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Investigating management control configurations using qualitative comparative analysis: an overview and guidelines for application

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Abstract

Configuration theory is concerned with understanding complex phenomena involving multiple and interacting attributes. The theory is consistent with a long line of research recognizing that management controls operate as complex packages or systems. However, empirical research aimed at understanding management control configurations is relatively scarce. One possible reason is the lack of appropriate methods. This paper introduces a promising case-oriented method for understanding complex phenomena called qualitative comparative analysis. This paper provides a basic guideline for applying the method, outlines how the method can be combined with more conventional research approaches, and offers suggestions for future research into management control configurations.

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Notes

  1. QCA is a widely adopted method in other disciplines, such as political science, and has seen increasing application in organization and management literatures (Rihoux et al. 2013). The method has not yet been applied in any published articles in MC literature, although there are a number of working papers (e.g. Bedford et al. 2014; Erkens and Van der Stede 2014; Sandelin 2014).

  2. While alternative approaches are available, such as data envelopment analysis (Sinha and Van de Ven 2005), these are not common in MC literature.

  3. Dichotomous sets are referred to as crisp sets, as opposed to fuzzy sets where inclusion in a set is in terms of degrees.

  4. In set theoretic terminology A\({\mathbf {\cdot }}\)B is referred to as a subset of the set of firms displaying the outcome.

  5. As is detailed in Sects. 4 and 5, whether the researcher makes that claim that A\({\mathbf {\cdot }}\)B is sufficient for the outcome to occur depends on the choice of the consistency threshold and the absence of logical contradictions.

  6. QCA does not assess interactions (multiplicative effects) in the same way as conventional statistical methods. QCA examines the membership of cases in the intersection of multiple attributes (variables) to determine which attributes must be present or absent to result in an outcome. There are also limits to the number of variables QCA can feasibly take into account. This is discussed in Sect. 4.

  7. Incorporating interaction terms into regression analyses (i.e. moderated regression) can take into account alternative paths to some degree, for example, by examining whether two MC attributes are substitutes (see Grabner and Moers 2013).

  8. Causal asymmetry can be examined with regression analysis by using certain research designs. One example is in the sticky cost literature that considers the differential effects of positive and negative changes in a cost driver on costs. This is modelled by using dummy variables (e.g. Anderson et al. 2003).

  9. The example that follows is an illustrative example based on the theory presented by Gerdin (2005). The actual data of Gerdin (2005) are not used.

  10. The researcher needs to ensure that the outcome varies across cases for a valid analysis of sufficient conditions. Variation in the outcome is not required for tests of necessary conditions. In this case it is the causal attributes that must vary (Ragin 2008).

  11. Exceptions include the frameworks of Simons (2005) and Speklé (2001).

  12. The number of possible configurations is equal to 2\(^{k}\) where k is the number of attributes.

  13. One way to overcome this limitation is to adopt a two-stage approach (Schneider and Wagemann 2006).

  14. Gerdin’s (2005) empirically derived MAS clusters show lower values of operating budgets for traditional MAS than for broad scope MAS. For illustrative purposes operating budgets are excluded from the definition of traditional MAS. Gerdin (2005) also includes an additional structure (simple) and MAS design (rudimentary). For convenience in illustration these are also excluded.

  15. Ideally the population of cases for the analysis of sufficiency is defined by the most similar different outcome (MSDO) principle (Przewroski and Teune 1970). See Mahoney and Goertz (2004) for a more detailed discussion on case selection.

  16. For example, the distinctive phases of H\(_{2}\)0 from solid to liquid to gas.

  17. There are two methods of calibration, direct and indirect. For further information see Ragin (2008).

  18. It should be noted that fuzzy set scores do not mean the same thing as raw scores. The raw score indicates “more frequent” or “less frequent” reporting in the observed firm. The fuzzy set score indicates whether the firm should be more or less in the set representing either “frequent” or “infrequent” reporting, as defined by the researcher.

  19. The crossover point is critical as it affects the conceptual meaning of the set. Upper and lower thresholds are instead useful for focusing the analysis on variation within a specific range. In other words, these thresholds can be used for removing irrelevant variation in an attribute or outcome.

  20. Firms that have a fuzzy set score of 0.5 on any indicator are not able to be categorized into a truth table row, as this score is indeterminate as to which set the firm should belong to and will be removed from the analysis in the following step. In order to retain all observations a common practice is to add or subtract a small fraction (e.g. 0.001) from fuzzy set scores (e.g. Crilly et al. 2012; Fiss 2011).

  21. Available software to conduct QCA include: fsQCA 2.5 (Ragin and Davey 2009), Tosmana 1.3.2 (Cronqvist 2011), R (Thiem and Dusa 2013), and Stata (Longest and Vaisey 2008). These programs, along with an overview of their advantages and disadvantages, are available at: http://www.compasss.org/software.htm.

  22. Consider the attributes of high competitive pressure and industry monopoly. Limited diversity would arise because the presence of both attributes is an illogical combination.

  23. Table 2 is not based on actual data, it is only for illustration.

  24. For a more comprehensive discussion the reader is referred to Ragin (2008, 2009) and Schneider and Wagemann (2012).

  25. Logical remainders are used as theory-based simplifying assumptions to overcome limited diversity in seeking solution parsimony. This is discussed in Sect. 4.8.

  26. Refer to Ragin (2008, pp. 44–68) and Schneider and Wagemann (2012, p. 126) on how to calculate consistency scores for fuzzy sets.

  27. Truth table rows may contain cases that are logical contradictions (see Sect. 4.8) which the researcher should review before the minimization of the truth table.

  28. Solutions should also be interpreted with respect to coverage. Coverage measures the proportion of cases that display the particular attribute combination with the outcome present. It provides an indication of the relative empirical importance of the configuration (Ragin 2008).

  29. See Ragin (2008) on calculating coverage scores.

  30. This assumes that the presence and absence of the outcome is calibrated symmetrically, which may not always be appropriate.

  31. The strength of prior empirical and theoretical knowledge is important. If counterfactuals are based on weak prior knowledge then the resulting solutions will lack validity. In such instances directional assumptions should not be made.

  32. For more detailed illustrations see Schneider and Wagemann (2012, pp. 168–175) and Ragin (2008, pp. 160–172).

  33. In crisp set QCA less than perfect consistency indicates a logical contradiction.

  34. Redundant attributes also deserve further inquiry in order to explain, for instance, situations where accounting is irrelevant (Choudhury 1988).

  35. Schneider and Rohlfing (2013) detail a number of other possibilities for further investigation based on QCA results.

  36. One way to do this is to purposively select the sample so that firms share a sufficient number of background conditions to make certain archetypes unlikely or impossible or so that firms share one or more transactional attributes.

  37. A recently developed temporal variant of QCA has opened up the possibility for examining causal paths through time (Schneider and Wagemann 2012).

  38. Ex-post statistical tests can also be conducted. As each case is traceable to a particular combination the researcher can assess differences in attributes that are not included in the main analysis (e.g. using t-tests).

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Acknowledgments

This article is partly based on a plenary session given by one of the authors at the 2014 European Accounting Association conference in Tallinn, Estonia.

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  1. University of Technology Sydney, PO Box 123, Broadway, NSW, 2007, Australia

    David S. Bedford

  2. Aalto University School Of Business, Helsinki, Finland

    Mikko Sandelin

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Bedford, D.S., Sandelin, M. Investigating management control configurations using qualitative comparative analysis: an overview and guidelines for application. J Manag Control 26, 5–26 (2015). https://doi.org/10.1007/s00187-015-0204-3

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