Description
In the wake of major accidents and crises in various industrial domains over the last decades, awareness has risen for the importance of managing risks in a proactive and systematic fashion (Power 2007). Risk management is in itself probably one of the fastest growing business fields.
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Risk management from an organizational
psychology perspective:
A decision process for managing uncertainties
Gudela Grote
Risikomanagement, Management von Unsicherheit, strategische Ent-
scheidungsprozesse, Stabilität, Flexibilität, Gestaltung von Geschäfts-
prozessen
Risk management, uncertainty management, strategic decision-ma-
king, stability, fexibility, design of business operations
Seit einigen Jahren ist Risikomanagement allgegenwärtig. In diesem
Artikel wird das Risikomanagement in Organisationen aus der Pers-
pektive des Managements von Unsicherheit diskutiert. Unsicherheit
wird als “neutrale” Quelle von Risiko angesehen; entsprechend ist der angemessene Um-
gang damit eine wesentliche Voraussetzung für effektives Risikomanagement. Anders als in
vielen Abhandlungen über Unsicherheit und Risiko werden sowohl die Reduktion als auch
der Erhalt oder sogar die Erhöhung von Unsicherheit als potentiell wirkungsvolle Strategien
einbezogen. Ein Entscheidungsprozess wird beschrieben, der Entscheidungsträger bei der
Wahl zwischen diesen Strategien unterstützt, wobei das übergeordnete Ziel eine adäquate
Balance zwischen Stabilität und Flexibilität der Geschäftsprozesse ist. Das Risikomanage-
ment eines Eisenbahnunternehmens dient als Illustration für den gewählten Fokus auf das
Management von Unsicherheit. Abschliessend werden einige Schlussfolgerungen zur Rolle
von Risikomanagement in Organisationen präsentiert.
In recent years risk management has become omnipresent. In this paper risk management
in organizations is discussed from the perspective of managing uncertainty. Uncertainty is
considered to be the „neutral“ source of risk and its management an important prerequisite
for effective risk management. Unlike in other treatments of uncertainty and risk, reducing
as well as maintaining or even increasing uncertainty are assumed to be potentially viable
strategies. A decision process is outlined that supports decision-makers in choosing among
these strategies with the overall aim of achieving an appropriate balance between stability
and fexibility in business operations. An example concerning risk management in a railway
company is used to illustrate the suggested focus on uncertainty management. Finally, some
conclusions are drawn concerning the role of risk management in organizations.
1. Introduction
In the wake of major accidents and crises in various industrial domains over the last dec-
ades, awareness has risen for the importance of managing risks in a proactive and system-
atic fashion (Power 2007). Risk management is in itself probably one of the fastest growing
business felds. In the most basic sense, risk management entails the identifcation and eval-
uation of risks, measures for handling the risks, and risk communication (Renn 2008).
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Many sophisticated methods have been suggested especially for the identifcation and eval-
uation of risks (e.g. Bedford/Cooke 2001), while the handling of risks and risk communica-
tion are not covered much by formal methods. However, there is an extensive literature on
risk communication in the social sciences that helps determine appropriate forms of com-
municating risks (Renn 2008). In this article, I will focus on the decision-making in relation
to different options for handling risks, which is the aspect of risk management least com-
prehensively treated in the literature. I will suggest a systematic process for making deci-
sions on risk, which is based on an organizational psychology approach to managing uncer-
tainty.
The risk management literature usually discusses four ways of handling risk: reduction,
retention, avoidance, and transfer (e.g. Renn 2008). This leaves out a ffth option, which
may be of equal importance, that is, deliberately increasing risk. The signifcance of increas-
ing risk in order to create business opportunities is particularly obvious in the fnancial sec-
tor (e.g. MacKenzie 2006). In the following, the reduction of risk, which in a more general
sense also comprises avoidance and transfer of risk, and the retention of risk, which is ex-
panded to also include increasing risk, will be considered.
Furthermore, a shift in perspective is proposed by focusing on uncertainty, not risk. Un-
certainty is considered to be the more generic concept, which can easily be linked to risk in
its most basic form as an uncertain event or in more specifc defnitions of risk such as the
product of probability and damage. Uncertainty in terms of insuffcient or ambiguous
knowledge about cause-and-effect relationships can be regarded as the “neutral” source of
risk. Uncertainty implies that predictability and transparency as crucial prerequisites of
control are reduced, which leads to insuffcient means of infuence for either avoiding dam-
ages or realizing opportunities. Power (2007) postulates that uncertainty is transformed
into risk when it becomes an object of management. When uncertainties are managed well,
a basic prerequisite for good risk management is established. With the proposed focus on
uncertainty it also becomes easier to appreciate the relevance of both decreasing and in-
creasing risk. Many business operations capitalize on exploring new territory, which by
defnition means to increase uncertainty and risk, even though this is often not made ex-
plicit due to the generally negative connotations of risk.
In the next section some principles are discussed which are relevant for the proposed
decision process. Subsequently, the decision process for handling uncertainty and risk is
described and an example given for its application.
2. Principles for decisions on managing uncertainties
In the following, four principles for the process and the content of decision-making regard-
ing the management of uncertainties will be derived. The decision process will be discussed
in light of research on the rationality of decision-making and the importance of individual
and collective beliefs when making decisions. The decision content will be deliberated upon
in terms of fnding the middle ground between minimizing of and coping with uncertainty.
2.1 (Ir-)Rationality in decision-making
Decision-making is probably one of the most researched psychological processes. For a long
time, research was mainly concerned with contrasting prescriptive mathematically based
models of rational decision-making with the actual decision-making behaviour of individu-
als and groups. Based on the fundamental differences found, the main thrust of the conclu-
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sions drawn has been to point to the fallibility of human decision-making and the need to
educate and support decision-makers in more rational decision-making (Mellers et al.
1998). More recently, some authors have begun to suggest that human decision-making
should be studied more from the point of view of its functionality in adapting to personal
and situational requirements instead of taking mathematical models as gold standard
(Shafr/LeBoeuf 2002). A brief outline of the current debate in the decision-making litera-
ture is provided in order to subsequently suggest two process-related principles for support-
ing decision-making on managing uncertainties.
The most pervasive prescriptive conception of decision-making is the maximization of
subjective expected utility, which postulates that the alternative with the highest expected
payoff gets chosen. In order to use this model, knowledge of probabilities and utilities is
needed and certain prerequisites have to be fulflled like absence of framing effects and of a
priori preference for (un)certainty. Research has provided convincing evidence that these
requirements are often not met, which substantially reduces the viability of the model
(Mellers et al. 1998; Shafr/LeBoeuf 2002). For instance, certainty is often preferred in deci-
sions on gains, but uncertainty is preferred when losses are to be decided upon (Kahneman/
Tversky 1979).
Another frequently used formal conception of decision-making is the maximization of
multi-attribute utility based on the knowledge of all relevant alternatives and all dimen-
sions and their relative weights for distinguishing among the alternatives. Simon (1955)
pointed out half a century ago already that people’s cognitive capacities are limited, which
he termed bounded rationality, leading them to accept “satisfcing” choices, for instance
based on an alternative’s acceptable level on one crucial dimension. In recent years, espe-
cially Gigerenzer has advocated the view that the use of simple decision heuristics is often
fully adequate even if more cognitive resources were available. He postulates that expert
intuition is about knowing which information is important and ignoring the rest (Gigeren-
zer/Goldstein 1996; Gigerenzer 2007).
The debate whether deviations from the formal prescriptive decision models make hu-
man decision-making irrational or whether the formal models are built on a very restricted
and possibly even irrelevant understanding of rationality is still on-going (Weber/Johnson
2009). The issue becomes even more complex when not only individual decision-making
but also groups of decision-makers are considered (Kerr/Tindale 2004). Groups may help to
overcome individual biases and faulty heuristics, but they may also exacerbate individual
inadequacies by, for instance, group pressures or diffusion of responsibility. Both for group
and individual decision-making recent research has stressed adaptive functioning as the ul-
timate criterion for good decision-making instead of some normative one best way (Kerr/
Tindale 2004; Kahneman/Klein 2009). This has been advocated in particular by researchers
following the so-called naturalistic decision-making approach, which focuses on studying
real life decision-making by professional groups instead of conducting laboratory experi-
ments (Klein 2008). Most recently, the apparent contradictions between heuristics-based
intuitive decision-making and formal rational decision-making have been built into dual-
process models (Evans 2008). These models assume the parallel functioning of both types
of decision-making, sometimes called system 1 and system 2, with more or less emphasis on
each, depending on situational requirements. While system 1 refers to intuition and is char-
acterized by implicit, automatic, low effort, holistic, fast, and emotional processes, system 2
entails reasoning with explicit, controlled, high effort, analytic, slow, and cognitive pro-
cesses.
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Research following the paradigm of naturalistic decision-making has also been very in-
fuential in demonstrating the importance of basic assumptions and belief systems in deci-
sion-making. An example is Feldman’s (2004) analysis of two major NASA accidents; the
explosions of the shuttles Challenger and Columbia. Feldman traces some of the faulty de-
cision-making involved in these tragedies back to an over-confdence in quantitative data
combined with neglect of non-quantifable data. As an underlying cause, he sees the culture
of objectivity at NASA, a culture he considers typical for an engineering organization.
“They (the NASA engineers) were not able to quantitatively prove fight was unsafe, so in
this culture it became easy for management to claim it was safe. […] Under conditions of
uncertainty, cultures dominated by the belief in […] objectivity must be silent. This silence
makes these cultures vulnerable to power and manipulation” (Feldman 2004, 708).
For our current purposes, two basic and uncontested principles for decision-making
processes can be derived. (1) Decisions are always based on some kind of subjective cost-
beneft analysis. (2) Individual and collective assumptions and beliefs about reality are at
least as important in decision-making as objectivist rationality.
2.2 Balancing di?erent modes of uncertainty management
As a starting point for making strategic decisions on how an organization should approach
uncertainties, minimizing uncertainties versus coping with uncertainties can be contrasted
(Grote 2009).
Scientifc treatment of organization design at the turn of the 20th century (Taylor 1911;
Weber 1947) was built on the assumption that organizations are closed systems, thereby
protected from external uncertainties. Internal uncertainties were to be minimized by
minute planning and continuous monitoring of the execution of these plans, providing min-
imal degrees of freedom to the people in charge of carrying out the plans and taking any
devia tion from the plans as signs for the necessity of even more planning and monitoring
(see Figure 1). Accordingly, the basic control mode is that of feedforward control. The Ford-
ist production lines are a prime example of the minimizing uncertainties approach. They
were tailored to mass production of standard products, that is: Model T in black. With the
acknowledgement of the open system nature of organizations the minimizing uncertainties
approach continued to be followed and even gained in fervour in order to keep systems
under control (Senge 1990). Weitz and Shanhav (2000) have suggested that engineers used
their success in handling technical uncertainties to expand their professional domain to in-
clude the reduction and elimination of organizational uncertainties as well. As the minim-
izing uncertainties approach promises maximum control, it is still the favoured approach in
many organizations.
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Figure 1: Basic principles of uncertainty management (adapted from Grote 2009)
A fundamentally different approach which has been promoted by organization theorists
and work scientists for several decades now is to enable all members of an organization to
cope with uncertainties locally and to rely on feedback control (e.g., Perrow 1967; Weick et
al. 1999; see Figure 1). From this perspective, planning is understood primarily as a re-
source for situated action (Suchman 1987), not as a blueprint for centrally determined and
monitored action. Local actors need to be given as many degrees of freedom as possible,
achieving concerted action mainly through lateral, task-induced coordination. Disturbances
are regarded as opportunities for use and expansion of individual competencies and for
organizational innovation and change. Cherns’ (1987) principles of socio-technical design
provide a good summary of the core ideas of this approach, especially the principles of
minimal critical specifcation for work processes and task allocation, of role breadth to en-
sure multifunctional expertise, and of controlling variances at their source.
Much of the earlier literature in organization theory was aimed at developing contin-
gency models for deciding between the minimizing and coping with uncertainty approaches
in light of the types and amounts of uncertainty a particular organization is faced with (e.g.
Burns/Stalker 1961; Thompson 1967; Van de Ven et al. 1976; Argote 1982; for a compre-
hensive review see Wall et al. 2002). The most basic understanding of these contingencies is
that minimizing uncertainties only works when the overall level of uncertainties an organi-
zation is confronted with is low. With higher levels of uncertainties, any attempt to design
them out of the system will fail and therefore the system has to be enabled to cope with
uncertainties locally.
More recently, research has been concerned with showing the need and also the possi-
bilities for overcoming the dichotomy between minimizing of and coping with uncertainty.
In 1976 Weick already argued that most organizations aim to achieve what he called loose
coupling, that is the concurrence of autonomy and dependence and thereby also a mix of
coping with and minimizing uncertainty. Elements of loose coupling are: intrinsic motiva-
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tion, which promotes using autonomy in line with superordinate goals; participation in
rule-making as a way to allow higher-order autonomy; mechanisms that support swift
shifts between the two modes of handling uncertainty; and fnally culture as a “soft” form
of centralization through values and basic assumptions (see Figure 1). Even for high-risk
industries, it is now acknowledged that organizations need both the stability created by
minimizing uncertainty and the fexibility achieved by coping with uncertainty. The con-
cepts of high-reliability organization (e.g., Weick et al. 1999) and of resilience engineering
(Hollnagel et al. 2006) are promiment examples of this change in thinking.
In 1991 March wrote a very infuential article, approaching the same issue from the per-
spective of learning in organizations. He argued that a balance is needed between explora-
tion of new possibilities, concerned with search, variation, experimentation and risk taking,
and exploitation of old certainties in terms of refnement, implementation, and effciency. In
strategic management, the organizational capabilities needed for concurrent exploitation
and exploration have also been termed ambidexterity (Tushman/O’Reilly 1996). In the
competition for scarce resources in organizations, exploitation tends to win because bene-
fts are more visible and short-term (Benner/Tushman 2003). As an example, March (1991)
discusses the socialization of newcomers into organizations, pointing to the attempts in or-
ganizations to ensure fast learning of organizational routines in order to quickly reach eff-
cient performance at the expense of the organization learning from the different viewpoints
and prior experience of the new employee. March’s work has motivated much research into
achieving a balance between exploitation and exploration and thereby also between stabil-
ity and fexibility in organizations. Especially the duality of fexibility and rigidity at differ-
ent levels of an organization has received much attention (Gupta et al. 2006), which exem-
plifes a basic tension in part-whole relationships described by organization theorists
(Astley/Van de Ven 1983). Autonomy at one system level is always linked to constraints at
another system level, and vice versa. Particular members of an organization can only act
autonomously if people at higher levels of the organization are prepared to restrict their
autonomy and delegate it to them.
From the preceding discussion, two further principles for decisions on managing uncer-
tainty can be derived: (1) Requirements of concurrent stability and fexibility need to be
fulflled. (2) Instead of choosing between a minimizing uncertainty versus coping with un-
certainty approach, a detailed consideration of reducing, maintaining and increasing uncer-
tainty in different business units and at different organizational levels is necessary.
3. Deciding on the management of uncertainty: A four-step process
We start with the fundamental premise that the overall objective in individual and organi-
zational decision-making is to gain and maintain control in order to achieve desired goals.
Because for decision-makers in organizations uncertainty may itself induce strong percep-
tions of threat beyond the actual threats of economic loss (Argote et al. 1989), the usual
frst reaction to uncertainties is to try to reduce them. The decision process described here
aims to achieve a more balanced assessment of ways of handling uncertainty by systemati-
cally considering advantages and disadvantages of reducing, maintaining and increasing
uncertainty.
Four steps are proposed (for more detail see Grote 2009), which should be carried out
by a team that includes the organization’s experts of the domains to be covered (for in-
stance, planners when suppy chain management is the focus of the decisions to be taken, or
process engineers when a new automation concept is to be developed), representatives of
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the people affected by the decisions (for instance, operational personnel), and the actual
decision-makers (for instance, the top management team or one representative from that
team). Depending on how broad the chosen content domain is and how much documenta-
tion already exists on the work processes related to that domain, the four steps can be dis-
cussed within a few hours, or they might form the basis of a project running weeks or even
months. If, for instance, the degree of standardization for the operative processes in one
subunit of the organization is the focus, this can be evaluated within a day or two. If the
general philosophy of the organization is at stake in an attempt to turn it into an ambidex-
trous organization, the necessary assessments of current practice and changes needed may
easily take several months.
In preparation for the suggested decision process, an uncertainty landscape needs to be
drawn up that contains as many of the relevant internal and external uncertainties as pos-
sible. Starting from the basic defnition of uncertainty as lack of information required to
perform a task, three variations of this lack of information should be considered: incom-
plete information, ambiguous information, and unclear response alternatives (Lipshitz/
Strauss 1997). These different kinds of lack of information may concern the organization’s
and the environment’s current or future states and cause-effect relationships related to dif-
ferent states and responses. Causes for uncertainties may lie within the organization, for
instance, they can be related to technologies and materials used and the interdependencies
between tasks, or they may be external to the organization, such as changing customer de-
mands or emerging competitors.
Subsequently, a systematic assessment is performed regarding the costs and benefts in-
volved in reducing, maintaining or increasing the identifed uncertainties. This assessment is
complemented by a refection of belief systems and their impact on cost-beneft assumptions
for the different ways of handling uncertainty. Beliefs about central controllability of pro-
duction processes, for instance, may easily produce an overly optimistic view on opportuni-
ties for minimizing uncertainty. Finally, the discussion on costs and benefts of different
management approaches will be summarized for all uncertainties concerned and decisions
taken on the most appropriate approach. The overall aim is to achieve a balance of stability
and fexibility ftted to the particular needs of the organization. The suggested steps in the
decision process are the following:
1. Analyze costs and benefts of reducing uncertainty;
2. Analyze costs and benefts of maintaining or increasing uncertainty;
3. Explore belief systems in the organization related to managing uncer tain ties;
4. Discuss anticipated effects of the recommendations derived in steps 1 to 3 and make
fnal decision.
Each step will now be decscribed in more detail.
Step 1: Analyze costs and benefts of reducing uncertainty. This step conforms to most
classic treatments of uncertainty. The aim is to increase transparency and predictability by
obtaining information and by eliminating causes of opaqueness and unpredictability. Often
this implies the use of power to force other actors to disclose their plans, to agree to bind-
ing arrangements or to accept that uncertainty is transferred to them (Hickson et al. 1971;
Marris 1996; Clegg et al. 2006). The more uncertainties there are the more costly any re-
duction strategy becomes. For instances, resources have to be spent on measurement and
control of internal and external processes. Also, suffcient power vis à vis other actors has
to be established and maintained. Moreover, while the benefts of reducing uncertainty are
quite obvious because control is increased, the costs are partially hidden. In particular, the
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loss of fexibility is not always suffciently taken into account. Reduction of uncertainties
focuses perception on the expected. Thereby, threats as well as opportunities may be over-
looked. Finally, it is important to note that more information does not necessarily reduce
uncertainty, but may create new uncertainties if it allows for different interpretations or
concerns events with unknown probabilities (Becker 2004).
Step 2: Analyze costs and benefts of maintaining or increasing uncertainty. To date, there
is little empirical research on the deliberate increasing of uncertainties due to the dominant
view of uncertainties as inevitable, but largely unwanted. The costs of acknowledging limit-
ed control, of increasing the variety in possible responses to external contingencies, and of
building resource buffers are seen to easily outweigh the benefts of fexibility and respon-
siveness. Probably the most discussed case concerns uncontrollable external uncertainties
which require an increase in internal fexibility and thereby often also internal uncertainty
due to more complex work processes. A classic example of this strategy is diversifcation, be
it with respect to products, markets or suppliers. “Unlike control and cooperation strategies
which attempt to increase the predictability of important environmental contingencies,
fexibility responses increase internal responsiveness while leaving the predictability of ex-
ternal factors unchanged” (Miller 1992, 324). Even in the innovation literature, uncertain-
ties tend to be acknowledged only to the extent that they are an unavoidable side effect of
discovery. It is assumed that control over innovation can be increased by partially routi-
nized processes (e.g. Nelson/Winter 1982; Brown/Eisenhardt 1995). Generally, uncertain-
ties may be increased by granting decision latitude to local actors, for instance by relaxing
rules. This promotes adaptive action, but reduces predictability and control for members of
management, which again often meets with resistance (Senge 1990)
Step 3: Explore belief systems in the organization related to managing uncer tain ties. The
third step involves switching perspective from rational accounts of cost-beneft analyses to
one of sensemaking and enactment (Weick 1995). This perspective holds that perceptions of
uncertainty are more relevant for decision-making than objective accounts of uncertainty,
and that these perceptions and the actions derived from them are embedded in and shaped
by decision-makers’ belief systems. Either minimizing or coping with uncertainty may be
the preferred way of managing uncertainty based on beliefs about control and trust, irre-
spective of the actual effectiveness of either strategy (Shapiro 1987). How powerful belief
systems are in shaping organizational practice as well as the underlying theoretical models
has been illustrated more generally by Ferraro and colleagues (2005). They discuss how the
– empirically contested – assumption that actors are generally motivated by self-interest
permeates much of economic thinking, explaining, for instance, the emphasis placed on
market mechanisms for handling conficts of interest or the importance given to external
incentives in infuencing behaviour.
Step 4: Discuss anticipated effects of the recommendations derived and make fnal deci-
sion. In the fourth step, an overall evaluation of the chosen modes for handling uncertain-
ties is carried out. The aim is to achieve a balance between stability and fexibility ftted to
the particular needs of the organization. The basic assumption is that reducing uncertainty
usually increases stability, while maintaining or increasing uncertainty supports fexibility.
However, aiming to reduce uncertainty that would be better maintained can actually desta-
bilize the system. For instance, if in production scheduling the sequence of orders is fxed
with no decision latitude given to people on the shop foor to adapt it in response to local
disturbances like machine breakdown, this may severely hamper the workfow in the af-
fected unit and beyond. Furthermore, the chosen modes for handling uncertainties may
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create the challenge to develop seemingly contradicting management styles (Smith/Tushman
2005), such as an empowering style in support of local coping with some uncertainties and
a directive style for binding actors to predetermined plans in order to reduce others. Instead
of making once and for all decisions on managing uncertainty, it often will be helpful to
defne probing strategies for continuous re-evaluations of the achieved balance.
4. An example: Managing uncertainty in a railway company
The example is situated in a railway company, which like the railway industry in general is
faced with many new uncertainties through technological developments, privatization, stiff
competition, especially regarding carriage of freight, and growing capacity demands. The
safety department in this particular railway company was charged with evaluating the ef-
fects of all these developments on the capabilities and needs of different groups of employ-
ees for performing their jobs effectively and safely. It was decided to perform this assess-
ment by means of a series of workshops in which the management of uncertainty
framework was used as a guiding principle. As no one specifc decision had to be made, but
rather an evaluation of anticipated new uncertainties and their potential effects was to be
carried out, costs and benefts of reducing, maintaining and increasing uncertainty were
discussed quite broadly for different business operations. Therefore, no strict application of
the decision process suggested in the previous section will be presented here. Instead the
example may help to illustrate the practical viability of the underlying conceptual frame-
work.
As a frst step, the technological and organizational changes which are underway or
planned for the next ten years were collated, highlighting three particularly important clus-
ters of changes: increasing automation of train control, centralization of traffc control, and
higher traffc density. In two one-day workshops with representatives from safety, quality
management, infrastructure, train operation, and maintenance, the effects of these changes
on the task profles for train drivers, signallers, shunters, and maintenance and construction
personnel were assessed. Each task profle was analyzed in detail in relation to assumed
changes in complexity and uncertainty through increased automation and task interdepend-
encies. For shunting, maintenance and construction, these analyses showed an increase in
uncertainties related to managing task interdependencies within more interlinked and more
tightly planned work processes. For train drivers, automation was considered to have the
greatest impact, which in the long run will reduce train driving to mere supervisory control
functions with the uncertainties particular to those functions like reduced system transpar-
ency and requirements for fast and fexible responses to non-routine events. Finally, for
traffc controllers and signallers, it appeared that the integration of these two functions in
highly automated central traffc control centres might lead to a new, more complex job pro-
fle for traffc controllers, and a less complex profle covering routine operation for the
former signallers. While some workshop participants saw centralization resulting in fewer
uncertainties for traffc controllers, others assumed that requirements for uncertainty han-
dling might even increase, as needs for local adaptations will remain and will be more dif-
fcult to handle in central control centres.
In the discussions, the central role of traffc control and of the changes in that function
through centralization and automation became very obvious. Depending on how the
changes in traffc control will be implemented, uncertainties may be transferred to other
actors and conditions for handling them may improve or worsen. One small example in
this respect is an already implemented change in rules concerning shunting of trains onto
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occupied tracks. Previously, signallers were required to communicate to train drivers if the
assigned track was occupied because the signal used for this operation is ambiguous and
only conveys the maximum speed of 40 km/h to the train driver. With increasing traffc
density and more traffc to be handled by signallers in more centralized control centres, this
communication requirement was dropped leaving the train driver with insuffcient informa-
tion regarding adequate shunting speed. This problem was addressed by yet another change
in rules which set the maximum shunting speed to 30 km/h in stations with particularly low
visibility where train drivers have little chance to discover track occupation in time to re-
duce speed suffciently. Thus, uncertainty was originally increased for train drivers and then
partially reduced again.
Overall, the most signifcant concern that emerged in the workshops was the growing
diffculty of managing task interdependencies due to the greater centralization of traffc
control, fewer buffers in resource planning, and loss of shared understanding of work pro-
cesses through divisionalization of the organization. Detailed analyses of the coordination
required between job functions showed that there is considerable potential for unduly
transferring uncertainties to other job functions, especially from traffc control to train driv-
ing and maintenance. In order to address these concerns, several measures were taken: more
integrated training across company divisions, the development of a guideline for job and
system design tailored to the needs of the different company divisions, and the development
of a guideline for rule management in cooperation with the railway regulator. An important
element of the rule management guideline as it now stands is a decision tree that helps to
clarify the amount of uncertainty to be handled in a given work process, the possibilities for
reducing that uncertainty, and the requirements for training and for support by fairly open
rules in case the uncertainty has to be maintained. Additionally and most importantly, an
annual risk assessment was introduced that will permit the monitoring of changes in the
uncertainty landscape for different job functions and of (mis-)matches between require-
ments and capacity for handling those uncertainties.
The integrated training and the participatory development of common guidelines for rule
management and job and system design across company divisions are important measures
in themselves, but they are also highly relevant for maintaining a shared culture. Culture is
seen as a crucial coordination mechanism for dealing with high levels of uncertainties in the
highly interlinked work processes in train operation and maintenance (Grote 2007).
At no point in the analyses undertaken in the railway company, was a systematic explo-
ration of belief systems and their effects on perceived costs and benefts of the different
ways of handling uncertainty carried out. In the workshops and in the subsequent develop-
ment of the various guidelines, differences in preferences and beliefs regarding effective or-
ganizational design became apparent, but were not dealt with explicitly. Instead of con-
fronting the different views – for instance regarding the appropriate distribution of power
and control between the different occupational groups – broad participation in guideline
development was sought as a means to further collective sensemaking and the building of
shared belief systems. This may actually be a better way of addressing belief systems than
trying to discuss them directly, especially in organizations with a rationalist culture, as in
this case. From this experience a modifcation of the decision process described in the previ-
ous section can be derived: Depending on decision-makers’ openness for refecting their
own decision premises, step 3 can be undertaken as suggested or may have to be embedded
in the other steps, the latter requiring particularly skilful moderation of the decision pro-
cess.
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5. Conclusion
In this article, management of uncertainty has been discussed as a more generic organiza-
tional task than and as a crucial prerequisite for risk management. In the suggested decision
process, reducing as well as maintaining or even increasing uncertainty are considered as
potentially viable options. From a risk management perspective, this may seem unusual,
because the focus there is usually to reduce or avoid risk and retain it only if all else fails.
However, also from that perspective it is important to acknowledge that uncertainty and
risk can, at best, be responsibly handled, but they cannot be managed away. Additionally,
uncertainty and risk may even be essential to some business opportunities. The task then
becomes to admit to necessary risks without unduly embracing risks.
Organizations have the power and presumably also the knowledge to make sensible deci-
sions on risk involved in business operations. This creates the rightful expectation that they
can also be held accountable for the decisions they take. However, the ensuing concern with
living up to this expectation may lead risk experts to frame their judgments more in terms
of reducing their personal, legal and reputational risks than in terms of providing honest
assessments of the risks at hand (Power 2004). This may create the paradox that focusing
too narrowly on risk management becomes itself a risk. In order to avoid this problem,
Power argues for a new politics of uncertainty that “would not seek to assuage public anxi-
ety and concerns with images and rhetorics of manageability and control, and would chal-
lenge assumptions that all risk is manageable. (...) Public understandings of expert fallibility
would be a basis for trust in them, rather than its opposite” (Power 2004, 63).
While this new politics of uncertainty is very useful to promote open dialogue about risk,
it clearly has the downside also that decision-makers may be encouraged to disclaim their
contribution to failures, as has happened in the recent fnancial crisis. In order to live up to
rightful expectations of responsible decision-making, decisions have to be based on explicit
scenarios that demonstrate how adequate coping with uncertainty and risk can be achieved.
However, these scenarios also have to include the acknowledgement of limits of controlla-
bility and the defnition of accountability for business processes within and outside these
limits. In view of responsibly handling the particularly high uncertainty and risk involved in
fnancial operations, MacKenzie (2006) similarly called for broad conversations on the de-
sign of fnancial markets in order to help build and maintain fnancial systems that may
serve the interests of all. The suggested decision process is hoped to promote such conversa-
tions in organizations and possibly beyond.
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Gudela Grote ist ordentliche Professorin für Arbeits- und Organisationspsychologie an der
ETH Zürich.
Anschrift: Departement Management, Technology, and Economics, ETH Zürich, Kreuz-
platz 5, 8032 Zürich, Tel.: +41 (0)44/632-7086, E-Mail: [email protected]
doc_495657287.pdf
In the wake of major accidents and crises in various industrial domains over the last decades, awareness has risen for the importance of managing risks in a proactive and systematic fashion (Power 2007). Risk management is in itself probably one of the fastest growing business fields.
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Risk management from an organizational
psychology perspective:
A decision process for managing uncertainties
Gudela Grote
Risikomanagement, Management von Unsicherheit, strategische Ent-
scheidungsprozesse, Stabilität, Flexibilität, Gestaltung von Geschäfts-
prozessen
Risk management, uncertainty management, strategic decision-ma-
king, stability, fexibility, design of business operations
Seit einigen Jahren ist Risikomanagement allgegenwärtig. In diesem
Artikel wird das Risikomanagement in Organisationen aus der Pers-
pektive des Managements von Unsicherheit diskutiert. Unsicherheit
wird als “neutrale” Quelle von Risiko angesehen; entsprechend ist der angemessene Um-
gang damit eine wesentliche Voraussetzung für effektives Risikomanagement. Anders als in
vielen Abhandlungen über Unsicherheit und Risiko werden sowohl die Reduktion als auch
der Erhalt oder sogar die Erhöhung von Unsicherheit als potentiell wirkungsvolle Strategien
einbezogen. Ein Entscheidungsprozess wird beschrieben, der Entscheidungsträger bei der
Wahl zwischen diesen Strategien unterstützt, wobei das übergeordnete Ziel eine adäquate
Balance zwischen Stabilität und Flexibilität der Geschäftsprozesse ist. Das Risikomanage-
ment eines Eisenbahnunternehmens dient als Illustration für den gewählten Fokus auf das
Management von Unsicherheit. Abschliessend werden einige Schlussfolgerungen zur Rolle
von Risikomanagement in Organisationen präsentiert.
In recent years risk management has become omnipresent. In this paper risk management
in organizations is discussed from the perspective of managing uncertainty. Uncertainty is
considered to be the „neutral“ source of risk and its management an important prerequisite
for effective risk management. Unlike in other treatments of uncertainty and risk, reducing
as well as maintaining or even increasing uncertainty are assumed to be potentially viable
strategies. A decision process is outlined that supports decision-makers in choosing among
these strategies with the overall aim of achieving an appropriate balance between stability
and fexibility in business operations. An example concerning risk management in a railway
company is used to illustrate the suggested focus on uncertainty management. Finally, some
conclusions are drawn concerning the role of risk management in organizations.
1. Introduction
In the wake of major accidents and crises in various industrial domains over the last dec-
ades, awareness has risen for the importance of managing risks in a proactive and system-
atic fashion (Power 2007). Risk management is in itself probably one of the fastest growing
business felds. In the most basic sense, risk management entails the identifcation and eval-
uation of risks, measures for handling the risks, and risk communication (Renn 2008).
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Many sophisticated methods have been suggested especially for the identifcation and eval-
uation of risks (e.g. Bedford/Cooke 2001), while the handling of risks and risk communica-
tion are not covered much by formal methods. However, there is an extensive literature on
risk communication in the social sciences that helps determine appropriate forms of com-
municating risks (Renn 2008). In this article, I will focus on the decision-making in relation
to different options for handling risks, which is the aspect of risk management least com-
prehensively treated in the literature. I will suggest a systematic process for making deci-
sions on risk, which is based on an organizational psychology approach to managing uncer-
tainty.
The risk management literature usually discusses four ways of handling risk: reduction,
retention, avoidance, and transfer (e.g. Renn 2008). This leaves out a ffth option, which
may be of equal importance, that is, deliberately increasing risk. The signifcance of increas-
ing risk in order to create business opportunities is particularly obvious in the fnancial sec-
tor (e.g. MacKenzie 2006). In the following, the reduction of risk, which in a more general
sense also comprises avoidance and transfer of risk, and the retention of risk, which is ex-
panded to also include increasing risk, will be considered.
Furthermore, a shift in perspective is proposed by focusing on uncertainty, not risk. Un-
certainty is considered to be the more generic concept, which can easily be linked to risk in
its most basic form as an uncertain event or in more specifc defnitions of risk such as the
product of probability and damage. Uncertainty in terms of insuffcient or ambiguous
knowledge about cause-and-effect relationships can be regarded as the “neutral” source of
risk. Uncertainty implies that predictability and transparency as crucial prerequisites of
control are reduced, which leads to insuffcient means of infuence for either avoiding dam-
ages or realizing opportunities. Power (2007) postulates that uncertainty is transformed
into risk when it becomes an object of management. When uncertainties are managed well,
a basic prerequisite for good risk management is established. With the proposed focus on
uncertainty it also becomes easier to appreciate the relevance of both decreasing and in-
creasing risk. Many business operations capitalize on exploring new territory, which by
defnition means to increase uncertainty and risk, even though this is often not made ex-
plicit due to the generally negative connotations of risk.
In the next section some principles are discussed which are relevant for the proposed
decision process. Subsequently, the decision process for handling uncertainty and risk is
described and an example given for its application.
2. Principles for decisions on managing uncertainties
In the following, four principles for the process and the content of decision-making regard-
ing the management of uncertainties will be derived. The decision process will be discussed
in light of research on the rationality of decision-making and the importance of individual
and collective beliefs when making decisions. The decision content will be deliberated upon
in terms of fnding the middle ground between minimizing of and coping with uncertainty.
2.1 (Ir-)Rationality in decision-making
Decision-making is probably one of the most researched psychological processes. For a long
time, research was mainly concerned with contrasting prescriptive mathematically based
models of rational decision-making with the actual decision-making behaviour of individu-
als and groups. Based on the fundamental differences found, the main thrust of the conclu-
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sions drawn has been to point to the fallibility of human decision-making and the need to
educate and support decision-makers in more rational decision-making (Mellers et al.
1998). More recently, some authors have begun to suggest that human decision-making
should be studied more from the point of view of its functionality in adapting to personal
and situational requirements instead of taking mathematical models as gold standard
(Shafr/LeBoeuf 2002). A brief outline of the current debate in the decision-making litera-
ture is provided in order to subsequently suggest two process-related principles for support-
ing decision-making on managing uncertainties.
The most pervasive prescriptive conception of decision-making is the maximization of
subjective expected utility, which postulates that the alternative with the highest expected
payoff gets chosen. In order to use this model, knowledge of probabilities and utilities is
needed and certain prerequisites have to be fulflled like absence of framing effects and of a
priori preference for (un)certainty. Research has provided convincing evidence that these
requirements are often not met, which substantially reduces the viability of the model
(Mellers et al. 1998; Shafr/LeBoeuf 2002). For instance, certainty is often preferred in deci-
sions on gains, but uncertainty is preferred when losses are to be decided upon (Kahneman/
Tversky 1979).
Another frequently used formal conception of decision-making is the maximization of
multi-attribute utility based on the knowledge of all relevant alternatives and all dimen-
sions and their relative weights for distinguishing among the alternatives. Simon (1955)
pointed out half a century ago already that people’s cognitive capacities are limited, which
he termed bounded rationality, leading them to accept “satisfcing” choices, for instance
based on an alternative’s acceptable level on one crucial dimension. In recent years, espe-
cially Gigerenzer has advocated the view that the use of simple decision heuristics is often
fully adequate even if more cognitive resources were available. He postulates that expert
intuition is about knowing which information is important and ignoring the rest (Gigeren-
zer/Goldstein 1996; Gigerenzer 2007).
The debate whether deviations from the formal prescriptive decision models make hu-
man decision-making irrational or whether the formal models are built on a very restricted
and possibly even irrelevant understanding of rationality is still on-going (Weber/Johnson
2009). The issue becomes even more complex when not only individual decision-making
but also groups of decision-makers are considered (Kerr/Tindale 2004). Groups may help to
overcome individual biases and faulty heuristics, but they may also exacerbate individual
inadequacies by, for instance, group pressures or diffusion of responsibility. Both for group
and individual decision-making recent research has stressed adaptive functioning as the ul-
timate criterion for good decision-making instead of some normative one best way (Kerr/
Tindale 2004; Kahneman/Klein 2009). This has been advocated in particular by researchers
following the so-called naturalistic decision-making approach, which focuses on studying
real life decision-making by professional groups instead of conducting laboratory experi-
ments (Klein 2008). Most recently, the apparent contradictions between heuristics-based
intuitive decision-making and formal rational decision-making have been built into dual-
process models (Evans 2008). These models assume the parallel functioning of both types
of decision-making, sometimes called system 1 and system 2, with more or less emphasis on
each, depending on situational requirements. While system 1 refers to intuition and is char-
acterized by implicit, automatic, low effort, holistic, fast, and emotional processes, system 2
entails reasoning with explicit, controlled, high effort, analytic, slow, and cognitive pro-
cesses.
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Research following the paradigm of naturalistic decision-making has also been very in-
fuential in demonstrating the importance of basic assumptions and belief systems in deci-
sion-making. An example is Feldman’s (2004) analysis of two major NASA accidents; the
explosions of the shuttles Challenger and Columbia. Feldman traces some of the faulty de-
cision-making involved in these tragedies back to an over-confdence in quantitative data
combined with neglect of non-quantifable data. As an underlying cause, he sees the culture
of objectivity at NASA, a culture he considers typical for an engineering organization.
“They (the NASA engineers) were not able to quantitatively prove fight was unsafe, so in
this culture it became easy for management to claim it was safe. […] Under conditions of
uncertainty, cultures dominated by the belief in […] objectivity must be silent. This silence
makes these cultures vulnerable to power and manipulation” (Feldman 2004, 708).
For our current purposes, two basic and uncontested principles for decision-making
processes can be derived. (1) Decisions are always based on some kind of subjective cost-
beneft analysis. (2) Individual and collective assumptions and beliefs about reality are at
least as important in decision-making as objectivist rationality.
2.2 Balancing di?erent modes of uncertainty management
As a starting point for making strategic decisions on how an organization should approach
uncertainties, minimizing uncertainties versus coping with uncertainties can be contrasted
(Grote 2009).
Scientifc treatment of organization design at the turn of the 20th century (Taylor 1911;
Weber 1947) was built on the assumption that organizations are closed systems, thereby
protected from external uncertainties. Internal uncertainties were to be minimized by
minute planning and continuous monitoring of the execution of these plans, providing min-
imal degrees of freedom to the people in charge of carrying out the plans and taking any
devia tion from the plans as signs for the necessity of even more planning and monitoring
(see Figure 1). Accordingly, the basic control mode is that of feedforward control. The Ford-
ist production lines are a prime example of the minimizing uncertainties approach. They
were tailored to mass production of standard products, that is: Model T in black. With the
acknowledgement of the open system nature of organizations the minimizing uncertainties
approach continued to be followed and even gained in fervour in order to keep systems
under control (Senge 1990). Weitz and Shanhav (2000) have suggested that engineers used
their success in handling technical uncertainties to expand their professional domain to in-
clude the reduction and elimination of organizational uncertainties as well. As the minim-
izing uncertainties approach promises maximum control, it is still the favoured approach in
many organizations.
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Figure 1: Basic principles of uncertainty management (adapted from Grote 2009)
A fundamentally different approach which has been promoted by organization theorists
and work scientists for several decades now is to enable all members of an organization to
cope with uncertainties locally and to rely on feedback control (e.g., Perrow 1967; Weick et
al. 1999; see Figure 1). From this perspective, planning is understood primarily as a re-
source for situated action (Suchman 1987), not as a blueprint for centrally determined and
monitored action. Local actors need to be given as many degrees of freedom as possible,
achieving concerted action mainly through lateral, task-induced coordination. Disturbances
are regarded as opportunities for use and expansion of individual competencies and for
organizational innovation and change. Cherns’ (1987) principles of socio-technical design
provide a good summary of the core ideas of this approach, especially the principles of
minimal critical specifcation for work processes and task allocation, of role breadth to en-
sure multifunctional expertise, and of controlling variances at their source.
Much of the earlier literature in organization theory was aimed at developing contin-
gency models for deciding between the minimizing and coping with uncertainty approaches
in light of the types and amounts of uncertainty a particular organization is faced with (e.g.
Burns/Stalker 1961; Thompson 1967; Van de Ven et al. 1976; Argote 1982; for a compre-
hensive review see Wall et al. 2002). The most basic understanding of these contingencies is
that minimizing uncertainties only works when the overall level of uncertainties an organi-
zation is confronted with is low. With higher levels of uncertainties, any attempt to design
them out of the system will fail and therefore the system has to be enabled to cope with
uncertainties locally.
More recently, research has been concerned with showing the need and also the possi-
bilities for overcoming the dichotomy between minimizing of and coping with uncertainty.
In 1976 Weick already argued that most organizations aim to achieve what he called loose
coupling, that is the concurrence of autonomy and dependence and thereby also a mix of
coping with and minimizing uncertainty. Elements of loose coupling are: intrinsic motiva-
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tion, which promotes using autonomy in line with superordinate goals; participation in
rule-making as a way to allow higher-order autonomy; mechanisms that support swift
shifts between the two modes of handling uncertainty; and fnally culture as a “soft” form
of centralization through values and basic assumptions (see Figure 1). Even for high-risk
industries, it is now acknowledged that organizations need both the stability created by
minimizing uncertainty and the fexibility achieved by coping with uncertainty. The con-
cepts of high-reliability organization (e.g., Weick et al. 1999) and of resilience engineering
(Hollnagel et al. 2006) are promiment examples of this change in thinking.
In 1991 March wrote a very infuential article, approaching the same issue from the per-
spective of learning in organizations. He argued that a balance is needed between explora-
tion of new possibilities, concerned with search, variation, experimentation and risk taking,
and exploitation of old certainties in terms of refnement, implementation, and effciency. In
strategic management, the organizational capabilities needed for concurrent exploitation
and exploration have also been termed ambidexterity (Tushman/O’Reilly 1996). In the
competition for scarce resources in organizations, exploitation tends to win because bene-
fts are more visible and short-term (Benner/Tushman 2003). As an example, March (1991)
discusses the socialization of newcomers into organizations, pointing to the attempts in or-
ganizations to ensure fast learning of organizational routines in order to quickly reach eff-
cient performance at the expense of the organization learning from the different viewpoints
and prior experience of the new employee. March’s work has motivated much research into
achieving a balance between exploitation and exploration and thereby also between stabil-
ity and fexibility in organizations. Especially the duality of fexibility and rigidity at differ-
ent levels of an organization has received much attention (Gupta et al. 2006), which exem-
plifes a basic tension in part-whole relationships described by organization theorists
(Astley/Van de Ven 1983). Autonomy at one system level is always linked to constraints at
another system level, and vice versa. Particular members of an organization can only act
autonomously if people at higher levels of the organization are prepared to restrict their
autonomy and delegate it to them.
From the preceding discussion, two further principles for decisions on managing uncer-
tainty can be derived: (1) Requirements of concurrent stability and fexibility need to be
fulflled. (2) Instead of choosing between a minimizing uncertainty versus coping with un-
certainty approach, a detailed consideration of reducing, maintaining and increasing uncer-
tainty in different business units and at different organizational levels is necessary.
3. Deciding on the management of uncertainty: A four-step process
We start with the fundamental premise that the overall objective in individual and organi-
zational decision-making is to gain and maintain control in order to achieve desired goals.
Because for decision-makers in organizations uncertainty may itself induce strong percep-
tions of threat beyond the actual threats of economic loss (Argote et al. 1989), the usual
frst reaction to uncertainties is to try to reduce them. The decision process described here
aims to achieve a more balanced assessment of ways of handling uncertainty by systemati-
cally considering advantages and disadvantages of reducing, maintaining and increasing
uncertainty.
Four steps are proposed (for more detail see Grote 2009), which should be carried out
by a team that includes the organization’s experts of the domains to be covered (for in-
stance, planners when suppy chain management is the focus of the decisions to be taken, or
process engineers when a new automation concept is to be developed), representatives of
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the people affected by the decisions (for instance, operational personnel), and the actual
decision-makers (for instance, the top management team or one representative from that
team). Depending on how broad the chosen content domain is and how much documenta-
tion already exists on the work processes related to that domain, the four steps can be dis-
cussed within a few hours, or they might form the basis of a project running weeks or even
months. If, for instance, the degree of standardization for the operative processes in one
subunit of the organization is the focus, this can be evaluated within a day or two. If the
general philosophy of the organization is at stake in an attempt to turn it into an ambidex-
trous organization, the necessary assessments of current practice and changes needed may
easily take several months.
In preparation for the suggested decision process, an uncertainty landscape needs to be
drawn up that contains as many of the relevant internal and external uncertainties as pos-
sible. Starting from the basic defnition of uncertainty as lack of information required to
perform a task, three variations of this lack of information should be considered: incom-
plete information, ambiguous information, and unclear response alternatives (Lipshitz/
Strauss 1997). These different kinds of lack of information may concern the organization’s
and the environment’s current or future states and cause-effect relationships related to dif-
ferent states and responses. Causes for uncertainties may lie within the organization, for
instance, they can be related to technologies and materials used and the interdependencies
between tasks, or they may be external to the organization, such as changing customer de-
mands or emerging competitors.
Subsequently, a systematic assessment is performed regarding the costs and benefts in-
volved in reducing, maintaining or increasing the identifed uncertainties. This assessment is
complemented by a refection of belief systems and their impact on cost-beneft assumptions
for the different ways of handling uncertainty. Beliefs about central controllability of pro-
duction processes, for instance, may easily produce an overly optimistic view on opportuni-
ties for minimizing uncertainty. Finally, the discussion on costs and benefts of different
management approaches will be summarized for all uncertainties concerned and decisions
taken on the most appropriate approach. The overall aim is to achieve a balance of stability
and fexibility ftted to the particular needs of the organization. The suggested steps in the
decision process are the following:
1. Analyze costs and benefts of reducing uncertainty;
2. Analyze costs and benefts of maintaining or increasing uncertainty;
3. Explore belief systems in the organization related to managing uncer tain ties;
4. Discuss anticipated effects of the recommendations derived in steps 1 to 3 and make
fnal decision.
Each step will now be decscribed in more detail.
Step 1: Analyze costs and benefts of reducing uncertainty. This step conforms to most
classic treatments of uncertainty. The aim is to increase transparency and predictability by
obtaining information and by eliminating causes of opaqueness and unpredictability. Often
this implies the use of power to force other actors to disclose their plans, to agree to bind-
ing arrangements or to accept that uncertainty is transferred to them (Hickson et al. 1971;
Marris 1996; Clegg et al. 2006). The more uncertainties there are the more costly any re-
duction strategy becomes. For instances, resources have to be spent on measurement and
control of internal and external processes. Also, suffcient power vis à vis other actors has
to be established and maintained. Moreover, while the benefts of reducing uncertainty are
quite obvious because control is increased, the costs are partially hidden. In particular, the
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loss of fexibility is not always suffciently taken into account. Reduction of uncertainties
focuses perception on the expected. Thereby, threats as well as opportunities may be over-
looked. Finally, it is important to note that more information does not necessarily reduce
uncertainty, but may create new uncertainties if it allows for different interpretations or
concerns events with unknown probabilities (Becker 2004).
Step 2: Analyze costs and benefts of maintaining or increasing uncertainty. To date, there
is little empirical research on the deliberate increasing of uncertainties due to the dominant
view of uncertainties as inevitable, but largely unwanted. The costs of acknowledging limit-
ed control, of increasing the variety in possible responses to external contingencies, and of
building resource buffers are seen to easily outweigh the benefts of fexibility and respon-
siveness. Probably the most discussed case concerns uncontrollable external uncertainties
which require an increase in internal fexibility and thereby often also internal uncertainty
due to more complex work processes. A classic example of this strategy is diversifcation, be
it with respect to products, markets or suppliers. “Unlike control and cooperation strategies
which attempt to increase the predictability of important environmental contingencies,
fexibility responses increase internal responsiveness while leaving the predictability of ex-
ternal factors unchanged” (Miller 1992, 324). Even in the innovation literature, uncertain-
ties tend to be acknowledged only to the extent that they are an unavoidable side effect of
discovery. It is assumed that control over innovation can be increased by partially routi-
nized processes (e.g. Nelson/Winter 1982; Brown/Eisenhardt 1995). Generally, uncertain-
ties may be increased by granting decision latitude to local actors, for instance by relaxing
rules. This promotes adaptive action, but reduces predictability and control for members of
management, which again often meets with resistance (Senge 1990)
Step 3: Explore belief systems in the organization related to managing uncer tain ties. The
third step involves switching perspective from rational accounts of cost-beneft analyses to
one of sensemaking and enactment (Weick 1995). This perspective holds that perceptions of
uncertainty are more relevant for decision-making than objective accounts of uncertainty,
and that these perceptions and the actions derived from them are embedded in and shaped
by decision-makers’ belief systems. Either minimizing or coping with uncertainty may be
the preferred way of managing uncertainty based on beliefs about control and trust, irre-
spective of the actual effectiveness of either strategy (Shapiro 1987). How powerful belief
systems are in shaping organizational practice as well as the underlying theoretical models
has been illustrated more generally by Ferraro and colleagues (2005). They discuss how the
– empirically contested – assumption that actors are generally motivated by self-interest
permeates much of economic thinking, explaining, for instance, the emphasis placed on
market mechanisms for handling conficts of interest or the importance given to external
incentives in infuencing behaviour.
Step 4: Discuss anticipated effects of the recommendations derived and make fnal deci-
sion. In the fourth step, an overall evaluation of the chosen modes for handling uncertain-
ties is carried out. The aim is to achieve a balance between stability and fexibility ftted to
the particular needs of the organization. The basic assumption is that reducing uncertainty
usually increases stability, while maintaining or increasing uncertainty supports fexibility.
However, aiming to reduce uncertainty that would be better maintained can actually desta-
bilize the system. For instance, if in production scheduling the sequence of orders is fxed
with no decision latitude given to people on the shop foor to adapt it in response to local
disturbances like machine breakdown, this may severely hamper the workfow in the af-
fected unit and beyond. Furthermore, the chosen modes for handling uncertainties may
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create the challenge to develop seemingly contradicting management styles (Smith/Tushman
2005), such as an empowering style in support of local coping with some uncertainties and
a directive style for binding actors to predetermined plans in order to reduce others. Instead
of making once and for all decisions on managing uncertainty, it often will be helpful to
defne probing strategies for continuous re-evaluations of the achieved balance.
4. An example: Managing uncertainty in a railway company
The example is situated in a railway company, which like the railway industry in general is
faced with many new uncertainties through technological developments, privatization, stiff
competition, especially regarding carriage of freight, and growing capacity demands. The
safety department in this particular railway company was charged with evaluating the ef-
fects of all these developments on the capabilities and needs of different groups of employ-
ees for performing their jobs effectively and safely. It was decided to perform this assess-
ment by means of a series of workshops in which the management of uncertainty
framework was used as a guiding principle. As no one specifc decision had to be made, but
rather an evaluation of anticipated new uncertainties and their potential effects was to be
carried out, costs and benefts of reducing, maintaining and increasing uncertainty were
discussed quite broadly for different business operations. Therefore, no strict application of
the decision process suggested in the previous section will be presented here. Instead the
example may help to illustrate the practical viability of the underlying conceptual frame-
work.
As a frst step, the technological and organizational changes which are underway or
planned for the next ten years were collated, highlighting three particularly important clus-
ters of changes: increasing automation of train control, centralization of traffc control, and
higher traffc density. In two one-day workshops with representatives from safety, quality
management, infrastructure, train operation, and maintenance, the effects of these changes
on the task profles for train drivers, signallers, shunters, and maintenance and construction
personnel were assessed. Each task profle was analyzed in detail in relation to assumed
changes in complexity and uncertainty through increased automation and task interdepend-
encies. For shunting, maintenance and construction, these analyses showed an increase in
uncertainties related to managing task interdependencies within more interlinked and more
tightly planned work processes. For train drivers, automation was considered to have the
greatest impact, which in the long run will reduce train driving to mere supervisory control
functions with the uncertainties particular to those functions like reduced system transpar-
ency and requirements for fast and fexible responses to non-routine events. Finally, for
traffc controllers and signallers, it appeared that the integration of these two functions in
highly automated central traffc control centres might lead to a new, more complex job pro-
fle for traffc controllers, and a less complex profle covering routine operation for the
former signallers. While some workshop participants saw centralization resulting in fewer
uncertainties for traffc controllers, others assumed that requirements for uncertainty han-
dling might even increase, as needs for local adaptations will remain and will be more dif-
fcult to handle in central control centres.
In the discussions, the central role of traffc control and of the changes in that function
through centralization and automation became very obvious. Depending on how the
changes in traffc control will be implemented, uncertainties may be transferred to other
actors and conditions for handling them may improve or worsen. One small example in
this respect is an already implemented change in rules concerning shunting of trains onto
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occupied tracks. Previously, signallers were required to communicate to train drivers if the
assigned track was occupied because the signal used for this operation is ambiguous and
only conveys the maximum speed of 40 km/h to the train driver. With increasing traffc
density and more traffc to be handled by signallers in more centralized control centres, this
communication requirement was dropped leaving the train driver with insuffcient informa-
tion regarding adequate shunting speed. This problem was addressed by yet another change
in rules which set the maximum shunting speed to 30 km/h in stations with particularly low
visibility where train drivers have little chance to discover track occupation in time to re-
duce speed suffciently. Thus, uncertainty was originally increased for train drivers and then
partially reduced again.
Overall, the most signifcant concern that emerged in the workshops was the growing
diffculty of managing task interdependencies due to the greater centralization of traffc
control, fewer buffers in resource planning, and loss of shared understanding of work pro-
cesses through divisionalization of the organization. Detailed analyses of the coordination
required between job functions showed that there is considerable potential for unduly
transferring uncertainties to other job functions, especially from traffc control to train driv-
ing and maintenance. In order to address these concerns, several measures were taken: more
integrated training across company divisions, the development of a guideline for job and
system design tailored to the needs of the different company divisions, and the development
of a guideline for rule management in cooperation with the railway regulator. An important
element of the rule management guideline as it now stands is a decision tree that helps to
clarify the amount of uncertainty to be handled in a given work process, the possibilities for
reducing that uncertainty, and the requirements for training and for support by fairly open
rules in case the uncertainty has to be maintained. Additionally and most importantly, an
annual risk assessment was introduced that will permit the monitoring of changes in the
uncertainty landscape for different job functions and of (mis-)matches between require-
ments and capacity for handling those uncertainties.
The integrated training and the participatory development of common guidelines for rule
management and job and system design across company divisions are important measures
in themselves, but they are also highly relevant for maintaining a shared culture. Culture is
seen as a crucial coordination mechanism for dealing with high levels of uncertainties in the
highly interlinked work processes in train operation and maintenance (Grote 2007).
At no point in the analyses undertaken in the railway company, was a systematic explo-
ration of belief systems and their effects on perceived costs and benefts of the different
ways of handling uncertainty carried out. In the workshops and in the subsequent develop-
ment of the various guidelines, differences in preferences and beliefs regarding effective or-
ganizational design became apparent, but were not dealt with explicitly. Instead of con-
fronting the different views – for instance regarding the appropriate distribution of power
and control between the different occupational groups – broad participation in guideline
development was sought as a means to further collective sensemaking and the building of
shared belief systems. This may actually be a better way of addressing belief systems than
trying to discuss them directly, especially in organizations with a rationalist culture, as in
this case. From this experience a modifcation of the decision process described in the previ-
ous section can be derived: Depending on decision-makers’ openness for refecting their
own decision premises, step 3 can be undertaken as suggested or may have to be embedded
in the other steps, the latter requiring particularly skilful moderation of the decision pro-
cess.
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5. Conclusion
In this article, management of uncertainty has been discussed as a more generic organiza-
tional task than and as a crucial prerequisite for risk management. In the suggested decision
process, reducing as well as maintaining or even increasing uncertainty are considered as
potentially viable options. From a risk management perspective, this may seem unusual,
because the focus there is usually to reduce or avoid risk and retain it only if all else fails.
However, also from that perspective it is important to acknowledge that uncertainty and
risk can, at best, be responsibly handled, but they cannot be managed away. Additionally,
uncertainty and risk may even be essential to some business opportunities. The task then
becomes to admit to necessary risks without unduly embracing risks.
Organizations have the power and presumably also the knowledge to make sensible deci-
sions on risk involved in business operations. This creates the rightful expectation that they
can also be held accountable for the decisions they take. However, the ensuing concern with
living up to this expectation may lead risk experts to frame their judgments more in terms
of reducing their personal, legal and reputational risks than in terms of providing honest
assessments of the risks at hand (Power 2004). This may create the paradox that focusing
too narrowly on risk management becomes itself a risk. In order to avoid this problem,
Power argues for a new politics of uncertainty that “would not seek to assuage public anxi-
ety and concerns with images and rhetorics of manageability and control, and would chal-
lenge assumptions that all risk is manageable. (...) Public understandings of expert fallibility
would be a basis for trust in them, rather than its opposite” (Power 2004, 63).
While this new politics of uncertainty is very useful to promote open dialogue about risk,
it clearly has the downside also that decision-makers may be encouraged to disclaim their
contribution to failures, as has happened in the recent fnancial crisis. In order to live up to
rightful expectations of responsible decision-making, decisions have to be based on explicit
scenarios that demonstrate how adequate coping with uncertainty and risk can be achieved.
However, these scenarios also have to include the acknowledgement of limits of controlla-
bility and the defnition of accountability for business processes within and outside these
limits. In view of responsibly handling the particularly high uncertainty and risk involved in
fnancial operations, MacKenzie (2006) similarly called for broad conversations on the de-
sign of fnancial markets in order to help build and maintain fnancial systems that may
serve the interests of all. The suggested decision process is hoped to promote such conversa-
tions in organizations and possibly beyond.
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Gudela Grote ist ordentliche Professorin für Arbeits- und Organisationspsychologie an der
ETH Zürich.
Anschrift: Departement Management, Technology, and Economics, ETH Zürich, Kreuz-
platz 5, 8032 Zürich, Tel.: +41 (0)44/632-7086, E-Mail: [email protected]
doc_495657287.pdf