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Model Documentation for:
Getting trapped in the suppression of exploration:
A simulation model
This document is made available to the reviewers. In case of publication of the manuscript, this
documentation will be available upon request from the authors.
The model was developed in VENSIM software. The full model, in terms of stock and flows, is given on
the next page (Figure 1). The model, grounded in the literature, was subjected to sensitivity analyses and
also served to run historyreplicating and historydivergent simulations (see the manuscript for the main
results).
The theoretical background of the model can be summarized as follows. First, the model considers the
dynamic effects of aligning exploitation and exploration with environmental aspects. Second, we assume
exploitation and exploration activities are two ends of one continuum that are constrained by a shared set
of (limited) resources. Third, the model focuses on the capabilities of top management to signal
environmental changes and translate these into a balanced portfolio of exploitation and exploration
projects. In this respect, we assume the existence of an ‘optimal’ (i.e. most profitable) exploitation
exploration balance. This managerial capability arises from the interaction between top management and
the Board of Directors. Fourth, myopic forces limit the speed in which strategic changes are made.
Finally, we assume the firm in our model is technically fit; that is, the model focuses on the firm’s
evolutionary fitness and, as such, on top management’s capability to align the exploitationexploration
mix with the environmental context.
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Figure 1: Overview of the complete model
The three feedback loops, as discussed in the manuscript, are given in different colors with the variable
names written in black. The ‘External pressure’ feedback loop is depicted in blue, the ‘Stick to
exploitation’ feedback loop in red, and the ‘Attempt to explore’ feedback loop in orange. Please note that
the External pressure and Stick to exploitation loops overlap (from RIE to Change in investment
exploitation). Moreover, the Attempt to explore feedback loop overlaps a critical part of the External
pressure loop (from Inv_Explore to RIE). The blue variables denote exogenous influences. The green
variables indicate adjustment times (delays). The unit of time in the model is weeks and the total
simulation time was 800 weeks (slightly more than 15 years). The simulation algorithm was Euler’s
method with a step size (dt) of 0.25 weeks.
Section 2 of this document describes all equations of the formal model in detail. Subsequently, we provide
an overview of the model settings and the sensitivity of the calibrated variables. Section 4 explores
whether the model should be deterministic or stochastic.
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1. Model description
Capabilities are often a matter of degree (Winter, 2000), and can therefore be modeled as continuous
variables. In our model, the balance between exploration and exploitation (comprising organizational
ambidexterity) is determined by the distribution of the available resources (AR) over the two ends.
Following our assumption described in the previous section, the amount of AR, an auxiliary variable, is
finite: it is calculated as a certain percentage (POR) of the operating result (OR) in a current period.
Nevertheless, we assume a minimum amount of resources (MAR) that will be available even when the OR
is negative or very small. MAR, an exogenous constant (set to 0.5), prevents negative amounts of AR and
thus simulation errors. In order to achieve this, the ‘MAX’ function is used. This function assesses if the
calculated AR is greater than the MAR and then returns the calculated value (if true) or an assumed fixed
minimum amount of resources (MAR) (if false). (Note that MAR does not influence the process theory as
outlined in the paper because a negative OR will only occur at the very end of the described sequences of
events.)
(1) AR = MAX (OR * POR, MAR)
The percentage of the AR invested in exploration is captured by the variable ‘Resource investment in
exploration’ (RIE) (see function 17). The stock ‘Investment in exploitation’ (Inv_Exploit) refers to the
amount of resources invested in exploitation in the current period. On the other end of the continuum, the
stock ‘Investment in exploration’ (Inv_Explore) denotes the level of resources allocated to exploration in
the current period. Recent studies show that implementing new innovation strategies and thus routines is
not simple; moreover, it takes considerable time and effort before these strategies and routines become
effective (e.g. Durmusoglu et al., 2008). The desired resource adjustment is therefore subject to an
adjustment time (AT) (exogenous constants). The AT is shorter for exploitation (AT_Exploit) than for
exploration (AT_Explore), since it involves more radical changes to the routines. This gives the following
equations:
Change in investment exploitation:
(2) d (Inv_Exploit) / dt = ((1  RIE) * AR – Inv_Exploit) / AT_Exploit
Change in investment exploration:
(3) d (Inv_Explore) / dt = (RIE * AR – Inv_Explore) / AT_Explore
The exogenous variable ‘Environmental competitiveness’ (EC) represents the level of competition
in the firm’s environment and captures the number and strength of competitors in the current period. This
exogenous variable ranges from 0 (monopolistic) till 1 (highly competitive). The EC variable was
estimated by calculating the Herfindahl index for the case firm. This index is calculated by subtracting the
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sum of the squared market shares from one. This is captured by the following equation, where si is the
market share of firm i in the market, and N is the number of firms:
(4) ECi = Herfindahl index = 1 
‘Environmental dynamism’ (ED) is an exogenous variable representing the level of dynamism in the
market in the current period. It ranges from 0 (extremely lethargic) to 1 (extremely dynamic). This
variable was estimated by rescaling the S&P 500 index (from the beginning of 1994 till the ending of the
3rd quarter of 2009). More specifically, the S&P 500 growth rate was calculated for every t (with t0 = 1)
and the result subtracted with 1. (This latter is done because the initial situation is assumed stable and the
starting values of ED should therefore be close to 0, rather than 1.) This operation is captured by gr. The
resulting data set (ranging from 0.0 to 2.3) was then divided by x to ensure fit with the given range for ED.
Lastly, the moving average over 26 weeks was taken in order to smooth out any nonsystematic changes.
This results in the following algorithm, where x will equal 3:
(5) EDt =
The variable ‘Environmental competitiveness and dynamism’ (ECD) represents the state of the
environment in the current period, which determines the most appropriate exploitationexploration mix at
a specific moment in time. ECD is a continuous variable ranging from 0 (extremely stable) till 1
(extremely instable). The ECD variable is determined by the two exogenous variables EC and ED. More
specifically, the two lookup variables ‘Effect of EC on ECD’ and ‘Effect of ED on ECD’ capture the
influence of EC and ED on ECD. Concerning the former, the Scurve (see Figure 2) represents the
situation in which high levels of dynamism bring along the need for exploitation, while low levels of
dynamism need a more balanced portfolio of exploitation and exploration activities. Concerning the latter,
the Scurve (see Figure 2) captures that high levels of dynamism require more exploration efforts, while
low levels of dynamism demand (mostly) exploitation initiatives.
å
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1
2
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Figure 2: Effect of EC and ED on the required exploitationexploration mix
As argued in the main text, the ED variable has more influence on the appropriate mix than the EC
variable which results in the following formula (note the two lookup functions) (where ω is the weight
factor, which is equal to 2/3 in our case):
(6) ECD = ω * Effect ED on ECD (ED) + (1 ω ) * Effect EC on ECD (EC)
The ECD variable thus captures the assumed ‘optimal’ balance between exploitation and exploration and
is the basis for both the reinforcing ‘Stick to exploitation’ and the balancing ‘External pressure’ loop.
Stick to exploitation
In general, perceptions tend to adjust to new circumstances with a certain delay, which can be modeled in
terms of the behavior of a firstorder adaptive system (Sterman, 2000). Top management’s perception of
the environment, denoted by the stock ‘Perceived environmental competitiveness and dynamism’ (PECD),
is thus subject to such a delay. This variable captures the perceived environmental situation in the current
period. The delay is specified by the variable ‘Perception adjustment time Management’
(AT_Management) (an exogenous constant).
Change in PECD:
(7) d (PECD) / dt = (ECD – PECD) / AT_Management
The operational balance between exploitation and exploration in the current period is captured by the
variable ‘Relative investment in exploitation’ (RI_Exploit). The balance is given in terms of the relative
investment in exploitation. Since both Inv_Exploit and Inv_Explore denote the investments in respectively
exploitation and exploration at a certain time, RI_Exploit is calculated by dividing the Inv_Exploit by the
sum of Inv_Exploit and Inv_Explore.
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(8) RI_Exploit = Inv_Exploit / (Inv_Exploit + Inv_Explore)
From the PECD and the RI_Exploit, the ‘Perceived alignment with the environment’ (PAE) can be
calculated. Here, 1 implies a perfect alignment, while 0 means no alignment at all. (Please note that the
kind of manufacturing firm modeled typically does not have very low values for RI_Exploit, given the
importance of efficiency.)
(9) PAE = 1 – (RI Exploit * PECD)
Subsequently, the PAE triggers managerial action – denoted in the stock ‘Perceived need to
explore’ (PNE). This variable constitutes the cognitive aspect of the behavior of top management in the
current period. More specifically, it denotes top management’s perceived appropriate balance in the
current period. Due to inertial forces (AT_Myopia; an exogenous constant), PNE is subject to a firstorder
delay.
(10) d (PNE) / dt = (1 – PAE – PNE) / AT_Myopia
External pressure
The alignment between the exploitation–exploration mix and the environment influences the return on
investment (ROI), and thus the operating result of the firm. In that respect, heavy investments in
exploration, when the environmental situation demands more exploitation, will result in an inferior return
on (exploration) investments. We thus consider two ROIs, one for exploitation and one from exploration
investments. The former one is captured by the stock ‘ROI_Exploit’ while the latter one is denoted by the
stock ‘ROI_Explore’. Both capture the level of ROI in a current period. Moreover, this sequence of events
(from investments to operating results) takes place with a certain delay because initial investments have to
be transformed into (money generating) innovation. This delay is smaller for returns related to exploitation
(exogenous constant RD_Exploit) than it is for exploration (exogenous constant RD_Explore), since the
latter needs significantly more time to generate market success (Burgelman et al., 2004). Moreover,
investments made in exploration that are aligned with the environmental situation (i.e. the alignment
between the exploitationexploration investments and the ECD; see functions 11 and 12) yield a higher
return on investment (Jansen et al., 2006; Uotila et al., 2009). For example, the identification of a new
market can, most likely, make a larger (positive) financial impact than the incremental improvement of a
product in an existing market. Therefore, two different constants are needed to create a distinction
between ROIs from exploitation and exploration: ‘Result factor exploitation’ (RF_Exploit) and ‘Result
factor exploration’ (RF_Explore).
Change in ROI_Exploit:
(11) d (ROI_Exploit) / dt = (Inv_Exploit * (1 – ECD) * RF_Exploit – ROI_Exploit) / RD_Exploit
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Change in ROI_Explore:
(12) d (ROI_Explore) / dt = (Inv_Explore * ECD * RF_Explore – ROI_Explore) / RD_Explore
OC denotes the 'Operating costs' (an exogenous constant), and OR (a variable) is a function of:
(13) OR = ROI Exploit + ROI_Explore  OC
Shareholders (the board) also perceive the ORs with a certain delay, implying the use of a firstorder
adaptive system regarding the trend of the OR. The perceived trend in the OR (captured by the stock
PTOR) is therefore calculated as the average (thus delayed) fractional growth rate (which is negative for
decline). As such, it provides a simple trend estimate for the currently perceived OR.
(14) PTOR = (OR – Average_OR) / (AT_Board * Average_OR)
(15) d (Average_OR) / dt = Change in Average_OR = (OR – Average_OR) / AT_Board
The PTOR determines the amount of external pressure to generate shortterm financial results. This is
captured by the stock ‘External pressure to exploit’ (EP) which refers to the level of pressure in a current
period. This effect is determined by the lookup variable ‘Effect of POR on EP’ (see Figure 3). This lookup
captures the process that when top management fails to achieve acceptable financial returns, this will
result in pressure from the owners on top management to generate shortterm financial results (i.e. a
pressure to exploit). On the contrary, when the board perceives the financial performance to be adequate,
top management will have the possibility to adjust the exploitationexploration mix as desired (the
influence of the EP becomes evident at the ‘Attempt to explore’ loop).
Figure 3: Effect of PTOR on EP
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The increase and decrease of external pressure is also subject to a delay, the pressure adjustment
time (exogenous constant AT_Pressure). This delay arises from the fact that, first, the Board of Directors
operates on the basis of quarterly reports of operating results (reporting delay), and second, the Board acts
on the basis of the trend rather than incidental fluctuations in OR. Therefore, the following equation was
used for the external pressure to exploit (EP) on top management:
(16) d (EP) / dt = Change in EP = (Effect of PTOR on EP (PTOR) – EP) / AT_Pressure
Attempt to explore
The subsequent interaction between the perceived need to explore (PNE) and the external pressure to
exploit (EP) determines the value of the variable RIE and reflects top management’s behavior (related to
the exploitationexploration balance). This variable can range from 0 to 1 (0 implying a sole investment in
exploitation projects while 1 means a mere investment in exploration initiatives). Because this variable
depends on both PNE and EP, it is calculated by multiplying top management’s desired and the
shareholder’s allowed investment in exploration activities. The result of this process is the actual
investment level in exploration as well as in exploitation which constitutes a key component of the
‘Attempt to explore’ feedback loop:
(17) RIE = PNE * (1  EP)
2. Model settings and sensitivity
This section presents all the values for the constants after conducting historyreplicating simulation based
on the obtained data (see the manuscript for more details regarding data collection). This implies that
certain constants were ‘calibrated’ to fit the model variables with corresponding data gathered on site. The
results can be seen in Table I where the variables are alphabetically ordered and their set value presented.
In this table, a ‘*’ denotes the variables that were taken into the calibration process. In addition, Table II
provides an overview of all the variables in the model and Table III gives an overview of all the functions.
As can be seen in Table I, certain variables were not estimated during the historyreplicating
simulation, but based on reasoning and case study observations. This can be explained by the fact that the
firm, from which we gathered our data, did not engage significantly in exploration. As such, it makes no
sense to calibrate the delays for exploration. This concerns the variables ‘AT_Explore’ and ‘RD_Explore’.
We manually set these variables to two years; in line with the literature that observes the development of
radical innovation is likely to take years (e.g. Burgelman et al., 2004). As described in the manuscript,
these two variables were subject to a multivariate sensitivity analysis. These variables were given a 5
percent range to vary within (101.4 < 104 weeks < 106.6). The result (of 200 runs) is reported in Figure 4,
which demonstrates that the confidence levels only drop somewhat in the last 200 weeks of the total
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simulation period. As such, all simulations up to the 95% confidence bounds follow the same trend as the
historyreplicating simulation. This implies the model is rather robust.
Confidence level:
Dotted white line represents the
history replicating simulation.
Figure 4: The sensitivity analysis of the manually estimated ‘exploration’ constants (AT_Explore and
RD_Explore)
Other variables not included in the calibration were the adjustment times (delays) that we could
estimate by means of case observations and reasoning: ‘AT_Management’, ‘AT_Board’, and
‘AT_Pressure’. Data related to these variables become (formally) available to the Board of Directors and
the executive board every quarter. However, only if a certain trend occurs over a period of two quarters
(e.g. negative operating result), the Board of Directors and the executive board are likely to perceive it as a
systematic trend. Therefore these variables were set to 26 weeks (six months). Also these three variables,
including the AT_Myopia variable, were subjected to a sensitivity analysis. All variables were allowed an
8 percent variation. For AT_Management, AT_Board, and AT_Pressure this resulted in the following
range: 24.96 < 26 weeks < 27.04. AT_Myopia had the following range: 438.4 < 456.7 weeks < 474.9).
The results (200 simulations) reported in Figure 5 once more indicate good model robustness.
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Confidence level:
Dotted white line represents the
history replicating simulation.
Figure 5: The sensitivity analysis of the manually estimated Adjustment Time constants (AT_Management,
AT_Board, AT_Pressure, and AT_Myopia)
The historydivergent simulations were also subject to sensitivity analyses. For this, the exogenous
ECD variable was (two times) randomly adjusted over 200 runs. The first set of runs randomly decreased
the ECD variable by up to 50%, simulating a decreased level of dynamism and increased level of
competitiveness (stablescenario). The second set of runs randomly increased the ECD variable by up to
50%, simulation an increased level of dynamism and a decreased level of competitiveness (unstable
scenario). Figures 6 and 7 depict the results of the sensitivity analyses of the chosen adjustment in the
ECD variable, in the stable respectively unstable scenarios. The results of both exercises further confirm
the robustness of the sequences of events described in the manuscript: for the stablescenario (Figure 6),
all 200 simulations end with a notably decreased external pressure (EP), while for the unstablescenario
(Figure 7) all simulation runs result in the success trap. As such, the sensitivity analysis for the stable
scenario underscores the robustness of our finding that when top management is able to cope with the
environmental change, a low level of external pressure results and the suppression process (and success
trap) is avoided. The sensitivity analysis for the unstablescenario confirms the robustness of the
conclusion that if top management is not able to cope with environmental change, it will trigger the
suppression process and eventually lock the firm in the suppression of exploration. (Note that from period
D onwards, it is very likely that the firm will need to engage in major reorganizations in order to survive.)
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Confidence level:
Dotted black line represents the
history replicating simulation.
Figure 6: Sensitivity of the history divergent process theory, stablescenario.
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Confidence level:
Dotted black line represents the
history replicating simulation.
Figure 7: Sensitivity of the history divergent process theory, unstablescenario.
 13 
Table I: Overview of all model constants and settings.
Variable name
Setting
Unit
95% CI
Comments/ Explanation of the source of delay
AT_Exploit
37.7085
Weeks
37.6968 
37.7228
Time necessary to bring about changes in the
routines in exploitation activities.
AT_Explore*
104
Weeks

Time necessary to create, or bring about changes in,
the exploration routines.
AT_Myopia*
456.754
Weeks
449.608 
465.622
Time necessary to overcome managerial myopia.
AT_Management*
26
Weeks

Time necessary to perceive a systematic change in
the environmental situation by the executive board.
AT_Board*
26
Weeks

Time necessary to perceive a systematic trend by the
Board of Directors.
AT_Pressure*
26
Weeks

Time necessary to perceive a systematic change in
the operating results by the Board of Directors.
Initial Inv_Exploit
1
Million
Euros

Necessary for starting the simulation. Initial
situation implies a mere focus on exploitation,
which is in line with the investigated firm.
Initial Inv_Explore
0
Million
Euros

Necessary for starting the simulation. Initial
situation implies a mere focus on exploitation,
which is in line with the investigated firm.
MAR
0.5
Million
Euros

Minimum amount of resources available, even when
the operating result is negative. Required to avoid
model errors.
OC
81.9477
Million
Euros
81.9469 
81.9486
Operating costs assumed as constant.
POR
0.0236391
Percentage
0.0236385 
0.0236396
Percent of the operating result that is available for
investment in exploitation and exploration.
RD_Exploit
35.5818
Weeks
35.596 
35.6136
Time necessary to turn investments in exploitation
into moneygenerating products/processes.
RD_Explore*
104
Weeks

Time necessary to turn investments in exploration
into moneygenerating products/processes.
RF_Exploit
127.774
Euros
127.775 
127.776
Factor to differentiate between the results from
exploitation and exploration. Lower for the former.
RF_Explore
1312.29
Euros
1301.6 
1321.02
Factor to differentiate between the results from
exploitation and exploration. Higher for the latter.
* Subject to sensitivity analysis.
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Table II: Overview of all model variables.
Variable name
Type
Unit
Comments
Time
reference
AR
Auxiliary
Euros
Resources available for both exploration and exploitation
initiatives.
Current
period
PAE
Auxiliary
Percentage
Perceived alignment with the environment. Can range
from 1 (no gap) till 0 (maximum gap).
Current
period
EP
Stock
Percentage
External pressure to exploit. Can range from 1 (only
invest in exploitation) till 0 (invest in exploitation and/or
exploration).
Current
period
ED
Exogenous data
variable
Percentage
Environmental dynamism (S&P 500 index). Can range
from 0 (extremely instable) till 1 (very stable).
Current
period
EC
Exogenous data
variable
Percentage
Environmental competitiveness (1  Herfindahl index).
Can range from 0 (monopoly) till 1 (extremely
competitive)
Current
period
ECD
Auxiliary

Environmental competitiveness and dynamism. Can
range from 0 (implying a sole need for exploitation) till 1
(implying a mere need for exploration)
Current
period
Inv_Exploit
Stock
Euros
Sum of Euros invested in Exploitation.
Current
period
Inv_Explore
Stock
Euros
Sum of Euros invested in Exploration.
Current
period
PNE
Stock
Percentage
Perceived need to explore. Can range from 0 (only invest
in exploitation) till 1 (only invest in exploration).
Current
period
OR
Auxiliary
Euros
Sum of exploitationexploration ROI’s minus the OC.
Current
period
PECD
Stock

Perceived environmental competitiveness and dynamism.
Can range from 0 (very stable) till 1 (extremely instable).
Current
period
PTOR
Auxiliary
Euros
Average fractional growth rate of OR.
Current
period
RI_Exploit
Auxiliary
Percentage
Percentage of total invested Euros in exploitation
compared to the sum of exploitation and exploration. Can
range from 0 till 1.
Current
period
RIE
Auxiliary
Percentage
Result of the interaction between management (PNE) and
the Board of Directors (EP). Can range from 0 (only
invest in exploitation) till 1 (only invest in exploration).
Current
period
ROI_Exploit
Stock
Percentage
Return on investment exploitation (considering
RF_Exploit and RD_Exploit).
Current
period
ROI_Explore
Stock
Percentage
Return on investment exploration (considering
RF_Explore and RD_Explore).
Current
period
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Table III: Overview of all functions.
Variable name
Function
AR
MAX (OR * POR, MAR)
PAE
(1 – RI Exploit) * PECD
Change in EP
d (EP) / dt = Change in EP = (Effect of PTOR on PE (PTOR) – EP) / AT_Pressure
ED
(for period t)
EC
(for period t)
1 
ECD
(ω = 2/3)
ω * Effect ED on ECD (ED) + (1 ω ) * Effect EC on ECD (EC)
Change in
Inv_Exploit
d (Inv_Exploit) / dt = ((1  RIE) * AR – Inv_Exploit) / AT_Exploit
Change in
Inv_Explore
d (Inv_Explore) / dt = (RIE * AR – Inv_Explore) / AT_Explore
Change in PNE
d (PNE) / dt = (1 – PAE – PNE) / AT_Myopia
OR
ROI Exploit + ROI_Explore – OC
Change in
PECD
d (PECD) / dt = (ECD – PECD) / AT_Management
PTOR
(trend)
(OR – Average_OR) / (AT_Board * Average_OR)
d (Average_OR) / dt = Change in Average_OR = (OR – Average_OR) / AT_Board
RI_Exploit
Inv_Exploit / (Inv_Exploit + Inv_Explore)
RIE
PNE * (1  EP)
Change in
ROI_Exploit
d (ROI_Exploit) / dt = (Inv_Exploit * (1 – ECD) * RF_Exploit – ROI_Exploit) / RD_Exploit
Change in
ROI_Explore
d (ROI_Explore) / dt = (Inv_Explore * ECD * RF_Explore – ROI_Explore) / RD_Explore
3. Deterministic versus stochastic
An important characteristic of exploration projects is their uncertain nature. That is, employing a
deterministic model, as described in the manuscript, might seem to bias the results (e.g. ROI_Explore).
Therefore, the effect of a stochastic return on exploration investment (ROI_Explore) was investigated. In
order to do so, a Pink Noise (PN) structure was adopted and its outcome multiplied with the ROI_Explore
variable.
Change in ROI_Explore (stochastic):
(18) d (ROI_Explore) / dt = (Inv_Explore * ECD * PN) / RD_Explore
PN is formed by firstorder exponential smoothing of White Noise (WN) and is often referred to
as firstorder autocorrelated noise (Sterman, 2000). The main difference between the two is that the
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former has a ‘memory’, and, therefore, the output of t + 1 is not independent from t. For example, if at a
certain t, the investment in exploration initiates is not as profitable as desired (e.g. 90 percent), it is
unlikely that at t + 1 the package projects will generate above expected returns (e.g. 110 percent). As such,
PN provides a more realistic noise process than white noise. The following formulas were used to generate
PN (CT equals correlation time). See Sterman (2000) for more details concerning (pink) noise generation.
Change in PN:
(19) d (PN) / dt = (WN  PN) / CT
(20) WN = Mean + SD * ( ( 24 * CT/dt)0.5) * UNIFORM(0.5, 0.5, Noise Seed)
Following the argumentation in the main text we assume that, effectively, failures will be counteracted by
successes. Therefore, the mean value was set to 1. The SD was set to 0.3, giving the PN variable a likely
range from about 0.95 till 1.05 and a possible range from slightly less than 0.9 and somewhat more than
1.1 (see Figure 8). The overall result of the PN process is depicted in Figure 3 which illustrates the
different confidence interval levels for this variable (based on 200 simulation runs). Figure 9 and 10
illustrate the behavior of the EP and OR variables in this stochastic model. The influence of PN on the
ROI_Explore variable can be seen in Figure 11.The results of the stochastic model (captured by the
confidence interval levels) can now be compared with the deterministic model (denoted by the doted white
lines). We concluded that the stochastic process (PN) does not alter the results of this study in a
noteworthy manner. As such, the model was kept deterministic, for reasons of readability.
 17 
Figure 8: Confidence interval levels for the Pink Noise (PN) variable.
Confidence level:
Figure 9: Confidence interval levels for the ROI_Explore variable (stochastic
model).
Confidence level:
Dotted white line represents the
history replicating simulation.
 18 
Figure 10: Confidence interval levels for the OR variable (stochastic model).
Confidence level:
Dotted white line represents the
history replicating simulation.
Figure 11: Confidence interval levels for the EP variable (stochastic model).
Confidence level:
Dotted white line represents the
history replicating simulation.
 19 
4. References
Burgelman, R.A., Christensen, C.M. and Wheelwright, S.C. (2004). Strategic Management of Technology
and Innovation. New York: McGrawHill.
Durmusoglu, S.S., McNally, R.C., Calantone, R.J. and Harmancioglu, N. (2008). ‘How elephants learn the
new dance when headquarters changes the music: three case studies on innovation strategy
change’. Journal of Product Innovation Management, 25, 386403.
Jansen, J.J.P., Van Den Bosch, F.A.J. and Volberda, H.W. (2006). ‘Exploratory innovation, exploitative
innovation, and performance: effects of organizational antecedents and environmental
moderators’. Management Science, 52, 16611675.
Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. New
York: McGraw Hill.
Uotila, J., Maula, M., Keil, T. and Zahra, S.A. (2009). ‘Exploration, exploitation, and financial
performance: analysis of S&P 500 corporations’. Strategic Management Journal, 30, 221231.
Winter, S.G. (2000). ‘The satisficing principle in capability learning’. Strategic Management Journal, 21,
981996.