keep a long-term vision while executing short-term plans.

It isn’t unusual for founders to set such visions, as they help keep the company on track in the long run.

And this is where a business strategy starts.

All the business models designed by Alibaba will follow its vision, mission, and values they aim to create in the long run.

Read : Alibaba Business Model

Alibaba ecosystem and value proposition

These elements gave rise to an ecosystem made of “consumers, merchants, brands, retailers, other businesses, third-party service providers and strategic alliance partners.”

As Alibaba points out in its annual report “our ecosystem has strong self-reinforcing network effects benefitting its various participants, who are in turn invested in our ecosystem’s growth and success.”

Network effects are a critical ingredient for marketplaces’ success.

To give you an idea, the more buyers join the platform, the more Alibaba’s recommendation engine will be able to suggest relevant items to buy for other customers, and at the same time the more merchants will join in, given the larger and larger business opportunities.

Keeping these network effects going is a vital element of long-term success but also among the greatest challenge of any marketplace that wants to be relevant.

Even though Alibaba’s essence is in online commerce, the company has several business model s running and a business strategy that at its core is evolving quickly.

Thus, the core commerce has made it possible for Alibaba to build a whole new set of “companies within a company.”

From digital entertainment and media, logistics services, payment, financial services, and cloud services with Alibaba Cloud.

Thus, from a successful existing online business model , Alibaba has expanded in many other areas.

And its future business strategy focuses on developing, nurturing, and growing its ecosystem.

More precisely, its strategic long-term goal is to “serve two billion consumers around the world and support ten million businesses to operate profitably on its platforms”

To achieve that Alibaba is focusing on three key activities:

• Globalization.
• Rural expansion.
• And big data and cloud computing.

For its core commerce activities, Alibaba has designed a value proposition that moves around a few pillars:

• Broad selection: over 1.5 billion listings as of March 31, 2018.
• Convenience:  seamless experience anytime, anywhere from online and offline.
• Engaging, personalized experience: personalized shopping recommendations and opportunities for social engagement.
• Value for money: competitive prices offered via a marketplace business model.
• Merchant quality: review and rating system to keep merchants’ quality high.
• Authentic products: merchant quality ratings, clear refund, and return policies, and the Alipay escrow system.

From that value proposition , Alibaba has been able to grow its customer base and offer wider and broader products, until it expanded in the service and cloud business.

Amazon Business Strategy

Business Model Essence : E-Commerce/Marketplace Distribution And Monetization Model Leveraging On Proprietary Infrastructure To Offer Third-Party Services

Starting in 1994 as a bookstore, Amazon soon expanded and became the everything store.

While the company’s core business model is based on its online store.

Amazon launched its physical stores, which generated already over five billion dollars in revenues in 2017.

Amazon Prime (a subscription service) also plays a crucial role in Amazon’s overall business model , as it makes customers spend more and be more loyal to the platform. 

Besides, the company also has its cloud infrastructure called AWS, which is a world leader and a business with high margins. Amazon also has an advertising business worth a few billion dollars.

Thus, the Amazon business model mix looks like many companies in one. Amazon measures its success via a customer experience obsession, lowering prices, stable tech infrastructure, and free cash flow generation.

Therefore, even though in the minds of most people Amazon is the “everything store.”

In reality, its revenue generation shows us that it has become a way more complex organization, that also has a good chunk of advertising revenue and third-party services.

For instance, Amazon is also a key player with its AWS in the cloud space.

And is well a key player in the digital advertising space, together with Google and Facebook :

Amazon has been widely investing in its technological infrastructure since the 2000s, which eventually turned into a key component of its business model .

Read : Amazon Business Model

Amazon’s vision, mission, and core values

Jeff Bezos is obsessed with being in “day one,” which as he puts it , “ day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always  Day 1. “

It all starts from there, and to achieve that Jeff Bezos has highlighted a few core values that makeup Amazon ‘s culture and vision :

• Customer obsession.
• Resist proxies.
• Embrace external trends.
• High-velocity decision-making.

As pointed out by Amazon , “w hen Amazon.com launched in 1995, it was with the mission “ to be Earth’s most customer-centric company, where customers can find and discover anything they might want to buy online, and endeavors to offer its customers the lowest possible prices. ” 

This goal continues today, but Amazon ’s customers are worldwide now and have grown to include millions of Consumers, Sellers, Content Creators, and Developers & Enterprises.

Each of these groups has different needs, and we always work to meet those needs, innovating new solutions to make things easier, faster, better, and more cost-effective.”

In this case, Amazon ‘s mission also sounds like a vision statement.

Whatever you want to call it, this input is what makes a company look for long-term goals that keep them on track.

Of course, that doesn’t mean a well-crafted vision and mission statement is all that matters for business success.

Yet, it is what keeps you going when things seem to go awry.

Amazon moved from an online book store to the A-to-Z store it kept its mission almost intact while scaling up.

Start from a proof of concept, then scale up

It is interesting to notice how businesses evolve based on their commercial ability to scale up.

When Amazon started up as a bookstore, it made sense for several reasons, that spanned from logistics to pricing modes and industry specifics.

Yet, when Amazon finally proved that the whole web thing could be commercially viable, it didn’t wait, it grew rapidly.

From music to anything else it didn’t happen overnight, but it did happen quickly.

Thus, this is how Amazon’s mission shifted from “any book in the world” to “anything from A-Z.”

This isn’t a size-fits-all strategy. Amazon chose rapid growth, similar to a blitzscaling process as aggressive growth was a way to preserve itself.

Hadn’t Amazon grown so quickly, it could have been killed.

The opposite approach to this kind of strategy is a bootstrapped business, which is profitable right away and self-sustainable.

Decentralized and distributed value creation: the era of platforms and ecosystems

Before we move forward, I want to highlight a few key elements to have a deeper understanding of both Amazon and Alibaba’s business models and their strategies.

Before digitalization would show its use and commercial viability, most of the value creation processes were internalized.

That meant companies had to employ massive resources to generate value along that chain.

That changed when digitalization allowed the value creation process to be distributed, and we moved from centralized to grassroots content creation.

This is even clearer in the case of platforms, and marketplaces like Amazon and Alibaba.

For instance, where in the past the review process and quality insurance would be done centrally by making sure that the supply complied with the company’s quality guidelines.

Introducing distributed review systems, where the end-users checked against the quality compliance, allowed companies like Alibaba and Amazon to generate network effects, where the more users enriched the platforms with those reviews the more the platform could become valuable.

For that matter though, the main platform’s role will be to fight spam and attempt to trick the system.

Other than that (fighting spam is a challenging task) all the rest is managed at the decentralized level, and the value creation happens when more and more users review products and services on those platforms.

We’re referring here to the review system, but it applies almost to any aspect of a platform.

Amazon for years allowed third-party to feature their stores on Amazon ‘s platform, while they kept the inventory.

This meant an outsourced and distributed inventory system, spread across the supply side.

Therefore, the supply side not only made the platform more valuable by creating compelling offerings.

But it also made it more valuable from the operational standpoint, by allowing a better inventory system, which could be turned quickly.

Therefore, the critical aspect to understand in the digital era is decentralized value creation, which makes the value creation process less expensive for an organization, more valuable to its end users, and more scalable as it benefits from network effects.

How do decentralize value creation?

Many platform-like business models have leveraged a few aspects:

• User-generated content (Quora, Facebook , Instagram).
• Distributed inventory systems ( Amazon , Alibaba).
• Peer-to-peer networks ( Airbnb , Uber).

This implies a paradigm shift.

When you start thinking in terms of platforms, no longer you’ll need a plethora of people taking care of each aspect of it.

Rather you’ll need to understand how the value creation can be outsourced to a community of people and make sure the platform is on top of its game in a few aspects.

For instance, Amazon and Alibaba have to make sure their review system isn’t gamed. Airbnb has to make sure to be able to guarantee safety in the interactions from host to guests and vice-versa.

Quora has to make sure to keep its question machine to keep generating relevant questions for users to answer (the supply-side).

If you grasp this element of a platform, you’re on a good track to understanding how to build a successful platform or marketplace.

Apple Business Strategy

Business Model Label : Product-Based Company Leveraging On Locked-In Ecosystems With A Reversed Razor And Blade Business Strategy

Apple sells its products and resells third-party products in most of its major markets directly to consumers and small and mid-sized businesses through its retail and online stores and its direct sales force.

The Company also employs a variety of indirect distribution channels , such as third-party cellular network carriers, wholesalers, retailers, and value-added resellers.

During 2017, the Company’s net sales through its direct and indirect distribution channels accounted for 28% and 72%, respectively, of total net sales.

Many people look at the iPhone, or the previous products Apple has launched successfully in the last decade and assume that their success is due to those products.

In reality, Apple has followed throughout the years a strategy that focused on five key elements:

• Strong branding.
• Beautifully crafted products.
• Technological innovation.
• Strong distribution.
• Locked-in ecosystems.

In short, Apple can sell an iPhone at a premium price because it employs a reversed razor and blade strategy.

This strategy implies free access to Apple’s Ecosystem (ex. iTunes, and Apple Store).

That makes the whole experience through Apple’s devices extremely valuable.

Thanks to that experience, the perception of high-end (luxury-like) products, together with a reliable distribution, justifies Apple’s premium prices.

Apple’s managed to build a business platform on top of the iPhone, thus creating a strong competitive moat, which lasts to these days:

Therefore, Apple’s future success can’t be measured with the same lenses as the last decade.

The real question is: what product will Apple  be able to launch successfully?

And keep in mind it’s not just about the product. Apple’s formula summarized above can be replicated over and over again.

But it isn’t a simple formula. And as locked-in ecosystems, in which Apple controls as much as possible, the experience of its users has proved quite successful in the last decade.

That might not be so in the next, given the rise of more decentralized infrastructure.

For that matter, Amazon might be well moving from a reversed razor and blade model:

To a service-based model:

This isn’t surprising, as a service business has a few compelling advantages:

• High margins.
• A relatively stable revenue stream.
• Scalability.

As Apple has relied on home runs with its products, from the new Mac to the iPod, iPhone, and iPhones, that kind of success isn’t easy to replicate, and it makes the company relies on a continuous stream of fresh sales to keep the business growing.

A service business would balance things out.

It is important to remark this isn’t something new to Apple :

When Apple introduced the iPhone, it isn’t like it was an overnight success. It was successful, but it had to create a whole ecosystem to make the iPhone a continuous source of growth for the company!

When it comes to business strategy, as pointed out in Apple’s annual reports:

The Company is committed to bringing the best user experience to its customers through its innovative hardware, software and services. The Company’s business strategy leverages its unique ability to design and develop its own operating systems, hardware, application software and services to provide its customers products and solutions with innovative design, superior ease-of-use and seamless integration.

Understanding this part is critical. As I explained above, at the time of this writing many think of Apple as the “iPhone company.”

Yet Apple is way more than that, and its business strategy is a mixture of creating ecosystems by leveraging on these pillars:

• Operating systems.
• Applications software.
• Innovative design.
• Ease-of-use.
• Seamless Integration.

Those elements together make Apple ‘s products successful. As Apple further explained:

As part of its strategy, the Company continues to expand its platform for the discovery and delivery of digital content and applications through its Digital Content and Services, which allows customers to discover and download or stream digital content, iOS, Mac, Apple Watch and Apple TV applications, and books through either a Mac or Windows personal computer or through iPhone, iPad and iPod touch® devices (“iOS devices”), Apple TV, Apple Watch and HomePod.

Once again, it isn’t anymore about creating a product, but about generating self-serve ecosystems.

How do you support those ecosystems?

It depends on what’s your target. A media company will primarily need an ecosystem made of content creators (take Quora or Facebook or YouTube ).

In many cases, a digital media company over time has to be able to nurture several communities to create a thriving ecosystem.

For instance, large tech companies or startups, often rely on several communities:

• Programmers and developers ( Google , Apple ).
• Content creators and publishers ( Google , Quora, YouTube ).
• Artists and creative talents ( Apple , YouTube ).

In Apple ‘s case though, the first ecosystem is the community of developers building third-party software products that complement the company’s offering:

The Company also supports a community for the development of third-party software and hardware products and digital content that complement the Company’s offerings.

When you combine that with a high-touch strategy (where skilled and knowledgeable salespeople interact with customers) you create a flywheel, where customers are retained for longer, the brand grows as a result of this high-touch activity which creates a better post-sale experience and triggers word of mouth and referral from existing customers:

The Company believes a high-quality buying experience with knowledgeable salespersons who can convey the value of the Company’s products and services greatly enhances its ability to attract and retain customers.Therefore, the Company’s strategy also includes building and expanding its own retail and online stores and its third-party distribution network to effectively reach more customers and provide them with a high-quality sales and post-sales support experience.The Company believes ongoing investment in research and development (“R&D”), marketing and advertising is critical to the development and sale of innovative products, services and technologies.

Read : Apple Business Model

Airbnb Business Strategy

Business Model Essence : Peer-To-Peer House-Sharing Network With Fee-Based Monetization Strategy

As a peer-to-peer network, Airbnb allows individuals to rent from private owners for a fee.

Airbnb charges guests a service fee between 5% and 15% of the reservation subtotal; While the commission from hosts is generally 3%.

Airbnb also charges hosts who offer experiences a 20% service fee on the total price.

The digitalization that happened in the last two decades has facilitated the creation of peer-to-peer platforms in which business models disrupted the hospitality model created in the previous century by hotel chains like Marriott, Holiday Inn, and Hilton.

Airbnb is quickly branching out toward offering more experiences. We can call Airbnb the “marketplace of experiences.”

In short, just like Amazon started from books, Airbnb has started from house-sharing.

But that is the starting point, which gives the innovative company enough traction to validate its whole business model and expand to other areas.

The principal aim of Airbnb is to control the whole experience for its users. This means creating an end-to-end travel experience that embraces the entire process .

Thus, it’s not surprising that we’ll see Airbnb expanding its marketplace to more and more areas. This is also shown by the fact that Airbnb might soon offer bundled travel packages .

Just as we’ve seen in the case of Alibaba and Amazon , Airbnb follows a marketplace logic, where it needs to make the interactions between its key users (hosts and guests) as smooth as possible, with an emphasis on safety.

As a platform, Airbnb initially used a strategy of improving the quality of its supply by employing freelance photographers that could take pictures of host homes.

This, in turn, made those homes more interesting for guests, as they could appreciate those homes more.

As many people in real estate might know, the quality of the pictures is critical.

Although this might sound trivial, this is what improved the Airbnb supply side.

Indeed with better and professionally taken images, Airbnb improved its reach via search engines (yes, search engines are thirsty for fresh and original content, images comprised).

And it enhanced the experience of its potential customers.

Now Airbnb is converting its business model to digital experiences. In addition to changing the whole strategy.

Whereas Airbnb focused in the past on covering major cities across the world.

Changing travel habits made Airbnb focus on digital experiences and local, extra-metropolitan areas throughout the pandemic.

While, post-pandemic, as people travel for longer stays, the whole platform has been structured around these. 

Read : Airbnb Business Model

Baidu Business Strategy

Business Model Essence :  Online Marketing Free Services Advertising-Supported Revenue Model

Baidu makes money primarily via online marketing services (advertising). In fact, in 2017, Baidu made about $11.24 in online marketing services and a remaining almost $1.8 billion through other sources. According to Statista,

Baidu has an overall search market share of 73.8% of the Chinese market. Other sources of revenues comprise membership services of iQIYI (an innovative market-leading online entertainment service provider in China) and financial services.

At first sight, Baidu might seem the mirror image of Google , but in China.

However, this is a superficial view. While Baidu has followed in China a similar path to Google , it did take advantage of the fact that Google wasn’t available there, to build its dominant position.

Baidu also has a more efficient cost structure than Google. It had also introduced innovations in its search products (like voice search devices for kids) at a time when Google wasn’t there yet.

Read : Baidu Business Model

Baidu mission: two-pillar business strategy and value propositions acting as a glue for its key users/customers

In the past years, Baidu has followed an expansion business strategy focused on acquiring assets and companies that complemented its core business model .

As the leading Chinese search provider, in 2017, Baidu updated its mission to “ Baidu aims to make a complex world simpler through technology.”

This mission is achieved via a two-pillar strategy:

• Strengthening the mobile foundation (similar to Google’s mobile-first).
• And leading in artificial intelligence.

Baidu’s key partners comprise users, customers, Baidu union members, and content providers.

For each of those critical segments, Baidu has drafted a fundamental value proposition .

Thus, to generate a value chain that works for these stakeholders, Baidu has to balance it with a diversified value proposition :

• Users:  enjoying Baidu search experience want a search engine that gives them relevant results.
• Customers: with 775,000 active online marketing customers in 2017, consisting of SMEs, large domestic businesses, and multinational companies, distributed across retail and e-commerce, network service, medical and healthcare, franchise investment, financial services, education, online games, transportation, construction and decoration, and business services. Those businesses look for a trackable, and sustainable ROI for their paid advertising campaigns. By bidding on keywords, they can target specific audiences.
• Baidu Union Member: share revenues with Baidy by displaying banner ads on their sites in relevant spaces filled by the  Baidu search algorithm (think of it as Google’s AdSense Network ). Those publishers and sites can generate additional revenues and monetize their content without relying on complex infrastructure, that instead is employed by Baidu.
• Content Providers:  video copyright holders, app owners who list their apps on the Baidu app store, users who contribute their valuable and copyrighted content to Baidu products, and publishers. Those users get visibility or money in exchange for this content. Baidu has to make sure to allow those content providers to get in exchange for their work and creativity visibility and revenues.

Understanding how the value proposition for each player comes together is critical to understanding the business decisions a company like Baidu makes over time.

For instance, as Baidu (like Google ) moves more and more toward AI, the need to balance the value proposition for Baidu Union Members might fickle.

Booking Business Strategy

Business Model Essence :  House-Sharing Platform Leveraging On A Two-Sided Marketplace With A Commission-Based Revenue Model

Booking Holdings is the company that controls six main brands that comprise Booking.com, priceline.com, KAYAK, agoda.com, Rentalcars.com, and OpenTable. 

Over 76% of the company’s revenues in 2017 came primarily via travel reservations commissions and travel insurance fees.

Almost 17% came from merchant fees, and the remaining revenues came from advertising earned via KAYAK.

As a distribution strategy, the company spent over $4.5 billion on performance-based and brand advertising.

Read : Booking Business Model

Booking mission, value proposition, and key players

Booking’s mission is to “help people experience the world.” Booking does that via a few primary brands:

• Booking.com.
• priceline.com.
• Rentalcars.com.

The mission of helping people experience the world is executed via three primary value propositions delivered to consumers, travelers, and business partners:

• Consumers are provided what Booking calls “the best choices and prices at any time, in any place, on any device.”
• People and travelers can easily find, book, and experience their travel desires.
• Business partners (like Hotels featured on Booking.com) are provided with platforms, tools, and insights in exchange.

Boomedium-term term strategy is focused on:

• Leveraging technology to provide the best experience.
• Growing partnerships with travel service providers and restaurants.
• Investing in profitable and sustainable growth.

DuckDuckGo Business Strategy

Business Model Essence : Privacy-based Search Engine Built On Google’s Weakness With An affiliate-based Revenue Model

DuckDuckGo makes money in two simple ways: Advertising and Affiliate Marketing.

Advertising is shown based on the keywords typed into the search box. Affiliate revenues come from Amazon and eBay affiliate programs.

When users buy after getting on those sites through DuckDuckGo the company collects a small commission.

While this model might not sound that exciting. DuckDuckGo managed to grow quickly by leveraging Google’s primary weakness: users’ privacy. Where Google’s primary asset is made of users’ data. DuckDuckGo throws that data away on the fly:

It is important to remark that DuckDuckGo is still figuring out a business model that can make it sustainable in the long term.

Indeed, the company got a venture round of $10 million back in August 2018.

DuckDuckGo will be tweaking its business model in the coming years, to reach a “ business model /market fit.”

Read : DuckDuckGo Business Model

Read : DuckDuckGo Story

Google (Alphabet) Business Strategy

Business Model Essence :  Free Search Engine Distributed Across Hardware, Browsers, And Members’ Websites With An Hidden Revenue Generation Model

As of 2017, over ninety billion dollars, which consisted of 86% of Google ’s revenues came from advertising networks.

The remaining fraction (about 13%) came from Apps, Google Cloud, and Hardware. While a bit more than 1% came from bets like Access, Calico, CapitalG, GV, Nest, Verily, Waymo, and X.

Google business model is changing over the years.

Even though advertising is still its cash cow, Google has been diversifying its revenues in other areas. 

While in 2015 90% of Google’s revenues came from advertising, in 2017, advertising revenues represented 86%.

Other revenues grew from about 10% in 2015 to almost 13% in 2017.

Why did Google get there? And where is Google going next? To understand that you need to understand the “moonshot thinking.”

Read : Google Business Model

Read : Google Cost Structure

Read : Baidu vs. Google

Understanding Google’s moonshot thinking and a breakthrough approach to business

As highlighted in the Alphabet annual report for 2018:

Many companies get comfortable doing what they have always done, making only incremental changes. This incrementalism leads to irrelevance over time, especially in technology, where change tends to be revolutionary, not evolutionary. People thought we were crazy when we acquired YouTube and Android and when we launched Chrome, but those efforts have matured into major platforms for digital video and mobile devices and a safer, popular browser. We continue to look toward the future and continue to invest for the long-term. As we said in the original founders’ letter, we will not shy away from high-risk, high-reward projects that we believe in because they are the key to our long-term success.

Understanding the moonshot approach to business is critical to understanding where Google (now Alphabet) got where it is today, and where it’s headed next.

Since the first shareholders’ letter from Google’s founders, Brin and Page they highlighted that “ Google is not a conventional company. We do not intend to become one.”

Google has successfully built ecosystems that today drive

To understand where Google is going next, you need to look at the AI Economy , in which the tech giant is trying to lead the pack.

Whether or not it will be successful will highly depend on its ability to keep creating successful ecosystems, just as Google has done with Google Maps (you might not realize but Google Maps powers up quite a large number of applications) and Android.

At the time of this writing, Google is widely investing in other areas, such as:

• Voice search.
• AI and machine learning applications.
• Self-driving cars.
• And other bets.

If that is not sufficient Google has made several moves in different spaces, to keep its dominance on mobile, while moving toward voice search, like the investment in KaiOS, which business model is interesting as it finally allows an ecosystem to be built on top of cheap mobile devices in developing countries:

That is why Google keeps making “smaller bets in areas that might seem very speculative or even strange when compared to its current businesses.”

Those other bets made “just” $595 million to Google in 2018.

This represented 0.4% of Google ‘s overall revenues , compared to the over $136 billion coming from the other segments.

Google ‘s North Star is its mission of “ organizing the world’s information and making it universally accessible and useful.” 

Read : KaiOS Business Model

Let’s go through a few other tips for a successful business strategy. 

Problem-first approach

The customer-problem quadrant by LEANSTACK’s Ash Maurya is a great starting point to define and understand the problem, that as an entrepreneur you will going to solve. 

Indeed, a successful business is such, based on the market’s rewards for the entrepreneur’s ability to solve a problem.

Keep in mind that defining and understanding problems in the real world is one of the most difficult things (that is why entrepreneurship is so hard).

To properly stumble on the right definition of the problem you’re solving, there might be some fine-tuning going on, which in the business world we like to call product-market fit . 

Business engineering skills

Another key element is not to lose sight of the context you’re operating.

As such, analyzing that properly might require some business engineering skills . 

To simplify your life you can use the FourWeekMBA business analysis framework.

Don’t strategize on a piece of paper

Strategies always work well on a piece of paper.

Yet when execution comes suddenly we can realize all the drawbacks of that.

In very few, rare cases, a designed strategy will work as expected.

However, the reason we plan and strategize isn’t just to make things work as we’d like them to.

But to communicate a vision we have to those people (employees, customers, stakeholders) who will help us get there. 

That is why when we strategize it’s important not to lose sight of the essence of our strategy, which is the long-term vision we have for our business.

The rest is execution, practice, and a lot of experimentation!

The innovation loop

Innovation starts by tweaking, testing, and experimenting also in unexpected ways.

Often though, as a business strategy is documented after the fact, it seems as if it was all part of a plan. 

In most cases, the innovation loop starts by stumbling upon that thing that will have a great impact on your business.

Therefore, as an entrepreneur, you need to keep pushing on those models that worked out.

But also to be on the lookout for new ways of doing things. 

Barbell approach 

In a barbel approach we want to have a clear distinction between two domains: 

• Core business : on the core business side, where you have a consolidated strategy, and a business model that has proved to work, it’s important to be structured. This means having a clear culture, following given processes, and slowly evolving your business model. 
• New bets : as your business model will become outdated over time, and that might happen also very quickly, you need to be on the lookout for new opportunities emerging, also in new, completely unrelated business fields. 

For instance, a tech giant like Google, has a part of its business skewed toward a few bets it placed on industries that are completely unrelated to its core business (search).

Those bets are not contributing at all to its bottom line (only some of those bets are generating revenues but those are extremely marginal compared to the overall turnover of the company). 

However, those might turn out widely successful (or huge failures) in the years to come. 

Thus, with a barbell approach, we want to consolidate what we have. But also be open to what might be coming next!

Business Explorers

Strategic analysis thinking tools.

Strategic analysis is a process to understand the organization’s environment and competitive landscape to formulate informed business decisions , to plan for the organizational structure and long-term direction. Strategic planning is also useful to experiment with business model design and assess the fit with the long-term vision of the business.

Business model canvas

The business model canvas aims to provide a keen understanding of your business model to provide strategic insights about your customers, product/service, and financial structure;

so that you can make better business decisions.

Blitzscaling canvas

In this article, I’ll focus on the Blitzscaling business model canvas. This is a model based on the concept of Blitzscaling.

That is a particular process of massive growth under uncertainty, and that prioritizes speed over efficiency. It focuses on market domination to create a first-scaler advantage in a scenario of uncertainty.

Pretotyping

Pretotyping is a mixture of the words “pretend” and “prototype,” and it is a methodology used to validate business ideas to improve the chances of building a product or service that people want.

The pretotyping methodology comes from Alberto Savoia’s work summarized in the book “The Right It: Why So Many Ideas Fail and How to Make Sure Yours Succeed.”

This framework is a mixture of the words “pretend” and “prototype,” and it helps to answer such questions (about the product or service to build) as: Would I use it? How, how often, and when would I use it?

Would other people buy it? How much would they be willing to pay for it? How, how often, and when would they use it?

Value innovation and blue ocean strategy

A blue ocean is a strategy where the boundaries of existing markets are redefined, and new uncontested markets are created.

At its core, there is value innovation, for which uncontested markets are created, where competition is made irrelevant. And the cost-value trade-off is broken.

Thus, companies following a blue ocean strategy offer much more value at a lower cost for the end customers.

Growth hacking process

Growth hacking is a process of rapid experimentation, coupled with the understanding of the whole funnel, where marketing , product, data analysis, and engineering work together to achieve rapid growth.

The growth hacking process goes through four key stages analyzing, ideating, prioritizing, and testing.

Pirate metrics

Venture capitalist , Dave McClure, coined the acronym AARRR which is a simplified model that enables us to understand what metrics and channels to look at. At each stage of the users’ path toward becoming customers and referrers of a brand.

Engines of growth

In the Lean Startup, Eric Ries defined the engine of growth as “the mechanism that startups use to achieve sustainable growth.”

He described sustainable growth as following a simple rule, “new customers come from the actions of past customers.”

The three engines of growth are the sticky engine, the viral engine, and the paid engine. Each of those can be measured and tracked by a few key metrics, and it helps plan your strategic moves.

The RTVN model is a straightforward framework that can help you design a business model when you’re at the very early stage of figuring out what you need to make it succeed.

Sales cycle

A sales cycle is the process that your company takes to sell your services and products.

In simple words, it’s a series of steps that your sales reps need to go through with prospects that lead up to a closed sale.

Planning ahead of time the steps your sales team needs to take to close a big contract can help you grow the revenues for your business.

Comparable analysis

A comparable company analysis is a process that enables the identification of similar organizations to be used as a comparison to understand the business and financial performance of the target company.

To find comparables, you can look at two key profiles: the business and economic profiles.

From the comparable company analysis, it is possible to understand the competitive landscape of the target organization.

Porter’s five forces

Porter’s Five Forces is a model that helps organizations to gain a better understanding of their industries and competition.

It was published for the first time by Professor Michael Porter in his book “Competitive Strategy” in the 1980s.

The model breaks down industries and markets by analyzing them through five forces which you can use to have a first assessment of the market you’re in.

Porter’s Generic Strategies

Porter’s Value Chain

Porter’s Diamond Model

Bowman’s Strategy Clock

VMOST Analysis

Fishbone Diagram

GE McKinsey Matrix

VRIO Framework

3C Analysis

AIDA stands for attention, interest, desire, and action. This is a model that is used in marketing to describe the potential journey a customer might go through, before purchasing a product or service. The variation of the AIDA model is the CAB model and the AIDCAS model.

PESTEL analysis

The PESTEL analysis is a framework that can help marketers assess whether macro-economic factors are affecting an organization.

This is a critical step that helps organizations identify potential threats and weaknesses. That can be used in other frameworks such as SWOT or to gain a broader and better understanding of the overall marketing environment.

Technology adoption curve

The technology adoption curve is a model that goes through five stages. Each of those stages (innovators, early adopters, early majority, late majority, and laggard) has a specific psychographic that makes that group of people ready to adopt a tech product.

This simple concept can help you define the right target for your business strategy.

Business model essence

A Business Model Essence, according to FourWeekMBA, is a way to find the critical characteristics of any business to have a clear understanding of that business in a few sentences.

That can be used to analyze existing businesses. Or to draft your Business Model and keep a strategic and execution focus on the key elements to be implemented in the short-medium term.

FourWeekMBA business model framework

An effective business model has to focus on two dimensions: the people dimension and the financial dimension. The people dimension will allow you to build a product or service that is 10X better than existing ones and a solid brand.

The financial dimension will help you develop proper distribution channels by identifying the people that are willing to pay for your product or service and make it financially sustainable in the long run.

TAM, SAM, and SOM

Understanding your TAM, SAM and SOM can help you navigate the market you’re in and to have a laser focus on the market you can reach with your product and service.

Brand Building

Value Proposition Design

Product-Market Fit

Freemium Decision Model

Organizational Design And Structures

Speed-Reversibility Matrix

Minimum Viable Product

SWOT Analysis

Revenue Modeling

Business Experimentation

Business Analysis

Ansoff Matrix

Key takeaway

I hope that in this guide you learned the critical aspects related to business strategy, with an emphasis on the entrepreneurial world. If business strategy would only be an academic discipline disjoined from reality, that would still be an interesting domain, yet purely speculative.

However, as a business strategy can be used as a useful tool to leverage on to build companies, hopefully, this guide will help you out in navigating through the seemingly noisy and confusing business world, dominated by technology. As a last but critical caveat, there isn’t a single way toward building a successful business.

And oftentimes the way you choose to build a business is really up to you, how you want to impact a community of people and your vision for the future!

Other resources: 

• Types of Business Models You Need to Know
• What Is a Business Model Canvas? Business Model Canvas Explained
• Blitzscaling Business Model Innovation Canvas In A Nutshell
• What Is a Value Proposition? Value Proposition Canvas Explained
• What Is a Lean Startup Canvas? Lean Startup Canvas Explained
• How to Write a One-Page Business Plan
• How to Build a Great Business Plan According to Peter Thiel
• How To Create A Business Model
• What Is Business Model Innovation And Why It Matters
• What Is Blitzscaling And Why It Matters
• Business Model Vs. Business Plan: When And How To Use Them
• The Five Key Factors That Lead To Successful Tech Startups
• Business Model Tools for Small Businesses and Startups

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Blue ocean player.

Blue Sea Player

Constructive Disruptor

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What is business strategy?

What are examples of business strategies.

Things like product differentiation, business model innovation, technological innovation, more capital for growth, can all be moats that organizations focus on to gain an edge. Depending on the context, industry, and scenario, a business strategy might be more or less effective; that is why testing and experimentation are critical elements.

Connected Strategy Frameworks

ADKAR Model

Business Model Canvas

Lean Startup Canvas

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Business Analysis Framework

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Blue Ocean Strategy 

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GE McKinsey Model

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Minimum Viable Audience

Business Scaling

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Speed-Reversibility

Asymmetric Betting

Growth Matrix

Revenue Streams Matrix

Pricing Strategies

Other business resources:

• What Is Business Model Innovation
• What Is a Business Model
• What Is Business Strategy
• What is Blitzscaling
• What Is Market Segmentation
• What Is a Marketing Strategy
• What is Growth Hacking

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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Mastering Payoff Matrices: Power of Strategic Decision-Making Tools

June 5th, 2024

Navigating intricacy and choosing prudently hold prime significance. Fortunately, payoff matrix emerge as potent sense-makers.

Through over two decades guiding transformational change, I’ve witnessed matrices’ gifts firsthand time and again across various terrains.

Essentially, these visual organizers furnish structured comprehension beyond instinct.

By mapping options and their effects, matrices illuminate the nuanced interweaving amid counterpart actions and reverberations – aiding intelligent navigation.

Originating from strategy analyses, matrices now uplift diverse domains beyond their theoretical roots.

Their knack for bringing clarity amid complexity and furnishing prudent guidance merits respect.

Ultimately, matrices empower optimized decisions driving continual progressions – whether navigating cooperation/competition waters or resolving production dilemmas beneficially.

By reflecting on such proven decision-aiding, ponder matrices’ value wherever intricate choice-points present.

Key Highlights

• A payoff matrix is a powerful decision-making tool that visually represents the potential outcomes of strategic choices made by multiple parties.
• It originated from game theory but has found widespread applications across various industries, including product management, business strategy, risk assessment, and resource allocation.
• The key components of a payoff matrix are the players (decision-makers), strategic choices, and potential outcomes or payoffs.
• Payoff matrices help decision-makers understand the interplay between different parties’ actions and their corresponding consequences.
• They provide a structured approach to analyzing complex scenarios, identifying optimal strategies, and making informed decisions.
• Advanced concepts like Nash equilibrium, dominant strategies, coordination games, and mixed strategies enhance the analytical depth of payoff matrix analysis.
• Practical applications include feature prioritization, risk assessment, resource allocation, stakeholder analysis, and decision optimization.
• Best practices involve addressing incomplete information, combining payoff matrices with other decision-making tools, conducting sensitivity analyses, and considering ethical implications.
• Payoff matrices offer a data-driven framework for strategic decision-making, with emerging applications in areas like AI integration and collaborative decision-making.

Introduction to Payoff Matrix: A Powerful Decision-Making Tool

A payoff matrix visualizes the potential outcomes of the strategic choices made by multiple parties involved in a decision-making scenario.

It provides a structured framework for analyzing the interplay between different players’ actions and the corresponding payoffs or consequences, allowing for informed and data-driven decision-making.

The Origins of Payoff Matrix in Game Theory

The concept of payoff matrices finds its roots in game theory, a branch of mathematics that studies strategic decision-making situations involving multiple players with potentially conflicting interests.

Game theorists developed payoff matrices as a tool to model and analyze the outcomes of various strategic interactions, such as competitive games, negotiations, and economic scenario.

Key Components: Players, Strategic Choices, and Outcomes

The key components of a payoff matrix are the players (decision-makers), the strategic choices available to each player, and the potential outcomes or payoffs resulting from the combination of those choices.

Players can be individuals, teams, companies, or any entity involved in decision-making. Strategic choices represent the available actions or strategies that each player can choose.

Outcomes or payoffs are the consequences or results associated with each combination of choices, often expressed as numerical values representing profits, losses, benefits, or any relevant measure of success or utility.

Creating and Interpreting Payoff Matrix

Building a payoff matrix is a structured process that involves several key steps:

• Identify the players or decision-makers involved in the scenario.
• List the strategic choices or actions available to each player.
• Determine the potential outcomes or payoffs resulting from each combination of choices.
• Organize the choices and outcomes in a grid or matrix format, with rows representing the choices of one player and columns representing the choices of the other player(s).
• Fill in the matrix with the corresponding payoffs for each combination of choices.

Understanding Payoff Matrix Results and Quadrants

Interpreting the results of a payoff matrix involves analyzing the quadrants or cells within the matrix.

Each quadrant represents a specific combination of choices made by the players and the associated payoffs.

The payoffs can be numerical values or qualitative descriptions, depending on the nature of the scenario.

It’s important to consider the preferences and objectives of each player when evaluating the payoff matrix results.

Symmetric vs. Asymmetric Payoff Matrix

Payoff matrices can be classified as symmetric or asymmetric, depending on the relationship between the players’ payoffs:

• Symmetric Payoff Matrix: In a symmetric payoff matrix, the payoffs for each player remain the same, regardless of the choices made by other players. The resulting outcomes are identical for all players.
• Asymmetric Payoff Matrix: In an asymmetric payoff matrix, the payoffs for each player depend on the specific choices made by other players. The resulting outcomes vary significantly based on the combinations of choices made.

Practical Examples from Product Management and Business

Payoff matrices find practical applications in various business domains, including product management, strategic planning, and decision analysis .

For example, in product management, a payoff matrix can be used to prioritize features or product development initiatives based on their potential benefits and the resources required for implementation.

In business strategy, payoff matrices can help assess the outcomes of competitive actions or market entry decisions.

Advanced Concepts in Payoff Matrix Analysis

Nash equilibrium is a fundamental concept in game theory and payoff matrix analysis.

It represents a situation where no player can improve their payoff by unilaterally changing their strategy, given the strategies chosen by the other players.

In other words, it’s a stable state where each player’s strategy is the best response to the strategies of the other players.

Dominant strategies are strategies that yield the best possible outcome for a player, regardless of the choices made by the other players.

If a player has a dominant strategy, it is always optimal to choose that strategy, regardless of the other player’s actions.

Coordination Games and the Prisoners’ Dilemma

Coordination games are scenarios where players’ interests are aligned, and they benefit from cooperating.

The classic example is the Prisoners’ Dilemma, where two prisoners must decide whether to cooperate or defect, resulting in different payoffs depending on their choices.

Anti-coordination Games and Conflict Resolution

Anti-coordination games are scenarios where players’ interests conflict, and they benefit from choosing opposite strategies.

These games model situations where players compete for limited resources or engage in conflicts.

Analyzing anti-coordination games can provide insights into conflict resolution strategies and negotiation tactics.

Mixed Strategies and Probabilistic Analysis

In some situations, pure strategies (deterministic choices) may not lead to optimal outcomes.

Mixed strategies involve players selecting their strategies probabilistically, assigning specific probabilities to each available choice.

This approach can lead to more favorable outcomes in certain scenarios and is often used in game theory and payoff matrix analysis.

Payoff Matrix in Practice: Applications and Case Studies

In product management, payoff matrices can be invaluable tools for prioritizing features or product development initiatives based on their potential benefits and the resources required for implementation.

By mapping out the potential outcomes of various product decisions, product managers can make informed choices that align with their strategic objectives and optimize resource allocation.

Risk Assessment and Scenario Analysis

Payoff matrices can be used for risk assessment and scenario analysis in various industries.

By modeling the potential outcomes of different risk scenarios and the associated payoffs, organizations can better understand the potential impact of risks and develop effective mitigation strategies.

Resource Allocation and Cost-Benefit Analysis with Payoff Matrix

In resource allocation and cost-benefit analysis , payoff matrices can help decision-makers evaluate the trade-offs between different investment or resource allocation options.

By considering the potential payoffs and costs associated with each option, organizations can make informed decisions that maximize returns while minimizing risks and costs.

Stakeholder Analysis and Decision Optimization

Payoff matrices can also be used for stakeholder analysis and decision optimization.

By modeling the preferences and objectives of different stakeholders, decision-makers can identify win-win scenarios or negotiate mutually beneficial outcomes.

This approach can foster collaboration, resolve conflicts , and optimize decision-making processes.

Limitations and Best Practices for Using Payoff Matrix

While payoff matrices are powerful tools, they rely on the availability of accurate and complete information.

In many scenarios, information is often incomplete or uncertain, which can impact the accuracy of the payoffs estimated in the matrix.

To address this limitation, decision-makers should complement the use of payoff matrices with other decision-making tools, such as sensitivity analysis, scenario planning, and real-time data collection.

Combining Payoff Matrix with Other Decision-Making Tools

To create a more robust decision-making process, it is recommended to combine the insights generated by payoff matrices with other analytical tools and techniques.

These can include decision trees , utility theory, and statistical modeling , among others.

By integrating multiple decision-making tools , organizations can gain a more comprehensive understanding of the decision landscape and make more informed choices.

Sensitivity Analysis and Continuous Monitoring

Payoff matrices are static representations of decision scenarios at a given point in time.

However, situations are dynamic, and the assumptions or conditions underlying the payoff matrix may change over time.

To address this, it is essential to conduct sensitivity analyses and continuously monitor the decision environment.

By regularly reviewing and updating the payoff matrix, decision-makers can adapt their strategies and maintain optimal decision-making processes .

Ethical Considerations and Responsible Decision-Making

While payoff matrices provide a structured approach to decision-making, it is crucial to consider ethical implications and responsible decision-making practices.

Decision-makers should ensure that the payoffs and outcomes considered in the matrix align with ethical principles, social responsibilities, and stakeholder interests.

Additionally, transparency and accountability should be maintained throughout the decision-making process.

Future Directions and Emerging Applications

Payoff matrix applications continually evolve across expanding domains. Data-driven resolutions and artificial intelligence’s expanding roles envision enhancing matrices’ predictive capacity and optimized decisions.

By leveraging machine learning and predictive modeling capacities, enterprises furnish more accurate estimations factoring complexity and patterns.

AI-empowered guidance systems further ease navigation.

Bridging theoretical frameworks and practical deployments, matrices illuminate complex landscapes comprehensively. Combining rigor and real-world comprehension, prudent decisions propel strategic visions.

Collaborative styles and emerging technologies’ integrations undoubtedly birth innovations amid uncertainty.

Therefore, maintaining ethics and responsibility amid transformations proves vital navigating intricacies successfully long-term.

Ultimately, matrices prove invaluable amid changing tides, mitigating risks while grasping opportunities.

Their structured yet flexible nature for analyzing intricate scenarios and identifying optimized routes merits faithful consideration wherever strategic determinations present intricate calculations.

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Experimental Use of Strategic Choice Approach (SCA) by Individuals as an Architectural Design Tool

• Published: 10 March 2018
• Volume 27 , pages 811–826, ( 2018 )

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• Elena Todella   ORCID: orcid.org/0000-0001-9107-7000 1 ,
• Isabella Maria Lami   ORCID: orcid.org/0000-0002-6468-3184 2 &
• Alessandro Armando   ORCID: orcid.org/0000-0003-0399-9764 1  

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The paper proposes the use of the Strategic Choice Approach as a way of structuring the architectural design process, done by individuals and partly supported by meetings and interviews with DMs, experts, and stakeholders. The aim is to stimulate a debate around the use of SCA and its possible merging with architectural design, also analysing how the micro-processes involved in this merging can work in practice. We reflect on the possible use of SCA to determine prescriptive conditions on physical form at a scale that is still intermediate between the single building and the urban tissue: the method is employed as a design tool to provide alternative transformation scenarios. It represents a way of approaching the challenge of planning in an uncertain world, eliciting guidelines and strategies, and furthermore it produces an architectural project or transformation in a physical sense. Moreover, by investigating what occurs during the different micro-processes with the interviewees, we focus on some behavioural issues and effects, in relation to the context, the models of the application and the different entities involved in the interventions. This proposal shows an application to a real-world problem, currently under debate by the City of Turin (Italy), the re-use of abandoned barracks located in a prestigious residential area.

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Source Reproduced with permission from Armando and Durbiano ( 2017 )

(Reproduced with permission from Armando et al. 2015 )

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Ackerman F, Eden C (2005) Using causal mapping with group support systems to elicit an understanding of failure in complex projects: some implications for organizational research. Group Decis Negot 14:355–376

Article   Google Scholar  

Ackermann F, Eden C (2011) Strategic management of stakeholders: theory and practice. Long Range Plan 44:179–196

Albrechts L, Balducci A (2013) Practicing strategic planning: in search of critical features to explain the strategic character of plan. Plan Rev 49(3):16–27

Google Scholar  

Armando A, Durbiano G (2017) Teoria del progetto architettonico. Dai disegni agli effetti, Carocci, Roma

Armando A, Bonino M, Frassoldati F (2015) Watersheds. A narrative of urban recycle. Sandu Publishing Co, Guangzhou

Coelho D, Antunes CH, Martins AG (2010) Using SSM for structuring decision support in urban energy planning. Technol Econ Dev Econ 16:641–653

Eden C, Jones S, Sims D, Smithin T (1981) The intersubjectivity of issues and issues of intersubjectivity. J Manag Stud 18(1):37–47

Faludi A (2000) The performance of spatial planning. Plan Pract Res 15(4):299–318

Faludi A, van der Valk A (1994) Rule and order: Dutch planning doctrine in the twentieth century. Geo J Libr 28:105

Ferraris M (2009) Documentalità. Perché è necessario lasciar tracce, Laterza, Roma-Bari

Franco LA (2006) Forms of conversation and problem structuring methods: a conceptual development. J Oper Res Soc 57:813–821

Franco LA, Montibeller G (2010) Facilitated modelling in operational research. Eur J Oper Res 205:489–500

Fregonara E, Curto R, Grosso M, Mellano P, Rolando D, Tulliani JM (2013) Environmental technology, materials science, architectural design, and real estate market evaluation: a multidisciplinary approach for energy-efficient buildings. J Urb Technol 20:57–80

Friend JK (1993) Planning in the presence of uncertainty: principles and practice. J Jpn Soc Civ Eng 476:1–9

Friend J, Hickling A (1987) Planning under pressure: the strategic choice approach, 1st edn. Pergamon, Oxford

Friend J, Hickling A (2005) Planning under pressure: the strategic choice approach, 3rd edn. Pergamon, Oxford

Giangrande A, Mortola E (2005) Neighbourhood renewal in Rome. Combining strategic choice with other design methods. In: Friend J, Hickling A (eds) Planning under pressure: the strategic choice approach, 3rd edn. Pergamon, Oxford, pp 322–326

Hämäläinen RP, Luoma J, Saarinen E (2013) On the importance of behavioural operational research: the case of understanding and communicating about dynamic systems. Eur J Oper Res 228(3):623–634

Latour B (1988) Science in action. How to follow scientists and engineers through society. Harvard University Press, Cambridge

Latour B (2013) Cogitamus. Sei lettere sull’umanesimo scientifico, Il Mulino, Bologna

Latour B, Woolgar S (1979) Laboratory life: the construction of scientific facts. Sage Publications, Beverly Hills, CA

Mastop H, Faludi A (1997) Evaluation of strategic plans: the performance principle. Environ Plan 24:815–832

Mingers J (2003) A classification of the philosophical assumptions of management science methods. J Oper Res Soc 54(6):559–570

Mingers J (2011) Soft OR comes of age—but not everywhere!. Omega. Int J Manag Sci 39:729–741

Mingers J, Rosenhead J (2001) Diverse unity: Looking inward and outward. In: Rosenhead J, Mingers J (eds) Rational analysis for a problematic world revisited: problem structuring methods for complexity, uncertainty and conflict. Wiley, Chichester

Mingers J, Rosenhead J (2004) Problem structuring methods in action. Eur J Oper Res 152:530–554

O’Keefe R (2016) Experimental behavioural research in operational research: what we know and what we might come to know. Eur J Oper Res 249:899–907

Rittel HWJ, Webber MM (1973) Dilemmas in a general theory of planning. Policy Sci 4:155–169

Tavella E, Lami IM (2018) Negotiating perspectives and values through soft OR in the context of urban renewal. J Oper Res Soc. https://doi.org/10.1080/01605682.2018.1427433

White L (2006a) Evaluating problem structuring methods: developing an approach to show the value and effectiveness of PSMs. J Oper Res Soc 57:842–855

White L (2006b) Aesthetics in OR/systems practice: towards a concept of critical imagination as a challenge to systems thinking. Syst Res Behav Sci 23(6):779–791

White L (2016) Behavioural operational research: towards a framework for understanding behaviour in OR interventions. Eur J Oper Res 249:827–841

Yaneva A (2009) Laboratory life: the construction of scientific facts. 010 Publishers, Rotterdam

Yaneva A (2012) Mapping controversies in architecture. Ashgate, Farnham

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Architecture and Design Department, Politecnico di Torino, Viale Mattioli 39, 10125, Turin, Italy

Elena Todella & Alessandro Armando

Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Viale Mattioli 39, 10125, Turin, Italy

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Todella, E., Lami, I.M. & Armando, A. Experimental Use of Strategic Choice Approach (SCA) by Individuals as an Architectural Design Tool. Group Decis Negot 27 , 811–826 (2018). https://doi.org/10.1007/s10726-018-9567-9

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Published : 10 March 2018

Issue Date : October 2018

DOI : https://doi.org/10.1007/s10726-018-9567-9

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