Research Analysis: Systems Archetype Process Explained

A systems archetype is a diagnostic tool that is typically used to interoperate common behavioural patterns in organisational systems. In this final stage of the research analysis, the author mapped these behaviour patterns as they occur in the system and followed the prescribed archetype assignment process designed to support the creator of the system models—firstly to make sense of the complexity in the system, and then to identify opportunities for improvement.

88 This approach is supported by Braun (2002) when he states that ‘the system archetypes are highly effective tools for gaining insight into patterns of behaviour, themselves reflective of the underlying structure of the system being studied,’ (Braun, 2002, p. 1).

The ‘limits to success’ archetype, also known as the ‘limits to growth’ archetype, was developed by Meadows, Meadows, Randers and Behrens (1972) and is used to identify and eliminate factors which are limiting a system's growth potential.

The selection criteria to determine a relevant systems archetype considers two standards when applying the limits to a success template (Figure 5.1), namely a review of the concerned behaviour over time by following Goodman and Klein’s mapped archetypes and their interactions, and aligning the most relevant systems theory to the observed scenario. In this case, the systems theory states that ‘a reinforcing process of accelerating growth (or expansion) [R1] will encounter a balancing process as the limit of that system [B2] is approached. It hypothesises that continuing efforts will produce diminishing returns as one approaches the limits,’ (Braun, 2002).

Figure 5.1 Limits to Success Template

Source: Kim. D.H., & Anderson, V. (1998). System archetype basics: From story to structure. Pg. 43

The reinforcing loop in Figure 5.2 labelled (R1) describes the actions that are accelerating growth according to the data in the mapped system, and include these variables: the quality of skills development, access to personal development, mentorship, level of start-up business support, and level of community support. The balancing loop (B2) represents the system variables that were limiting the growth of the system. These variables are: the quality of education, the degree of digital inclusion, the degree of financial inclusion, propensity towards social entrepreneurship (culture), and access to social entrepreneurial bridging programmes. As per the relevant research question, they all have a material impact on how effective youth social entrepreneurial development programmes can be in terms of creating more inclusive self-employment opportunities for the participating excluded youth.

89 Figure 5.2 Limits to Success Archetype: Causal Model 1

Based on this system-modelling phase, a hypothesis emerged that says social entrepreneurship development could potentially accelerate the entrepreneurial ecosystem by addressing the limiting actions (B2) that were restraining the system. Hence, in theory, if youth social entrepreneurship development agencies focused their efforts (R1) on developing social entrepreneurs who worked on solving social issues which where specifically limiting the system, they could accelerate the growth and success of the system. As such, they could potentially accelerate the entrepreneurial ecosystem in Cape Town if implemented at scale.

The next stage of analysis focused on the key findings in the literature review and mapped these concepts further as illustrated in Figure 5.3 (orange) and 5.4 (green). The process by which to achieve this aim included mapping the insights and topics discussed in the literature review as they relate to one or more of the eleven variables in the causal loops (reinforcing or balancing).

Figure 5.3 Limits to Success Archetype: Causal Model 2

For example, the variable called ‘access to mentorship’ maps the literature review findings presented in the section titled ‘The Role of Mentorship’. Meanwhile, the variable called ‘propensity towards social entrepreneurship’ illustrates the findings discussed in the

R1 Degree of

Inclusion B2

Quality of

Education Degree of Digital Inclusion Degree of Financial Inclusion Propensity Towards SE Access to Bridging Programmes Quality of Skills Dev. Access to Personal Dev. Access to Mentorship Start-up Business Support Level of Community Support Constraint: Level of Exclusion (participating equitably in mainstream economic activities)

90 subchapter titled ‘Role of Community and Culture' (refer to 4.1.3.2) . This mapping process was followed for all variables and resulted in a simplified account of the critical factors influencing the system.

Figure 5.4 Limits to Success Archetype: Causal Model 3

Figure 5.5 Limits to Success Archetype: Causal Model 4

The next step in the process, illustrated in figure 5.5 (light blue), presents the interconnectivity of the relationships between the individual variables, data themes, and topics. For example, access to quality

91 tertiary education has a connection with the degree of financial inclusion (Spaull, 2015; GEM, 2017, etc.). Mapping these relationships assisted in identifying the opportunities for improvement that have been further expanded upon in figures 5.6 (purple).

The final analysis phase illustrated in figure 5.6 continues the data mapping process until potential intervention points are identified. These intervention points represent opportunities to innovate and develop new models, processes, products, or services which could have a positive effect on the system, and could potentially address the research problem. They will be discussed further in the next chapter when the business plan is introduced as a recommendation.

Figure 5.6 Limits to Success Archetype: Causal Model 5

In summary, this final phase of analysis—the application of the systems archetype ‘limits to success’— was a useful diagnostic tool for consolidating and simplifying a complex system as well as identifying the variables and the factors impacting the system positively or limiting its potential growth.

It was through evaluating these criteria and scenarios, based on the participant data and the literature reviewed, that exciting new ideas and opportunities to innovate were able to emerge. Each variable and its influencing factors could be viewed as opportunities to improve the system and/or to innovate. However, given the scope of this project, not all of these possibilities could be explored to their full conclusion. As such, and where relevant, these opportunities have been included for future research in Chapter 6 or ‘banked’ to investigate independently of this study.

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