Niche market growth demands increasingly more resources (as the market grows). ... (Once more, the constants .2 and .8 denote dimensionless constants that ...
Model appendix for: “Overcoming transformational failures in the dynamics of technological innovation systems” Technological Forecasting and Social Change Rob Raven* Bob Walrave+ * Monash University + Eindhoven University of Technology The system dynamics (SD) model developed and calibrated in Walrave and Raven (2016) is adopted here. The model was originally developed, and also adjusted for the purpose of this study, in Ventana’s software package ‘Vensim’ (version 6.1C). Adopting this model and its empirical setting allows for experimentation with the relevant variables by means of socalled if-then simulation experiments. Here we focus on the adjustments made to this model, which were required for testing the propositions presented in the paper. In this respect, we also refer to the model appendix developed for Walrave and Raven (2016), for a complete description and validation of the original model. For your convenience, a copy of the latter appendix has been attached to this document. We adopt this model in order to study how technological innovation systems respond to policy interventions, directed to overcome transformational failures. As explained in the paper, we experiment with ‘hybrid’ resourcing conditions in combination with a second round of resource provisions (which equal 50% of the initial resource provision), distributed over a period of 5 years. Note that the manuscript details how these resources are distributed over the different TIS functions/structures. Adjustments to the original model – Walrave and Raven (2016) Some relatively small adjustments to the original model were required in order to execute the proposed experiments. These concern (a) adjustments to the TIS functions/structure (to allow for resource input); and (b) the development of an intervention timing model, which makes sure that correct amount of resources is injected into the system at the right moment in time (captured by the ‘Intervention size’ and ‘Intervention trigger’ variables respectively). Concerning the adjustments to the TIS functions, four variables were added to the system, which reflect interventions against the four different transformational system failures. Equations 1-4 differ in terms of ‘Intervention type’ (see Table 1, page 2), ‘Intervention trigger’ (detailed later), and the constant ‘intervention size’ (see Table 2, page 4): (1) Directionality intervention [Euro] = IF THEN ELSE (Intervention trigger = 1 :AND: Intervention type = 1, Intervention size, 0) (2) Demand articulation intervention [Euro] = IF THEN ELSE (Intervention trigger = 1 :AND: Intervention type = 2, Intervention size, 0)
-1-
(3) Policy coordination intervention [Euro] = IF THEN ELSE (Intervention trigger = 1 :AND: Intervention type = 3, Intervention size, 0) (4) Reflexivity intervention [Euro] = IF THEN ELSE (Intervention trigger = 1 :AND: Intervention type = 4, Intervention size, 0)
1= 2= 3= 4=
Table 1. Intervention types. Directionality failure Demand articulation failure Policy coordination failure Reflexivity failure
Subsequently, these 4 interventions influence the existing TIS functions/structure in the following manner (equations 5 till 10; adjustments to the original equations in bold.) (5) Change in Guidance of the Search [Euro/month]: d (Guidance of the Search) / dt = ((Technological knowledge developed * Technological knowledge diffused * Effectiveness and efficiency factor * Resources to technology development + (1 – Effectiveness and efficiency factor) * Resources to technology development + 0.8 * Directionality intervention) – Guidance of the Search) / AT GoS (6) Change in Niche market [dmnl/month]: d (Niche market) / dt = (MIN ((Entrepreneurial activity * TIS structures + 0.8 * Demand articulation intervention * Euro to change in Niche market (Niche market), 1) – Niche market) / AT NM The MIN function is part of a first-order control to prevent the stock from growing above 1 (the theoretical maximum for the Niche market stock). Note that the .8 (a constant) in equation 5 and 6 reflects a percentage (% of resources devoted to the associated inflow, as detailed in the paper). Where ‘Euro to change in Niche market’ denotes a lookup, dependent on the ‘Niche market’, as illustrated by Figure 1. This formulation ensures that Niche market growth demands increasingly more resources (as the market grows).
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Euro to Change in Niche market
0.00001 0.000008 0.000006 0.000004 0.000002 0 0
0.2
0.4
0.6
0.8
1
Niche market
Figure 1. Non-linear relationship between ‘Niche market’ and ‘Euro to Change in Niche market’ (7) Change in TIS structures [dmnl/month]: d (TIS structures) / dt = (1 – TIS structures) * ((Effect of EA on TISS (Entrepreneurial activity) + Effect funding on TISS) – TIS structures) / AT TISS where the ‘Effect funding on TISS’ equals: (8) Effect funding on TISS [dmnl/Euro] = MIN (Euro to change in TISS * (Funding TISS + 0.8 * Policy coordination intervention), 1) (9) Sailing ship effect [dmnl] = ( Effect SSEs on SSE (SSE status) – ((0.2 * Directionality intervention + 0.2 * Demand articulation intervention + 0.8 * Reflexivity intervention + 0.2 * Policy coordination intervention) * Effect intervention SSE)) * Maximum effect size SSE Where ‘Effect intervention SSE’ denotes a constant listed in Table 2. (10) Change in Perceived legitimacy of the TIS [dmnl/month]: d (Perceived legitimacy of the TIS) / dt = ((Technological knowledge diffused * Technological knowledge developed * TIS structures * (1 – Regime resistance toward TIS) + Euro to change in PLT * 0.2 * Reflexivity intervention) – Perceived legitimacy of the TIS ) / AT PLT Note that ‘Euro to change in PLT’ is a constant that is adopted from the original model. (Once more, the constants .2 and .8 denote dimensionless constants that reflect the percentage of resources that is injected in the associated inflow.) Concerning intervention timing, we assumed that the second round of investment is triggered by a negative value of the 3-year moving average of ‘Change in niche market’, which is modelled as equation 11:
-3-
(11) Change in Moving average Change in Niche market [dmnl/month]: (Change in niche market – Moving average Change in niche market) / AT MA CINM Where ‘AT MA CINM’ denotes the adjustment time (3 years, see Table 2). Once we observe a structural negative trend in Niche market growth, we set the stock ‘Intervention trigger’ to ‘1’ (which subsequently influences equations 1-4). Equation 12 details the function. Note that once the ‘Intervention duration’ exceeds the ‘Maximum intervention duration’ (constant, see Table 2), the ‘Intervention trigger’ is set (once more) to ‘0’. (12) Change in Intervention trigger [dmnl/month]: IF THEN ELSE (Intervention trigger=0 :AND: Moving average Change in niche market < 0 :AND: Intervention duration Maximum intervention duration :AND: Intervention trigger = 1, –1/TIME STEP, 0)) We capture the current intervention duration by the stock ‘Intervention duration’. As in equation 13: (13) Change in intervention duration [month/month]: IF THEN ELSE (Intervention trigger > 0, 1, 0) Table 2. List of new constants and initial values. Name Value Unit Effect intervention SSE 5e-05 Euro/Dmnl Initial value: Moving average Change in Niche market 0 Dmnl AT MA CINM 36 Months Initial value: Intervention trigger 0 Dmnl Maximum intervention duration 60 Months Intervention size 10000 Euro
Sensitivity Analyses We ran Monte-Carlo simulations, 100 runs per experiment, while varying the distribution between primary and supportive function, uniformly, between .1/.9 and .3/.7. The results, presented below, indicate a high level of robustness with respect to variations in resource distribution. De-alignment and re-alignment Directionality failure:
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De-alignment Re-alignment - Directionality failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
450
600
450
600
Demand articulation failure: De-alignment Re-alignment - Demand articulation failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
-5-
Policy coordination failure: De-alignment Re-alignment - Policy coordination failure - Sensitivity 50% 75% 95% 100% Niche market 0.7
0.525
0.35
0.175
0
0
150
300 Time (Month)
450
600
450
600
Reflexivity failure: De-alignment Re-alignment - Reflexivity failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
-6-
Reconfiguration Directionality failure: Reconfiguration - Directionality failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
450
600
450
600
Demand articulation failure: Reconfiguration - Demand articulation failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
-7-
Policy coordination failure: Reconfiguration - Policy coordination failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
450
600
300 Time (Month)
450
600
Reflexivity failure: Reconfiguration - Reflexivity failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
-8-
Technological substitution Directionality failure: Technological Substitution - Directionality failure - Sensitivity 50% 75% 95% 100% Niche market 0.3
0.225
0.15
0.075
0
0
150
300 Time (Month)
450
600
450
600
Demand articulation failure: Technological Substitution - Demand articulation failure - Sensitivity 50% 75% 95% 100% Niche market 0.3
0.225
0.15
0.075
0
0
150
300 Time (Month)
-9-
Policy coordination failure: Technological Substitution - Policy coordination failure - Sensitivity 50% 75% 95% 100% Niche market 0.3
0.225
0.15
0.075
0
0
150
300 Time (Month)
450
600
450
600
Reflexivity failure: Technological Substitution - Reflexivity failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
300 Time (Month)
- 10 -
Transformation Directionality failure: Transformation - Directionality failure - Sensitivity 50% 75% 95% 100% Niche market 0.3
0.225
0.15
0.075
0
0
150
300 Time (Month)
450
600
450
600
Demand articulation failure: Transformation - Demand articulation failure - Sensitivity 50% 75% 95% 100% Niche market 0.3
0.225
0.15
0.075
0
0
150
300 Time (Month)
- 11 -
Policy coordination failure: Transformation - Policy coordination failure - Sensitivity 50% 75% 95% 100% Niche market 0.3
0.225
0.15
0.075
0
0
150
300 Time (Month)
450
600
300 Time (Month)
450
600
Reflexivity failure: Transformation - Reflexivity failure - Sensitivity 50% 75% 95% 100% Niche market 0.8
0.6
0.4
0.2
0
0
150
- 12 -
Original Model documentation for: Modelling the dynamics of Technological Innovation Systems
Bob Walrave*, Rob Raven+ * Eindhoven University of Technology, School of Industrial Engineering, Eindhoven, The Netherlands + Utrecht University, Copernicus Institute, Utrecht, The Netherlands
- 13 -
The model was developed in Ventana System’s VENSIM software. Figure 1 gives an overview of the full model. The model, grounded in the literature, was subjected to robustness analyses and served to run the different scenarios in the context of technological innovation systems. Following modelling best practices (Holtz, 2011), we focus only on one part the overarching transition dynamic. That is, rather than modelling a full transition, we aim to replicate growth (or decline) of a TIS in the context of a dominant socio-technical regime. As such, the ‘goal’ of any given simulation is to arrive at the state where the ‘Market motor loop’ (R.5, Figure 1) grows strong enough (i.e., generates enough resources) to drive the whole system, independent of any form of external resources, in the context of a dominant socio-technical regime. The six loops (Figure 1) are grounded in the concept of motors of innovation (Suurs, 2009), which is combined with the notion of ‘transition pathways’ (Geels and Schot, 2007). These six feedback loops, which are discussed in the manuscript, are presented in different colours with the variable names written in black (in Figure 1). The green variables concern resources that are being fed into the system—following from ‘Fixed external resources’ [Euro] and/or the ‘Niche market’ [dmnl] itself—needed to drive the system. The brown variable denotes strong exogenous influences (‘Landscape pressure’ [dmnl] and ‘Fixed external resources’ [Euro]) on the system. The orange variables indicate adjustment times (delays) or ratios. The pink constants represent resource distribution factors. The purple variables cause for random variations to selected flows. The unit of time in the model is months and the total simulation time is 600 months. The simulation algorithm is Euler’s method with a step size (dt) of 0.0625 months. As explained in the manuscript, the model assumes a ‘single TIS – single regime’ situation and is not attuned to a technology-specific innovation system. The main ingredients of system dynamics models are stocks, flows, and variables. In the diagramming notation, stocks are represented by rectangles and denote a particular level (e.g., the amount of technological knowledge developed). Flows are depicted as pipes with valves and are responsible for changes (e.g., decrease of increase of the technological knowledge) in the stocks. Variables are those constructs that are dependent on stocks and flows (e.g., the ‘Resources to technology development’). Finally, the clouds represent infinite sources or outcomes of particular flows that are beyond the scope of the model. - 14 -
Regime resistance loop
Figure 1. Overview of the endogenous model structure. - 15 -
MODEL DESCRIPTION As explained in the manuscript, the model is composed of two main parts: technology development and market development.
Technology development The knowledge development motor loop – Loop B.1 The knowledge development motor serves as prerequisite for innovation processes to occur. Activities that underlie this loop are, for instance, academic studies and laboratory trials. Such research and development (R&D) related activities constitute a source of variation to the system (Suurs, 2009) – and it is assumed that this results in the development of a certain pathbreaking technology. We capture the current state-of-the-art, with respect to technological knowledge developed, by the stock ‘Technological knowledge developed’ [dmnl]. (This stock thus captures all the knowledge, distributed over all different agents, related to the pathbreaking invention.) This stock can vary between 0 (lowest level = no knowledge) and 1 (highest level = a nearly, for that moment in time, ‘complete’ understanding of the technology). It is widely acknowledged that the improvement of technological performance becomes increasingly more difficult once that technology matures and starts to reach it performance limit (De Liso & Filatrella, 2008). Indeed, development of technological performance typically follows a S-shaped growth curve over time (Christensen, 1992a, 1992b; Schilling & Esmundo, 2009; Suurs, 2009). We adopt this conception in our model by making technological progress increasingly difficult the higher the state of development/diffusion. More specifically, increasingly more resources are needed to achieve technological performance growth, the higher the state-of-the-art. In this respect, growth in this part of the system is bounded by a performance ceiling. This makes these two loops ‘goal-seeking’ in nature. If, for some reason, knowledge development stops (or is severely limited), the current body of knowledge becomes obsolete over time (due to new technological developments, etc.). As such, the ‘Technological knowledge developed’ [dmnl] stock is subject to an outflow, called ‘Knowledge decay rate’ [dmnl/month]. Here we assume an exponential decay structure (Sterman, 2000) (i.e., knowledge decreases at a rate proportional to its current value: The more - 16 -
knowledge there is, the more knowledge is subject to becoming out-dated). As such, we adopt an adjustment time (Sterman, 2000) given by the constant ‘Knowledge decay time’ [month]. Equation 1 captures the behaviour of the knowledge decay rate (where the ‘Knowledge development rate’ [dmnl/month] is given in Equation 2). (1) Knowledge decay rate [dmnl/month]: d (Technological knowledge developed) / dt = – Technological knowledge developed / Knowledge decay time [+ Knowledge development rate]
Vice versa, due to investments in, for instance, R&D (captured by the variable ‘Resources to R&D’ [Euro]), the stock ‘Technological knowledge developed’ [dmnl] increases. This increase is captured by the inflow ‘Knowledge development rate’ [dmnl/month]. Here, we assume an exponential growth structure. That is, we assume that improvement of technological performance becomes progressively more difficult once that technology matures and starts to reach it performance limit (De Liso and Filatrella, 2008). This growth characteristic is captured by Equation 2, by the introduction of an adjustment time (i.e., the ‘AT TKDe’ [month]) in combination with a increasingly limited potential maximum ‘Technological knowledge developed’ (i.e., 1 – ‘Technological knowledge developed’). This setup allows the stock ‘Technological knowledge developed’ to follow a S-shaped growth curve, typical for technological developments (Christensen, 1992a, 1992b; Schilling and Esmundo, 2009; Suurs, 2009). Subsequently, we calculate the inflow as given in Equation 2—where the constant ‘Euro to knowledge development rate’ [dmnl/Euro] captures how much knowledge is (initially) gained per invested Euro—and where the ‘Knowledge decay rate’ [dmnl/month] is presented in Equation 1. (2) Knowledge development rate [dmnl/month]: d (Technological knowledge developed) / dt = + ((1 - Technological knowledge developed) * (Resources to R&D * Euro to knowledge development rate)) / AT TKDe [– Knowledge decay rate]
The stock ‘Technological knowledge developed’ [dmnl] (partially) determines the levels of the stocks ‘Guidance of the search’ [dmnl] and ‘Resource mobilization’ [Euro]. As can be seen
- 17 -
in Figure 1, these stocks are also part of ‘The knowledge diffusion motor’ (loop B.2) and, therefore, are discussed in the next section. The latter stock captures the total amount of resources (in Euro’s) put to use for both technology development and diffusion. In this respect, a certain percentage of the available budget (denoted by ‘Knowledge resource distribution’ [dmnl—%]) is made available to knowledge development, while the rest (i.e., 1 – ‘Knowledge resource distribution’) is directed toward knowledge diffusion. Subsequently, the ‘Resources to R&D’ [Euro] can be calculated as in Equation 3. (3) Resources to R&D [Euro] = Knowledge resource distribution * Resource mobilization
The ‘Resources to R&D’ [Euro] subsequently drives the already discussed ‘Knowledge development rate’ [dmnl/month]. As such, this completes the description of the balancing ‘Knowledge development motor loop’ (loop B.1). The knowledge diffusion motor loop – Loop B.2 In order for the technology and a market to develop, the developed technological knowledge need to diffuse among relevant actors (through, e.g., conferences, workshops, and alliances). We capture the current state of knowledge diffused with the stock ‘Technological knowledge diffused’ [dmnl]. This stock also varies between 0 (implying no technological knowledge is diffused) and 1 (meaning that nearly all currently available technological knowledge is diffused). This stock is also subject to exponential decay due to knowledge loss over time, which is captured by the ‘Knowledge loss rate’ [dmnl/month], with adjustment time ‘Knowledge decay time’ [month]. Furthermore, if new knowledge is developed (i.e., a positive ‘Knowledge development rate’ [dmnl/month]), the level of ‘Technological knowledge diffused’ [dmnl] should, pro-rata, decrease. This then results in Equation 4—where the ‘Knowledge diffusion rate’ [dmnl/month] is explained by Equation 5. (4) Knowledge loss rate [dmnl/month]: d (Technological knowledge diffused) / dt = – (Technological knowledge diffused / Knowledge decay time) – (Technological knowledge diffused * (Knowledge development rate / Technological knowledge developed)) [+ Knowledge diffusion rate]
- 18 -
The level of ‘Technological knowledge diffused’ [dmnl] increases through investments (captured by the variable ‘Resources to knowledge diffusion’ [Euro] – see Equation 6). Similar to the structure underlying the ‘Knowledge development rate’ [dmnl/month], we assume that it becomes progressively more difficult to make progress and, as such, introduce an adjustment time ‘AT TKDi’ [month] in combination with a increasingly limited potential maximum ‘Knowledge diffusion rate’ [dmnl/month] (i.e., 1 – ‘Technological knowledge diffused’). Equation 5 captures this – where the constant ‘Euro to knowledge diffusion rate’ [dmnl/Euro] denotes how much knowledge is (initially) gained per invested Euro—and where the ‘Knowledge loss rate’ [dmnl/month] is given in Equation 4. (5) Knowledge diffusion rate [dmnl/month]: d (Technological knowledge diffused) / dt = + ((1 – Technological knowledge diffused) * (Resources to knowledge diffusion * Euro to knowledge diffusion rate)) / AT TKDi [– Knowledge loss rate]
Following the logic underlying Equation 3, the ‘Resources to knowledge diffusion’ [Euro] can be calculated as follows (Equation 6). (6) Resources to knowledge diffusion [Euro] = (1 – Knowledge resource distribution) * Resource mobilization
‘Technological knowledge developed’ and ‘Technological knowledge diffused’ shape the ‘guidance of the search’. For example, the successful realisation of a research project, contributing to ‘Technological knowledge developed’ and/or to 'Technological knowledge diffused’ can result in high expectations which contributes to the function ‘Guidance of the Search’ (Suurs and Hekkert, 2012). As such, we consider the interaction between the ‘Technological knowledge diffused’ [dmnl] and the ‘Technological knowledge developed’ [dmnl] to reflect the status of such shared development vision, influencing how effectively the ‘Resources to technology development’ can be utilized. Yet, we consider only a certain percentage (i.e., captured by the constant ‘Effectiveness and efficiency factor funding’ [dmnl— %]) of all available resources for technology development (i.e., ‘Resources to technology
- 19 -
development’ [Euro]) to be subject such the interaction—as in Equation 7, where ‘AT GoS’ [month] is the adjustment time:
(7) Change in Guidance of the Search [Euro/month]: d (Guidance of the Search) / dt = ((Technological knowledge developed * Technological knowledge diffused * Effectiveness and efficiency factor * Resources to technology development + (1 – Effectiveness and efficiency factor) * Resources to technology development) – Guidance of the Search) / AT GoS
Furthermore, as there is a time delay between the accreditation of resources and the actual resource division and utilization (i.e., ‘Resource mobilization’ [Euro]), we utilize a simple first-order adaptive structure (Sterman, 2000) for calculating the change to ‘Resource mobilization’ [Euro]. As such, we can describe Equation 8 – where ‘AT RM’ [month] denotes the adjustment time: (8) Change in Resource mobilization [Euro/month]: d (Resource mobilization) / dt = (Guidance of the search – Resource mobilization) / AT RM
The ‘Resources to knowledge diffusion’ [Euro] drives the already discussed ‘Knowledge diffusion rate’ [dmnl/month]. This then completes the description of the balancing ‘Knowledge diffusion motor loop’ (loop B.2).
Market development The entrepreneurial motor loop – Loop R.3 The manuscript outlines that the ‘Perceived legitimacy of the TIS’ is dependent on both technological legitimacy and market legitimacy. We consider the interaction between ‘Technological knowledge developed’ [dmnl] and ‘Technological knowledge diffused’ [dmnl] to represent such for former type of legitimacy. This latter type of legitimacy follows from (a) the level of ‘TIS structures’ [dmnl]; which positively influences the legitimacy—see Equation 16);
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and (b) the level of ‘Regime resistance toward TIS’ [dmnl], negatively influencing the legitimacy—see Equation 24). The perceived technological legitimacy and perceived market legitimacy combined determine the stock ‘Perceived legitimacy of the TIS’ [dmnl]. This stock can vary between 0 (no legitimacy) to 1 (full legitimacy). In general, perceptions adjust to new circumstances with a certain delay, which can be modelled in terms of the behaviour of a first-order adaptive system (Sterman, 2000). As such, we model change to the stock ‘Perceived legitimacy of the TIS’ [dmnl] as a first-order adaptive system, in Equation 9, with adjustment time ‘AT PLT’ [month]. (9) Change in Perceived legitimacy of the TIS [dmnl/month]: d (Perceived legitimacy of the TIS) / dt = ((Technological knowledge diffused * Technological knowledge developed * TIS structures * (1 – Regime resistance toward TIS))-Perceived legitimacy of the TIS ) / AT PLT
The stock ‘Entrepreneurial activity’ [dmnl] represents the number of firms and entrepreneurs becoming active in the innovation system. This stock, like the formerly described stocks, also varies between 0 (implying no entrepreneurial activity) and 1 (meaning full ‘saturation’ with respect to entrepreneurial activity). The stock ‘Entrepreneurial activity’ [dmnl] is expected to change, with a certain delay, as the result of change within the ‘Perceived legitimacy of the TIS’ [dmnl]. As explained in the manuscript, we assume a non-linear relationship, which is modelled by means of a so-called lookup function (denoted by the lookup variable ‘Effect of PLT on EA’). This relationship is depicted in Figure 2. This relation thus follows a ‘band-wagon effect’ (Geels, 2005), where the rate of change in entrepreneurial activity increases the more entrepreneurial activity is installed.
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1
1
Entrepreneurial activity
Entrepreneurial activity
0.9
.75
0.8 0.7 0.6
.5 0.5
.25
0.4 0.3 0.2 0.1
0
0 1
0.95
0.9
0.25 0.5 0.75 Perceived legitimacy of the TIS Perceived legitimacy of the TIS
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
1
Figure 3. Non-linear relation between ‘Perceived legitimacy of the TIS’ and ‘Entrepreneurial activity’. Entrepreneurial activity can, furthermore, be spurred by external actors, for instance, by external agents approaching firms and research institutes to participate in development initiatives aimed at the realization of pilots and demonstrations. In this respect, it is very likely that for instance governments, aiming to expedite the development and introduction of the socially desirable new technology and belonging solutions (e.g., more sustainable), are backing entrepreneurs with project specific subsidies (Suurs, 2009). This reduces perceived entrepreneurial risks—effectively resulting in more ‘Entrepreneurial activity’ [dmnl] respective to the current level of ‘Perceived legitimacy of the TIS’ [dmnl]. We modelled this by introducing the variable ‘Effect funding on PLT’ [dmnl] (see Equation 11) within the lookup section of Equation 10. Equation 10 captures the described dynamics—with adjustment time ‘AT EI’. (Note that growth in ‘Entrepreneurial activity’ [dmnl] is bounded by the ‘1 – Entrepreneurial activity’ term.) (10) Entrepreneurial interest rate [dmnl/month]: d (Entrepreneurial activity) / dt = + ((1 – Entrepreneurial activity) * (Effect of PLT on EA (Perceived legitimacy of the TIS + Effect funding on PLT)) / AT EI [– Entrepreneurial disinterest rate]
- 22 -
In this respect, following equation 11, the variable ‘Effect funding on PLT’ [dmnl] effectively converts the amount of resources available to stimulate ‘Entrepreneurial activity’ [dmnl] (i.e., ‘Funding EA’ [Euro], see Equation 23) to an increase in the level of ‘Perceived legitimacy of the TIS’ [dmnl], through introduction of the constant ‘Euro to change in PLT’ [dmnl/Euro]. For model robustness, we limit the maximum outcome of Equation 11 to 1 by means of a so-called ‘MIN’ statement. (11) Effect funding on PLT [dmnl] = MIN (Euro to change in PLT (Funding EA), 1)
Yet, if the ‘Perceived legitimacy of the TIS’ [dmnl] drops, so does entrepreneurial interest in the TIS—albeit with a certain delay. We adopt an exponential decay structure to model this; with adjustment time ‘AT EDI’ [month], as presented by Equation 12. (12) Entrepreneurial disinterest rate [dmnl/month]: d (Entrepreneurial activity) / dt = – Entrepreneurial activity / AT EDI [+ Entrepreneurial interest rate] As ‘Entrepreneurial activity’ [dmnl] increases, so do the resources provided to further system development, as initial entrepreneurial profits feedback into the system. We model this through introducing the constant ‘Variable input percentage’ [dmnl—%], which makes a part of the total amount of external resources (i.e., ‘Fixed external resources’ [Euro], see the section ‘Exogenous inputs’) dependent on the level of ‘Entrepreneurial activity’ [dmnl]. Equation 13 details the mechanism. (13) External resources [Euro] = (1 – Variable input percentage) * Fixed external resources + Variable input percentage * Fixed external resources * Entrepreneurial activity
The resources are subsequently distributed over technology development—through ‘Funding technology development (External resources)’ [Euro]—and market development— ‘Funding market development (External resources)’ [Euro], through a given percentage (denoted by ‘External resources distribution’ [dmnl—%]). From here, we can describe Equation 14 and 15.
- 23 -
(14) Funding technology development (External resources) [Euro] = External resources * External resources distribution
(15) Funding market development (External resources) [Euro] = External resources * (1 – External resources distribution)
The ‘Funding technology development (External resources)’ [Euro] (Equation 14) subsequently serves, partly, as input for the variable ‘Resources to technology development’ [Euro] (see Equation 22), which in its turn feeds into ‘Resource mobilization’ [Euro] (Equation 8). The resources for market development (i.e., Equation 15) are further split into resources directed to stimulate entrepreneurial activity (i.e., ‘Funding EA’ [Euro], Equation 23); and funds directed to stimulate growth in TIS structures (i.e., ‘Funding TISS’ [Euro], Equation 24)—see section on the ‘Market motor loop’ (loop R.5). As such, this completes the description of the reinforcing ‘Entrepreneurial motor loop’ (loop R.3).
The system building motor loop – Loop R.4 As argued in the manuscript, as ‘Entrepreneurial activity’ [dmnl] increases, ‘TIS structures’ [dmnl] arise. The ‘TIS structures’ [dmnl] is modelled as a stock, and can vary between 0 (no TIS structures) and 1 (a ‘complete’ set of TIS structures). We assume that the influence of entrepreneurs on the formation processes of ‘TIS structures’ [dmnl] becomes increasingly large as ‘Entrepreneurial activity’ [dmnl] increases—reflecting the need for a certain critical mass before substantial influence can be exercised. We model this relation between ‘Entrepreneurial activity’ [dmnl] and ‘TIS structures’ [dmnl] through a lookup function; denoted by the variable ‘Effect of EA on TISS’ [dmnl] and depicted in Figure 3.
- 24 -
Formal institutions and systems
1
TIS structures
.75
1 0.9 0.8 0.7 0.6
.5 0.5 .25
0
0.4 0.3 0.2 0.1 0 1
0.95
0.9
0.5 0.75 Entrepreneurial activity Entrepreneurial activity
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.25
0.3
0.25
0.2
0.15
0.1
0.05
0
0
1
Figure 4. Non-linear relation between ‘Entrepreneurial activity’ and ‘TIS structures’. Other actors can also stimulate the development of ‘TIS structures’ [dmnl]. For instance, governments can support the building of institutions by introducing new environmental standards fit to the emerging TIS (Suurs, 2009). We model this influence by introducing the variable ‘Effect funding on TISS’ [dmnl/Euro] in Equation 16, which serves to translate the input that originates from the variable ‘Funding TISS’ [Euro] (see Equation 24) into an increase in the ‘TIS structures’ [dmnl]. Note that, once more for model robustness, we limit the potential ‘Effect funding on TISS’ [dmnl] to 1 by means of a ‘MIN’ statement. From here, we can define Equations 16 and 17.
(16) Change in TIS structures [dmnl/month]: d (TIS structures) / dt = (1 – TIS structures) * ((Effect of EA on TISS (Entrepreneurial activity) + Effect funding on TISS) – TIS structures) / AT TISS
(17) Effect funding on TISS [dmnl/Euro] = MIN (Euro to change in TISS * Funding TISS, 1)
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The existence of ‘TIS structures’ [dmnl] influences the further development of the TIS in two manners: First, the existence of FIS provides face validity for the emerging technology, directly influencing the ‘Perceived legitimacy of the TIS’ [dmnl] (see Equation 9). Furthermore, the existence of ‘TIS structures’ [dmnl] negatively influences the ‘Regime resistance toward TIS’ [dmnl] as broadening networks and the establishment of intermediaries enforce growth of the emerging niche market, thereby counteracting regime resistance. Also see Equation 25. This concludes the discussion on the reinforcing ‘System building motor loop’ (loop R.4). The market motor loop – Loop R.5 As explained in the manuscript, growth in ‘Entrepreneurial activity’ [dmnl] also results, albeit with a delay (‘AT NM’ [month]), in ‘Niche market’ [dmnl] growth (once more, we adopt a firstorder adaptive system). Yet, the ‘Niche market’ [dmnl] can only truly develop when innovation system actors successfully navigate the creation of ‘TIS structures’ [dmnl]. As such, we consider the interaction between ‘Entrepreneurial activity’ [dmnl] and ‘TIS structures’ [dmnl] to indicate change in the ‘Niche market’ [dmnl]—See Equation 18. (18) Change in Niche market [dmnl/month]: d (Niche market) / dt = ((Entrepreneurial activity * TIS structures) – Niche market) / AT NM
The reinforcing ‘Market motor loop’ (loop R.5) can, once dominant, sustain all other motors (albeit that this requires a rather large ‘Niche market’), by generating ‘Niche input’ [Euro] to the funds going into technology development. In such situation, there is no need for ‘External resources’ [Euro] anymore, as a self-sustaining TIS has been created. The size of the variable ‘Niche input’ [Euro] is determined by multiplying the size of the ‘Niche market’ [dmnl] with the constant ‘Niche to Euro factor’ [Euro/dmnl]—as in Equation 19.
(19) Niche input [Euro] = Niche to Euro factor * Niche market
Subsequently, the available resources, following from the ‘Niche market’ [dmnl] are distributed over technology development and market development. The constant ‘Niche input
- 26 -
distribution’ [Dmnl—%] determines this division; similar to the constant ‘External resources distribution’—see Equations 14 and 15. From here, we formulate Equations 20 and 21. (20) Funding technology development (Niche) [Euro] = Niche input * (Niche input distribution)
(21) Funding market development (Niche) [Euro] = Niche input * (1 – Niche input distribution)
The ‘Funding technology development (Niche)’ [Euro] (Equation 20) and the ‘Funding technology development (External resources)’ [Euro] (Function 14) determine the variable ‘Resources to technology development’ [Euro] (which serves as input for Equation 8), as described by Equation 22. (22) Resources to technology development [Euro] = Funding technology development (External resources) + Funding technology development (Niche)
The resources following from ‘Funding market development (External resources)’ [Euro] and from ‘Funding market development (Niche)’ [Euro] are utilized for the development of ‘Entrepreneurial activity’ [dmnl] and ‘TIS structures’ [dmnl]. We do so by introducing two constants: (1) ‘Market resource distribution (External resources)’ [dmnl—%], and (2) ‘Market resource distribution (Niche)’ [dmnl—%]. This results in Equations 23 and 24—which feed into Equations 11 and 17. (23) Funding EA [Euro] = Funding market development (External resources) * Market resource distribution (External resources) + Funding market development (Niche) * Market resource distribution (Niche)
(24) Funding TISS [Euro] = Funding market development (External resources) * (1 – Market resource distribution (External resources)) + Funding market development (Niche) * (1 – Market resource distribution (Niche))
This concludes the description of the reinforcing ‘Market motor loop’ (loop R.5).
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The regime resistance loop – Loop B.6 The final loop in the model is the ‘Sailing ship loop’ (B.6). This loop is added to the existing TIS framework to account for the potential feedback loops between a TIS and its context. First, the growth of a TIS ‘Niche market’ [dmnl] triggers the ‘Sailing ship effect’ [dmnl], which implies an increase in the ‘Regime resistance toward TIS’ (ranging from 0-1: 0 implying no selection pressure at all, 1 means a severe selection environment; i.e., no ‘Niche market’ [dmnl] can develop—see Equation 9). The stock, model as a first-order adaptive system, is subject to adjustment time ‘AT RRT’ [month]. As explained, we assume that the development of ‘TIS structures’ [dmnl] lowers the ‘Regime resistance toward TIS’ [dmnl]. In order to control for the effect that ‘TIS structures’ [dmnl] has on ‘Regime resistance toward TIS’ [dmnl], we introduce the constant ‘Effect FIS on RRT’ [dmnl—%]. More specifically, this latter constant causes that a fully developed ‘TIS structures’ [dmnl] diminishes the ‘Regime resistance toward TIS’ [dmnl] to a certain degree— rather then completely (this is in line with our aim to replicate growth/decline of a TIS in the context of a dominant regime, rather than modelling a full transition). Yet, simultaneously, due to ‘Niche market’ [dmnl] growth the ‘Regime resistance toward TIS’ [dmnl] increases as a result of the ‘Sailing ship effect’ [dmnl] (De Liso and Filatrella, 2008). Note that, in Equation 25, we bounded growth to the stock ‘Regime resistance toward TIS’ [dmnl] to 1—by means of a ‘MIN’ statement. The assumed persistence of the ‘Sailing ship effect’ [dmnl], in combination with a decline in the ‘TIS structures’ [dmnl] (as the result of a failing TIS) can, without such ‘MIN’ statement, result in the situation where the ‘Regime resistance toward TIS’ grows larger than the theoretically possible value of 1 (and compromise model robustness). Finally, the ‘Regime resistance toward TIS’ [dmnl] is subject to ‘Landscape pressure’ [dmnl]—see section on ‘Exogenous inputs’. Such influences (e.g., oil price changes, climate change, etc.) put significant pressure on the currently dominant regime (Suurs, 2009), causing a drop in the ‘Regime resistance on TIS’ [dmnl]. As such, we can model change in the ‘Regime resistance toward TIS’ [dmnl] as follows (Equation 25). (25) Change in the Regime resistance toward TIS [dmnl/month]: - 28 -
d (Regime resistance toward TIS) / dt = (MIN (1 – TIS structures * Effect FIS on RRT + Sailing ship effect – Landscape pressure, 1) – Regime resistance toward TIS) / AT RRT
The modelled ‘Sailing ship effect’ can be described in the following manner: phase A) Initially, the ‘Sailing ship effect’ will build up as a result of ‘Niche market’ [dmnl] growth. phase B) Subsequently, once a certain ‘Niche market’ [dmnl] size has been crossed, the ‘Sailing ship effect’ will retain its maximum effect size (and not grow any stronger). phase C) Then, if the ‘Niche market’ [dmnl] has been able to keep a certain size, for a given amount of time, the ‘Sailing ship effect’ declines again. As such, in order to determine the ‘Sailing ship effect’ [dmnl], two elements of the ‘Niche market’ [dmnl] need to be captured: First, its historically largest size. And second, the duration the ‘Niche market’ [dmnl] exceeded a certain assumed threshold size. That is, the sailing ship effect is not likely to last indefinitely and, as such, a niche market, exceeding a certain size (captured by the constant ‘Size threshold SSE’ [dmnl]) for a given amount of time (captured by the constant ‘Duration threshold SSE’ [month]), is likely to overcome the ‘Sailing ship effect’ [dmnl]. For the former element, we introduce the stock ‘Historically largest niche market’ [dmnl], which records the historically largest ‘Niche market’ as a one-way adaptive system (implying the stock can never decline) with no delay (adjustment time equals ‘Time Step’). As in Equation 26. (26) Change in Historically largest Niche market [dmnl]: d (Historically largest Niche market) / dt = IF THEN ELSE (Historically largest niche market < Niche market, (Niche market – Historically largest niche market) / TIME STEP, 0)
The latter element is captured by the stock ‘Duration above threshold’ [Month] (once more, an one-way adaptive system with no delay), which keeps track of the amount of months
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the ‘Niche market’ exceeded the given ‘Size threshold SSE’ [Dmnl]—as described by Function 27. (27) Change in Duration above threshold [month]: d (Duration above threshold) / dt = IF THEN ELSE (Niche market > Size threshold SSE, 1, 0) / TIME STEP
Equation 26 and 27 combined determine the ‘Sailing ship effect’ [dmnl] (following the outlined reasoning). In order to do so, we first determine the ‘SSE status’ [dmnl] (a stock, ranging from 0 to 1). This stock serves to translate the values of ‘Historically largest niche market’ [dmnl] and ‘Duration above threshold’ [dmnl] into a specific value that can be used to calculate the ‘Sailing ship effect’ [dmnl]. We do so as follows: If the ‘Historically largest niche market’ [dmnl] did not exceed the ‘Size threshold SSE’ [dmnl], the ‘Sailing ship effect’ [dmnl] is calculated with the ‘Historically largest niche market’ [dmnl] (phase A). If the size threshold has been passed, we need to determine if the duration threshold has been passed. Note that we assumed the ‘Sailing ship effect’ [dmnl] to be highly persistent, and its effect is not likely to decline before the duration threshold has been reached. As such, if the duration threshold has not been passed, then the ‘Sailing ship effect’ [dmnl] is, calculated by means of the ‘Size threshold SSE’ [dmnl]. This implies the highest possible effect size (phase B). If the ‘Duration threshold SSE’ [month] has been exceeded, then the ‘Sailing ship effect’ [dmnl] is, once more, determined by the ‘Historically largest niche market’ [dmnl]; following Figure 5, this implies a potential decline of the ‘Sailing ship effect’ [dmnl] (phase C). Figure 4 outlines the reasoning behind Equation 28.
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Evaluate: Historically largest niche market < Size threshold SSE
YES (Build-up phase of the Sailing ship effect-phase A). Historically largest niche market
NO (Size threshold has been reached). Evaluate: Duration above threshold < Duration threshold SSE
YES (Sailing ship effect remains at maximum level until duration threshold has been exceeded-phase B): Size threshold SSE NO (Duration threshold reached, decline in Sailing ship effect possible-phase C): Historically largest niche market
Figure 4. Reasoning behind Equation 28. Given that this effect follows from the response of incumbents on TIS growth, we model the delay ‘AT SSEs’ [month]. This results in Equation 28. (28) Change in SSE status [dmnl/month]: d (SSE status) / dt = IF THEN ELSE ( Historically largest niche market < Size threshold SSE, Historically largest niche market, IF THEN ELSE ( Duration above threshold < Duration threshold SSE, Size threshold SSE, Historically largest niche market) / AT SSEs
Subsequently, the ‘SSE status’ [dmnl] determines the variable ‘Sailing ship effect’ [dmnl]. Given the non-linear nature of this relationship (De Liso and Filatrella, 2008), we adopted a
Sailing ship effect
lookup variable ‘Effect SSEs on SSE’ (see Figure 5).
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0.295 0.290 0.285 0.280 0.276 0.271 0.266 0.261 0.256 0.251 0.246 0.241 0.237 0.232 0.227 0.222 0.217 0.212 0.207 0.202 0.000
SSE status Figure 5. Non-linear relation between ‘SSE status’ and the ‘Sailing ship effect’. - 31 -
As can be seen in Figure 5, the potential maximum ‘Sailing ship effect’ [dmnl] equals 1. Following Equation 25, such effect size would result in a ‘Regime resistance toward TIS’ of 1 and a ‘Perceived legitimacy of the TIS’ of 0; and thus guaranteed TIS failure. As such, we scale the ‘Sailing ship effect’ (i.e., the outcome of the lookup function given by ‘Effect SSEs on SSE’) with the constant ‘Maximum effect size SSE’ [Dmnl]—as in Equation 29. (29) Sailing ship effect [dmnl] = Effect SSEs on SSE (SSE status) * Maximum effect size SSE
The Sailing ship effect then feeds into the ‘Regime resistance toward TIS’ [dmnl] (Equation 25), which subsequently determines part of the ‘Perceived legitimacy of the TIS’ [dmnl] (Equation 9). As such, this concludes the description of the balancing ‘Sailing ship effect loop’ (loop B.6).
EXOGENOUS INPUTS The model, as described so far, is fully endogenous in nature. Yet, as noted throughout this text, there are two important exogenous inputs that influence TIS development. First of all, there is ‘Landscape pressure’ [dmnl], which lowers the ‘Regime resistance toward TIS’ [dmnl]. Secondly, the model’s dynamics are started by means of ‘Fixed external resources’ [Euro]. As explained, these funds can feed into technology development as well as the market development (through ‘External resources’ [Euro], Equation 13). In an attempt to keep the model parsimonious, we describe both inputs in terms of (a) size [dmnl or Euro], (b) timing [month], and (c) duration [month]. Equation 30 details the ‘Landscape pressure’ [dmnl]; and Equation 31 details the ‘Fixed external resources’ [Euro]. (30) Landscape pressure [dmnl] = IF THEN ELSE (Time >= Timing LP :AND: Time < Timing LP + Duration LP, Size LP, 0)
(31) Fixed external resources [Euro] = IF THEN ELSE (Time >= Timing FER :AND: Time < Timing FER + Duration FER, Size FER, 0)
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RANDOM VARIATIONS As outlined in the manuscript, we included sources of random variation to the model. More specifically to the growth and decline in ‘Technological knowledge developed’, ‘Technological knowledge diffused’, ‘Entrepreneurial activity’, ‘TIS structures’, and ‘Niche market’. We modelled this by means of so-called ‘Pink noise’ [dmnl—%] (Sterman, 2000). More specifically, the following stock and flow structure was used—with adjustment time ‘AT noise’ [month]. (32) Change in Pink noise [dmnl—%] d (Pink noise) / dt = (1 + Normal distribution – Pink noise) / AT noise
(33) Normal distribution [dmnl] = RANDOM NORMAL (-5, 5, 0, 5, Seed x)
Five different ‘seeds’ (see Equation 33) were used to create five different ‘versions’ of noise, each version influencing one or more flows. Table 1 detail which flow was subject to what ‘version’ of noise. Table 1. Pink noise – Flow relationships. Pink noise ‘version’
Introduced to flow
Pink noise 1
Knowledge development rate (Equation 2); Knowledge loss rate (Equation 4)
Pink noise 2
Knowledge decay rate (Equation 1); Knowledge diffusion rate (Equation 5)
Pink noise 3
Entrepreneurial interest rate (Equation 10)
Pink noise 4
Change in niche market (Equation 18)
Pink noise 5
Entrepreneurial disinterest rate (Equation 12); Change in FIS (Equation 18)
We multiplied the ‘Pink noise’ [dmnl—%] with the numerator of the targeted flow. The following adjustments were made to Equations 1, 2, 4, 5, 10, 12, 16, and 18—resulting in
- 33 -
Equations 1a, 2a, 4a, 5a, 10a, 12a, 16a, and 18a. We present the new functions below. For the reader’s convenience, first the original function is given, after which the adjusted equation is presented. (1) Knowledge decay rate [dmnl/month]: d (Technological knowledge developed) / dt = – Technological knowledge developed / Knowledge decay time [+ Knowledge development rate] (1a) Knowledge decay rate [dmnl/month]: d (Technological knowledge developed) / dt = – (Technological knowledge developed * Pink noise 2) / Knowledge decay time [+ Knowledge development rate]
(2) Knowledge development rate [dmnl/month]: d (Technological knowledge developed) / dt = + ((1 - Technological knowledge developed) * (Resources to R&D * Euro to knowledge development rate)) / AT TKDe [– Knowledge decay rate] (2a) Knowledge development rate [dmnl/month]: d (Technological knowledge developed) / dt = + ((1-Technological knowledge developed) * Pink noise 1 * (Resources to R&D * Euro to knowledge development rate)) / AT TKDe [– Knowledge decay rate]
(4) Knowledge loss rate [dmnl/month]: d (Technological knowledge diffused) / dt = – (Technological knowledge diffused / Knowledge decay time) – (Technological knowledge diffused * (Knowledge development rate / Technological knowledge developed)) [+ Knowledge diffusion rate] (4a) Knowledge loss rate [dmnl/month]: d (Technological knowledge diffused) / dt = – (Technological knowledge diffused / Knowledge decay time) – (Technological knowledge diffused * (Knowledge development rate * Pink noise 1 / Technological knowledge developed )) [+ Knowledge diffusion rate]
(5) Knowledge diffusion rate [dmnl/month]:
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d (Technological knowledge diffused) / dt = + ((1 – Technological knowledge diffused) * (Resources to knowledge diffusion * Euro to knowledge diffusion rate)) / AT TKDi [– Knowledge loss rate] (5a) Knowledge diffusion rate [dmnl/month]: d (Technological knowledge diffused) / dt = + ((1 – Technological knowledge diffused) * (Pink noise 2 * Resources to knowledge diffusion * Euro to knowledge diffusion rate)) / AT TKDi [– Knowledge loss rate]
(10) Entrepreneurial interest rate [dmnl/month]: d (Entrepreneurial activity) / dt = + ((1 – Entrepreneurial activity) * (Effect of PLT on EA(Perceived legitimacy of the TIS + Effect funding on PLT)) / AT EI [– Entrepreneurial disinterest rate] (10a) Entrepreneurial interest rate [dmnl/month]: d (Entrepreneurial activity) / dt = + ((1 - Entrepreneurial activity) * Pink noise 3 * Effect of PLT on EA(Perceived legitimacy of the TIS + Effect funding on PLT)) / AT EI [– Entrepreneurial disinterest rate] (12) Entrepreneurial disinterest rate [dmnl/month]: d (Entrepreneurial activity) / dt = – Entrepreneurial activity / AT EDI [+ Entrepreneurial disinterest rate] (12a) Entrepreneurial disinterest rate [dmnl/month]: d (Entrepreneurial activity) / dt = – Entrepreneurial activity * Pink noise 5 / AT EDI [+ Entrepreneurial disinterest rate]
(16) Change in TIS structures [dmnl/month]: d (TIS structures) / dt = (1 – TIS structures) * ((Effect of EA on TISS(Entrepreneurial activity) + Effect funding on TISS) – TIS structures) / AT TISS (16a) Change in TIS structures [dmnl/month]:
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d (TIS structures) / dt = (1 – TIS structures) * Pink noise 5 * ((Effect of EA on TISS(Entrepreneurial activity) + Effect funding on TISS) – TIS structures ) / AT TISS
(18) Change in Niche market [dmnl/month]: d (Niche market) / dt = ((Entrepreneurial activity * TIS structures) – Niche market) / AT NM (18a) Change in Niche market [dmnl/month]: d (Niche market) / dt = (MIN (Entrepreneurial activity * TIS structures * Pink noise 4, 1) – Niche market) / AT NM
Due to the ‘Pink noise’ [dmnl] introduced in Equation 18a, the stock ‘Niche market’ [dmnl] can grow larger than 1 (which should, theoretically, not be possible). As such, a ‘MIN’ statement is introduced to prevent the stock from growing larger than 1.
EXPERIMENTAL SETUP In order to conduct the experiments as outlined in the manuscript, the following constants were adjusted (Note that all the constants [incl. units and values] are listed in the section ‘Constants and initial values’.): (a) ‘Landscape pressure’ [dmnl] was the same (in terms of size, timing, duration) for every experiment. (b) The ‘Fixed external resources’ [Euro] varied among the different experiments in terms of: 1. Timing: a proactive resource condition (i.e., ‘Fixed external resources’ [Euro] introduced before ‘Landscape pressure’ [dmnl]) versus a reactive resource condition (i.e., ‘Fixed external resources’ [Euro] introduced at the same time as ‘Landscape pressure’ [dmnl). 2. Duration: Vary between 10 and 15 years of ‘External resources’ (while keeping the ‘size’ the same). (c) We varied the ‘Regime-TIS relation’ by manipulating:
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1. The ‘Duration threshold SSE’ [month] (shorter for a ‘Symbiotic’ relationship versus longer for a ‘Competitive’ relationship). 2. The ‘Maximum effect size SSE’ [dmnl] (lower for a ‘Symbiotic’ relationship versus higher for a ‘Competitive’ relationship). (d) Finally, we modelled three resource conditions through adjustment of the constant ‘External resources distribution’ [dmnl—%]: 1. Technology-oriented resource conditions. 2. Market-oriented resource conditions. 3. Hybrid conditions. Table 2, as also presented in the manuscript, provides an overview of all conducted experiments. Table 2. Experimental setup.
Regime-TIS relation TIS developed before landscape event? Technology-oriented resource conditions Market-oriented resource conditions Hybrid conditions
De-alignment and realignment Symbiotic No
Reconfiguration
Transformation
Symbiotic Yes
Competitive No
Technological substitution Competitive Yes
Experiment 1a
Experiment 4a
Experiment 7a
Experiment 10a
Experiment 1b
Experiment 4b
Experiment 7b
Experiment 10b
Experiment 2a
Experiment 5a
Experiment 8a
Experiment 11a
Experiment 2b
Experiment 5b
Experiment 8b
Experiment 11b
Experiment 3a Experiment 3b
Experiment 6a Experiment 6b
Experiment 9a Experiment 9b
Experiment 12a Experiment 12b
CONSTANTS AND INITIAL VALUES Table 3 lists all the constants (incl. values and units). Table 4 gives all the initial values (incl. values and units). Table 3. List of all the constants used. Name of constant AT EI
Value 12
Unit Month
- 37 -
Remark
Name of constant AT TISS AT GoS AT NM AT noise AT PLT AT RM AT RRT AT SSEs AT TKDe AT TKDi Duration FER
Value 60 3 24 6 12 6 12 12 3 3 180 OR 240
Unit Month Month Month Month Month Month Month Month Month Month Month
Duration LP Duration threshold SSE Effect size FIS on RRT Effectiveness and efficiency factor Euro to change in TISS
60 12 OR 180 0.4 0.25
Month Month Dmnl—% Dmnl—%
1/50.000
Euro to change in PLT
1/15.000
Euro to knowledge development rate Euro to knowledge diffusion rate External resources distribution
1/10.000
Euro / dmnl Euro / Dmnl Dmnl / Euro Dmnl / Euro Dmnl—%
Knowledge decay time Knowledge resource distribution Market resource distribution (External resources) Market resource distribution (Niche) Maximum effect size SSE Niche input distribution Niche to Euro factor Seed 1 Seed 2 Seed 3 Seed 4
1/1.000 0.75 OR 0.25 OR ‘hybrid’1 60 0.8
Experiments a versus experiments b (see Table 2) Symbiotic OR Competitive
Technology push OR Market pull OR Hybrid. See footnote.
Month Dmnl—%
0.5
Dmnl—%
0.1
Dmnl—%
0.05 OR 0.25 0.05 175.000
Dmnl Dmnl—% Euro / dmnl Dmnl Dmnl Dmnl Dmnl
3425 11 4312 12355
Remark
Symbiotic OR Competitive
1
The hybrid development, as outlined in the manuscript, goes from 0.9 to 0.1 over a period of 36 months (after 36 month, it remains 0.1 until the ‘Duration FER’ has been reached—as determined by Equation 31). This was modelled as follows: IF THEN ELSE (Time > Timing FER :AND: Time < Timing FER + 36, 0.9 – (0.8 / 36) * (Time – Timing FER), IF THEN ELSE (Time
