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Management consulting is a professional service that is concerned with “the rendering of independent advice and assistance about the process of management” (ICMCI, 2002). It comprises consulting in various management areas including finance, human resources, information technology, marketing, operations, organization, and strategy (e.g., Alpha Publications, 2002: 6–7; FEACO, 2004: 8; Kennedy, 2003a: 13–14; Kubr, 2002: 261–604). Like for other professional services such as auditing, commercial law, executive search, investment banking, and tax advisory, the economic good exchanged is a highly customized service — not a physical product — and is provided by trained professionals (Greiner & Metzger, 1983: 7; Haywood-Farmer & Stuart, 1990: 337; Lowendahl, 2000: 18–23; Morgan & Quack, 2004: 1). With the rise of the service economy at the outgoing industrial age, the industry that provides consulting services has developed into a sizeable economic sector and employment market (Ringlstetter et al., 2004a: 9–10; Thommen & Richter, 2004: 5).

The contingency theory of organizations is a subset of the contingency approach in social and natural sciences. At an abstract level, the contingency approach describes a relationship between two phenomena. As its name indicates, the contingency approach holds that the effect of one phenomenon A on another phenomenon B depends on some third aspect C. Typically A represents an input that can be influenced while B is an outcome. C moderates the relationship between A and B, without necessarily being correlated with either of them (Howell et al., 1986: 89). When the value of C is low, the value of B in response to a certain value of A differs from B’s value when C is high for the same value of A. The relationship between A and B might even reverse with a change in the value of C. As shown in Figure 3, an increase in the value of A might have a positive effect on the value of B with a high C. When C is low, an increase in A might have a negative effect on B.

The management consulting industry exhibits characteristics that make it distinct from many other industries. Such peculiarities need to be considered when choosing a research methodology. For example, consulting is a projectbased activity (section 3.2.1.1). As projects are by definition unique and different from each other, the applicability of standard rules and procedures for ‘production’ as included in the concept of formalization is at least challenged. Formalization is a theoretical concept that needs to be operationalized for empirical research by proxy measures that can adopt different values (Bortz, 1999: 10; Bronner et al., 1999: 39; Heinze, 2001: 47; Neuman, 2000: 160). The measures of formalization need to be defined differently than in research on industries such as manufacturing or governmental. Similar arguments can be found for structural differentiation, specialization, and centralization (section 3.3.1) as well as for the task uncertainty contingency (section 3.2.1.1). The measures of these theoretical concepts cannot be completely specified in advance but need to be explored from the data in order to then test hypotheses. Explorative research with measures that are not specified before data collection provides a strong argument for choosing a qualitative research methodology (Miles & Huberman, 1994: 11–12; Neuman, 2000: 123, 163, 420; Rynes & Gephart, 2004: 455). As qualitative research is little standardized and “there are no algorithms for producing it” (Rynes & Gephart, 2004: 461), the research methodology is outlined in the following. It is also shown how qualitative elements are combined with quantitative ones.

The coding process as described in section 3.5.3 resulted in an extensive node tree for task uncertainty with the interview text being coded by topic at numerous hierarchically structured nodes as shown in Appendix IV-a. Each of these nodes is reviewed in detail to discern appropriate measures of task uncertainty. If the analysis finds that different values exist among consulting firms, the node’s topic is considered a measure of task uncertainty. Ordinal scales are used for each of the measures of task uncertainty as the analysis revealed that the consultancies’ values are not widely distributed but rather concentrated at different points of a scale. The consulting firms are therefore assigned to one of the categories of an ordinal scale.

The empirical test of hypotheses finds that four of the seven hypotheses are supported by the data whereas three hypotheses are not supported (Table 10). The outcome is consistent in the sense that the findings for different hypotheses do not logically contradict each other (Figure 63). For example, if size had been found to be positively related to specialization (H2), there would have been a logical contradiction with the other results. As specialization and task uncertainty are negatively related with each other (H4), and size and task uncertainty are not negatively but rather positively related (H5), size could not be positively related to specialization. Contradictions of this kind, however, are not part of the findings.

This thesis has been concerned with the diversity in the organizational structure of management consulting firms. The diversity has evolved as an outcome of two decades of dramatic expansion of the consulting industry and its stagnation since 2001 (section 1.1.1). The way in which consultancies are structured has far reaching implications regarding their overall business and employment models (section 1.1.2). In addition, the organizational structure of consultancies has been outside the focus of traditional organization theory research. Little explanation has been offered for the phenomenon of organizational diversity in consulting (section 1.1.3). Therefore, this thesis has investigated the factors accounting for the diversity in the organizational structure of management consulting firms.