Organisational Response to Crisis

This section discusses how organisations respond to crises. The literature is introduced by reviewing the Disaster Research Center (DRC) typology which was one of the first typologies developed to study organisational responses to crises. Five approaches to organisations’ response to crises are then presented and reviewed. The five approaches are adaptive fit, the edge of chaos, power laws, crisis turnarounds and high reliability organisations. The contribution of each approach to this thesis is also discussed.

Organisations respond to disruption and uncertainty in ways which may show different levels of resilience or alternatively failure:

• They centralise internal controls (Pfeffer, 1978); • they adapt (Ashkanasy, et al., 2000; Webb, 1999); • they learn (Carroll, 1998; Weick, et al., 2005); and • they are creative (Kendra & Wachtendorf, 2003a).

Dynes and Quarantelli (1968) were among the first to focus on organisational responses, as opposed to individual reactions to disaster. In the 1960’s they combined organisational and behaviour theories and proposed the Disaster Research Center (DRC) typology consisting of four types of organised behaviour in disaster. The typology is shown as Figure 2.5. The typology identifies four types of organisation; established, expanding, extending and emergent. Each of the types has the potential to be resilient, however the emergent organisations, those that do not exist prior to the disaster or crisis, are themselves a resilient response. The act or development of their emergence is a resilient response from a group, a realisation that something needs to be done, and an ad hoc solution to the problem.

Figure 2.5: The DRC Typology of Organised Behaviour in Disaster

(Dynes & Quarantelli, 1968, p. 419)

Quarantelli (1995) later updated the typology to reflect different types of emergence that they observed through DRC studies. They found that emergent behaviours existed, not only in emergent groups, but also in non-emergent groups. Examples of this were provided by groups that “…often underwent no major alterations in their structures or functions but nonetheless…exhibited emergent qualities” (Quarantelli, 1995, p. 17). Here Quarantelli (1995) notes how some organisations respond to disaster by expanding their ability to respond, without altering their structure or core business.

Lengnick-Hall and Beck (2005, p. 738) discuss adaptive fit as the ability of an organisation to “…accommodate the level of complexity presented by its environment”. They go on to argue that organisations adapting to uncertainty can take “…deliberate, intentional and rational steps to reach equilibrium” (Lengnick-Hall & Beck, 2005, p. 738). This is perhaps the most practiced approach where organisations do just enough to regain a balance and to survive, while maintaining their existing organisational structure and values. Chakravarthy (1982) describes three states of adaptive fit; unstable, stable, and neutral. Lengnick-Hall and Beck (2005) discuss the characteristics of each of Chakravarthy’s states of adaptive fit; this is presented in Table 2.3.

Type 1: Established Already established and have a

specified role to play in responding to the disaster, such

as the police and fire service

Type 3: Extending Not expected to respond to disasters but they perform non-

regular tasks using their existing structures

Type 2: Expanding Organisations such as the Red

Cross that are expected to be involved in the response and perform business-as-usual tasks

but transform structurally (i.e. they expand)

Type 4: Emergent Characterised by both a new structure and the performance

of non-regular tasks. These emergent organisations do not

exist prior to the disaster Tasks Regular Non-Regular S tr u ct u re O ld N ew

Table 2.3: Lengnick-Hall and Beck’s (2005) Adaptive Fit and Underlying Assumptions

Fit Strategic Posture

Focus Characteristics

Unstable Defensive • Reducing a firm’s

interactions with its environment

• Mechanistic organisational design or

structure

• Very vulnerable to external elements • Reliance on buffers for protection from

adverse consequences

• Relies on passive insulation for survival • Best suited to an environment that changes

slowly and predictably Stable Reactive _• Trying to meet

every environmental change with a corresponding organisational action

• Bureaucratic organisational structure • Adequate resources to respond to

environmental shift but constrained by administrative processes

• Attempt to sense and respond to

environmental changes in ways that conserve resources

Neutral Proactive _• Forecasting and pre-emptive judgement

• A natural match between a firms material

resources and its ability to exploit them

• Ability to reduce vulnerability in highly

complex environments

• Able to anticipate and capitalise on external

shifts

Dervitsiotis (2003) discusses organisational resilience as business landscape fitness. Organisations’ resilience is the fit between their competitive environment, and their performance at a specific point in time. He goes on to argue that conventional business excellence, such as that measured by the EFQM model or the Baldridge Awards, is a goal based on the idea that all organisations are competing on the same business landscape towards the same goals. However, this ignores the fact that organisations’ environments are continuously changing and that every organisation faces different challenges. As a result, Dervitsiotis (2003) argues that business excellence is single loop learning, and that double loop learning is required,

“…in which there must be a search for different more fundamental goals…in view of the inadequacy of the ones presently taken for granted and the prospect of decline or collapse”.

The idea of a resilient organisation that questions their assumptions and is aware of the fallibility of their organisational system, is also reflected in high reliability organisations theory as a preoccupation with failure (Weick & Sutcliffe, 2007) and in resilience engineering asrestlessness(Hollnagel, et al., 2008).

Dervitsiotis (2003) conceptualises a more uneven business landscape, and includes the concept of the edge of chaos as a property of complex adaptive systems. Based on a definition of the business environment as characterised by rapid change Dervitsiotis (2003, p. 255) argues,

“As the value of a particular system variable is changed, a complex system suddenly exhibits ordered behaviour and then may become disordered again. The region where such changes occur is called the edge of chaos”.

Figure 2.6 shows the edge of chaos on a curve representing an organisation’s performance over time. The diagram shows how an environmental change can create an inflection point; shown on the diagram as point I. An inflection point is the point at which there is “…a critical shift in a company’s performance curve” (Dervitsiotis, 2003, p. 259). Following this, the organisation operates on the edge of chaos and, at point A on the diagram, can either make the decision to jump the curve, or to remain on its current course which will eventually enter a state of decline. If the organisation chooses to jump the curve, it will enter another curve with potentially higher achievements. Dervitsiotis’s (2003) model provides some useful ideas for the study of organisational resilience. The idea that an organisation can jump ahead of its current performance curve is a useful analogy, because it provides a visual of what it means to be resilient.

Figure 2.6: Organisational Performance at the Edge of Chaos

(Dervitsiotis, 2003, p. 255)

Kauffman (1995) also refers to the concept of the edge of chaos, Anderson (1999, p. 223) reviews this and notes that,

“…all complex adapting systems evolve to the edge of chaos, the point where small and large avalanches of coevoluntionary change cascade according to a power law”.

Here Anderson (1999) describes how systems evolve to a state of self-organised criticality, in which changes in the environment appear to have a disproportionate impact on the system. Using a normal distribution, practitioners often refer to disasters and crises as high impact low probability events. However, Anderson (1999) uses power laws to account for the way in which organisations experience large fluctuations, or crises, more often than expected. The term power law refers to a mathematical relationship between two variables, where the frequency of an event, such as a crisis or

Environmental Challenge Time P er fo rm a n ce Curve S-1 Edge of Chaos A BE B Curve S-2 Legend:

BE = Point of assumed Business Excellence I = Strategic inflection point

A = Best point and time for “jumping the curve” B = Bad point and time for “jumping the curve”

disaster, varies as a power of some attribute of that event. This relationship can be seen in the equation below:

P(x) = Cx−α With C = ec

(Newman, 2005, p. 323)

Newman (2005) discusses power law curves and notes that they are often characterised by fat tails, or a high frequency of large events. He provides examples of power laws including the magnitude of large earthquakes. Buchanan (2004) uses the diagram shown as Figure 2.7 to illustrate how the power law curve differs from the bell-shaped curve, and how this affects organisations. Normal statistics practices use the bell-shaped curve as a normal distribution which can be applied or expected in most situations. The bell- shaped curve on Figure 2.7 shows how the tails are quite ‘thin’; this means that, according to the bell-shaped curve, there is a very low probability of high impact events such as earthquakes and industrial accidents. However, high magnitude events occur more often than the bell-shaped curve suggests (Perrow, 1999). The power law curve on Figure 2.7 accounts for this variance. The tails on this curve are ‘fatter’ showing that, according to power law, the probability of high impact events, is in fact much higher. This has significant implications when estimating risks and assessing the likelihood of high impact events.

Figure 2.7: The Bell-shaped Curve vs. the Power Law: The Importance of 'Fat Tails'

(Adapted from Buchanan, 2004, p. 5)

Bell-shaped Curve

Buchanan suggests that organisations can use the power law curve, and the knowledge that high impact low probability events happen more often than we might think, to develop strategies to manage crises.

Smith and Sipika (1993) present a model of the post-crisis turnaround stages of crisis management which is shown as Figure 2.8. Within this, they identify three stages; defensive phase, consolidation phase and offensive phase.

Figure 2.8: Post-crisis Turnaround Stages

(Smith & Sipika, 1993, p. 33)

During the Defensive Phase organisations activate their formal and informal response mechanisms. Working within their continuity plans and arrangements, organisations

Defensive Phase Consolidation Phase Offensive Phase Contingency Planning Communications Coupling and Complexity

Crisis Decision Units Scapegoating

Constrained Decision Horizons Control of the Media

Systems’ Design Divestment Cost Control Compensating Victims Financial Restructuring Investment Restructuring Calming the Market Information Management - Controlling Disinformation

Restructuring for a Responsive Structure

Review of Management Style Change of Personnel Marketing Acquisition Configuration Culture Crisis Event O v er la p B et w ee n P h as es Period of Intense Threat F ai lu re R ec y cl in g o f T u rn ar o u n d O p ti o n s

often operate under uncertainty where the flow and availability of information is restricted. Smith and Sipika (1993) include coupling and complexity in the model, not only because these are key factors in creating crises, but because it is important for organisations to ensure that they have identified the cause of crises and fixed any problems, before moving on. During the Consolidation Phase, organisations place more emphasis on strategy, and focus on organisational recovery. As indicated in the model, organisations also look to restore confidence to their internal and external stakeholders and networks. The Offensive Phase is characterised by changes to the organisations’ culture, and configuration or structure. Here, the organisation is experiencing the crisis of legitimisation (Smith, 1990), and it is important to reassure stakeholders further by restructuring the organisation to improve systems and processes to prevent future crises, and by reviewing existing management. This phase is also linked to cultural change within the organisation because leadership, or management and structure, are both integral to the re-positioning, maintenance or change of an organisation’s culture.

Smith and Sipika’s model of post-crisis turnaround is useful for the discussion of organisational resilience because it provides a description of how organisations can respond to a crisis that they have failed to prevent.

The concept of High Reliability Organisations (HRO’s) originates from the military. These organisations have a collective preoccupation with the possibility of failure (Reason, 2000); they expect things to go wrong and errors are generalised rather than isolated. High Reliability theory was originally developed to explain how organisations such as nuclear submarines and air traffic control centres maintain high levels of safety and low incidence of accidents despite operating in hazardous and continuously changing environments. Reason (2000, p. 768) characterizes HRO’s as those organisations “…which have less than their fair share of accidents”. The concept has since been extended to describe space agencies, chemical facilities and clinical environments. Bigley and Roberts (2001) argue that reliability is an increasingly critical quality and competency for organisations responding to crises. They go on to define reliability as “…the capacity to continuously and effectively manage working conditions” (Bigley & Roberts, 2001, p. 1281). Bigley and Roberts discuss a fire department and argue that a structure based on an incident command system can be highly reliable; they go on to note that,

“They appear able to structure and restructure themselves on a moment- to-moment basis and to provide members with means to oscillate effectively between various preplanned organisational solutions to the more predictable aspects of a disaster circumstance and improvised approaches for the unforeseen”.

(Bigley & Roberts, 2001, p. 1282)

This describes more than being able to manage working conditions, and is very similar to resilience. HRO’s can,

“…reconfigure themselves to suit local circumstances. In their routine mode, they are controlled in the conventional hierarchical manner. But in high tempo or emergency situations, controls shift…The organisation reverts seamlessly to the routine control mode once the crisis has passed”. (In doing so they recognise) …that human variability in the shape of compensations and adaptations to changing events is one of the system’s most important safeguards”.

(Reason, 2000, p. 770)

This provides an example of a strategy which achieves a balance between anticipation and resilience and is discussed in more detail in Section 2.5. Reason (2000, p. 769) goes on to argue, “High reliability organisations…offer important models for what constitutes a resilient system”. The similarities or cross-over between HRO and resilience theory are further emphasised by Weick and Sutcliffe (2007) who use HRO theory as a basis for their organisational resilience audits which measure organisational resilience and ask to what extent organisations display HRO characteristics. They go on to discuss patterns of organisational resilience and argue,

“HROs overcome error when interdependent people with varied experience apply a richer set of resources to a disturbance at great speed and under the guidance of swift negative feedback”

Vogus and Sutcliffe (2008) discuss organisational resilience and HRO’s interchangeably, however they do argue that “…resilience and reliability are not identical constructs” (Vogus & Sutcliffe, 2008, p. 3421). Figure 2.9 summarises the characteristics of HRO’s and how they prevent or respond to crises.

Figure 2.9: High Reliability Organisations

Source Characteristics of HRO’s

Roberts (1990) HRO’s are sufficiently technologically advanced that errors can have far- reaching negative consequences

Tightly coupled interdependencies between system components and functions

Complexity which is embedded in system components and the way in which they come together

Grabowski and Roberts (1999)

HRO’s prioritise safety and reliability as organisational goals.

The use of effective and varied communications to reduce uncertainty Reason (2000) The ability to switch from business-as-usual mode to crisis mode and back

again quickly and efficiently Clearly defined and shared goals Weick and

Sutcliffe (2007)

Mindfulness

HRO Crisis Prevention and Response Strategies Perrow (1984) Organisational learning

Weick (1987) Strong organisational culture to reinforce safety and reliability as goals Roberts (1990) Continuous staff training

Responsibility and ownership for problems at all levels Multiple communications pathways or redundancies In-built system flexibility

Resources redundancy Grabowski and

Roberts (1999)

Redundancy in staff and technology Decentralised high reliability culture

Continuous development of interpersonal trust

Reason (2000) Reconfiguring and restructuring to suit the business environment and migrating controls and decision making

Encouraging variability

HRO’s not only address surface problems but also seek to improve underlying system conditions that contribute to crisis

Training staff to recognise and report early warning signs Weick and

Sutcliffe (2007)

Preoccupation with failure which leads them to continuously question their environment and their current assumptions

Commitment to safety and reliability as goals

They defer decisions to those with appropriate knowledge and skills rather than hierarchical position

Vogus and Sutcliffe (2008)

Use information about ‘near misses’ as information about the underlying health of the system and as a source of learning

Note: Sources are listed in chronological order.

The study of HRO’s has been dominated by post-disaster analyses of major accidents such as Bhopal (Roberts, 1990), the Columbia space shuttle (Mason, 2004), and the

Challenger space shuttle (Weir, 2002). Few studies have been conducted on organisations that have the potential for catastrophe but have not experienced major accidents (Roberts, 1990). The characteristics that contribute to organisations’ reliability, may also contribute towards their resilience. Normal Accident Theory (NAT) argues that the cause of accidents is in “…the complexity and coupling of the system itself, not in the failures of its components” (Perrow, 1999, p. 354) and that as a result, accidents are inevitable. This theory is not in competition or contradiction to HRO theory (HRT) but is complimentary. Rijmpa (1997, p. 21) examines the two theories and argues,

“NAT does not only explain normal accidents: it can also be used to explain overall reliability. HRT explains more than overall highly reliable performance: it also highlights factors which contribute to an organisation’s proneness to system accidents”.

In his seminal work, ‘Normal Accidents’ Perrow (1999) discusses high risk organisations as those organisations that combine a complex environment with tightly coupled operations and have the potential to fail with catastrophic consequences (Perrow, 1999). Weick (1987) argues that organisational culture determines whether or not high risk organisations can transform into HRO’s. In terms of organisational resilience, high reliability is a desirable trait (Vogus & Sutcliffe, 2008).