Description of MM research area

MMs are models of objective assessment of status quo for object improvement in a step-by-step pattern (Gausemeier, 2009). They are structured in levels for mirroring stages of growth that an organization passes through, which have three main distinctive elements (Gottschalk, 2009): (i) they follow a particular order, (2) they have a hierarchical progression not easily reversible, (3) they involve lots of organizational activities and structures. Hence they provide a basis for ‘strategic planning’ of investments, so as to ensure continuous improvement and to move towards corporate objectives (Hackos, 1997). Fraser et al. (2002) have provided a clear classification of maturity models according to three typologies identified. Next characterization descends form that work. First typology is the maturity grid, which typically illustrates maturity levels in a simple and textual manner, structured in a matrix or a grid. They are of somehow simple and they do not specify direction towards which to aim. They only identify some characteristics that processes, and enterprises should have in order to reach high performance (Maier, Moultrie, &

Clarkson, 2011). Second typology is the Likert-like, that are constructed by “questions” as statements of good practices. Responders to the questionnaire must score the related performance on a scale of 𝑁 values. Hybrid models as a combination of the questionnaire approach with the maturity grid do exist.

Finally, the CMM-like models (CMM stands for Capability Maturity Model) identify the best practices for specific processes and measures the maturity of organizations in terms of how many practices are implemented (Maier et al., 2011). Their architecture is more structured and complex, compared to the

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other. Structure is divided into process areas sharing some common features, which specify several key practices to address a series of goals. Typically, also the CMM-like models exploit Likert questionnaires to assess the maturity.

A further development of CMM-like model is the Capability Maturity Model Integration that provides guidance to perform processes of different nature⁷⁷.

Generally, MMs share some common proprieties (De Carolis, Macchi, Negri, et al., 2017b; Fraser et al., 2002):

• Maturity stages: it is generally adopted a scale of levels from three to six, but this is not mandatory.

For instance, the MM for IoT of Jæger and Halse (2017) has eight levels. Levels cope with the step-by-step stage of growth of the system towards the maturity, i.e. the highest level in the scale.

• Descriptors for each level: these provide a suitable name for each level.

• Description of the characteristics of each level.

• Dimensions: they are the clusters for mapping the maturity into different areas, providing a detailed representation of the current and future state.

• Items: are elements or activities grouped into dimension, for surveying the system.

• Descriptions of activities that must be performed at each maturity level.

Another key characteristic for distinguishing the design of MMs is represented by the assessment and the measurement typology (Schumacher et al., 2016). The measurement typology and tools to visualize measures depend on the purpose to achieve, and the main purposes of assessment instruments are: (i) descriptive, i.e. providing a picture of the AS-IS situation of the organization; (ii) prescriptive, i.e.

indicating how to approach maturity improvement; (iii) comparative, i.e. enabling benchmarking across companies (De Carolis, Macchi, Negri, & Terzi, 2017a).

4.1.1 The MM research area in industrial context

Useful attempts to organize the MM design have been made in last twenty years, aiming at (i) providing the main phases of generic model development (De Bruin, Freeze, Kaulkarni, & Rosemann, 2005), and (ii) defining a structured method to develop a specific MM (Becker et al., 2009). The design method proposed by Becker et al. (2009) is well-accepted by academia and practitioners: it has been cited 846 times in Google Scholar, until 27^th March 2020. Authors define a general procedure model in eight phases and seven requirements. Phases are: (P1) problem definition, (P2) comparison of existing models, (P3) determination of the design strategy, (P4) iterative maturity model development (broke up into selecting the design level, selecting the approach, designing the model section, and testing the results), (P5) conception of transfer and evaluation, (P6) implementation of the transfer media, (P7) evaluation, and finally (P8) rejection of the model. Requirements are: (R1) Comparison with existing maturity models, (R2) Iterative Procedure, (R3) Evaluation, (R4) Multi-methodological Procedure, (R5) Identification of Problem Relevance, (R6) Problem Definition, (R7) Targeted Presentation of Results, (R8) Scientific Documentation. Each phase meets one or more requirement as its developing procedure, hence a matrix to indicate the relation between phases and stages is proposed in Table 4.1.

On the other hand, De Bruin et al. (2005) are pioneers of the definition of MM design. Their six-step framework entails defining the (i) ‘scope’ of the model and its (ii) ‘design’ as the architecture of the model.

Next steps deal with (iii) ‘populating’ the model, i.e. where to search subjects to be surveyed and how to

77 Source: CMMI Project Team (2002). Capability maturity model® integration (CMMI SM), version 1.1. Retrieved from ftp://ftp.sei.cmu.edu/public/documents/02.reports/pdf/02tr028.pdf. Last access: 2020.08.27

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gather the content. Finally, follows the (iv) ‘test’, (v) the ‘deployment’, and the (vi) ‘maintenance’ phase, which asks to continuously update the model. First two steps are relevant since are preparatory to others.

The former step allows to set the outlying boundaries for model application and use: it requires to define the focus of the model and the development stakeholders. The latter forms the basis for further development and application. It requires to define five criteria: (i) the audience, (ii) the method and the (iii) the driver of application, (iv) the respondents and finally (v) the end application.

Table 4.1 - requirements to develop each phase of the procedure model in Becker et al. (2009)

R1 R2 R3 R4 R5 R6 R7 R8

P1 X X X

P2 X X

P3 X

P4 X X

P5 X X

P6 X X

P7 X X

P8 X

Figure 4.1 simply depict the framework for designing maturity models according to De Bruin et al. (2005).

The cycling graphics wants to highlight that the maintenance phase allows to update and improve the model redefining what was set in the previous design.

Figure 4.1. Six-step framework for designing a MM rearranging De Bruin et al. (2005)

The follow analysis of research on MM comes from the quantitative study of Bertolini et al. (2019) pertaining the studies carried out for this thesis, in which authors have analyzed 65 relevant papers on maturity in business management towards both business intelligence and operations management.

Firstly, they have grouped the papers over years of publication, determining the frequency of publications per year for providing the trend of the general literature (Figure 4.2).

Figure 4.2. Publications over years on MM in the timespan 2002-2017 (Bertolini et al., 2019)

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The number of publications is basically increasing since 2002, i.e. the year of the first publication of list of 65 papers, and form 2015 onwards a considerable interest in it has been arising. The analysis stops in 2017 because of its set-up in retrieving the papers.

Next, authors have identified 19 research areas and 8 subject categories clustering the areas, by the content of titles and abstracts, and keywords. Then, the documents have been grouped with regards to the subject categories and the research areas by reading the full paper. Finally, the frequencies of each research area and subject category are determined. Figure 4.3 and Figure 4.4 provide the full lists of research areas and subject categories, respectively, and their occurrences.

Figure 4.3. Research Areas of studies on MM (Bertolini et al., 2019)

Figure 4.4. Subject Categories of research on MM (Bertolini et al., 2019) Follow considerations stick to the authors’ considerations (Bertolini et al., 2019):

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The most of publications belongs to the manufacturing area, and they aim at defining a MM for the digitalization of business and the development of products and services. Other research area interested in MM belongs to the business design, and the information systems: a possible cause of this result is the relation between these research areas, which are concurrent because of their multi-disciplinary. It is important to note that 20 of 65 documents are Literature Reviews and just provide insights on the maturity model for specific research area.

The analysis shows that the concept of maturity and MMs have attracted in recent years lots of research communities as about the 70% of the literature on this topic in the timespan surveyed (i.e. 2002-2017) has been published in its last five-year period (2013-2017). Research cores relate to digitalization of business, product and service provisioning, and IT infrastructure deployment. This sounds as I4.0 is somehow affecting MM research, and the result is not fuzzy. In fact, since the structured idea of I4.0 has appeared as a complex system of technologies and their paradigms for achieving a higher level of operational efficiency and productivity, manufacturing operations, and business strategies (Hermann et al., 2016; Kagermann et al., 2013), it attracted more and more attention in several research field, hence also the MM.