For the semi-systematic identification process of adaptability capabilities, the literature listed in Table 18 has been used. This list contained academic literature on adaptability constructs, both on a higher, generic DBE level and more specific on adaptability, incorporating information from several reference disciplines, as illustrated in Figure 12. These papers proved useful for the identification of (higher-level) design requirements yet lacked sufficient detail and empirical data for identifying capabilities. This finding is further substantiated by Senyo et al. (2019), who present a classification on empirical and non-empirical research performed in the field of DBE research. From their figures, it becomes apparent that more than 50% of the published DBE research is not
based on any research methodology or method and can, therefore, be categorised as ‘non-
empirical’. Therefore, the decision has been made to apply a semi-systematic approach for identifying capabilities, as was further elaborated in Section 2.1.3.
Table 18 listed a total of fifteen unique papers. The reviewing process resulted in the exclusion of three additional papers, as they lacked relevant information for the answering of this section’s
research question. The twelve remaining papers resulted in the semi-systematic identification of forty capabilities, as illustrated in Appendix B.5. SSLR - Adaptability Capabilities. The obtained capabilities contained different granularities and were not mapped onto the previously identified requirements. Moreover, most of the capabilities (78%) were merely mentioned once, which could be explained by the fact that the papers originated from numerous reference disciplines and were
mostly based on their authors’ conceptual orientation.
To ensure the generalisability and applicability of the method, the incorporation of these results was restricted to capabilities that were explicitly mentioned at least twice. This prerequisite resulted in the selection of the following capabilities:
1. (Platform) Independence
Fricke & Schulz (2005) describe the principle of independence as the ability to minimise the impact of changing design parameters. When described in the context of adaptive applications, Dantas & Borba (2003) argue that the general structure of an application should apply to a comprehensive set of systems, ranging from enterprise applications to embedded systems.
2. Agility
Agility is one of the adaptive principles proposed by Gill (2015). Being agile in the context of adaptive Enterprise Architecture implicates that it, as a design and practice, could enable flexible and timely responses to both expected and unexpected changes by adopting lean and learning techniques in support of adaptation (Korhonen et al., 2016).
1. Analytics
Yu et al. (2012) stress the relevance of adopting (data) analytics to advance the adaptiveness of enterprises. The capability enables organisations to obtain insights in their environments and internal operations more quickly and recognise potential needs for change so that appropriate actions can be taken in response. This statement is further substantiated by Korhonen et al. (2016),
who define it as “the ability to monitor, collect, analyse, and interpret data and information for
actionable insights or changes or decision making”.
2. Autonomy
Fricke & Schulz (2005) mention that independence could be perceived as a prerequisite for autonomy. This capability is defined by objects, capable of providing basic functionality for ensuring their independence from, for example, embedded systems. This principle is key to achieving adaptability (Fricke & Schulz, 2005).
3. Leanness
In their study, Masuda et al. (2017) have categorised leanness as an element related to agility. Moreover, the element describes the operation of EA with minimal resources without compromising quality. Korhonen et al. (2016) acknowledge that observation and describes leanness as a principle of agility
4. Loose Coupling
This capability involves applying poorly coupled processing modules and an additional communication layer, allowing the digital entities in the digital ecosystem to be decoupled in time and space (Averian, 2018b). Furthermore, van de Wetering & Bos (2017) describe loose coupling as a design principle for modular systems resulting in efficacious adaptive enterprises.
5. Modularity
Modular architectures can support the reuse of elements, modules or an entire section of an EA, within a particular scope of functionality and defined interfaces (Fricke & Schulz, 2005).
Modularity depicts the clustering of the system’s functions into various modules, while moreover
minimalizing the coupling amongst these modules and maximising their cohesion (Fricke & Schulz, 2005).
6. Security
Averian (2018b) stresses the fact that some areas of a digital ecosystem will imply a strong connection between the digital world and the physical world. The connection is made to realise a secure system, and the DE model should include multilevel security measures, including authorisation and identification of digital entities, users, protection of data and authentication.
2.6.1
Capability Relations
The definitions of the selected definitions presented in the previous section highlight several dependencies among multiple capabilities. These dependencies and an overview of the remaining capabilities are illustrated in Figure 16.
3
RESEARCH METHOD
In this section, the applied research methods for numerous steps of this research are addressed. In the first sub-section, the conceptual adaptability framework, interpreting the identified findings from the literature review phase, is illustrated and discussed. The following section described the conducted interview method for the collection of additional data. After that, the methods involved in the extension of an existing EA method are described, comprising an explanation for the selection of an existing EA method and the intended steps for the extension of the selected method. In the final section of this chapter, the validation method is addressed.