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Pre-publication version of paper submitted to the Second World-wide Research
Symposium on Purchasing and Supply Chain Management, 1-3 April, 1998, London
Generalising from single samples - Evaluating the contribution of casestudy analysis to empirical purchasing research
Introduction
[The ’one-shot’ case-study] design is an observation only of what exists
at the time of the study, as such, it has no control over extrinsic and
intrinsic factors. In addition, it does not allow for manipulation of the
independent variable or for before-after or control-group-experimentalgroup comparison. Furthermore, since case studies analyze single
unsampled systems, they are weak on generalization as well. Studies
that employ the One-Shot Case-Study design have no checks on
internal validity and thus are of little use in testing causal relations.
Indeed this design has been denoted by some methodologists as preexperimental.
Nachmias & Nachmias, (1976), p.42
Criticisms of this kind may have encouraged the abandonment of the case-study approach to
empirical research in some academic fields. Mitchell (1983), p. 187 observes that it seems to
have disappeared, for example, in Sociology in the early 1950s when it was replaced by the
familiar jargon of variable analysis, sampling techniques et al. Much the same is true of
Economics where the approach has been largely relegated to the status of a teaching aid.
Nevertheless empirical research based on case-studies is alive and well in a variety of other
fields such as Politics, Anthropology and Psychology. Moreover, a brief survey of the
Purchasing literature indicates that the case-study approach has, in the absence of any critical
debate, already become an extremely popular research strategy in this newly formed field of
study.i In the light of the severity of some of the methodological attacks on the case-study, it
would seem prudent for purchasing academics to pause and take a considered look at the
debate before continuing with their current, apparently unquestioning embrace of this
particular research strategy.
The Nachmias attack is based on an application of the ontology and epistemology of Humean
positivism which regards knowledge of the world as proceeding from a search for, and
uncovering of, patterns or regularities in samples of data relating to events in the empirical
realm. The resulting regularities may then be formulated as ‘laws’ that can be applied as
generalisations concerning the relationships between the relevant variables in the parent
population of phenomena. The subsequent explanation of phenomena in social systems thus consists of generalisable laws that permit the researcher to make predictions concerning future
relationships between variables.
If the positivist approach to understanding social phenomena is accepted, then it is
reasonable to ask if the case-study research strategy is capable of uncovering generalisable
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relationships between variables. Stated baldly, this line of criticism asks - if the research
objective is the creation of a generally applicable understanding of purchasing phenomena,
what is the point of looking closely at a sample of one? How can a general law be drawn from
an appreciation of the way in which one company, department or individual behaves? It is, of
course, possible to challenge the positivist ontological and epistemological foundations upon
which such questions rest, and that task will be tackled below. However, before the validity of
the positivist methodology is itself brought into question, the claim of Nachmias & Nachmias
and othersii that the case-study approach is incapable of producing useful and valid
generalisations, will be challenged on two grounds. One major dissenting line of thought deals
with the nature of the process of causal explanation, the other with the nature of the
phenomena under examination.
The process of explanation - analytical induction and logical inference
In the 1930s, before positivism had established its grip upon much empirical research and the
plethora of statistical techniques and PCs capable of easily performing the necessary
calculations had been invented, voices could be heard resisting the new epistemology. Thus
Znaniecki (1934) can be found arguing strongly in favour of analytical induction in which:
...certain particular objects are determined by intensive study, and the
problem is to define the logical classes which they represent. No
definition of the class precedes in analytical induction the selection of
data to be studied as representative of this class....
Znaniecki, (1934), p. 249
The procedure he championed involved establishing definitions or descriptions of concepts
and processes based upon an identification of their ’essential’ characteristics:
Analytic induction abstracts from the given concrete case, characters
that are essential to it and generalizes them, presuming that in so far as
essential, they must be similar in many cases......Thus, when a
particular concrete case is being analyzed as typical or eidetic, we
assume that those traits that are essential to it, which determine what it
is, are common to and distinctive of all cases of a class.
ibid., p. 250
If the essential characteristics of a phenomenon can indeed be identified, then it will be
logically possible to generalise from a single sample of a phenomenon by means of, for
example, case study analysis.
Half a century later, in a development of this kind of inductive argument, Mitchell
(1983) can be found discussing the difference between statistical and logical inference,
stating:
Scientific or causal - or perhaps more appropriately - logical inference,
is the process by which the analyst draws conclusions about the
essential linkage between two or more characteristics in terms of some
systematic explanatory schema - some set of theoretical propositions.
Mitchell, (1983), p. 199-200
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and concluding that:
... the process of inference from case studies is only logical or causal
and cannot be statistical and that extrapolability from any one case
study to like situations in general is based only on logical inference.
[Thus] We infer that the features present in the case study will be
related in a wider population not because the case is representative but
because our analysis is unassailable.
Mitchell, (1983), p. 200
Hence, if research into a single sample/case leads to the discovery of an ‘unassailable’ causal
explanation of a phenomenon, then the researcher may be confident that the same explanation
will apply to other members of the parent population from which the sample was taken. iii
The excavation of causation - skeletal specifications
Mitchell and Znaniecki provide arguments for the combination of very small or single
samples with induction in the search for generalisable causal explanations. However, neither
author explores the question of precisely how causation might be uncovered in the practice of
empirical research, and the limitations that practice might impose upon the possibility of
producing generalisable causal explanations. Some light is thrown on this subject by the
literature surrounding the idea that it is possible to usefully distinguish between an entire
phenomenon and a sub-section of that whole that can be called an underlying ‘process’.
Writing in the context of educational research, Howard Becker (1990) cites the
example of empirical research in mens’ and womens’ prisons. At first sight, the phenomena
are quite different, Male prisons have, inter alia, well developed markets for drugs, clothes
and so on, and the inmates devise prison-specific homosexual relationships that do not
threaten their heterosexuality in the outside world. They also maintain a ‘convict code’ of
behaviour based on loyalty towards other inmates and distrust of the guards. Female prisons,
on the other hand, do not have developed markets, sexual relationships resemble pseudofamilies and they have no convict code. Nevertheless, Becker argues that although the details
of the phenomena are quite different, there is an underlying common ‘process’ in which the
deprivations of prison life lead to the creation of a prison culture. Consequently he argues:
.....generalizations are not about how all prisons are just the same, but
about a process, the same no matter where it occurs, in which variations
in conditions create variations in results.
Becker, (1990), p. 240
Similarly, in a discussion of organisational research that echoes Mitchell’s earlier thoughts,
Hartley (1994) argues that:
The detailed examination of organizations in a context can reveal
processes which can be proposed as general or as peculiar to that
organization. The detailed knowledge of the organization and especially
the knowledge about the processes underlying the behaviour and its
context can help to specify the conditions under which the behaviour
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can be expected to occur. In other words the generalization is about
theoretical propositions not about populations.
Hartley, (1994), pp. 225-6:
This focus on underlying processes appears to be an attractive suggestion. In the field of
purchasing phenomena for example, although it may well prove impossible to produce a
general theory about, say, buyer-supplier negotiations that could be applicable to all types of
company regardless of size, supplier base, product base or market structure, and every
negotiator regardless of personal style, personality and the like, it might be possible to identify
some underlying processes in negotiation practice that are so generalisable. This proposition
appears all the more promising in view of the fact that it is reasonable to expect to find
similarities in the underlying processes of individual cases in purchasing. Many of the buyers
in companies have been through the same education process. In the UK there is only one
professional Institute that represents professional buyers, consequently many buyers have
studied the same academic syllabi and the same limited literature. Furthermore, with the
increase in the popularity of practices such as bench-marking and the seeking out of ‘best
practice’, many companies actively try to copy each other’s operations. In such circumstances
it would be extremely surprising if empirical research did not uncover a number of similar,
underlying processes.
We may usefully take a leaf out of the systems analysts’ book at this point. Significant
proportions of their lives are concerned with the stripping away of the idiosyncratic surface
details of processes within companies with a view to identifying and specifying what might be
called underlying, ‘skeletal’ processes:
The physical model is contaminated with many physical and
technology-related activities and memories simply because that is the
way things are done in the current system. We want to separate the
what from the how. Namely we want to separate the things that do the
data transformations from the things that service the processes that do
these transformations. Simply stated, we want to know what processes
and data stores serve the users and which ones serve the system. In this
way, we can distinguish between changes in the technology of how
things are done from changes in the fundamental business practices
implemented in the system.
Kowal, (1988), p. 155
Analysts seek to uncover what they term a logical model with which they can:
...briefly and concisely explain the system under study. ......[and]
describe the system in terms of its most important activities and task, its
essential data, and the events to which the system responds.’
ibid., (1988), p. 271
There would seem to be little doubt then, that it is possible to strip away surface detail and
reveal underlying processes. It is, moreover conceivable, as we shall see below, that those
same processes may well be present in the broader population of phenomena.
In summary, support for the argument that it may be possible to use the case-study
research strategy to produce valid, generally applicable explanations from a single sample of a
phenomenon can be found in the ideas of:
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 analytical induction and the identification of the ‘essential’ characteristics of a
phenomenon.
 causal analysis and the construction of ‘unassailable’ analyses.
Moreover, the task of revealing underlying causal mechanisms may be facilitated by a search
for:
 skeletal specifications and the uncovering of common, underlying processes.
Unfortunately, both the ideas and the suggested empirical technique suffer from a variety of
individual and shared defects.
Limitations on reliability - The problem of induction
Both analytical induction and causal analysis are necessarily subject to a potentially crippling
logical difficulty memorably presented by Popper thus:
It is usual to call an inference ‘inductive’ if it passes from singular
statements ..., such as accounts of the results of observations or
experiments, to universal statements such as hypotheses or theories.
Now it is far from obvious, from a logical point of view, that we
are justified in inferring universal statements from singular ones, no
matter how numerous: for any conclusion drawn in this way may
always turn out to be false; no matter how many instances of white
swans we may have observed, this does not justify the conclusion that
all swans are white.
Popper, (1972), p.27
This is the notorious ‘Problem of Induction’ originally formulated by Hume over two hundred
years earlier.iv More recent treatments of the problem divide it into two parts:
............ the difficulty occasioned by the fact that all our putative
knowledge of the natural world is in principle revisable (the little
problem of induction)...[and] the difficulty occasioned by the fact that
even if we had perfect knowledge of the natural world another world
could come into being at any time in which that knowledge would be
useless (the big problem of induction)
Harré and Madden, (1975), p.75
For positivists, by casting logical doubt on the reliability of any inductively generated theory,
the problem of induction constitutes both a powerful argument for avoiding any inductivelybased research technique and support for the hypothetico-deductive search for eventregularities adopted by positivism. However, the positivist research methodology is, of course,
not without its own critics. The Realists have convincingly demonstrated that the reliable
identification of cause and effect through the use of statistical analysis to uncover patterns in
event is only possible in closed systems with stable environments and constant, unchanging
processes and actors.v Such conditions rarely apply fully even under controlled conditions in
chemistry laboratories. In social systems such as companies, where the external business
environment is notoriously unstable and the presence of human beings in the internal company
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environment with their reflexive,vi unpredictable behaviour, unequivocally prevents system
closure. The reliable identification of cause and effect from event-regularities as proposed by
Humevii is thus rendered impossible. Under the kind of conditions existing in purchasing
environments, the casual significance of patterns in data is indeterminate.
The realisation that neither inductive approaches nor positivism have a reliable method
of uncovering causal explanations in social systems might be regarded as extremely unsettling
in a field like purchasing that is tightly tied to practitioners and their interests. Deep
uncertainty about the reliability of research results is unlikely to be well received in business
circles. Although it might be possible to publish such research in disciplines less intimately
connected with the commercial world, businesses will want to be reassured that the risk of
failure and loss is small before they invest in and implement change. ‘It might work in your
company.’, or ‘It seemed to work in a very small Canadian moccasin business that employed
four people.’ will not normally be good enough in such an environment. Business may have
more demanding standards of explanatory reliability than the academic referees of, say, the
Economic Journal or the American Sociological Review.viii
Skeletal process banality - explanation or generalisation
The logical difficulties that beset an approach based on inductive inference are compounded
by practical restrictions on the feasibility of using the method, suggested above, of uncovering
skeletal specifications as a basis for the construction of generalised causal explanations.
Systems analysts may well have techniques for excavating underlying system specifications,
but in the main that profession is uninterested in either generalisability or causal explanations.
They are trying to extract the essence of specific systems within a company with a view to
digitising them. It is possible, therefore that the techniques they employ may ignore or even
destroy the very information needed to form causal explanations. Consider the following two
descriptions of the same (imaginary) company’s system for purchasing new products.
Detailed description
The company’s engineers produce drawings of the component they require. Frequently these
drawings are not based directly on the firm’s technical needs. Because of long-standing
corrupt arrangements between the engineers and unscrupulous engineering suppliers, some of
the company’s essential needs are modified until they can only be supplied rapidly by the
engineer’s favourite suppliers. The resulting (biased) drawings are supplied to the company’s
Purchasing Department. They are frequently delivered more slowly than is necessary. This
delay reduces the amount of time the buyers have to perform the task of identifying and
establishing suitable suppliers. Moreover, the purchase of new components is not given the
highest priority by the Purchasing Department’s senior management. Their focus is on the
demanding task of maintaining supplies of some 15,000 current production parts to the
company’s production lines. The net result is that the purchasing department is frequently
happy to rely on the engineer’s suggested suppliers This maximises the likelihood of the
buyer selecting suppliers suggested by the engineers, but sub-optimises the company’s
purchasing performance.
The Company has a sourcing procedure drafted by the Finance Director stipulating that
three formal enquiries should be sent out for any order worth more than £10,000/annum. In
practice this is rarely followed, either because of lack of time, or because the Chief buyer
considers herself to be something of a law unto herself, deeply resents the ideas that Finance
should be controlling the Purchasing Function’s behaviour, considers her department to be
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grossly understaffed and does not have the resources to carry out what she regards to be
unnecessarily bureaucratic procedures.
Skeletal process description
1. Company objective: find a source of supply for a new component.
2. Engineering department produces drawing of required component.
3. The drawing is passed to Purchasing
4. Purchasing employ available resources - purchasing Staff and their knowledge of sourcing
techniques, sources of data on potential suppliers such as staff knowledge and trade directories
- to match the technical characteristics of the company with the technical capabilities of
potential suppliers.
5. Purchasing selects the supplier(s) most likely to minimise the total, long-term costs of
purchasing the specified component.
The difference between these two descriptions is analogous to the concept of thick and thin
descriptions referred to by the Anthropologist Geertz (1972). Thus, Geertz cites Ryle’s (1971)
philosophical discussion of the range of nuances associated with the act of twitching the
muscles of the right eyelid; everything from a simple involuntary twitch through winking,
using the act of winking as a means of transmitting other information, pretending to wink and
so on into ironic uses of the wink and the like. Ryle coins the concepts of ‘thick‘ and ‘thin’
descriptions. Where a thin description of, say, the ‘complex’ phenomenon of someone
pretending to wink as a means of satirising the behaviour of someone who actually had
winked, might be: ‘rapidly contracting his right eyelids’, a thick description of the same event
might be:
.....practising a burlesque of a friend faking a wink to deceive an
innocent into thinking a conspiracy is in motion.
Geertz, (1972), p.7
The two descriptions of the sourcing process offered above are thick and thin versions
of the same phenomenon. If the empirical research objective is a causal explanation of the
company’s new product sourcing performance, then it is likely that the researcher will need to
consider such details as the relationships between the various functions involved, the nature of
the interactions between the staff and the data processing and paperwork systems and so on.
The thick description would appear to be the appropriate vehicle for such an analysis. God
may not be in the details, but it is likely that casual explanations will be found there. However,
much of the thick detail of any case will tend to be heavily company-specific, and could never
form part of a generally applicable theory or description of the way companies source new
components. If, on the other hand the research objective is the creation of generally applicable
theories or explanations, then the skeletal, thin description of the same process offered above
is indeed so fundamental that it could be applied to the majority of medium, and large-sized
manufacturing companies in the world. Sadly, however, although it may well be generalisable,
the thin description may also be so elementary as to be banal, and offers few causal insights.
In other words researchers may find that they frequently have to choose between either
creating causal explanations that are not generalisable, or generalisable explanations that do
little to identify cause and effect.
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At this stage, the task of deriving generalisable causal explanations from single casestudies thus appears possible, but both logically unreliable and unlikely to produce valuable
findings. Nevertheless, all is not yet lost.
Side-stepping the problem of induction
Harré & Madden (1975) argue that the problem of induction stems from positivism’s
erroneous foundation of Humean ontological and epistemological beliefs. If those errors are
removed, they argue, then the little problem of induction is no longer a concern for inductive
causal explanations. Specifically they point out that the positivist Humean scheme of laws
deriving from event regularities depends upon an ontology of discrete, atomistic events that:
....can be understood best as temporal cross-sections of what we would
ordinarily call a physical object. ‘x is red at t1’, ‘x is round as t1’, ‘x is
sweet at t1’ etc. Would each be an event in this sense....Humean events
are momentary ‘happenings’ in consciousness, the immediate
impression of the moment...Our experience of a physical object [or
process] is conceived to be a construction out of such events....[which
are] ... instantaneous in nature, punctiform and elementary.’
Harré & Madden, (1975), p.109-110
Moreover, Hume considered it reasonable to treat events as though they were entirely
independent.
It is assumed that if a material thing undergoes a sequence of changes,
thus generating successive events, that these events are absolutely
independent of one another, that is, no matter what the structure of the
previous succession has been, it is possible that the thing could take on
any property whatever in the future. Similarly, it is assumed that no
matter what properties a body has, it could, for all we can know, at any
time take on simultaneously any other properties whatever.
Harré and Madden, (1975), p. 3-4
Thus, the individual members of any given sequence of events are assumed to be
unconnected in any manner. Consequently, from an epistemological point of view, it is
impossible to understand a world of atomistic, independent events in terms of causal
relationships based on connections between such events. Moreover the effect of these
ontological assumptions is to remove the logical necessity for the repetition of any particular
sequence of events in the future. Without the possibility of connections between events, it is
logically possible that any ‘explanation’ based on specific sequences of events may be
incorrect and hence in need of future revision. From these foundations they argue, springs the
little problem of induction.
Whereas Hume and the positivists focus on patterns in the events we experience, Harré
& Madden propose an alternative Realist ontology that stresses the importance of the nature of
the things:
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....whose interactions produce the flux of events. The system of things,
of ultimate and derived individuals, is the permanent structure of the
universe.
Harré & Madden, (1975), p. 4
Shackled by the logical limitations of its chosen ontology, positivism is incapable of
generating causal explanations in open social systems; only descriptions of patterns in
data/events and unreliable predictions. In contrast, Harré & Madden’s universe of things
allows them to offer a radically different method of explaining empirical experiences which
stresses the role of ‘powerful particulars’:
Our conception of causality is deliberately in keeping with one of the
commoner ways in which this concept is employed. In this sense,
causation always involves a material particular which produces or
generates something. But what may be singled out as the cause may be
an event, a state of affairs, or even in certain contexts, a material
substance.....in any specific application of the notion of causality, the
crucial element, we argue, the presence of which makes the action
causal, is a powerful particular.
Harré & Madden, (1975), p. 5
Freed from the restraints imposed by the positivist event ontology, they argue for the renewed
validity of the process of induction:
... if we see that certain powers and capacities are explained by the
nature of certain particulars and are necessarily attendant upon them
then we have, in turn, an explanation of why certain things must go
together and happen as they do......
The claim that inductive inference carries its own internal
warrant and needs no bolstering from the outside can be justified
directly in the following way. Providing we have no reason for thinking
we were perceptually mistaken and have positive evidence for the truth
of the description of a certain causally efficacious particular, then we
can confidently say of a past occurrence of a causal production that the
expected outcome did not simply happen to occur but had to occur,
since the citation of the causally efficacious particular explains why the
outcome and not something else occurred.
Harré & Madden, (1975), p. 71
By challenging the epistemological and ontological foundations of positivism they argue for
the ability to identify explanations based on the effects of ‘causally efficacious particulars’
and casual connections between the events we experience.ix Harré & Madden devote little
attention to the question of generalisability,x however, we may surmise that if, in the course of
empirical research, case-study work were to uncover a ‘causally efficacious particular’ that
permitted a causal explanation of some aspect of a phenomenon, then it would be logically
possible to generalise that causal explanation out to all other examples of the phenomenon
containing similar powerful particulars.
Unfortunately for the empirical researcher in search of practical guidance on how to
proceed in case-study analysis, although Harré & Madden may well have produced a
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convincing argument for ignoring the problem(s) of induction, the introduction of their
‘causally efficacious particular’ does little to clarify the practical task of identifying causal
explanations. Unlike the problem of induction, this is not so much a problem of pure logic, as
one of epistemology concerning the way in which we can gain reliable knowledge of the
empirical world.
Thus two decades after Znaniecki (1934) was published, Robinson (1951) argued that
analytical induction is limited to the identification of the:
‘necessary conditions for..[a] phenomenon, to conditions which must be in existence
before the phenomenon occurs but which are not shown to be sufficient to produce the
phenomenon.
Robinson, (1951), p. 817
In order to find out the sufficient conditions for the existence of a phenomenon it would be
necessary to compare cases where it occurs, with those where it does not. Furthermore, even if
it is felt that the definition of necessary conditions only will suffice in an explanation, the
researcher is left with the problem of deciding whether or not the characteristics the research
has identified are indeed the ‘essential‘ ones. Mitchell’s (1983) development of Znaniecki’s
ideas has some initial persuasive power, but, it might be argued, does little more than change
the problem of identifying the ‘essential characteristics’ of a phenomenon into one of
assessing whether or not a causal analysis of a phenomenon is ‘unassailable’. Harré &
Madden’s (1975) resolution of the problem of induction is elegant, but even they are left with
the question, in this context, of how researchers can be sure that they have correctly identified
a ‘causally efficacious particular’. Finally the discussion of skeletal specifications highlighted
the apparent conflict between generalisability and causal understanding. However, even if the
underlying process was useful in producing a causal explanation without descending into
banality, researchers would still face the problem of identifying the point at which the analysis
is ‘thick’ enough to facilitate the act of casual explanation.
Researchers attempting to apply any of the ideas advocated above can thus reasonably
ask the following questions:
 how will I know if the characteristics I have identified are ‘essential’?
 how will I know if the analysis or theory I have constructed is ‘unassailable’?
 how will I be able to tell when I have successfully identified ‘causally efficacious
particulars’?
 How can I be sure that the process description is ‘thick enough’ to permit causal
explanation?
Without answers to these questions, researchers’ confidence in the reliability of their own
results will be extremely limited.
Improving reliability - the need for corroboration
One strategy that might be employed to improve reliability would be to use any
theories or hypotheses thrown up by the original ‘explanation’ to generate new research
questions that might be answered by reference back to the phenomenon in the case-study. If,
following further investigation, those questions are answered in the affirmative, then this
could be regarded as what might be called internal corroboration of the explanation.xi Such a
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process is illustrated in diagram 1 in which the researcher approaches the research task with
pre-existing, opinions, experiences and so onxii, forms a hypothesis and seeks evidence from a
search for causal explanation in an analysis of the case (the numbers alongside the arrows
between stages in the process indicate the sequence in which the various stages occur). When
the results of the research are compared with the original hypothesis, this may prompt a
revision of the original hypothesis (represented by arrow 4) and a re-examination of the casestudy, and so on until a tentative, working explanation is developed.
Alternatively, external corroboration that characteristics of a phenomenon are
‘essential’; an explanatory analysis was ‘unassailable’; a description ‘thick enough’ or that a
‘causally efficacious particular’ had been correctly identified, might be sought from an
attempt to identify the proposed or identified characteristics, analysis, description or powerful
particular in a larger sample of phenomena similar to that found in the case-study. This kind
of process is illustrated in diagram 2. In this way statistical analysis of large samples may
have a role to play in conjunction with individual case-study investigations. The purpose of
the statistical analysis in this context however is not to seek out event-regularities from which
to draw up universal laws, but merely to attempt to corroborate the theories or explanations
uncovered by inductive, causal analysis.
Moreover, there is no reason why the sequence of events should not be reversed and
the process begun with statistical analysis techniquesxiii that facilitate the investigation of large
numbers of cases or samples of the target phenomenon. Once more, however, the purpose of
that analysis would not be to use patterns in the data to infer causality. That task would fall to
case-study research employed as a follow-up technique. See diagram 3. In this way an attempt
to derive generally applicable causal explanations might be undertaken without recourse to the
usual panoply of random sampling techniquesxivintended to ensure the selection of
‘representative samples’ from parent populations. The choice of the sequence of research
approaches may then be determined largely by the availability of data. Where large amounts
of data on a number of different variables in a variety of cases is readily available, statistical
analysis might be a reasonable approach with which to begin. Where such data is not easy to
obtain, a single case-study may well indicate where data collection in the parent population
could usefully be concentrated with a view to corroborating a tentative causal explanation.
The combination of a realist epistemology and ontology with the positivist epistemology
One of the strengths of the inductive realist approach, as applied through case-study research,
is the promise of causal explanations resulting from examinations of the thick details of small
samples. However the weakness of this approach is the unreliability of any resulting causal
explanations, and thus the inability to reliably suggest generally applicable patterns, theories,
explanations or laws. Conversely, one of the strengths of positivism, as applied through largesample statistical analysis, is its ability to identify generalised patterns of event-regularities. Its
primary weakness, meanwhile, is the inability to generate a reliable causal explanation in open
social systems. The strengths and weaknesses of each approach display a curious specular
quality. It is proposed therefore that researchers in the field of purchasing adopt a form of
triangulationxv involving the combination of small-sample causal and large-sample statistical
analyses of the same phenomena, with the strength of one approach being used to compensate
for the weakness of the other. This suggestion rests upon an abandonment of the positivist,
Humean ontology of atomistic, independent events and the embrace of the realist ontology of
things. However, since no attempt is being made to use statistical analysis as a means of trying
to identify cause and effect directly, it is not necessary to jettison the positivist data pattern-
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searching epistemology which can, instead, be combined with the causal explanation
epistemology of the realists. Data patterns may serve well the limited purpose of indicating
potentially fruitful avenues for causal investigations using other techniques.
Working explanations
However, even a triangulated research strategy combining complementary statistical and
causal analyses in order to produce corroborative evidence will be unable to produce
conclusive causal explanations in open systems. Consequently Popper’s celebrated warning xvi
that it is ultimately impossible to verify theories only refute them, remains valid.xvii Hence the
conclusion that not only all hypotheses, explanations or theories drawn from small or singlesample research, but also the results of such research carried out in combination with large
sample statistical analysis, should be understood, and clearly labelled as being, of a ‘working’
nature. They are what might be called ‘working explanations’. (See diagrams 1-3)
Conclusions
This paper has attempted to answer the question of whether it is possible to use the case-study
research strategy to uncover generalisable relationships between variables or identify causal
mechanisms within open systems. Clearly, the strategy is well suited to the task of generating
thick descriptions of phenomena, and if it were to enable researchers to define ‘essential’
characteristics or produce ‘unassailable’ theories, then the answer would be an unequivocal
‘yes’. However Robinson’s (1951) objection that analytical induction is able to describe the
‘necessary’ conditions, but not the ‘sufficient’ conditions for the existence of a phenomenon
constitutes a serious logical restriction on the efficacy of the strategy. Moreover, the
discussion of the use of triangulation above indicated that the case-study strategy may be most
effective when it is deployed as part of (at least) a two-track approach in collaboration with
the statistical analysis of data patterns in larger samples of phenomena. Even then, given the
open nature of social systems, empirical researchers in Purchasing environments can logically
never be certain that they have correctly identified causal explanations of isolated phenomena,
let alone produced a reliable, generalisable theory.
Some academics in other fields have reacted to the extreme problems of attempting to
generate generalisable theories in social systems by abandoning the attempt all together. xviii
However, there is no need to adopt quite such a fatalistic stance in Purchasing. As this paper
has argued, single-sample case-study analysis and large sample statistical techniques may be
seen as complementary in nature, and used in tandem to generate explanatory theories of
greater reliability than could be achieved by the application of either approach on its own.
Nevertheless, given the unavoidable residual causal uncertainty in open systems, total
reliability will always remain unattainable. Consequently purchasing researchers should make
every effort to refrain from the temptation of either generalising freely from single cases, or
using large sample statistical analysis techniques to try to justify statements of the form:
‘changes in variable x cause changes in variable y’. It will, of course, be possible to draw firm
conclusions of this nature when examining banal circumstances involving inanimate objects,
viz.: ‘Turning this ignition key to the right causes the starter motor in the purchasing
department’s van to engage.’ or ‘Pressing this button causes some data to be sent to suppliers
through the department’s EDI system connections.’. However, in the main, empirical
researchers seeking to uncover causal relationships in purchasing system phenomena will have
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to reconcile themselves to the fact that their best efforts will be incapable of generating
anything more than tentative, working explanations.
Finally, researchers in this field might also usefully avoid replicating the behaviour of
other academic disciplines that have dug themselves into entrenched methodological positions
over the years. This kind of behaviour merely leads to the expenditure of intellectual energy
and physical resources on unnecessary, wasteful territorial trench warfare. Qualitative and
quantitative research techniques are not mutually exclusive; the use of case-study analysis is
not necessarily a sign that a discipline is ‘pre-experimental’ or ‘non-scientific’. On the
contrary, a willingness to embrace and combine a range of research methodologies may be
interpreted as a sign of sophistication and pragmatic maturity.
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ii See, for example, Campbell (1957), Campbell & Stanley (1966), Katzer, Cook & Crouch (1982).
iii Space constraints led to extremely brief summaries of Znaciecki and Mitchell’s work. All that
has been left is the core of their arguments. Much meaning is lost in such a savage process of
reduction, and the reader is recommended to read the originals.
iv Hume, D. (1975), Book I, part iii, section vi
v See Bhaskar (1978) and (1979), Sayer (1984) and Collier (1994).
vi See Mehan & Wood (1975), Chapter 2, Coulter (1991), p. 34, Sharrock & Anderson (1986), p.103.
viiHume, ibid., p. 87
viii On the other hand, the fondness many companies display for embracing inappropriate or
largely unproven fads suggests that the quality of their critical faculties also frequently leaves a
lot to be desired.
ix Space prevents all but a sketch of the complexities offered by Harré & Madden. The interested
reader should consult their 1975 text in full.
x See Harré & Madden (1975), p. 151-2
xi It is tempting to use the word ‘confirmation’ here, but that would involve denying the truth of
Popper’s argument that is only possible to refute theories, not confirm them - see Popper (1972),
p. 33, and ibid., appendix ix, pp. 387-419
xii Pace the ethnomethodologists! See Mehan & Wood (1975), for example, who argue passionately
that researchers should make every effort to abandon pre-existing theories, ideas and the like
before research is begun.
xiii Some authors use the phrase ‘statistical inference’ in this context [see Mitchell (1983)], but as
the Realists have conclusively established [See Bhaskar (1978) and (1989), Sayer (1984) and
Collier (1995)], it is impossible to infer reliable causal explanations from statistically generated
relationships between variables in open-systems such as organisations. Statistical analysis in
these circumstances is only capable of description not explanation.
xiv See Katzer, Cook & Crouch (1982), p.148.
xv Cassell & Symons (1994), p. 4, Rossman & Wilson (1985), Jick (1979), Patton, (1990, pp. 464470.
i
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Popper (1972), p. 33.
See also Bhaskar’s powerful argument concerning the impossibility of performing decisive tests
of theories in open-systems, Bhaskar (1989), p. 83.
xviii In Educational research for example Schofield (1990), p.280-9 suggests that:
...there is broad agreement that generalizability in the sense of producing laws that apply
universally is not a useful standard or goal for qualitative research........
Similarly, in Sociology Denzin (1983), p. 133 states that:
The interpretivist rejects generalization as a goal and never aims to draw randomly
selected samples of human experience.
xvi
xvii