Construction model by Bruin et al. [2005]

The model byDe Bruin et al.[2005] suggests the development of maturity models along the phases1) Scope, 2) Design, 3) Populate, 4) Test, 5) Deploy, and 6) Maintain.

The first phase,Scope, contains the definition of the model’s focus, which can be either general or domain-specific. Examples for a general model are the supply chain maturity model by Lockamy and McCormack [2004] and the excellence model by the European

Foundation of Quality Management (EFQM) [EFQM,2012].¹ General models are nei-ther industry specific nor focused on a specific topic in a certain field [De Bruin et al., 2005].

Contrary, domain specific models as the capability maturity model for software devel-opment [Paulk et al.,1993] or the model byMarx et al. [2012] focusing on management control systems, are more specialized on one topic. The second part of the initial Scope phase outlines the focus-related stakeholder identification. The stakeholders in focus have an influence both on the later defined items as well as the documentation of the final model.

The subsequent second step - Design - is primarily characterized by decisions regard-ing the application of the constructed model. Crucial aspects are the audience of the final model, the drivers for application, as well as the intended spread of the application throughout the company.

Additionally, the decision about the population approach has to be made. The selected approach depends on the maturity of the domain in focus (which will be explained in detail in the third construction step). In general, the existing population approaches can be split into bottom-up and top-down. FollowingDe Bruin et al.[2005], bottom up is suitable for mature domains, top-down for immature domains.²

When using bottom-up approaches, suitable for mature domains, the elements, inde-pendently of maturity levels, are defined first. In a second step, the maturity elements are assigned to different maturity levels. In a third step, based on the elements per ma-turity level, the mama-turity principles are defined.³ This procedure offers the possibility of applying quantitative models. This approach is only applied loosely in current model research-driven maturity model constructions.

For younger disciplines, the maturity levels with the related maturity principles are set up first and succeeded by the identification of maturity elements for each maturity level.

This procedure is called top-down.

The differentiation into these two population approaches is based on the expectation, that the identification of maturity elements in a first step is hard to achieve due to the lack of experience, both of practitioners as well as scientists in the related field. Vice versa, for a mature discipline it is assumed that potential maturity elements are already

1Subject of the model are criteria for the evaluation of the quality management maturity in a company.

2The description of these aspects can be found in Chapter3.

3The notions maturity measurements and maturity elements are used synonymously.

well known.

However, in step 2, only the decision for a topdown or bottomup approach is made -the execution follows in step 3.

After setting the focus and boundaries in the steps 1 and 2, the model population as step 3 includes the identification of measurements and maturity indicators. It is thus the most comprehensive step.

Following De Bruin et al. [2005], the methods used for the identification and measure-ment of maturity depends again on the maturity of the domain. For a more mature domain, a literature-based identification is possible, whereas for younger disciplines, the processing of the results from expert interviews, case studies, or Delphi techniques is recommended. These maturity indicators are identified both on a high level, represented by dimensions that describing the domain (e.g. technological infrastructure as a domain of a business intelligence system), as well as on a low level, regarding indicators within a dimension.

The developed model and the model instruments - used for the model population - are tested for validity, reliability, and generalizability in phase 4,Test. The model validation can be segmented into face and content validity. Face validity refers to the quality of the translation of the construct, targeting the accuracy and completeness of the model.

Validation techniques are focus groups or interviews. Following De Bruin et al. [2005], the use of different techniques in the population phase can foster the validation as well.

Content validity is targeting the completeness of the representation of the topic. This depends on the extend of the literature review carried out in step one and two.

The same techniques are used for the model instrument validation. A focus group is used to validate the survey, which is used for the model assessment. De Bruin et al. [2005]

state that a validation of the used instruments lead to a reliable model. Generalizability is achieved by a high volume of deployment in different environments regarding company characteristics.

The aspect of application is connected with step 5, the model deployment, which is carried out in a two-step approach. The initial deployment - the maturity evaluation of a company utilizing the developed model - as a first step takes place with collaborators from the model development process, as they are already familiar with the concept of maturity models. In the second step, the model is applied to organizations that have not

been involved in the construction process. The goal of this step is to evaluate up to what degree the model can be applied to companies with different characteristics regarding industry, size, and region. De Bruin et al.[2005] do not describe in detail how the model is supposed to be changed based on the received feedback.

The final, continuous maintaining phase comprises of fitting the developed model to the dynamics of the domain. Maturity is understood as a relative characteristic, based on the comparison with other companies’ maturity. In the course of time when the knowledge in a domain broadens and deepens, characteristics associated with a high maturity can change. High and low maturity is partly determined by the best and the worst performing companies in the domain analyzed. Consequently, the model maintaining allows for the relative character of the maturity concept. As companies continuously strive to improve their capabilities, the model indicators have to be fitted to these changes in order to keep the maturity model up-to-date. De Bruin et al.[2005]

point out the needed resources and partners for the model maintaining, which should be incorporated already in the initial scoping but do not describe the maintaining process in detail.