Learning, Environmental Dynamism
and the Evolution of Dynamic Capabilities
Abstract
This paper presents a simulation model of the development of knowledge, routines
and dynamic capability in organizations. We draw on system dynamics to explore
trade-offs arising in the decision to invest in deliberate learning processes to enhance
the development of dynamic capabilities. Four different effects, namely the tool
utility and consciousness (positive) effects as well as the inertia and experience base
(negative) effects on dynamic capability are considered. The model also distinguishes
the two forms of deliberate learning (knowledge articulation and codification) along
these effects as well as cognitive and other resource constraints. The simulation
results show differing levels of effectiveness as environmental dynamism increases.
Knowledge codification appears to be the optimal strategy at intermediate levels and
only a combined articulation and codification approach is capable to maintain the
firm’s ability to adapt its operating routines in highly turbulent environments.
Introduction
The study of dynamic capabilities, the organization’s capacity to change its
operations and adapt them to the environmental requirements, has taken center stage
in the debate on strategic management as well as organization theory (Teece, Pisano
and Shuen 1997, Eisenhardt and Martin 2000, Zollo and Winter 2002, Winter 2003,
Helfat and Peteraf 2003). The notion, which has received several, and only partially
aligned, definitions, lies at the heart of of the organization’s ability to enact change in
a systematic and fruitful way. Winter (2003) clarifies that organizational change
happens in one of two ways: the first with ad-hoc problem driven search, and the
second through the action of “stable patterns of activity aimed at creating or
changing operating routines in pursuit of enhanced organizational effectiveness”, the
definition of dynamic capabilities in Zollo and Winter (2002).
Whereas the question 'what are dynamic capabilities?' seems to have found
increasing convergence in its response (cf. Helfat et al. 2006), the related question
'how do dynamic capabilities develop?' remains open to debate and scholarly
inquiry. Zollo and Winter (2002) offer a conceptual model that takes into explicit
consideration the role of intentionality in the learning process, distinguishing
between semi-automatic learning (e.g. learning-by-doing) and deliberate types of
learning (i.e. knowledge articulation and codification). They argue the evolution of
both dynamic capabilities and operating routines, through a recursive cycle of
variation, selection, replication and retention processes, is fundamentally determined
by the relative effectiveness of these learning mechanisms.
2
The following question, however, remains: what influences the relative
effectiveness of these different learning mechanisms in producing this particular type
of change capacity? Under what conditions, in other words, are investments in
deliberate learning processes warranted?
Whereas Zollo and Winter (2002) focus on task characteristics as moderating
factors for the relative effectiveness of deliberate learning investments, this paper
considers environmental dynamism as a key condition to explore the role of learning
mechanisms in the evolution of dynamic capabilities. The driving question in this
study, therefore, is: to what extent and how does the degree of dynamism in
environmental change influence the development of the firms’ ability to adapt its
operating processes to the new demands and conditions of their environment?
The question is tackled with a simulation model based on system dynamics
principles. We adopt system dynamics modeling because assumptions are (a) made
explicit and (b) observed simultaneously in their interdependent and non-linear
influences on the outcome under study (e.g. Rudolph and Repenning 2002, Sterman
2000). Moreover, system dynamics modeling is built on the explicit distinction and
analysis of (both tangible and intangible) stocks and flows, which are acknowledged
to be increasingly important in developing our understanding of organizational
knowledge and dynamic capabilities (Helfat 1997, Helfat and Peteraf 2003). System
dynamics suggests that any kind of capability can be modeled as a stock, or a set of
related stocks, that accumulates or depletes over time as a result of flows in and out
of this stock (Sterman 2000). As such, the model developed in this paper attempts to
bridge and integrate the stock versus flow conceptualization of dynamic capability.
3
In order to focus on the development of this specific kind of capabilities in a
parsimonious and tractable way, the model presented below will make two
important simplifying assumptions. First, that the development of dynamic
capabilities is first and foremost an internal process, that it is not influenced in a
significant way by external or institutional pressures, other than an aggregate
variable 'environmental dynamism'. Second, we assume the effectiveness of the
learning mechanisms underlying the evolution of dynamic capabilities are
substantially independent from ad-hoc problem driven processes that constitute the
most diffused and frequent way organizations cope with environmental dynamism.
In the next section, we will develop the theoretical arguments underlying the
model. Subsequently, key features of the simulation model are described. We then
run several simulation experiments with the model and conclude with
interpretations for theory development, empirical inquiry and management practice.
Theoretical Background
Previous studies have focused on dynamic capability as arising from routines for
variation, selection, replication and retention (e.g. Helfat and Raubitschek 2000, Zollo
and Winter 2002, Zott 2003). For example, the simulation model developed by Zott
(2003) shows how certain attributes of dynamic capability contribute to the
emergence of differential firm performance within the same industry. We are
particularly interested in the intra-organizational tradeoffs inherent in the evolution
of dynamic capability. In this respect, Zollo and Winter (2002) distinguish between
three types of knowledge (processes) that interact to produce dynamic capability:
4

Tacit experience that represents the accumulation of lessons learnt directly from
external stimuli.

Articulated knowledge which consists of the result of efforts at articulating
experiential knowledge.

Codified knowledge which, in a similar vein, is built up of the codification of
articulated knowledge.
For example, a consulting firm has an experiential knowledge base that consists of
the sum of all (partly tacit) experience embedded in its consultants. In addition, it has
an articulated knowledge base residing in the heads of the individual consultants
that took form through contact with others, taking in others’ representations and by
articulation efforts of one’s own (e.g. by brown bag seminars, project reviews, and
senior-junior mentoring). Finally, a subset of articulated knowledge is being codified
(e.g. in formal audits, manuals, procedures, tools) on some kind of independent
medium like paper, hard disks, and information databases.
In this respect, Hansen, Nohria and Tierney (1999) observe that consulting firms
can employ different knowledge management strategies. In many consulting firms
knowledge is closely tied to the person who developed it and is shared mainly
through direct person-to-person communication; knowledge codification plays a
minor role in these firms. In other firms more attention is given to codifying and
storing knowledge in databases, where it can be easily accessed and used by anyone
in the company. Hansen et al. (1999) suggest that, to be effective in the consulting
business, a firm needs to excel in one of the two strategies and use the other strategy
in a supporting role. A similar need to focus on one particular knowledge strategy,
5
carefully selected and customised to the specific organizational context, has also been
observed for firms in other industries (e.g. Prencipe and Tell 2001).
We thus adopt a dynamic and path-dependent view of dynamic capability
development. A key assumption in this respect is that knowledge is a construct that
accumulates and diminishes over time. Therefore, each type of knowledge defined
by Zollo and Winter is susceptible to accumulation as well as obsolescence and
depletion. Regarding the depletion effect: employee turnover and cognitive
limitations related to the retention in short-term memory lead to a loss of experiential
as well as articulated knowledge, since both kinds of knowledge reside in the heads
of the employees. Codified knowledge will deplete through the redundancy of
available information and the obsolescence of knowledge contained in the artifacts,
due to environmental dynamism in its institutional, technological and competitive
dimensions and the costs related to the maintenance and updating of the codes.
The rate with which these knowledge levels grow partly depends on dedicated
investments in articulating knowledge (e.g. mentoring systems, de-briefing
processes) and codified knowledge (e.g. in information systems, manuals). These
articulation and codification efforts reflect the level of intentionality in the learning
process (Zollo and Winter 2002). Investments in articulation and codification take the
form of time and resources spent and, as such, may directly influence the time (and
resources) available for direct exposure to events that trigger experiential
knowledge1.
This reflects the off-line versus on-line distinction made by Gavetti and Levinthal (2000) in their
study of search in strategy-making processes.
1
6
Dynamic capability arises from the interplay between experience accumulation
from the enactment of operating routines, and experiential, articulated and codified
forms of knowledge. We conceptualize it as a set of related stock variables that
accumulate or deplete over time as result of in- and outflows of knowledge related to
processes of building, integrating or reconfiguring operating routines (cf. Dierickx
and Cool 1989, Helfat and Peteraf 2003).
The key intuition relates to the problem that increasing levels of deliberate
learning investments produce both positive and negative effects on the
organization’s ability to adapt its operations. Positive effects have been connected to
the development of higher levels of knowledge, particularly of causal nature, due to
both the increasing levels of consciousness about the performance outcomes of prior
experiences and the inquiries on its root causes and contingencies (Zollo and Winter
2002). In the case of codification, this 'consciousness effect' (see below) adds to the
utility drawn by the use of the artifacts created to share agreed procedures and
coordinate the execution of future routines. On the other hand, deliberate learning
investments, especially if in the form of knowledge codification processes, comes
with high costs related to the allocation of scarce resources (managerial time and
attention, primarily), as well as with increasing levels of organizational inertia
connected to the role of artifacts as institutionalized 'truth', which can become
increasingly difficult to challenge.
As a possible explanation of this conundrum, we suggest the levels of articulation
and codication of knowledge in the organization can be part of the distinction
between the virtuous and the vicious effects of deliberate learning investments on the
7
evolution of dynamic capabilities. At lower levels of articulated and codified
knowledge, the virtuous effects will predominate, whereas the opposite effect may
become increasingly dominant at higher levels of articulation and codification of
knowledge. This argument is similar to Adler and Borys' (1996) distinction between
the enabling and the coercive effect of bureaucracy. The key difference is that Adler
and Borys identify specific organizational features potentially responsible for this
crucial distinction, whereas we identify specific contingencies related to the stocks of
different types of knowledge present at a given time in the organization.
We will therefore, first of all, distinguish between the effects of articulation and
codification processes in the formal model that will be simulated. Second, we will
consider explicitly, and jointly, the non-linear dynamics of both the positive and the
negative effects of deliberate learning on the evolution of dynamic capabilities.
This will allow us to explore how variations in investments in deliberate learning
strategies may lead to different and counterintuitive patterns of dynamic capability
development and experience accumulation under different contingencies related to
environmental dynamism.
Model Structure
The model was developed in Vensim and Ithink simulation software. A complete
description of the model, including all equations, is given in the Appendix (attached
as a separate document).
The model draws on a so-called capacitated delay structure (Sterman 2000). This
structure arises when the impact of a set of related stocks (e.g. experience and
8
knowledge) depends on the level of flows in and out of these stocks, but is also
constrained by capacity boundaries (e.g. resource constraints).
Figure 1 provides an overview of the structure of the model. In this model,
deliberate efforts to articulate and codify knowledge affect knowledge flows and
levels, that in turn have an impact on dynamic capability, particularly in the form of
the organization's ability to generate effective proposals for change in its operating
routines. This ability to generate change proposals as well as environmental
dynamism affect change in operating routines; moreover, environmental dynamism
also influences knowledge development and attrition.
Figure 1 involves a stylized picture of the full model, that is described in more
detail in the Appendix. In the diagramming notation in this figure, flow variables are
depicted as pipes with valves. The rectangles represent the accumulated level of a
particular variable (e.g. tacit experience). Clouds represent the sources for some
flows and sinks for some outflows; these sources and sinks are outside the
boundaries of the model. There are three inputs in the model that can be externally
manipulated (e.g. in setting up experiments): articulation effort, codification effort,
and environmental dynamism; the range of all three input variables is from 0 to 1.
The model draws on a number of key assumptions. First, we assume
organizations are exposed to volatility in their product, market and technological
environments. These types of dynamics are aggregated in the variable environmental
dynamism (defined as a continuous variable from 0 to 1).
Second, we assume that dynamic capability arises from the interaction of tacit
experience, articulated knowledge, codified knowledge and environmental
9
dynamism, generating the ability to develop and implement change proposals.
Dynamic capabilityis thus a second order quality of the entire system modeled here.
Operating routines (OR) are the key object of dynamic capability. Operating
routines, embedded for instance in operating systems and procedures, change over
time as a result of (a) inputs from either stable patterns of routine development or adhoc adjustments and (b) attrition. The development of operating routines occurs
when a given group in the organization2 produces change proposals in response to
the environmental dynamism and opportunities the current routines are exposed to.
Attrition of operating routines is a continuous process related to memory decay in
the absence of frequent execution and possibly reinforced by increasing dynamism in
the environment (e.g. e-banking that makes certain sales routines in the financial
services industry less relevant).
As discussed in the previous section, tacit experience (TE) is an automated
accumulation process in which people deal with their experiences in daily
operations. The development of tacit experience therefore arises from engaging in
operating routines, but may suffer from increasing environmental dynamism. This
existing experience base is in fact exposed to a continuous process of attrition: some
of the experience relevant today, will become obsolete tomorrow. Higher levels of
environmental dynamism will reinforce this process.
The current base of tacit experience also feeds the process of knowledge
articulation resulting in articulated knowledge (AK). The latter process, represented
in the model as the Articulation rate, is a function of the effort made to articulate tacit
Note that the group proposing changes to a given routine does not need to be one that executes it.
For example, manufacturing routines are usually adapted by a specialized group responsible for
technical improvements (process innovation unit), which is the holder of the dynamic capability.
2
10
experience (Articulation effort) and the available tacit experience (TE) divided by the
current level of articulated knowledge (AK). Not all experience can be spoken. The
TE/AK ratio therefore serves as an algorithm representing decreasing (articulation)
returns to each additional effort to articulate tacit experience:
Articulation rate = ArticulationEffort * TE /AK
The development of codified knowledge (CK) is fundamentally different from
knowledge articulation. Whereas each piece of tacit experience that is articulated
leaves the stock of experience, codified knowledge can persist in its artifacts. In other
words, knowledge that is effectively codified, can continue to be articulated, shared
in face-to-face settings, and eventually adapted. In the model, codified knowledge is
a function of the codification effort and the current pool of articulated knowledge
(AK) divided by current stock of codified knowledge (CK). Not all articulated
knowledge can be written and codified (Winter 1987, Kogut and Zander 1992,
Cowan, David and Foray 2000); moreover, some articulated knowledge is
deliberately not codified because of its sensitive and classified nature (Prencipe and
Tell 2001). In this respect, the AK/CK ratio represents the decreasing nature of
returns to codification efforts when CK is growing relative to AK (cf. Cowan and
Foray 1997):
CK inflow = CodificationEffort * AK / CK
11
Figure 1: Overview of the Model
Resource
Constraints
Articulation
Ef f ort
Codif ication
Ef f ort
CK attrition
ToolUtility & Inertia
Ef f ect
Codif ied
Knowledge
~
Ability to Generate
Proposals
CK inf low
Consciousness &
Experience Base Ef f ect
~
AK attrition
Articulated
Knowledge
Operating
Routines
OR
dev elopment
OR attrition
Articulation
rate
TE attrition
Tacit
Experience
TE
dev elopment
Env ironmental
Dy namism
Tool Utility
effect
Consciousness effect
1
Effect on
Ability to
Generate
Change
Proposals
Inertia effect
Experience
Base effect
1
0,8
0,8
0,6
0,6
0,4
0,4
0,2
0,2
0
0
0
0,2
0,4
0,6
0,8
0
1
0,2
0,4
0,6
(0.8*CK+0.2*AK) / T E ratio
CK / AK ratio
12
0,8
1
In addition, codified knowledge may become obsolete, due to technological and
market developments and changing product portfolios. Even if this kind of
information is filed and retained, for example in a computer database or archive, we
assume the stock of codified knowledge depletes when no one in the organization
uses it or continues to contribute to its further development. Again, increasing
environmental dynamism will reinforce the process of attrition of codified
knowledge (cf. Figure 1).
Codification and articulation efforts use resources – for example, staff hours and
attention – that cannot be allocated elsewhere in the organization (e.g. for client
acquisition). In this respect, we assume the organization has a certain amount of staff
resources available for three activities: knowledge articulation, knowledge
codification, and operating routines. Resources spent in one of these three activities
are not available to the other two.
The Development of Dynamic Capability
A key element of the model involves the role of knowledge articulation and
knowledge codification in the evolution of dynamic capability. Based on the received
literature, we identify four different effects of knowledge codification and
articulation on the evolution of dynamic capability:
1. Tool Utility Effect: the first virtuous effect of knowledge codification follows from
the usefulness of the tools that are produced in the process (Cowan and Foray
1997). These tools, often embedded in manuals, standard operating procedures,
software applications and so on, help the organization to adapt its operating
routines by enhancing the cognitive alignment among dispersed members of
13
“how process X should be executed”, and facilitating the post-execution
evaluation for eventual adaptation. For example, establishing a shared
communication protocol facilitates the exchange of information and learning
outcomes across individuals or groups (e.g. Dhanaraj, Lyles, Steensma and
Tihanyi 2004) and allows the members of the groups to evaluate the effectiveness
of their communication processes, eventually developing corrections and
refinements. Holding environmental dynamism and other conditions constant,
we will assume that increasing levels of knowledge codification will increase the
organizational ability to generate change in operating routines.
2. Inertia Effect: when the level of codified knowledge becomes relatively high,
compared to other forms of knowledge in the organization, this may stiffle the
ability to produce effective proposals for changing operating routines. Codes
represent the “way things should be done” and can discourage the challenge of
the status quo, weakening the ability of the organization to adapt its operating
routines. In the language of Leonard Barton (1992), increasing capability levels
caused by investments in deliberate learning processes with highly inertial
properties (i.e. knowledge codification, rather than articulation) can turn the same
capabilities into rigities. We propose that the levels of knowledge codification
present in the organization can be an important explanation of this, hitherto
unresolved, puzzle. For example, Tripsas and Gavetti's (2000) describe Kodak’s
failure to seize the opportunities from the digital revolution of photography. This
failure might be explained, in addition to the role of cognitive inertia, by possibly
high levels of accumulated codified knowledge, which in turn sustain strong but
14
obsolete beliefs in the organization about the appropriate business model. Thus,
we assume that increasing levels of codified knowledge produce a negative effect
on the organizational ability to generate change in operating routines, and that
this effect actually increases in strength with the rising stocks of codified
knowledge.
3. Consciousness Effect: the process through which knowledge is articulated (e.g.
team meetings, conversations between junior staff and their mentor) and codes
are developed (e.g. in manuals, software and procedures) requires a significant
amount of questioning about the causal linkages between actions and outcomes.
Thus, the articulation and codification process therefore has a second, less
obvious, virtue with regard to the organizational ability to adapt its operating
routines. Members involved in articulation and particularly codification
processes, in fact, are somewhat forced to ask questions – for example about the
reasons for successes and failures in their prior experiences – thereby unveiling
some of the causal ambiguity that covers most organizational activities (Zollo and
Winter 2002). In a similar vein, knowledge articulation and codification raises the
level of mindfulness about the effectiveness of its own processes (Weick, Sutcliffe
and Obstfeld 1999) and draws attention to the needs to respond flexibly to
contextual cues (Levinthal and Rerup 2006). We thus model the consciousness
effect as an increasing level of dynamic capability at higher levels of articulated
and codified knowledge. Note the effect of articulation will be of a lower
magnitude, compared to codification, because the consciousness effect of codified
15
knowledge is stronger in view of its focus on tangible artifacts (e.g. written
protocols and manuals).
4. Experience Base Effect: this effect captures the conundrum that involves the implicit
(negative) effect of codification process on the development of tacit experience,
given the cognitive constrain introduced above. The more an organization
invests in knowledge codification, the less time and resources it has to dedicate to
the actual experiencing of interactions with the world. However, the development
of tacit experience not only is a gradual cumulative process, but is also necessary
to effectively codify, since it is the basis for the sense-making effort (Weick 1995,
Cowan et al. 2000, Dhanaraj et al. 2004). For example, a firm's codified knowledge
of a market is often facilitated through the kind of strong social ties that promote
experiential learning between buyers and sales staff (Uzzi 1997). Thus, effective
proposals for changes in operating routines need a substantial pool of tacit
experiences to translate (e.g. written) proposals into actual development of the
routines. Therefore, in line with the literature on implicit learning and
consciousness (e.g. Cleeremans and French 2002), we assume a high level of tacit
experience, relative to explicit knowledge, is required to maintain the
consciousness effect referred to earlier. This assumption implies that a high ratio
between explicit knowledge and tacit experience will decrease the ability to
generate proposals for changes in operating routines.
One of the graphs in Figure 1 summarizes the consciousness and experience base
effects, as a function of CK*w + AK*(1-w) divided by TE. The constant w represents
16
the stronger impact CK has, compared to AK, on consciousness and the ability to
generate change proposals – this constant is exogenous (set at 0.8) in the model. The
consciousness effect involves a positive effect at lower levels of the ratio, with
increasing marginal returns that subsequently turn into decreasing marginal returns.
The idea that ad-hoc problem driven search provides a base kind of change
capability is acknowledged here: if codified knowledge completely breaks down, the
graph for the tool utility effect implies that the impact on the ability to change
operating routines still is 0.2 (see the graph in Figure 1). The experience base effect
involves a negative effect at high levels of the CK/AK ratio, with increasing marginal
changes at first and decreasing marginal changes when the ratio moves toward 1. We
assume an exogenously determined limit to the experience base effect, with a loss of
0.4 of the ability to generate change proposals.
The tool utility and inertia effects on the ability to generate effective change are
assumed to be a function of the ratio between CK and AK. These effects are also
summarized in Figure 1. The tool utility effect involves a positive effect at low levels
of the CK/AK ratio, with increasing marginal returns that turn into decreasing
marginal returns when this ratio moves to 1. We assume here that if codification is
completely absent and the level of CK is thus zero, the net effect on the ability to
generate change proposals is 0,2 – this represents the idea that a substantial pool of
articulated knowledge creates an ability to change even without any knowledge
codification (cf. ad-hoc kinds of search and problem solving). The inertia effect
involves a negative effect of the same ratio at higher levels, with increasing marginal
returns that turn into decreasing marginal returns when CK/AK is close to 1.
17
In sum, the ability to generate effective change proposals (GP) for transforming
operating routines is defined as follows:
GP
= ToolUtility&Inertia Effect * Consciousness&ExperienceBase Effect
= f [CK/AK ] * g [ {w*CK + (1-w)*AK} / TE ]
GP operates on the process of changing operating routines, reflecting the idea
that a larger ability to generate high-quality change proposals will facilitate efforts to
actually change routines to make them more effective in response to environmental
cues. Moreover, resources are scarce, that is we assume there is a fixed amount of
resources available in the organization to either execute operating routines or
articulate and codify knowledge. (The amount of resources necessary to codify
knowledge is considered significantly larger than in knowledge articulation
processes.) As such, we can define change in operating routines as a function of the
ability to generate effective change proposals (GP), environmental dynamism (ED),
and resource constraints (RC):
OR development = (GP/ED) * RC
In sum, dynamic capability is thus conceived as a second order quality of the
entire system. In addition to the effects of resource constraints and environmental
dynamics, five feedback loops determine the evolution of organizational ability to
change operating routines. The critical dimension of each of these five feedback loop
arises from how tacit experience, articulated knowledge and codified knowledge
affects either the Tool Utility-Inertia or the Consciousness-ExperienceBase effect on
18
the ability to generate change in operating routines. Each feedback loop affects the
ability to generate proposals and change operating routine development differently,
in line with the assumptions discussed earlier.
Simulation Findings
The model outlined in the previous section can be used to simulate the evolution of
deliberate learning, tacit experience accumulation and dynamic capability
development. In particular, we explore how knowledge articulation and knowledge
codificaton contribute to (or undermine) the development of dynamic capability, in
response to increasing environmental dynamism. The model in the previous section
serves to simulate a large number of different settings, characterized by (initial levels
of) environmental dynamism, ability to generate change, and available resources. In
this section we focus on knowledge/learning systems that have a low ability to
generate change in operating routines at the outset, as a result of a relatively low
consciousness level and a moderate tool utility effect (cf. Figure 1), in an environment
that is relatively stable and under-resourced at the outset. This situation is often
observed in small and medium sized (e.g. family-owned) firms that tend to react to
rather than anticipate developments in their products, markets and technologies (e.g.
Christensen 1997, Leonard-Barton 1992, Tripsas and Gavetti 2000).
To be able to capture short-term as well as long-term patterns, the model is
simulated over a period of 200 quarters (or 50 years). In the simulation experiments
that follow, the system begins in a steady state in which the inflow in each stock
equals its outflow, environmental dynamism is 0.3 (on a scale from 0 to 1), and the
19
articulation and codification effort is 0.05 respectively 0.05 – implying that the
remaining 0.9 staff resources are allocated to operating processes.
To understand the behavior of the system in disequilibrium, the model was
tested using a variety of changes in environmental dynamism, articulation effort and
codification effort. This section describes a small but representative set of these
simulation experiments.
The simulation run in Figure 2 is produced by exposing the system, in steady
state conditions, to a 10% structural increase of environmental dynamism. In this
simulation run, increased environmental dynamism directly affects the level of
operating routine development, whereas the attrition in existing routines increases
(see also Figure 1). Moreover, the (partial) breakdown in existing routines implies
the development of tacit experiences is also affected; thus, the stock of tacit
experiences diminishes. With the articulation or codification effort unchanged, the
decreasing stock of experiences reduces the articulation rate, which in turn decreases
the existing body of articulated knowledge (reinforced by an increasing attrition rate,
that is higher anyway for articulated knowledge in the model). The net effect is that
the organizational ability to change operating routines in response to new
environmental imperatives is too low to cope with these imperatives. Therefore, the
firm is not able to effectively develop new operating routines in response to
environmental demands.
Figure 3 reports similar results of an experiment with a more substantial change
in environmental conditions of 40 percent. This figure suggests that this more
20
dramatic change in environmental conditions puts enormous strain on the system,
undermining a major part of its operating routines.
Figure 4 shows the results of an experiment in which environmental dynamism
increases by 50 percent. Under this additional strain, the system's operating routines
and knowledge resources completely break down. Unlike the previous experiment
the system cannot settle at a new steady state. These first three experiments suggest
the model in this paper has a threshold, or tipping point, beyond which the behavior
of the system in response to external impulses changes fundamentally (cf. Rudolph
and Repenning 2002). Below the threshold the system is able to find a new steady
state, but beyond the threshold vicious reinforcing feedback prevails and the system
breaks down.
-----------------------------------------Insert Figure 2, 3 and 4 here
-----------------------------------------Figures 5 and 6 illustrates how a simple one-time additional effort to articulate or
codify knowledge changes the behavior of the system exposed to the 10 percent
increase in environmental dynamism (cf. Figure 2). Figure 5 shows the long-term
effects of an additional effort to articulate knowledge during two subsequent
quarters (starting half a year after the environment starts changing); this additional
effort doubles the 0.05 structural effort made each quarter. Evidently, the additional
effort invested in talking to each other (e.g. during strategy workshops, extra team
meetings, etcetera) does not pay off, but rather reinforces the process pictured in
Figure 2. This counter-intuitive result is mainly due to the loss in tool utility effect,
that is not effectively compensated by the growing consciousness of the agents (see
Figure 5).
21
By contrast, Figure 6 shows how effective an additional effort in codifying
knowledge in this particular setting is. Doubling the codification effort in response
to new environmental cues reinforces the tool utility effect, whereas the existing
consciousness level is maintained by somewhat shifting the knowledge base. The
difference between the patterns in Figure 5 and 6 is remarkable, and arises from the
different impact of codified and articulated knowledge as well as the cumulative
nature of the effects of articulation and codification interventions (cf. their path
dependency).
------------------------------------------Insert Figure 5 and 6 here
------------------------------------------A similar set of experiments can be done in response to the more dramatic
scenario of 40 percent change in environmental dynamism (cf. Figure 4). Figure 7
illustrates how the system behaves when the articulation effort is stepped up in two
subsequent quarters. This gives similar patterns over time as for the response to the
10 percent change in environmental dynamism, described earlier.
In Figure 8 we tested the response to stepping up the codification effort. This
result is strikingly different from the response to the same intervention in Figure 6. In
this case, the system's change capacity diminishes, since the tool utility and
consciousness effects created by knowledge codification fail to overcome the stronger
inertial effects due to stronger dynamics in the environment. The patterns observed
in Figure 8 hold for different sizes of the additional codification effort. This suggests
that under more dramatic environmental change conditions, knowledge codification
alone fails to support the firm’s ability to adapt.
22
The simulation run in Figure 9 shows how the system can deal quite effectively
with the 40% increase in dynamism by stepping up both the codification and the
articulation effort during two quarters. These findings suggest that high levels of
environmental dynamism need to be dealt with by investing in both knowledge
articulation and codification, rather than relying only on one of the two deliberate
learning tools.
------------------------------------------Insert Figure 7, 8 and 9 here
-------------------------------------------
Discussion and Conclusions
This paper set out to study the mechanisms underlying the development of dynamic
capability in firms and the impact of environmental dynamism on their relative
effectiveness. The model proposed tries to be sensitive not only to the contrasting
(positive and negative) effects related to deliberate learning investments, but also to
the nuances between the role of different types of deliberate learning processes
(knowledge articulation vs. knowledge codification) and to the trade-offs inherent in
the choice of one or the other, or of none at all, that is relying on tacit knowledge
accumulation processes.
The system simulated by the model shows, first of all, clear boundaries for the
reliance on tacit knowledge accumulation processes, as environmental dynamism
increases. This is an important result, given the strong emphasis in the literature on
the virtues of tacit knowledge for organizational learning and adaptation processes
(e.g. Nonaka and Takeuchi 1995, Dhanaraj et al. 2004). Whereas tacit experience does
23
have a role to play in determining the firm’s ability to adapt, prior work may not
have given sufficient consideration to the fact that the usefulness of tacit kinds of
knowledge is particularly sensitive to the stability of the environmental conditions.
A second implication of the results of simulation runs is that investments in
deliberate learning effects are not a homogeneous category, that is their effects on the
development of dynamic capability differ in kind, not only in magnitude. More
precisely, codification seems to be a much more effective learning and adaptation
strategy at intermediate levels of environmental dynamism.
At high levels of dynamism, though, even a knowledge codification strategy
(by itself) shows clear limitations. The inertial effects produced by the artifacts
developed in the codification process become powerful enough to counteract the
positive effects on the ability to develop higher causal knowledge (consciousness
effect, in particular). In these contexts, similar to those described by Eisenhardt
(1989) in her work on high velocity environments, knowledge articulation regains the
upper hand when combined with codification efforts. Intuitively, articulation can
strike the appropriate balance between the need to penetrate causal ambiguity and
the pressure to reduce the inertial effects of codification processes. This result can
also be understood in terms of the notion of 'simple rules' that Eisenhard and Sull
(2001) describe. Simple rules do require a (low) dose of knowledge codification
processes, and a (more abundant) dose of investment in knowledge articulation to
figure out how to cope with complexity, eventually adapting the few rules the firms
evolves on.
24
Putting it all together, the managerial insight that emerges from these results
is that it is crucial for any firm to understand how to adapt its deliberate learning
approach to the environmental conditions it is facing. The relative effectiveness of
the two deliberate learning processes changes, in fact, quite radically depending on
whether the firm faces an intermediate or a high level of dynamism.
Despite this set of contributions to the received literature, there are several
simplifying assumptions we had to make in this initial simulation effort, which
correspond to future research work to be done. First, we have not modeled the
characteristics of the tasks that identify the operating routines in eventual need of
adaptation. Zollo and Winter (2002) develop theory on some of these characteristics
and their impact on the relative effectiveness of deliberate learning mechanisms
(homogeneously considered). One possible way to expand on the results of our
model is to consider explicitly the influence of task frequency, heterogeneity and
causal ambiguity on the way the environment affects the optimal learning and
adaptation strategy.
A second set of contingencies that has been left out of the analysis has to do
with firm-level effects, such as its size, structural form, strategic posture and overall
performance, just to name a few of the most important ones. It would be very
important, to further develop our current understanding of how organizations learn
to adapt, if we could integrate their own structural features in the model.
Of course, putting task effects, firm effects and environmental effects in the
same simulation model would produce results that could be difficult to analyze, let
alone understand. That is where simulation approaches might be leveraged to guide
25
empirical inquiry, the ultimate test of validity and generalizability of the theoretical
effort. We certainly look forward to the future development in at least some of these
directions and hope that this particular paper serves to stimulate future scholars’
curiosity toward the next steps in our quest to understand how organizations learn
to change and adapt.
References
Adler, P. S. & B. Borys. 1996. Two types of bureaucracy: Enabling and coercive.
Administrative Science Quarterly, 41, 61-89.
Christensen, C. 1997. The Innovator's Dilemma. Harvard Business School Press,
Boston, MA.
Cleeremans, A. & R. M. French (eds.) 2002. Implicit Learning and Consciousness: An
Empirical, Philosophical and Computational Consensus in the Making. Psychology Press,
Brighton, NY.
Cowan, R. & D. Foray. 1997. The economics of codification and the diffusion of
knowledge. Industrial and Corporate Change, 6, 595-622.
Cowan, R., P. David & D. Foray. 2000. The explicit economics of knowledge
codification and tacitness. Industrial and Corporate Change, 9, 211-253.
Dhanaraj, C., M. A. Lyles, H. K. Steensma & L. Tihanyi. 2004. Managing tacit and
explicit knowledge transfer in IJVs: The role of relational embeddedness and the
impact on performance. Journal of International Business Studies, 35, 428-442.
Dierickx, I. & K. Cool. 1989. Asset stock accumulation and sustainability of
competitive advantage. Management Science, 35, 1504-1511.
Eisenhardt, K. M. 1989. Making fast strategic decisions in high-velocity
environments. Academy of Management Journal, 32, 543-576.
Eisenhardt, K. M. & J. A. Martin. 2000. Dynamic capabilities: What are they? Strategic
Management Journal, 21, 1105- 1121.
Eisenhardt, K. M. & D. N. Sull. 2001. Strategy as simple rules. Harvard Business
Review, 79 (1), 107- 116.
Gavetti, G. 2005. Cognition and hierarchy: Rethinking the microfoundations of
capabilities' development. Organization Science, 16, 599-617.
Gavetti, G. & D. Levinthal. 2000. Looking forward and looking backward: Cognitive
and experiential search. Administrative Science Quarterly, 45, 113-137.
Hansen, M.T., N. Nohria & T. Tierney, T. 1999. What's your strategy for managing
knowledge? Harvard Business Review, 77 (2), 106-116.
26
Helfat, C. E. 1997. Know-how and asset complementarity and dynamic capability
accumulation: The case of R&D. Strategic Management Journal, 18, 339-360.
Helfat, C. E., S. Finkelstein, W. Mitchell, M. Peteraf, H. Singh, D. Teece & S. Winter.
2006. Dynamic Capabilities: Understanding Strategic Change in Organizations. Blackwell
Publishing, Oxford (UK), in press.
Helfat, C. E. & M. A. Peteraf. 2003. The dynamic resource-based view: Capability
lifecycles. Strategic Management Journal, 24, 997-1010.
Helfat, C. E. & R. S. Raubitschek. 2000. Product sequencing: Co-evolution of
knowledge, capabilities and products. Strategic Management Journal, special issue, 21
(10-11), 961-979.
Kogut, B. & U. Zander. 1992. Knowledge of the firm, combinative capabilities, and
the replication of technology. Organization Science, 3, 383-397.
Leonard-Barton, D. 1992. Core capabilities and core rigidities: A paradox in
managing new product development. Strategic Management Journal, Summer special
issue, 13, 111-126.
Levinthal, D. & C. Rerup. 2006. Crossing an apparent chasm: Bridging mindful and
less-mindful perspectives on organizational learning. Organization Science, 17, 502513.
Nonaka, I. & H. Takeuchi. 1995. The Knowledge-Creating Company. Oxford University
Press, Oxford.
Prencipe, A. & F. Tell. 2001. Inter-project learning: Processes and outcomes of
knowledge codification in project-based firms. Research Policy, 30, 1373-1394.
Rudolph, J. W. & N. P. Repenning. 2002. Disaster dynamics: Understanding the role
of quantity in organizational collapse. Administrative Science Quarterly, 47, 1-30.
Sterman, J. D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex
World. McGraw-Hill, Boston.
Teece, D. J., G. P. Pisano & A. Shuen. 1997. Dynamic capabilities and strategic
management. Strategic Management Journal, 18, 509-533.
Tripsas, M. & G. Gavetti. 2000. Capabilities, cognition, and inertia: Evidence from
digital imaging. Strategic Management Journal, 21, 1147-1161.
Uzzi, B. 1997. Social structure and competition in interfirm networks: The paradox of
embeddedness. Administrative Science Quarterly, 42, 35-67.
Weick, K. E., K. M. Sutcliffe & D. Obstfeld. 1999. Organizing for high reliability:
Processes of collective mindfulness. Research in Organizational Behavior, 21, 81-123.
Weick, K. E. 1995. Sensemaking in Organizations. Thousand Oaks, Sage.
Winter, S. G. 1987. Knowledge and competence as strategic assets. In: D. Teece (ed.),
The Competitive Challenge: Strategies for Industrial Innovation and Renewal.
Ballinger, Cambridge.
Winter, S. G. 2003. Understanding dynamic capabilities. Strategic Management Journal,
24, 991-995.
27
Zollo, M. & S. G. Winter. 2002. Deliberate learning and the evolution of dynamic
capabilities. Organization Science, 13, 339-351.
Zott, C. 2003. Dynamic capabilities and the emergence of intraindustry differential
firm performance: Insights from a simulation study. Strategic Management Journal, 24,
97-125.
28
Figure 2: The development of AK, CK, TE, OR and the ability to change OR:
simulation results of a 10% structural increase in Environmental Dynamism
(system starts in steady state).
1: Articulated Kno… 2: Codif ied Knowle… 3: Operating Routi… 4: Tacit Experience
1:
2:
3:
4:
5:
5: Ability to Gener…
200
30
1000
1
3
3
3
3
4
4
1:
2:
3:
4:
5:
100
15
500
1
1:
2:
3:
4:
5:
0
0
0
0
1
4
1
1
5
5
2
0.00
1
5
2
4
5
2
50.00
2
100.00
Quarters
Page 1
150.00
200.00
20 Sep 2006
10:50
ED Step (0.03, 10)
Figure 3: The development of AK, CK, TE, OR and the ability to change OR:
Simulation results of a 40% structural increase in Environmental Dynamism
(system starts in steady state).
1: Articulated Kno… 2: Codif ied Knowle… 3: Operating Routi… 4: Tacit Experience
1:
2:
3:
4:
5:
5: Ability to Gener…
200
30
1000
1
3
1:
2:
3:
4:
5:
4
100
15
500
1
3
1
3
4
1
1:
2:
3:
4:
5:
2
0.00
Page 1
2
50.00
4
1
5
5
5
0
0
0
0
3
4
1
2
100.00
Quarters
ED step (0.12, 10)
5
2
150.00
10:52
200.00
20 Sep 2006
Figure 4: The development of AK, CK, TE, OR, and the ability to generate change
in OR: Simulation results of a 50% structural increase in Environmental
Dynamism (system starts in steady state).
1: Articulated Kno… 2: Codif ied Knowle… 3: Operating Routi… 4: Tacit Experience
1:
2:
3:
4:
5:
50
1000
1
1:
2:
3:
4:
5:
100
25
500
1
5: Ability to Gener…
200
1
5
1
2
1:
2:
3:
4:
5:
3
0
0
0
0
2
4
3
0.00
5
1
2
4
50.00
5
3
4
100.00
Quarters
Page 1
1
5
2
3
150.00
4
200.00
17 Sep 2006
19:42
Untitled
Figure 5: Stepping up the Articulation Effort in quarter 12 and 13, in response to
Environmental Dynamism increasing 10% (compare OR with Figure 2).
2: Ability to Generate Pr… 3: C&EB Ef f ect
1: Operating Routines
1:
2:
3:
4:
50
1
1:
2:
3:
4:
25
4
1
4
4
1
4
1
0
1
1
3
3
3
1:
2:
3:
4:
4: TU&Inertia Ef f ect
2
2
2
3
2
0
0.00
50.00
100.00
Quarters
Page 1
150.00
Response by increasing AE with 0.05 in period 12-13
30
14:04
200.00
20 Sep 2006
Figure 6: Stepping up the Codification Effort in quarter 12 and 13, in response to
the 10% Environmental Dynamism increase (compare OR with Figure 2).
2: Ability to Generate Pr… 3: C&EB Ef f ect
1: Operating Routines
1:
2:
3:
4:
50
1
1:
2:
3:
4:
25
4: TU&Inertia Ef f ect
4
4
1
1
4
4
1
1
1
2
1:
2:
3:
4:
3
2
3
2
3
2
3
0
0
0.00
50.00
100.00
Quarters
Page 1
150.00
200.00
20 Sep 2006
14:38
CE increase with 0.05 in period 12-13
Figure 7: Stepping up the Articulation Effort in quarter 12 and 13, in response to
40% increase in Environmental Dynamism (compare OR with Figure 3).
2: Ability to Generate Pr… 3: C&EB Ef f ect
1: Operating Routines
1:
2:
3:
4:
50
1
1:
2:
3:
4:
25
4: TU&Inertia Ef f ect
4
4
4
1
1
4
1
1
3
1
3
3
3
1:
2:
3:
4:
2
2
0
2
2
0
0.00
50.00
100.00
Quarters
Page 1
150.00
Response by stepping up AE 0.05 in period 12-13
31
14:48
200.00
20 Sep 2006
Figure 8: Stepping up the Codification Effort in quarter 12 and 13, in response to
40% increase in Environmental Dynamism (compare OR with Figure 3).
2: Ability to Generate Pr… 3: C&EB Ef f ect
1: Operating Routines
1:
2:
3:
4:
50
1
4: TU&Inertia Ef f ect
4
4
4
1:
2:
3:
4:
25
4
1
1
1
1
1
2
1:
2:
3:
4:
3
2
3
3
3
2
0
2
0
0.00
50.00
100.00
Quarters
Page 1
150.00
200.00
20 Sep 2006
14:56
Response by stepping up CE with 0.05 in period 12-13
Figure 9: Stepping up both the Articulation and Codification Effort in quarter 12
and 13, in response to 40% increase in Environmental Dynamism.
2: Ability to Generate Pr… 3: C&EB Ef f ect
1: Operating Routines
1:
2:
3:
4:
50
1
1:
2:
3:
4:
25
4: TU&Inertia Ef f ect
4
4
4
1
1
1
1
2
3
4
1
2
3
2
3
3
1:
2:
3:
4:
2
0
0
0.00
50.00
100.00
Quarters
Page 1
150.00
12:49
200.00
20 Sep 2006
AE increase 0.05 and CE increase 0.1 in period 12-13 (additonal resources)
32