Abstract
Published case studies contain examples of the ability of the organizational change method appreciative inquiry (AI) to facilitate emergent organizational change, but fail to explain how these results are reached. This paper presents the outcomes of a qualitative study on how and to what extent AI is able to create emergent organizational change. Practical application of AI methodology was captured in five variables. Interviews were performed with 15 AI consultants from The Netherlands. The collected interview data was scanned for manifestations of the influence of the five variables on emergent organizational change. Results show that emergent organizational change created through AI application is primarily facilitated by the generation of new ideas and perspectives, by including relevant stakeholders and explicit appreciation of all contributions to the AI process.
Preface
- Everyone thinks of changing the world, but no-one thinks of changing himself- (Tolstoj)
- The thought we shall think, the deed we shall do, even the fate we shall lament tomorrow, all lie unconscious in our today-
(C.G. Jung)
The above displayed quotes probably hardly concern quotes you’d expect in a thesis in business administration and I cannot blame you. I’ve selected these quotes as they emphasize the deviation that Appreciative Inquiry represents from traditional organizational change approaches. I myself believe that the positive, appreciative philosophy on which AI is based on has the potential at least to become a valuable contribution to the multitude of organizational change approaches if not revolutionize the way we think about organizing and the value of open-minded human interaction in striving for organizational excellence. Performing this research and writing this thesis made me realize the potential richness for personal and organizational development captured in other people’s perspectives of the same subject. For those interested, the Dutch IT company Schuberg Philis represents an excellent example of how commitment to AI philosophy can create not only a very pleasant work atmosphere but also excellent business results. They have gone through a well documented AI summit process and their case now serves as a business case for Nyenrode business school as well as for Harvard University. For the less interested, but who still enjoy watching a movie, at some point in the process of writing my thesis I remember watching the movie Big Fish by Tim Burton. It was in the small hours of the day, and I realized that, besides it being a beautiful film, the movie provided an excellent example of social constructionism in action. It’s about a son whose father is about to die, and he feels he doesn’t know who his father is because his father always told him these unbelievable stories of his own life. This sounds kind of sad, but it’s really a beautiful film and I would recommend you all to watch it.
On a more personal note; it has been a long journey to finally reach the point I am at now and some, including myself, may have lost all hope of me ever graduating. I want to thank all people that supported me in whatever way during all these years. I explicitly want to thank Alexandra van Smoorenburg, Joep de Jong, Theo van den Eijnden, Cora Reijerse, Heike Aiello, Rombout van den Nieuwenhof, Alice Beijker, Annet van de Wetering, Daan van der Weele, Frank Burkels, Luc Verheijen, Marcel van Marrewijk, Ronald van Domburg, Robbert Masselink and Wick van der Vaart for their valuable contributions to my research. Special thanks goes out to Ben Emans, my supervisor, with whose much appreciated supervision and valuable tips and suggestions I finally managed to finish my masters.
Contents
Introduction ... 4
Emergent change ... 4
Appreciative Inquiry as a strategy for organizational change ... 9
Principles of AI ... 10
AI Process: the 4-D cycle ... 12
AI & Emergence ... 17
Intermezzo; AI-thinking in a wider context ... 23
Research methodology ... 27
Results ... 28
Discussion ... 37
Conclusion ... 43
Introduction
Appreciative Inquiry (AI) is an approach to organizational change that is said to ‘generate spontaneous, unsupervised, individual, group and organizational action toward a better future’
(Bushe, 2007, p.30), emphasizing AI’s ability to generate emergent organizational change. However, a critical inquiry into how and to what extent AI is able to support emergent organizational change is lacking in organizational change literature. The central premise of this thesis is that AI methodology can create emergent organizational change. This has to do with the creation of the right circumstances for change to take place in, as emergent change inherently by definition is characterized by an absence of planning: “Emergent change consists of ongoing accommodations, adaptations and alterations that produce fundamental change without a priori intentions to do so”
(Weick, 2000, p.237). Examples abound in literature (i.e. the Syntegra case, authored by Joep de Jong in Watkins, Mohr & Kelly (2011), the United States navy case (Powley, Fry, Barret & Bright, 2004), the sustainable Cleveland case (Meyer-Emmerick, 2012), the Avon of Mexico case, authored by Marjorie Schiller in Watkins & Mohr (2001), the Hunter-Douglas case, authored by Amanda Trosten-Bloom in Fry et al (2002), the ORD case (Bright, Cooperrider & Galloway, 2006) of AI interventions preceding emergent change, but what it exactly is about the methodology that creates the right circumstances for emergent organizational change to take place remains unclear; What are these circumstances, and how does AI methodology contribute to the creation hereof? The goal of the current research is to critically investigate how and to what degree AI creates favorable circumstances for emergent change to take place. This is done by relating four distinctive elements of the AI approach and one element that has received substantial critique in organizational change theory, problem acknowledgement (Grant & Humphries, 2006; Bushe & Kassam, 2005, Fineman, 2006) to emergent change. The process of variation, selection, retention (VSR) from Darwin’s theory of evolution is used to define emergent change.
To reach this goal, the next section will first discuss variation, selection and retention. The principles and process of AI are discussed next, an insight in the background of AI philosophy is provided after which the AI elements under investigation and the research question and sub- questions are introduced.
Emergent change
As the definition by Weick emphasizes, emergent change is about continuous adaptation and experimentation, which is performed to create an optimum ‘fit’ between organizational entities and the organization as a whole to the environment it operates in, without planning these adaptations beforehand. The concept of ‘fit’ is borrowed from the field of biology and specifically from Darwin, who used the term ‘survival of the fittest’ to describe the process of natural selection. ‘Survival of the fittest’ essentially says that the entity that is best capable of adapting to its environment will become the most successful survivor of environmental change. According to Darwin the process of continuous adaptation to the environment can be divided into three steps that repeat over and over again: variation, selection and retention (VSR). From a biological perspective the process of VSR starts with a genetic mutation that results in some kind of physical adaptation (variation), which fits better or worse with the environment the organism lives in. In case the genetic mutation results in a physical adaptation that constitutes a better fit with the environment compared to that of the
current standard, for example a little bit longer beak for a bird so it can reach its food better, the organism will be able to survive better compared to its relatives with a little smaller beak. As a result of being able to gather food more efficiently, the organism will become stronger than its relatives and produce offspring that will for some part inherit the mutation and for some part will not. The part of the offspring that did inherit the beneficial mutation will also be able to survive better than the part of the offspring that did not inherit the mutation and will consequently produce more effective offspring again, in other words, the mutation is selected. When this process is repeated generation after generation ultimately the whole population will have a little longer beak than its ancestors and the mutation is retained. In case the initial mutation would have resulted in a shorter beak, i.e. a worse fit with the environment, the mutation would not have been selected and died off.
Although the concept of VSR originated from the field of biology, it can also be applied to the field of organizational theory. For example, someone is working in an organization and has a certain set of tasks to be performed. When this person finds a way to perform these tasks more efficiently, a variation is created. When this person’s manager notices that this person is performing better compared to his colleagues and the manager considers this way of working as preferable over ‘the old way’ of doing things, the variation is selected. When the operation manual of this organization is consequently adapted to instruct people to perform the set of tasks in ‘the new way’, the variation is retained. Although this example constitutes a very simple depiction of the process of VSR and in practice the process will be more complicated, it does contain the basic process of VSR from an organization theory perspective. In essence, variation from an organization theory perspective starts with an idea, a different way of looking at things.
The person from the example had an idea, and consequently put it into practice, which constitutes the other aspect of variation. Instead of simply putting it into practice, this person could also have opted for discussing the idea with colleagues first, before either putting it into practice or rejecting the idea. If this person had the idea but did not put it into practice, or at least discuss it with colleagues before rejecting it, the variation would have died off prematurely. So, from an organization theory perspective, variation includes both the mental part (the idea) and an action part: the part of either the sharing of the idea with colleagues or simply putting it into practice. Again from the perspective of organization theory, the action part of variation is a prerequisite for selection to take place, whether this takes place as depicted in the example by the manager selecting a new way of doing things or discussing the idea with colleagues before rejecting or accepting the idea. So, selection happens when the variation is 1) somehow shared with other persons and 2) the variation is either considered promising and consequently acted upon or considered as unpromising and discarded. In case the variation is considered promising and consequently acted upon and over time becomes the standard way of doing things, the initial variation is retained.
Where the biological process of VSR can take a very long time as you have to wait for genetic mutations to manifest themselves and over the course of generations either be selected and retained or discarded, the process of VSR from an organizational perspective can manifest itself a lot faster. Despite that emergent change in organizations can happen a lot faster than in biological processes, little is known about how to organize emergent change in organizations. In literature examples abound (Burnes, 2004; Bamford & Forrester, 2003; Esain, Williams & Massey, 2008;
Wallace & Schneller, 2008) of emergent change and the fruitful results emergent change can bring about are recognized within the scientific community, yet theory on the organization of emergent change remains ambiguous. It has to do with creating the right circumstances for change to appear and prosper, but what these circumstances exactly are remains unclear.
Another relevant question, besides which circumstances facilitate emergent organizational change pertains to how the person from the example got to the point where he or she felt that a change was needed or preferable. In organizational change literature, the way and extent to which people make sense of proposed changes is believed to be of crucial importance to the acceptance of these proposed changes (Weick, 1995; Gioia, Thomas, Clark & Chittipeddi, 1994; Bartunek, Rousseau, Rudolph & DePalma, 2006; Ford, Ford & D’Amelio, 2008). According to Weick, Suthcliffe & Obstfield (2005, p. 409) the actual change is preceded by a process of sensemaking, which “involves turning circumstances into a situation that is comprehended explicitly in words and that serves as a springboard into action”. In fact, for people to change their patterns of thought and action, “the proposed change must make sense in a way that relates to previous understanding and experiences”
(Gioia, Thomas, Clark & Chittipeddi, 1994, p. 365). This definition emphasizes the process of sensemaking as the crucial first step in (emergent) change processes. Without sensemaking no change can occur, as people need to grasp a situation or experience in schemata and words before it can be processed by the brain and communicated to other people. This is emphasize by (Weick, Suthcliffe & Ostfield, 2005, p. 410), who state that “The operative image of organization is one in which organization emerges through sensemaking, not one in which organization precedes sensemaking or one in which sensemaking is produced by organization”. It is, among others characteristics, retrospective, social & systemic, about noticing & bracketing, action and organizing through communication (Weick et al, 2005). Sensemaking concerns basic cognitive processes through which people categorize and label experiences into schemata or ‘cognitive groups’ to make the processing of the abundance of experiences by the brain a feasible task. Inertia, or the inability to change, is contributed to forces at the organizational level such as institutionalized routines and practices embedded in organizational structure and culture (George & Jones, 2001). This implies that for emergent organizational change to be realized, one needs to go beyond organizational structures, cultures and routines, question them and consequently alter the status quo through a process of sensemaking.
Basic to this process of sensemaking are symbols and especially language symbols, such as visionary images and metaphors (Gioia et al, 1994, p. 365). The process of sensemaking essentially proceeds through four steps (Weick, 1995): first someone notices something in a flow of events that comes as somewhat of a surprise, the event can’t be directly fitted into an existing frame of reference or cognitive scheme. Second, the observed phenomena that constitute a discrepancy with existing mental models are spotted in a retrospective act of looking back at phenomenon experienced. Third, plausible explanations of the observed phenomena are developed and fourth, these explanations are put into words, written or spoken, creating meaning that wasn’t ‘out there’
before. This implicitly defines sensemaking as an emergent process as meaning is created without a priori planning to do so. According to Gioia et al (1994) the reading, writing, conversing and editing of these plausible explanations for observed phenomena are crucial actions that serve as the media through which conduct is shaped in organizations, emphasizing the importance of sensemaking in the process of emergent organizational change.
The planned and emergent approaches seem to be opposing and mutually exclusive approaches to organizational change, emphasized by Bamford & Forrester (2003, p. 547) who state that ‘the supporters of the emergent approach appear more united in their stance against planned change than their agreement on a specific alternative’. However, organizational change theorists and practitioners are starting to embrace a combined ‘planned – emergent’ approach to change (Bamford & Forrester, 2003; Burnes, 2004; Esain, Williams & Massey, 2008).
Weick (2000, p.223), in an effort to describe the relationship between planned and emergent change, argues that “emergent, continuous change forms the infrastructure that determines whether planned, episodic change will succeed or fail”, thereby emphasizing the ability of people to deal with change as a factor of acceptance for organizational change. The author reasons that the degree to which employees are comfortable dealing with change is to be nurtured by continuous small, incremental and emergent changes, which consequently influences the acceptance of and ability to deal with major, planned change initiatives. Bamford & Forrester (2003), in their effort to simultaneously manage planned and emergent change, identify information gathering, communication and learning to be key organizational activities that support emergent organizational change. Burnes (2004) argues that in change efforts, ‘structure’ needs to be changed quickly first, through a planned approach or ‘bold stroke’ (Kanter, Stein & Jick, 1992) to be followed by the development of an appropriate culture through an emergent process or ‘long march’ (Kanter et al, 1992). He identifies participation, commitment and taking responsibility for opportunities and success as crucial for realizing emergent change.
The validity of the claims made by these authors on the combination of planned and emergent approaches to change in organizational change efforts and the elements supposedly supporting emergent change is not disputed. However, they do all fail to specify how these elements should be operationalized for the creation of emergent change, or the right circumstances for emergent change to surface. When accepting that organizational change efforts to be successful require some combination of the planned and emergent approaches to change, knowledge of which circumstances facilitate emergent organizational change and how to create these circumstances is crucial to the success of change initiatives. That is where the added value of this research resides: it is an effort to contribute to our knowledge on which circumstances facilitate emergent change and how these circumstances can be created by using the organizational change approach of AI as a vehicle to study organizational elements that promote emergent change. Through this approach this research builds and develops both the theory on AI and the small body of research on emergent organizational change.
Turning to the defining of variation, selection and retention from an organizational change perspective, the research of Ghoshal & Bartlett (1988) represents a good starting point. Ghoshal &
Bartlett (1988) introduce creation, adoption and diffusion as synonyms in organization theory for the biological process of VSR. The authors define ‘creation’ as the development of new products and processes, ‘adoption’ as subsidiaries being required to adopt innovations developed elsewhere, and
‘diffusion’ as the mandatory sharing of innovations with the parent company. The concept of VSR from an organizational change perspective as creation, adoption and diffusion does not do justice to the process of VSR under research in this study, as the context this representation was developed in concerned innovations created by subsidiaries of multinationals. From this perspective a) creation is viewed as a collective act and b) the process of creation, adoption and diffusion is viewed as a deliberate, planned course of action. This contrasts with the current study, as variation here is considered to be the result of individual thinking processes and the progression of this variation is considered an emergent process as opposed to a planned process.
Instead of ‘creation’ or ‘variation’ the term ‘generation’ (G) is considered more suitable in the context of this study as it concerns the generation of new ideas or new perspectives by individuals included in the AI process for looking at existing organizational elements originating. So variation in the context of this study is viewed as ‘generation’ and is defined as ‘novel ideas concerning all
organizational elements or novel perspectives of looking at existing organizational elements as a result of individual cognitive processes’.
Considering the collective nature of AI processes, selection represents somewhat of a deviation from traditional selection, or in other words, from an organization theory perspective, decision making. In more traditional forms of organization, decision making is usually performed by a relatively small proportion of the organization size, i.e. management or upper management while in AI processes, decision making is done collectively, based on free choice, prescribed by the ‘free choice’ and ‘wholeness’ principles (discussed in the next section). Consequently the term ‘collective agreement’ (CA) will be used to describe selection. Collective agreement is defined as ‘the collective process of agreeing to pursue one or more courses of action resulting from the AI process’. AI process is also defined in the next section.
When trying to define retention from an organization theory-, or more specific an AI - perspective, the purpose of the AI intervention should be considered. The purpose of an AI intervention can be characterized to be somewhere on the continuum between two paradigms. On the one end of the continuum is the functionalist paradigm, which assumes that society has a
“concrete, real existence and a systemic character oriented to produce an ordered and regulated state of affairs” (Morgan, 1980, p.608). On the other end is the interpretive paradigm, which assumes that “the social world has a very precarious ontological status, and that what passes as social reality does not exist in any concrete sense, but is the product of the subjective and inter-subjective experiences of individuals” (Morgan, 1980, p.608). When AI methodology is viewed from a dominant functionalist paradigm, it is likely that the AI process is considered to be a single autonomous event with a beginning and an end, when the determined courses of action are implemented. When the AI intervention is viewed from a purely interpretive paradigm, the adoption of an AI philosophy, embracing the principles of social constructionism and generativity (to be discussed in the next section) might be considered the purpose of the intervention. When taking the stance of the former paradigm, retention might be defined in terms of the degree to which the goals determined in the AI process are implemented and functioning. Taking the stance of the latter paradigm, retention might be defined in terms of adaptation to the environment, so measures like customer satisfaction, employee satisfaction or innovative capacity could serve as measures of retention. As it is the objective of this research to determine the capacity of AI to generate emergent change, the definition of retention from a slightly dominant interpretive paradigm is considered more appropriate. Consequently retention will be called ‘persistent adaptation’ (PA), defined as ‘the continuous pursuit/development of initiatives, resulting from the intervention as well as new initiatives resulting from the new mindset created in the AI intervention’. Following from this redefinition, the process of VSR mentioned earlier is defined as the process of G/CA/PA for the remainder of this study.
In the current research the organizational change approach of AI, as it is said to generate emergent organizational change, will serve as a vehicle to assess the process of G/CA/PA from an organizational perspective. As a start to shed some light on what these favorable circumstances for emergent change are, and the way and extent to which AI can contribute to the creation of these circumstances, the following section turns the attention to the guiding principles and process for AI application, which, if AI is truly capable of facilitating emergent change, provides a framework for the analyses of how emergent change is facilitated.
Appreciative Inquiry as a strategy for organizational change
Originating from the Weatherhead school of management, Case Western Reserve University in Cleveland, Ohio, David Cooperrider and Suresh Srivastva presented a first outline on AI in their seminal article from 1987. The foundation for the presentation of this AI philosophy, which differed from traditional problem solving paradigms, was laid when David Cooperrider was intrigued by the level of cooperation and innovation reported when asking for success stories about the Cleveland Clinic, where he was performing his doctoral study in 1986 (Mishra & Bhatnagar, 2012). The difference between the traditional problem-solving paradigm and the appreciative paradigm towards organizational change is depicted in figure 1.
AI is an organizational change method which since its introduction has revolutionized the field of OD, is said to be more capable of generating innovative change compared with conventional action research (van der Haar & Hosking, 2004, p. 1018) and has attracted many followers. The AI approach is based on two central concepts: social constructionism (SC) and generative capacity (GC)(Gergen, 1978).
Figure 1: Juxtaposing problem solving & appreciative inquiry. (Copied from Barret & Cooperrider, 1990)
SC is essentially a branch of philosophy that asserts that the reality we see ‘out there’ is socially constructed through a collective process of interpreting events and assigning meaning to them. The use of language plays an important role within SC as the way we put things into words influences the way the subject at hand is perceived. In other words, SC puts forth that there are no empirical truths ‘out there’, just socially constructed interpretations of reality, which can be altered through the use of language. The conception of organizational life from an SC perspective is an interesting subject which opens up new ways of interpreting organizational activities. However, for this research the interpretation of SC as ‘the assumption that different people might regard the same situation differently as their frame of reference differs resulting from differing experiences accumulated through life’ will suffice. For a more in depth analyses of SC I refer to the intermezzo section on AI thinking in a wider context.
Problem Solving Appreciative Inquiry
“Felt need”
Identification of problem
Appreciating Valuing the best of ‘what is’
Analysis of causes Envisioning ‘what might be’
Analysis of possible solutions Dialoguing ‘what should be’
Action planning (treatment)
Innovating ‘what will be’
Basic assumption:
Organizing is a problem to be solved
Basic assumption:
Organizing is a miracle to be embraced
Bushe (2007, p.30) argues that “one of the central sources that influenced the creation of AI was {…} generative theory”. Generative theory, stemming from the field of social psychology, was developed by Kenneth Gergen. Gergen (1978, p. 1346) argues that theoretical accounts should be considered in terms of their GC, that is, “the capacity to challenge the guiding assumptions of the culture, to raise fundamental questions regarding contemporary social life, to foster reconsideration of what is ‘taken for granted’, and thereby furnish new alternatives for social action”. Bushe (2007, p.30) claims that when the concept of generativity is successfully incorporated in AI application, “AI generates spontaneous, unsupervised, individual, group and organizational action toward a better future”. This means that an AI intervention is considered to be unsuccessful when it does not create emergent change, a view supported by a body of literature on AI in which the most successful cases are considered to be the ones which generate spontaneous action and
‘new lenses’ for looking at organizational life.
So, recapitulating the two base-concepts of AI, SC & GC, and applying them to the process of organizational change, the core of the AI approach is formed by two guiding assumptions. The first assumption, from the SC perspective, is that every stakeholder in an organizational change effort views the situation in which the organizational change effort is taking place differently, based on his personal frame of reference. These different perspectives hold a richness that can and should consequently be captured to adequately understand the complexity of organizational change. The second assumption, from the perspective of GC, is that new ways of thinking about organizations lead to new options for action. Bushe (2007, p. 30) claims that AI can be generative in a number of ways, that is “the quest for new ideas, images, theories and models that liberate our collective aspirations, alter the social construction of reality and, in the process, make available decisions and actions that weren’t available or didn’t occur to us before”.
I realize that this conception of AI is rather vague and hardly constitutes a structured way of realizing organizational change, emphasized by the claim that AI is missing a critical contextualization of the organization within the wider social, economic and political landscape (Grant & Humphries, 2006). To make the application of AI philosophy more concrete, the next section discusses the principles on which AI application is build, after which the actual implementation of AI is discussed in the process-section.
Principles of AI
The seminal paper by Cooperrider & Srivastva (1987) that introduced AI to the realm of organizational change primarily presented a theoretic foundation for AI but it did not provide a clear procedure for practical application. As AI lacked a formal procedure and application of the theoretical foundations raised questions about the place of problems and problem solving in organizational change, a set of 5 (1-5) principles was developed by Cooperrider, Sorensen, Whitney
& Yeager (2001), which was complemented with another set of 3 principles (6-8) by Whitney &
Trosten-Bloom (2003). These eight AI principles form the core of AI application and are defined as
‘the principles of AI application each specific intervention in the AI process should adhere to at least 1 off’ (AI process refers to the practical application of AI methodology, discussed in the next section).
An in depth analyses of the eight principles lies outside of the scope of this research. Instead a short description of the eight principles and the consequences thereof for AI application is provided (Table 1). For more information on the principles I refer to the books by Cooperrider et al (2001) and
Whitney & Trosten – Bloom (2003). The important thing here is to know that the eight principles of AI are reflected in the process of AI application, which is the subject of the next section.
Principle Definition Meaning Consequences for the AI
process 1. The
constructionist principle
Words create worlds
Reality as we know it is a subjective versus objective state
It is socially created, through language and conversation
How we know and what we
know are closely interwoven.
Organizations are socially co-constructed realities, and
so AI should attempt to engage as many members of the system as possible in the
AI process and focus on articulating desirable
collective futures.
2. The simultaneity principle
Inquiry creates change
Inquiry is intervention
The moment we ask a question, we begin to create change
As we inquire into human systems, we change them.
Requirement of spending considerable time and effort
to identify what the AI process is about and paying
close attention to the exact wording and provocative potential of the questions that will be asked right from
the entry of the consultant into the system.
3. The poetic
principle We can choose what we study
Organizations, like open books, are endless sources of study and learning
What we choose to study makes a difference. It describes – even creates – the world as we know it
The words and topics that we choose to talk about have an impact far beyond just the
words themselves.
The language of the AI process has important outcomes in and of itself. In all phases of the AI process, effort must be put into using words that point to, enliven,
and inspire the best in people.
4. The anticipatory principle
Image inspires action
Human systems move in the direction of their image of the future
The more positive and hopeful the image of the future, the more positive the present-day action
What we do today is guided by our image of
the future.
The future image of the organization inspires action
today, so these future images should be carefully selected and communicated.
5. The positivity principle
Positive questions lead to positive change
Momentum for large-scale change requires large amounts of positive affect and social bonding
This momentum is best generated through positive questions that amplify the positive core
Momentum and sustainable
change requires positive affect
and social bonding.
All language used in an AI process should have a positive message, even when dealing with problematic issues.
6. The wholeness principle
Wholeness brings out the best
Wholeness brings out the best in people and organizations
Bringing all stakeholders together in large group forums stimulates creativity and build collective capacity
Experiencing
‘the whole’
creates deeper understanding
To meet the required level of complexity to deal with complex change, all relevant
stakeholders should be included in the process.
7. The
enactment principle
Acting ‘as if’ is self-fulfilling
To really make a change, we must ‘be the change we want to see’
Positive change occurs when the process used to create the change is a living model of the ideal future
By enacting things as they
should be, giving a good
example, people follow.
Enacting a desired future behavior or giving a good example helps people move
in the desired direction
8. The free choice principle
Free choice liberates power
People perform better and are more committed when they have freedom to choose how and what they contribute
Free choice stimulates organizational excellence and positive change
People will perform better
when they choose themselves
instead of being chosen
for.
People have the freedom to commit to themes or courses of action based on
their own motivation.
Table 1: AI principles, definitions, meanings and implications
AI Process: the 4-D cycle
The process of AI application is designed to inquire into organizational strengths that could be exploited to counteract current organizational problems and the development of the organization in general. AI is usually practiced through the application of the 4-D cycle, containing four steps (Cooperrider et al, 2001): Discover, Dream, Design & Destiny, sometimes complemented with a fifth D: Definition (Watkins & Mohr, 2001). For the remainder of the current thesis the term ‘AI process’
refers to the 4-D cycle. The terms Discover(y), Dream, Design, Destiny and Definition refer to the separate phases of AI application, depicted in figure 2 (with ‘Definition’ being the ‘Affirmative topic choice’ in the middle if the figure) and explained in more detail below.
The 4-D cycle is characterized by the translation of proven historical organizational capabilities and strengths (to be surfaced in the discovery phase) to the future (in the dream phase) and relating this back to the present (in the design and destiny phases). The circular notion of the model, with the fourth D again connecting with the first D, emphasizes the continuous, dynamic, organic process of organizational change, development and adaptation to the environment. For the purpose of this research, establishing the capabilities of AI to create emergent organizational change, the main focus is on the discovery and dream phases and the first part of the design phase. This focus is based on the way the AI 4-D process is equipped; the dream and discovery phases are primarily aimed at the generation of new ideas and perspectives while the latter two phases, design and destiny, are aimed at turning these new ideas and perspectives into actionable ideas, so the actual emergent part of AI application primarily resides in the first two phases of discovery and dream.
Figure 2: the 4-D cycle
The process starts with the definition of an affirmative topic, the topic that is under investigation in the AI process. The affirmative topic is extracted from the problem or question based on which the AI consultant got invited to the client system. It concerns the subject to be inquired into to solve current organizational issues. For example, when the consultant is invited to resolve issues around not making deadlines on time, the affirmative topic could be something like ‘keeping appointments’ or ‘taking responsibility’. When applying the 4-D cycle the affirmative topic is decided on before the discovery phase starts, when following the 5-D cycle, the determination of the affirmative topic is done in the definition phase. ‘The most common application of AI is the summit methodology, in which large numbers of organizational stakeholders come together for a series of structured discussions over three or four days’ (Bright, Cooperrider & Galloway, 2006, p.290), although examples abound of application of AI at lower levels of aggregation like groups (Peele, 2006; Hart, Conklin & Allen, 2008) or even individual settings (Elliot, 1999; Hart, Conklin & Allen, 2008). Roughly three forms of application of AI can be identified (Masselink & Ijbema, 2011); the first form spreads the AI process over time where each of the four steps is tackled separately with time in between to process the results of the preceding phase and prepare for the next phase. In the second form the four steps are followed consecutively, usually in the form of a 1 to 5 day working conference or summit. In the third form the complete 4-D process is followed for each meeting, whereby the results of the learning process are applied in practice, after which a new cycle can be started. This form is usually encountered in settings of group or individual development. I will shortly describe the four phases of the 4-D cycle below before turning to the introduction of the research variables.
Preliminary phase: definition
Although not part of the dominant 4-D cycle but introduced as the fifth D by Watkins & Mohr (2003), the process of inquiry starts with the definition of an ‘affirmative topic’. “To AI proponents, the framing of a topic is the most important factor for the success of any change initiative” (Bright,
Cooperrider & Galloway, 2006, p.290). AI is considered to be a narrative (DeMatteo & Reeves, 2011) or linguistic approach which, from a social constructionist perspective, makes the first question asked of critical importance as “organizations tend to evolve toward the image evoked by the most commonly asked questions of their members” (Bright, Cooperrider & Galloway, 2006, p.290). The definition of an affirmative topic is usually done by creating a steering committee with representatives from every group of stakeholders from the client system, which guided by the AI consultant comes to a topic through appreciative interviewing. A common and persistent misconception among AI practitioners is that AI is only concerned with ‘the positive’ while AI is actually about uncovering the strength of a community without labeling or judgment. A useful and commonly applied tool for identifying the ‘right’ topic for the inquiry is the Wall of Wonder (WoW).
This technique is meant to create space for the sharing of events, both negative and positive, whereby daring to narrate and show the groups history in such a way that the important themes contributing to the affirmative topic surface, is of utmost importance. The technique is applied by first separating a group’s time line in past, present and future, after which participants are asked to identify important events, both negative and positive, related to the affirmative topic and write them down. The group is then asked to identify the critical factors (themes) that contributed to these important events. Common practice in this stage is the teaching of AI principles and techniques to the steering committee so they can guide the process with the whole client system. In situations where AI is used not as a large group intervention but at a lower aggregation level, for example a small group, the definition of the affirmative topic is customarily done by the manager or a representative from the group in cooperation with the AI consultant.
The Discovery phase
Typical activities in the discovery phase are the shaping of the process, creation of appreciative questions, appreciative interviewing, analyses of interview results and distilling the
‘positive core’ out of the interview data. Based on the affirmative topic defined in the preliminary phase, in 1-on-1 interviews, participants in the AI process are asked to share peak experiences concerning this affirmative topic. The interviews are performed by the participants in the process, the client system, in pairs. The pairs ideally should differ as much as possible in terms of position in the organization, personal norms & values and any other way to maximize the possibility of learning from each other. The process of appreciative interviewing is best described through an example. Say the affirmative topic is ‘taking responsibility’. In the appreciative interviews, 1-on-1, interviewees ask each other to share a peak experience of when they really took responsibility for something; let’s say the development of a new product. Consequently it is inquired into why the interviewee felt responsible for the development of the product, which could be because he came up with the idea for the new product himself. It is then inquired into what made it possible that the interviewee got to develop the idea, the new product. A possible answer could be that he was given the time and resources to develop the product by his manager. It could then be asked why he was given the time and resources, and a possible answer could be because his manager trusts him. The basic value, the root cause for success in this example that made the taking of responsibility possible is ‘trust’. The appreciative interviewing process ideally continuous to the point where these basic values are uncovered, depending on the level of commitment to the process by the client organization. When the level of commitment to the process in the example had been lower, the inquiry could have stopped at the point where the giving of time and resources had been identified as a root cause of success for taking responsibility.
The appreciative interview forms the core of the AI process which, opposed to traditional fact-finding interviews, constitutes a learning process for both interviewer as interviewee. Moreover, different perspectives are surfaced through the process of appreciative interviewing. These differences are not labeled wrong or right but are inquired into to uncover which values and assumptions they are based on. The key is to engage in a judgment free dialogue, to keep asking questions; ‘who was involved in your peak experience’, ‘what conditions made it possible?’, until the core values underlying these peak experiences are uncovered. A point that needs to be emphasized is that appreciative interviewing is not all about positivity and peak experiences, but differences in perspectives can also be appreciated. Appreciation of differences may even be more valuable to the AI process as differences in perspectives, values and assumptions create a possibility for learning.
The root causes for success identified in the 1-on-1 interviews are consequently shared with the whole client system and grouped into ‘themes’ to be taken into the dream phase. Grouping and deciding on themes is done collectively. The themes should include all the root causes for success identified in the paired interviews.
The discovery phase aims at uncovering the ‘best of what is’, it is designed to identify and enhance an organization’s ‘positive capacity’. Primary concern here is to create an awareness of images, stories and capacities which are most likely to inspire and guide future organizing.
The Dream phase
In the dream phase the focus of the inquiry is shifted from the identification of existing strengths to a consideration of how these strengths can be leveraged. The themes resulting from the discovery phase are developed further. Key in this phase is the concept of ‘generativity’; the themes identified based on the subject’s own history are reframed in terms of ‘what could be’, thus placing historical strengths in the future, expanding aspirations for change and challenging the status quo.
An important intervention in the dream stage is the use of metaphors, as they have the capacity to see situations anew, facilitate learning of new knowledge, create scenarios of future action and overcome areas of rigidity (Barret & Cooperrider, 1990), thus assisting in amplifying the range of possibilities for future action. According to Barret & Cooperrider (1990) a good metaphor adheres to four principles or can have four functions; the transfer of meaning to another domain (so that people can converse on the subject at hand more freely, openly), the facilitation of the learning of new knowledge (by studying the subject at hand through a different lens), metaphors can have a guiding function and they invite to active experimentation. Morgan (1980) asserts that metaphor proceeds through assertions that subject A is, or is like B, whereby the process of comparison, substitution, and interaction between the image A and B facilitates the generation of new meaning. Morgan (1980) identifies a creative potential of using metaphors and also a potential drawback of the metaphor usage; ‘Metaphor is thus based on partial truth, it requires of its user a somewhat one- sided abstraction in which certain features are emphasized and others suppressed in a selective comparison’. The choice of a proper metaphor therefore must be a careful one as it might emphasize or suppress the wrong features.
The dream stage typically starts with assigning participants to the themes identified in the discovery phase through a process of free choice; participants can choose which theme they want to commit to and realize its potential. For each of the themes the group is asked to come up with a challenging dream in the form of a provocative proposition and present this dream to the other groups in a creative manner. The ‘dream’ concerns an envisioned future state of organization concerning the theme. For example, when the theme is ‘customer satisfaction’, the dream can be
something like ‘this organization only has 100% satisfied customers’. The presentation of this dream may take the form of paintings, sketches, poetry or any other creative form of expression. These presentations of dreams are discussed and possibly enriched through suggestions from the other groups. As a next step the groups are typically asked to identify the most promising dreams and the meaning of these dreams in practical terms. A provocative proposition forms the end of the dream stage and concerns a statement that is challenging, of fundamental belief and aspiration about human organizing, it stretches the status quo and provides the organization with a goal to grasp for (Watkins, Mohr & Kelly, 2011). Through the provocative, challenging nature of the proposition it should provide the client system some kind of touchstone against which proposed changes can be measured for appropriateness. It is directly build on the stories of peak experiences and of what gives life to the organization collected in the discovery phase and the theme’s consecutively identified from these interviews and symbolizes the transition from inquiry to action.
The Design phase
In the design phase a shift is made from reflection to action; concrete, actionable ideas are generated to move the subject closer to the envisioned future state. Courses of action to be pursued to realize the envisioned future state are decided upon collectively. Preferably actions that can be taken immediately to assist in the realization of the future state are decided upon and implemented to sustain the momentum and energy generated in the dream phase. Action groups are formed around the prioritized dreams, again based on free choice of the participants, with the focus on operationalization and implementation. Aspiration statements are developed and a consecutive change program is created for the realization of these aspirations. The first actions to be taken are preferably quickly realizable to sustain the momentum generated in the discovery and dream phases.
Preconditions for realizing the dream are identified and design elements are chosen that assist in the best realization of the dreams.
The Destiny phase
In the destiny phase the step from planning to deployment is made. The preferably quickly realizable first steps of the action plans are implemented, thus perpetuating the momentum from the AI intervention. When organizational members and stakeholders are truly committed, the courses of action resulting from the AI process and additional developments are actively pursued and people are actively encouraged to develop their ideas adding to the development of the organization. The momentum is ideally sustained indefinitely as participants return to their day-to- day responsibilities.
AI & Emergence
This section first places this research in perspective to developments in organization theory in general and emergent organizational change literature specifically, providing the foundation for this research to build on, before turning to the presentation of the research questions.
Organizational change is considered to have become normality and it is widely agreed upon by organization theorists and practitioners that change is more frequent, of a greater magnitude and much less predictable than ever before (Burnes, 2004). With change becoming the standard, the staggering 70% failure rate (Amis, Slack & Hinings, 2004; Beer & Nohria, 2000; Clegg & Walsh, 2004;
Craine, 2007; Ford & Ford, 2009; Cartwright & Schoenberg, 2006; Washington & Hacker, 2005) reported on organizational change efforts in both private and public sectors emphasizes that organizational change is inherently difficult and points to our inability to successfully understand how change is accomplished. When accepting that change has become normality, the question arises of how to deal with the continuous stream of adaptation needed to ensure optimal fit between an organizations internal and external environment. One way of trying to ensure optimal fit is by constantly scanning, interpreting and reacting to changes, the approach favored by the planned school of change. Another way is to increase the capacity of people and organizations to deal with and proactively facilitate change, an approach favored by the emergent school of thought on organizational change.
Many organizational change theories, such as total quality management (TQM), business process reengineering (BPR), just-in-time (JIT) and creative problem solving (CPS), have been introduced to the realm of organizational change which, so far, have all failed to successfully understand the complex process of organizational change, reflected in the high failure rates concerning organizational change efforts. Common denominator regarding those organizational change theories is that they are all based on problem solving and follow a planned approach. The planned change approach has dominated both theory and practice of change management for the last 50 to 60 years, and still continues to do so. The planned approach holds the view that organizational change concerns a process that moves an organization from one “fixed state” to another through a series of pre-planned steps. The problem solving paradigm, through its process of problem identification, solution generation and treatment of the problem, views organizational life as a ‘problem to be solved’.
AI on the other hand, views organization as ‘a miracle to be embraced’. This has a bit ‘happy- clappy’ feel to it, but success stories of AI application abound in literature. A critical investigation of how different components of AI methodology create change is lacking however. Assuming the stance that emergent change cannot occur without a preceding process of sensemaking, with the goal of this research to critically investigate the role of AI in creating favorable circumstances for emergent change, the question arises how the process and techniques of AI facilitate generativity and the sensemaking process, which, from a social constructionist perspective, is the change in itself.
When accepting that the eight principles of AI are reflected in the process of AI application, combined with the notion that if AI is truly capable of facilitating emergent organizational change these principles form a framework for analyses of how emergent change is facilitated. The question becomes through which AI interventions these principles are practiced and which influence they assert on the process of G/CA/PA. To assess the emergent capabilities of AI four distinctive AI interventions (generative metaphor, provocative proposition, appreciative interviewing and
stakeholder inclusion, to be discussed in the next section) reflecting the eight working principles are introduced as independent variables influencing the process of G/CA/PA in the next section. These four distinctive AI interventions are supplemented with a fifth independent variable, the handling of problematic issues (also discussed in the next section), which AI is said to ignore or go around, representing one of the major critiques on AI. This variable is included because I believe the notion that AI tends to ignore problems to be misguided and resulting from a discrepancy between the theory and practice of AI.
Research questions
It is the goal of this research to assess the capability of AI interventions to generate emergent change. As emergent change by definition cannot be preceded by any form of planning, but change has to ‘arise’, originate in some sort of way, attention needs to be turned to the circumstances in which emergent change can blossom. This leads to the following research question:
In what way and to what extent does AI create the right circumstances for emergent change?
Contributing to the answering of this question, the five variables under investigation believed to influence the process of G/CA/PA are discussed and defined below. All independent variables, Generative Metaphor (GM), Provocative Proposition (PP), Appreciative interviewing (AA), Stakeholder Inclusion (SI) and Problem Acknowledgement (PA) are considered to be interventions within the AI process. The extent to which the independent variables are employed in the process depends on specific actions by the consultant and the influence the variables exercise on the process of G/CA/PA can consequently be steered upon.
Generative metaphor
Two kinds of metaphors can be identified in AI application; narrative metaphors and physical metaphors. The first form is actively used throughout the entire 4-D cycle while the latter is predominantly operationalized in the dream phase where theme related strengths proven in the past identified in the discovery phase are placed in the future. The use of physical metaphors in the AI process occurs in two ways; the first is that people are asked to create a creative image in the form of a painting, sketch, poetry, or any other form of creative expression of their desired future concerning the theme at the end of the dream phase. This creative depiction of the person’s desired future around the theme serves as a metaphor. Another specific use of metaphor regularly applied in AI processes is the creation of an image on the basis of the provocative proposition created at the end of the dream phase. This image usually is created by an external artist and then placed at a central position in the organization like the main entrance. Through the active use of metaphors embedded in the process, it adheres to the anticipatory and enactment principles while simultaneously facilitating generativity and sensemaking. For the purpose of this study, generative metaphor is defined as ‘a symbolic, narrative or physical representation of a part of everyday life employed in the AI process to generate different perspectives’.
The narrative metaphor is expected to heighten generation in the AI process because of the transfer of meaning, the facilitation of learning new knowledge and the invitation to experiment (fig.
3). The physical metaphor is believed to influence collective agreement and persistent adaptation in the AI process as the physical presence of a depicted desired future might perform the guiding function of a metaphor by constantly reminding people of the point at the horizon they cooperatively
