SHAPING COLLABORATIVE

IMAGINATIONor analyzing and attaining competent decisions in genuine uncertainty through structured stakeholder involvement and opportunity space concretization.

Management Report

For any questions, to inquire to opportunities of using morphological analysis or to discuss the content, reach the author at stiannielsenman@gmail.com

EXECUTIVESUMMARYRESEARCH PROBLEM

Real collaboration between very different stakeholders is tough but needed in an increasingly interconnected world brimming with insolvable, or wicked issues. While intentions may be good, several paradoxes ensures that worthwhile results often prove elusive. In essence, decision making is invariably a political process where we’ll want to satisfy as many stakeholders as possible to reduce the risk of future disruptions, while the decisions to be taken also needs to be extremely effective in an instrumental way - we need objectively better solutions, which requires active knowledge and strategically non-compromising concepts. This is inherently very difficult to attain when we rely on qualitatively different knowledges, let alone when the knowledge needs to be merged by stakeholders who disagree heartily.

For all its difficulty however, it seems that as the world grows smaller and more uncertain, these collaborations are nonetheless what may help stabilize the local environments of firms as well as produce sustainable business value based on sustainable practices, embedded in the environment. In conclusion, though it seems difficult, it also seems very worthwhile.

TENTATIVE SOLUTIONGeneral Morphological Analysis (MA) is a qualitative decision support method which excels in

genuinely uncertain environments, and is dusted off and investigated. It seems promising in dealing with the paradox of collective creation among dissatisfied stakeholders by producing a thorough audit trail and exploring solution breadth instead of focusing on contention points (which may be irrelevant) or building trust through extraneous exercises. However, a couple of aspects seems troubling: [1] it hasn’t been used much since its conception in the 1940’, which is odd if it is as good as it seems; [2] it is a socially emergent method, whose results are difficult to predict and control based on theory only, so knowing how to facilitate it effectively is crucial but not operationally described anywhere; and [3] it is resource intensive, requiring three days of analysis, adding to this specialist (likely stakeholder) identification prior and decision analysis after, for a total of probably no less than two weeks, whose result is not a ‘best course of action’ but a shortlist of opportunities. In other words, it is effective only for very central decisions. The research then aims to find if it is useful and if so to operationalize it for implementation.

RESEARCH OUTCOMEMorphoogical Analysis was tested and a process-oriented artefact developed and instantiated through

a functional prototype to ensure the social emergence was adressed and adequately controlled. Based on innovation literature, it was enhanced with ‘synergies’, slightly cumbering the analysis process but allowing quick subsequent action by finding the ‘probably best’ solutions for all involved stakeholders (if any). Operationalizing MA was found to benefit tremendously by using ‘soduko operations’, doubling complexity reduction and enforcing ‘the progress of mindstates’ which dictates the mental processes dialectically needed throughout the analysis process - in this way, MA is similar to creative-analytical processes of concept or idea development.

MA as well as the added synergies were overall found to be useful to the wary analyst. Two reasons MA may not have gained traction earlier were found to be [1] its qualitative - that is - non-dictating nature which hasn’t been a good fit with the more stable 20th century decision environments it comes from; and [2] its markedly different approach which stems from a different way of seeing and understanding decisions. Expecting managers to see its merit when sold as a method may not resonate. Instead, perhaps MA needs to be seen as part of a methodology, thus explicating its alienness and being more honest to its powers and weaknesses.

A QUICK INTRODUCTION TOMORPHOLOGICAL ANALYSIS

Morphological Analysis can be seen as a qualitative, multi-criteria decision support method, excelling in genuinely uncertain decision environments and supplying a long-list of all truly possible solutions. One of the central developments of this study was modifying it to enable generation of a short-list as well, speeding up subsequent work by identifying the ‘proably best’ solutions.

Morphological Analysis (MA) was developed by Fritz Zwicky in 1948 to systematically map huge and complex, multi-dimensional solution spaces. His essential insight was that certain combinations are impossible, identifying which will decrease cognitive load on managers significantly and potentially allow complete mapping, and we might even find unconceived, novel solutions among what is left.

Managers already do multidimensional mappings with simple two-by-two matrices. These are the realistically most complicated multi-dimensional charts we can sketch - three dimensions is possible but cumbersome, while with four we would not even know how to envision it. This requires us to reduce the problem field to two-by-two to envision it, which is often dangerous. We need to identify the two most central ‘market drivers’ or ‘risk dimensions’, neglecting all others based on current data. Though decisions become robust to changes within these boundaries, neglected parameters may suddenly prove significant, changing the company’s environment dramatically. Morphological Analysis accommondates this by easily including as much as seven dimensions of seven discreet states each, for a solution space of over 800.000 ‘formal solutions’.

The product of MA - the ‘Virtual laboratory’. Location, metaphor, experience and theme are all parameters / questions, while university, large room etc. are elements / answers. Any solution is a combination of any one element from each parameter. The formal solution space is 5 x 7 x 5 x 7 = 1225 solutions. Greyed out elements are inconsistent based on choices (marked by checkmarks), while green are ‘synergistic’. With inconsistencies, 469 solutions remain, while of entirely synergistic solutions there are only about 10 (0,8% of formal solution space).

THE PROCESSMorphological Analysis can be performed in two distinctly different ways - with a backoffice-approach,

by a solo analyst (perhaps conducting interviews and other investigations), or through a specialist-reliant workshop. The latter approach is the investigated use of MA, as it has several advantages - two of which are [1] by including stakeholders, different knowledges interact and the risk of error is smaller, while precise contention points are identified quickly; and [2] by finding points of non-contention among stakeholders, trust may be build which has a chance of coordinating tasks and expectations, reducing disruptions in the wicked issue.

figure 7.3 - CCA Field, taking A - I and dynamically constraining the VL in real time.

1. The central contention point of the workshop is firstly defined as a ‘bottleneck’ decision to be taken. This may take several forms, such as a concept, resource allocation, or capacity building. Morphological Analysis is most effective with a normative aim.

2. A number of parameters or questions are defined, who are all part of the central decision and whose answers interact with one another.

3. A set of elements, or answers are developed for each question, between two and ten.

4. Element interactions are assessed through a cross-consistency assessment (CCA), pairing all elements thorougly in pair-wise fashion - if two elements cannot co-exist they are deemed inconsistent (marked by D, E and G in the figure above), and all formal solutions containing that combination are removed from the solution space. They may also ‘create something more’, which makes then synergistic. This interaction type was one of the outcomes of the study. This assessment is a time-consuming process which scales factorially with problem size. However, achieving the same thoroughness without MA would require assessing all formal solutions, which scales exponentially with problem size (the seven by seven formal solution space from above would have 1.029 cross-consistency pairs for evaluation instead of 800.000 solutions).

5. Having completed this, a computer-aided virtual laboratory allows playful interaction with the results as well as a thorough audit trail for all stakeholders.

PROJECTMETHODOLOGY

Three central aspects were under scrutiny in the study. Firstly, would it be possible for a nowcomer to morphological analysis to conduct a reasonable process? Secondly, if so, what would be instrumental facilitation learning points, and how can they be embedded into the process? Thirdly, with the aim of innovation in WI management, might MA be modified to improve performance? The research question then became:

How may a first generation MA-artefact designed to enable innovation for WI management look?

This research question importantly serves two masters - firstly and directly, it asks to the implementation of Morphological Analysis as an artefact (an idealized software). And secondly, to achieve this, theory relevant to innovation, wicked issues and morphological analysis has to be investigated and synthesized. Because MA is a socially emergent method a thorough understanding of it requires implementation, and implementation requires thorough understanding. The only useful way to tackle this paradox then, is with a clumsy prototyped approach, and that is why the research question needs to be seen as serving two masters - neither theory development nor artefact development has good prospects of achieving anything worthwhile on their own.

The project is conducted with a pragmatist world-view relying an open, encompassing and complex ontology; a partial, plural and provisional epistemology, as well as an ethics based in an understanding of incommensurability, social agonism and coercion. The project is carried out as soft design science research relying on abductive reasoning. Qualitative and quantitative data is developed from video-recordings of a quasi-experiment utilizing a concurrent embedded mixed-method strategy. The experiment is transcribed, and the MA-based virtual artefact is designed.

morphologicalanalysis

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experimentalworkshopand results

PROJECT PROCESS MODEL

artefact design

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icked issues

figure 1.5 - the research process simplified and named for research-specific procresses. This model is used throughout the thesis to supply an overview.

The central data collection of the study is the quasi-experiment - a morphological analysis workshop where the board of DANSIC15/16 participated to analyze the opportunity space of conducting the DANSIC16 conference. This experiment was transcribed and data was gathered qualitatively in the form of sense-impressions, process evaluations and participant behavior and feedback. This was extremely important because the study was explorative, which produces an invariably unforseeable outcome, towards which the researcher needs to be sensitive. On the other hand, qualitative data was gathered in the form of ‘cognitive load elements’, which tells on process-participant interaction and process breakdown.

THEORETICALOUTCOMES

Where other research often focus on the social or perhaps epistemological sphere of wicked issues, I’ve found ontology to be central in my understanding of their insolvability, emphasizing that solutions often emerge not from within the problem sphere. Rather, these issues are often symptoms of resource scarcity, which might also be solved by innovation efforts that creates more value for all stakeholders.

COMLEXITY AND COGNITIONNine central causes were identified for the complexity of wicked issues, the prevalence of which

should determine if MA is a suitable analysis method. These are divided into which spheres they act in - ontological, empirical and ethical. With fast-paced dynamism and/or active coersion among stakeholders, MA is probably not effective. In meeting overwhelming amounts of complexity, we have various

DRIVERS OF COMPLEXITY

System behavior is constrained deterministically,allowing historicity (selection).

Process irreversibility and bifurcation sensitivity produces essentially incalculably dynamical systems

Mutual dependencies and non-linear, cyclical causal relationships self-organizes system

Partiality - We cannot know all there is to know about the world

Plurality - Our inclinations, tools and cultures determines what world we see

Provisionality - Todayʼs truths will be tomorrowʼs falsehoods

Incommensurability - Value systems which peoplejudge and act by are completely incomparable

Agonism - People act unpredictably and spon-taneously, producing unintended consequences

People willfully employs coercionto achieve their ends

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coping mechanisms, whose ultimate goal is to close the gap between cognition and complexity. Firstly, allowing us to act at all and secondly, with enough surplus cognition, to attain creative solution capacity.

This can basically be done in two ways - reducing complexity or increasing cognition. These are not mutually exlusive, but different scientific traditions have different ‘favourite’ ways of doing it, and so different management cultures will trust different strategies for decision making. In authoritative wicked issue management strategies, for example, a subtype of complexity reduction is used - decompose and discard, focusing on the social sphere where it aims at removing actors, allowing a few key speciaists to basically create autocratic solutions. MA performs both - it reduces complexity by investigating it, thus casting off false solutions and possibly stabilizing volatile dynamics by aligning stakeholder expectations; and it increases cognition by being computer-aided, thus a built specialized cognition, as well as insourcing cognition in the form of outside specialists.figure 3.2 - the drivres of complexity

COLLABORATIVE IMAGINATION SHAPERTo allow innovation among feuding stakeholders, the MA-arterfact was developed as a mapping of

collaborative imagination. Imagination is extremely important both in trying to alleviate wicked issues because they are so engrained in tradition and culture as well as in innovation in general. It allows the merging of existing solutions as well as the dreaming up of entirely new solutions firmly anchored in existing knowledge. As the case WI was not overly antagonistic, performance here is still uncertain.

DATA COLLECTIONOUTCOMES

The MA workshop process was tested and evaluated thoroughly. Central outcomes are presented here. The greatest pitfalls turned out to be the cognitive load groupings [1] inadequately described interaction type requirements, [2] lost data, [3] explaining the division of parameters/questions and [4] choosing MA archetype (all having CLE ≥ 4). A quality function deployment was conducted to ensure all cognitive load groups with CLE ≥ 2 were addressed.

Assessing the time spent (5 hours), the processes conducted and their value, the same results could be achieved in 3:40 hours.

SUDOKU, MIND-STATES, CARDS AND SOFT LANDSCAPEFive central, qualitative outcomes emerged.

Firstly, being able to model element-to-element requirements (when one element needs another) proved very useful, as well as how to achieve this, which requires parameters to be divided in a specific manner. This was termed sudoku-operations, because it’s essentially second-degree inferences. These allows including requirements as well as inconsistencies, potentially doubling final constrainment and thus doubling the value of the entire analysis.

The second insight was the realization of the importance of using the proper mental faculties at the right times - the first three phases of MA has no use of normative judgment - it only slows the process down. This should be verbalized and enforced throughout. It is perhaps no great surprise that this is so - however the exact nature of when to do what has been defined, which is a more nuanced result.

Interaction attribution can be rather repetitive which fosters mistakes and process instability. It should be avoided by having all participants secretly selecting an interaction type (inconsistent, consistent and synergistic) they believe to apply for a given pair, then revealing at the same time, for example using cards. This guarantees everyone has considered every interaction and does not ‘skip’ some, and that the first to open his/her mouth does not determine the battleground.

Fourthly, synergies proved to produce a soft landscape which produces ‘solution archetypes’. An affinity diagram was conceptualized over this, allowing reviewing the landscape structurally for ‘central synergies’, which seems to allow very refined decision analysis. With a formal solution space of 1225 combinations, the DANSIC15/16 board reduced this to 469 solutions (reduced to 38 %) through constrainment, while under 10 archetypes resulted from synergies (0,8 %), which is easily analyzable.

Fifthly, the constants for the decisions, the invariables, should be identified and written down as quickly as possible, likely before parameters / questions are developed, if possible.

EXPLORATION ANDMA OPPORTUNITIES

Besides being a Collaborative Imagination Shaper, MA seems to have several developmental opportunities. One reason scholars have thus far not investigated it might be because it excels over other methods only in genuinely uncertain and complex decision environments. If so, MA seems to be a better fit with present day challenges than ever before. MA has a series of archetypical, potential use cases.

COORDINATION PLATFORMIn a research and/or design-dominated environment, MA may be able to centre coordination around

the core functionality, hypotheses etc. in a way which accepts ambiguity while still focusing on the design. This might be between developmental departments placed physically apart or perhaps among all relevant people in an entire crowd-based open sourced project in a wikipedia-fashioned homepage. This would point to the central uncertainties still present and solution breadth, while allowing decision makers to participate in the development process to ensure strategic adherance. Using MA in this fashion relies on dispersed creation, maintenance and assyncronous software interaction, and thus not the workshop creation.

STAKEHOLDER ALIGNMENTUsing MA to align stakeholders requires leveraging the structured workshop process to achieve second

order consensus (accepting other’s standpoints from their points of view, not necessarily agreeing) and allows involving stakeholders in formation of values. This may reduce expectations, thus creating common ground or coomon ground may be found when focus in diverted from contention points to opportunity breadth.

AUTOMATED DECISION SUPPORTCognitive computing could potentially allow decision makers to simply describe their desired decision

as well as the key questions they are asking themselves to try to answer it in a nuanced manner (perhaps in a couple of different ways). Supplying the cognitive computer with databases of relevant knowledge, it might come up with several different opportunities, and might also assess the fit among these. It should probably be reviewed, but might produce a first approximation of the solution space. This would be a back-office imlpementation.

BACKCASTING FRAMEWORKBecause MA accepts genuine uncertainty, it has considerable affinity with backcasting, a kind of

‘proactive forecasting’ which is centered around scenario analysis and which tries to maneuver to some desired end-point. By making a morphology of a timeline, several potential desired outcomes may be envisioned, and decision makers may then try to sail the high seas of navigating to any of these harbors, which produces a strategically flexible present-future dialectic.