11.5: Comparisons with Traditional Research
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Positivist case research, aimed at hypotheses testing, is often criticised by natural science researchers as lacking in controlled observations, controlled deductions, replicability, and generalisability of findings—the traditional principles of positivist research. However, these criticisms can be overcome through appropriate case research designs. For instance, the problem of controlled observations refers to the difficulty of obtaining experimental or statistical control in case research. However, case researchers can compensate for such lack of controls by employing ’natural controls’. This natural control in Markus’ (1983) study was the corporate accountant who was one of the system advocates initially, but started resisting it once he moved to the controlling division. In this instance, the change in his behaviour may be attributed to his new divisional position. However, such natural controls cannot be anticipated in advance, and case researchers may overlook them unless they are proactively looking for such controls. Incidentally, natural controls are also used in natural science disciplines such as astronomy, geology, and human biology—for example, waiting for comets to pass close enough to the earth in order to make inferences about comets and their composition.
The problem of controlled deduction refers to the lack of adequate quantitative evidence to support inferences, given the mostly qualitative nature of case research data. Despite the lack of quantitative data for hypotheses testing (e.g., -tests), controlled deductions can still be obtained in case research by generating behavioural predictions based on theoretical considerations and testing those predictions over time. Markus employed this strategy in her study by generating three alternative theoretical hypotheses for user resistance, and rejecting two of those predictions when they did not match observed behaviour. In this case, the hypotheses were tested using logical propositions rather than mathematical tests, which are just as valid as statistical inferences since mathematics is a subset of logic.
Third, the problem of replicability refers to the difficulty of observing the same phenomenon considering the uniqueness and idiosyncrasy of a given case site. However, using Markus’ three theories as an illustration, a different researcher can test the same theories at a different case site, where three different predictions may emerge based on the idiosyncratic nature of the new case site, and the three resulting predictions may be tested accordingly. In other words, it is possible to replicate the inferences of case research, even if the case research site or context may not be replicable.
Fourth, case research tends to examine unique and non-replicable phenomena that may not be generalised to other settings. Generalisability in natural sciences is established through additional studies. Likewise, additional case studies conducted in different contexts with different predictions can establish generalisability of findings if such findings are observed to be consistent across studies.
Lastly, British philosopher Karl Popper described four requirements of scientific theories: theories should be falsifiable, they should be logically consistent, they should have adequate predictive ability, and they should provide better explanation than rival theories. In case research, the first three requirements can be improved by increasing the degrees of freedom of observed findings—for example, by increasing the number of case sites, the number of alternative predictions, and the number of levels of analysis examined. This was accomplished in Markus’ study by examining the behaviour of multiple groups (divisional accountants and corporate accountants) and providing multiple (three) rival explanations. Popper’s fourth condition was accomplished in this study when one hypothesis was found to match observed evidence better than the two rival hypotheses.