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Introduction
Knowledge is power (Francis Bacon)
Today’s electronically mediated global economy is the biggest, most
complex system ever created.
The rise of a New Economy characterized by dramatic innovations in
Information and Communication Technology is generating new important
challenges for strategic management, revolutionizing rules for competition,
sorts of organizations and their structures, and causing rapid change to
become a constant (J.Browning and S. Reiss, 1998).
The growing environmental complexity is in part an effect of the increase in
globalization, enhanced by an easier and quicker communication and driven
by the consequent need to foster growth, competitiveness and vitality.
The opening of a global market accompained by greater ease of reach by
service providers has lead to major increases in competition, which caused a
growing speed of decision making (together with the risk that such faster
decisions may bring) and a rising pressure on strategy making. This was also
due to the consequent increase in business complexity and competitive
uncertainty, to the escalating costs of delay and error, and to the explosion of
internal and external data that augmented the risk of information overload
and strategic disorientation (Noonan and Tenaglia, 1988; Czinkota and
Ronkainen, 1997).
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Moreover, the flexibility enabled by the digitalization of information and by
the opportunities provided by the progressive convergence of media has
caused major “competitive borders” to collapse, generating unexpected
phenomena like competitive leapfrogging within different business arenas,
and fostering the most intense and revolutionizing process of mergers and
acquisitions (among which many hostile take-overs) of our era.
Management today has to learn how to deal with the dynamic complexity
arisen due to unforeseeable and continuous shifts in the competitive and
technological environment, and is forced to do it increasingly faster: an
organization that cannot anticipate or at least respond swiftly to rapid and
discontinuous change will not endure long. A quick responsiveness to weak
signals is therefore necessary for survival, while the ability to achieve foresight
and to develop intuition for strategic opportunities despite the obstacles posed
by complexity will determine the future leadership of organizations.
Thus, a company’s success will depend mainly on how effectively and
flexibly it can manage and develop its intangible assets, in other words, on its
capacity to learn and rapidly acquire the skills required by a quickly evolving
environment.
In an attempt to understand how the acquisition of this necessary learning
skills may be fostered, we will study how organizational learning in strategic
processes may be supported by computer-based simulations. Our objective is
to find out whether these simulation tools enable to overcome the typical
obstacles to learning posed by environmental (time delays, spatial distance)
and cognitive constraints (both individual and social ones), and whether this
could exert a positive impact on the effectiveness of strategy making in a
turbulent environment. In this regard, we will consider how the enhanced
systemic perspective and the facilitated communication of mental models
enabled by computer-based simulations promote a shared vision - stimulating
a concentration of efforts toward a common goal - and support the collective
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decision making processes by both reinforcing the perception and
interpretation of weak environmental signals and by encouraging a
collaborative, creative and proactive attitude to strategy making.
This study explores a collection of related theories and methodologies
derived from different research fields, such as cognitive psychology,
operational research, decision theory and strategy. In particular, we will
mainly refer to the works of Senge (Systems Thinking), Mintzberg (Emergent
strategies), Vicari and von Krogh (Autopoiesis), and Forrester (System
Dynamics).
The first two parts of this work are theoretical and provide a framework
where the advantages of simulation-enhanced organizational learning are
presented and evaluated. In the third and last part two brief cases are exposed
more with the aim to facilitate the understanding of the simulations’ tools’
potential applications, than to provide empirical evidence to support our
argumentation.
In the first part, we address how the revolutionizing progress has forced to
re-consider traditional strategy management, shifting its focus from
deterministic planning with a problem-solving and static approach to a more
dynamic, flexible and experimental strategy making, enhanced by a holistic
view of the company and its potential in an unpredictable environment.
The first chapter provides an introduction on the concepts of complexity,
dynamism and change, and on the impact of these forces on the organization
considered from the perspective of three different methodological approaches:
Complexity Theory, the resource-based view of System Dynamics, and
Autopoiesis, besides Holism which is inherent in all the above.
In the second, we move to analyze the strategic implications of cognition,
presenting Senge’s disciplines of Systems Thinking and Mental Models. We
will discuss the benefits of encouraging managers to gain a systemic view of
their company, and we will describe the obstacles that mental models and
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cognitive bias may pose to an organization’s knowledge sharing and learning,
along with the opportunities that the recognition of these obstacles brings
about.
Further on we will consider how the structure and the culture of an
organization may affect its ability to learn and communicate effectively,
evaluating the benefits of network or enacting structures and explaining the key
role of culture to promote knowledge sharing and encourage learning.
Then we will consider the motivational potential of a Vision which is the
product of a cultural diversity and is deeply shared among all the
organization’s members. We will also discuss the apparent trade-offs that rise
between the organizational variety requisite to cope with complexity and the
need to align the organization’s members in the pursuit of a common dynamic
goal.
After considering the impact that complexity operates on decision making,
in the third chapter we will describe the reasons why today’s strategy making
is more effective when it is managed as an emergent, intuition-driven
spontaneous process and not as a precisely stated and implemented planning
process.
Because of the unreliability of predictions and the need to be rapidly
adapting to -or even anticipating- change, we will explain why the only way
an organization may prepare itself to face -or generate- future environmental
revolutions is to adopt a learning approach to the future.
In this regard, the learning opportunities provided by visioning and
futuring activities will be introduced, stating in particular the fundamental
relevance of intuition and creativity in today’s strategic management.
Finally, the first chapter will supply a description of the different processes
that generate learning, distinguishing between individual and organizational
learning, adaptive or generative learning, and discussing the advantages of
learning by experience, besides considering the limits of traditional learning
techniques.
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In the second part we propose the use of simulation games to overcome said
limits and foster an organization’s potential for learning by encouraging its
members to gain a systemic view of the interrelated complex they contribute
to create.
After introducing the use of games and simulation in history, we will
consider the benefits of creating virtual “practice fields” - where managers are
motivated to play and are able to learn by experience in a risk-free
environment that provides immediate feedback - in order to encourage
learning and support strategic processes.
The beneficial effects of introducing simulated environments (Microworlds)
as a means for accelerating learning are many, among which we will analyze
in particular how microworlds foster a systemic view, by inviting any member
of a company to “play president for a day”, how they enhance experiential
and experimental learning by providing an environment where errors are
considered as opportunities for further understanding, and how they support
decision making.
We will point out how the access to a simulated environment may also
promote cooperation, communication and the emergence of a shared vision
through the creation of a common language and through providing managers
with a tangible dimension where to surface and challenge their individual
mental models of the future.
Next, the design process of a practice field - or learning laboratory, where a
microworld is encompassed- will be examined, with a particular emphasis on
the importance of facilitation (briefing and de-briefing), and the requisites of
an effective interface will be pointed out.
It will be argued that the use of simulations to enhance strategic processes
presents some pitfalls, like the development of a video-game mentality and
other counter-productive attitudes that limit the potential of simulation tools
to stimulate augmented insight and learning.
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We will then explain that the process of developing the model that underlies
the functioning of a simulation can be included in the laboratory in order to
contrast the above mentioned perils. Modeling also produces a more effective
and enduring learning than the playing a simulation, as it teaches managers to
employ a dynamic analysis method that can be easily transferred for
applications to other contexts.
In particular, we will briefly introduce the modeling methodology of System
Dynamics, which is at the basis of most simulations and enables to depict the
accumulation or depletion over time of variables representing the resources of
the company, creating a stock and flow framework that captures the causal
interrelatedness between them.
Finally, after bringing about the issues regarding the evaluation and
measurement of the effectiveness of simulation-based experiential learning
and modeling, we will briefly consider the opportunities for intelligence
amplification provided by the products of Information Technology.
In the third part we will show some application examples of simulated
environments based on the collection of modeling software tools developed by
Powersim Company.
Simulations may serve different categories of purposes, among which the
exploration of digital scenarios for analysis. In this regard, one example is
represented by British Telecom who created the simulation of a competitive
telecommunication market with the objective to create a game for strategic
exploration and for supporting organizational change.
A different example is the simulation developed by Help S.p.A. The
company created the Dashboard Management System in order to support the
decision making process of winery businesses, integrating it with the
Management Accounting area of the firms.
Last, we will point out the advantages of extending the accessibility of
simulations to the Web.
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Part I
The Organization
and its Learning
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1. The Organization System
Complexity is the best stimulus to knowledge and development.
(Seres)
Notions on Dynamic Complexity and Change
As companies need to cope with an ever evolving system and not a static set
of conditions, they need to study the competitive arena and the environment
upheavals that challenge them in terms of complexity and dynamism.
Before attempting to define complexity, it should be pointed out that the
concept of complexity is usually different according to the point of view of
individual companies, and cannot be objectively measured: what maybe
classified as a simple environment by external observers, may be seen as very
complex from the view of one firm (Vicari and von Krogh, 1993).
Literature has provided interesting considerations on the definition of
complexity; generally, it is put in reference with the multiplicity of variables
that must be considered and the variety of relationships that can exist among
them (Ward and Schriefer, 1988). Complexity is therefore supposed to increase
when there is a large number of strategic issues under analysis, or when those
issues are more instable, interconnected, diverse, and develop more quickly
from a weak signal.
Further on, from a human resources perspective, a strategic problem is
considered to be complex if its analysis is intractable because of incomplete or
overwhelming information, competing values and views, and power
differential between team members (Eden and Radford, 1990).
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If we look at the complexity that characterizes the environment of
organizations, we learn that the number of different parts in a system and
their manner of organization and interdependence are not the only drivers of
complexity: a system is complex also if its operations are neither strictly cause
and effect nor entirely random, but rather relate to one another in a contingent
way. This contingency is expressed by a certain degree of homogeneity in
organizational forms and practices that arises from a diffused isomorphic
process that considers “intelligent firms” to respond to an environment that
consists of other firms responding to their environment, which consists of
organizations responding to an environment of organizations’ responses
(Powell, Di Maggio, 1991, in: Brioschi, 1999, p. 9).
A similar complex system is inherently unpredictable, full of feedback loops,
decentralized with any decision points, and, most important, non-
decomposable: it cannot be understood by reducing it to its component parts.
Therefore, any attempt to analyze and manage the relationships that exists
between an organization and its environment, and within the organization
itself, needs to develop a systemic view that takes into account the holistic
nature of those interrelationships.
In his work on the strategic relevance of adopting a systemic approach to
conceptualization and analysis1 Senge has made a significant differentiation
between detail and dynamic complexity: detail complexity is mainly
associated with an overload of information and with the amount of variables
kept in mind while performing an analysis. Dynamic complexity instead
occurs when the results of actions are not obvious, and the effects over time of
the same action are profoundly different from the short term to the long term,
or from local to large scale, due to the multi-loop, multi-state, nonlinear
feedback systems in which organizations operate (Warren and Langley, 1999,
Flood, 1999).
1
The Fifth discipline, 1995
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When an action has a set of local consequences and a totally different set of
consequences in another distant part of the system, the system is dynamically
complex. An example of a similar system could be the competitive arena of the
converging telecommunications, media and Internet-based businesses, as well
as a complicated globally dispersed organization system. Despite the
increasingly rapid dynamics of complexity, currently most of the analysis of a
system are concentrated on the detail complexity of the system and not on its
dynamic complexity. A reason for this behavior may be the mistaken
assumption that complexity is the only way to deal with complexity, and
therefore more and more complex solutions are projected (Senge, 1990).
Instead, we will shortly point out that to manage dynamic contexts a different
approach could be better suited.
The notion of complexity and its dynamics is deeply linked with that of
change. Necessary for evolution, change is often viewed as a succession of
periods of stability intermittently discontinued by major changes produced by
an increase in complexity.
Gursel and Ercil (1997, p. 319) recognized three types of change occurring in
an environment: closed change: in such a situation, changes have predictable
consequences and in a short time period they may occur repeatedly. In
contained change, there are some other occurring events and actions being
taken in the current time which are less exact repetitions of the past. The
possibility of making forecasts extends some way further into the future than
is the case with closed change, but the prediction’s time period has to be short
enough to allow to ignore any build up of small unnoticeable changes, which
may have major consequences. Open-ended change is the new type for business
and is qualitatively different from closed and contained change.
While those two changes involve some repetition or large number of events,
and were the main concern of managers in the past decades, open-ended
change is unique and characterizes the current business environment. In this
situation, changes may seem small and insignificant at first, but after a period
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of time, they escalate with major consequences for many companies. In open-
ended change, information is always inadequate and uncertainty is at its
highest.
Our approach to change occurring in the inside of an organization derives
from different assumptions at the basis of the methodologies described in the
following paragraphs. We assume the dynamic of change not be directed by
central authority and as we suppose that organizational change cannot simply
result from formal rules and practices, but that “changes can be considered as
an emergence resulting from a spontaneous self-organization of interpretive
beings around an issue” (Flood, 1999).
In fact, individuals do not merely follow social rules and practices, they
might wish to change them because rules and practices are modifiable, adding
to the complexity of human systems. These systems involve many people, each
with their own interpretation and experiences of the rules and practices that
affect them. The great uncertainty that this complexity brings about prevents
human systems to be predictable (Flood, 1999).
This has dramatic implications for the strategy making process, as its
effectiveness will no longer depend on control, but on how to facilitate the
emergence of desired individual and organizational behavior, i.e., change.
Given the impossibility for managers to influence their employees’ behavior
and their organization’s evolution in the business arena deterministically,
recent literature affirms that the only way for a company to achieve success is
to influence change by adopting an active role in increasing its capacity to
learn and to develop its intellectual capital (Brioschi, 1999).
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Non traditional theories on the evolution of the organization in a
dynamic complex environment
Having introduced the major environmental factors and characteristics
that are challenging the organization’s success (and even survival), it is now
interesting to consider two different “families” of theoretical approaches
aimed at understanding an organization and its relationship to the
environment. The first family considers organizations as systems that in their
evolution strive to adapt themselves to the changing environment in order to
be successful. A second family instead does not operate a distinction between
an organization’s evolving process and the environment’s one, but it considers
organizations to be self-referential and eventually coevolving with their
environment.
Approaches that consider adaptation
According to Complexity theorists, today’s predominant management view is
that human agents in an organization can foresee the future outcomes of their
actions sufficiently well jointly to intend comprehensive organization
outcomes. This predominant view is based on the metaphor of an organization
as a machine or as an organism adapting to a given environment, and utilizes
a linear model to represent it.
This current management thinking is argued to be descending from
Newton’s mechanicistic model of the universe which used linear
approximations (of admittedly nonlinear systems) to predict the behaviors of a
system. The Newtonian model explains how the system works, leaving nothing
to chance and illuding human beings to be “in control” of perfectly
deterministic organization systems.
The same theorists also argue that the studies of the interaction between the
organization and its environment have been strongly influenced by
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equilibrium models. This influence is evidenced by the presumption of an
evolutionary drive of the organization and its environment towards
equilibrium or stability; in fact, successful organizations are considered as
systems tending to states of equilibrium adaptation to their environments, i.e.
systems that will continue to move to equilibrium unless they are disturbed
from such states (Brioschi, 1999; Stacey, 1996).
In contrast, the new approach provided by the science of complexity studies
the fundamental properties of an organization viewing it as a complex
adaptive system, i.e., a network characterized by a nonlinear feedback
structure that is built through a process of spontaneous self-organization and
produces emergent outcomes.
The outcomes are emergent as the system is considered to be creating
patterns in behavior, and to consist of a network of agents driven by iterative
nonlinear feedback to produce unknowable outcomes. The behavior of an
organization is thus presumed to be unpredictable, as the interaction itself
creates patterns that no individual -or manager- may foresee (Stacey, 1996).
Complex adaptive systems consist of a number of components interacting
with each other according to sets of rules that require them to examine and
respond to each other’s behavior so as to improve their behavior and thus the
behavior of the system which they comprise. This leads to the particular
consideration of such systems to be operating in a manner that constitutes
learning. Pushing this further, “since those learning systems operate in
environments that consist mainly of other learning systems, it follows that
together they form a co-evolving supra-system that creates and learns its way
into the future” (Stacey, 1996).
A second interesting point of this theory considers the agents of such
nonlinear feedback networks to be generating unexpected outcomes from a
sort of “fertile mess” in the organization’s design that fosters creativity as it
encourages open experimentation and play - a point that will be at the basis of
our analysis.
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The limits of a linear analysis to understand a system have also been
pointed out in the literature on System Dynamics. The assumption that the
relationships characterizing a system are linear implies the stability of the
system under study, and therefore it excludes the study of systems bounded by
nonlinear influences, which do not tend toward a state of static equilibrium.
Social systems are in fact characterized by a “continuously shifting balance of
forces among the system nonlinearities” (Forrester, 1961, p. 66). System
Dynamics is a modeling methodology that is based on a resource-based view
of the firm. Under this perspective, in an evolving business environment, the
resource system of a company has to change from one competitive era into the
next. To achieve this adaptability a company should be able to successfully -
and flexibly- manage its own metamorphosis from dependence on one source
of sustainable advantage on another (Glucksman and Morecroft, 1998).
The value of an organization’s capabilities is assumed to be context
dependent. That is, depending to the competitive context, some capabilities
arise as important to achieve competitive advantage, and to the extent that
the firm’s competitive environment changes, the advantages identified by
traditional resource analysis at a prior points in time may not lend themselves
to a competitive advantage in subsequent time periods (Mollona, 1996).
The particular value of this approach is given by its dynamic analysis tools.
Conventional static systems analysis tools offer no understanding of how the
problems we have today have developed over time, especially if the causes are
nonobvious. Also, this tools won’t be able to help understanding the likely
consequences of managers’ future efforts at change, especially where these
might take actions that are oriented to the short term, risking to make things
better today but worse tomorrow (Senge, 1990).
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