Building towards a Software Based Innovation Modelling Tool Paidi O’Raghallaigh*, David Sammon, and Ciaran M urphy Business Information Systems, University College Cork, Cork
[email protected] http://www.ucc.ie
Abstract. M anagement literature is renowned for producing concepts and frameworks but few of these are translated into software-based tools, even when they could be valuable to management. The area of innovation modelling is unfortunately a case in point. M anagement has heretofore been unable to effectively capture, communicate, and share its innovation models. Owing to the limited cognitive abilities of the human mind and the complexity and ambiguity surrounding innovation in organisations, the role of software-based tools in the future of innovation modelling should not be overlooked. But no standard approach to representing innovation models exists. The objective of this paper is to describe a prototype of an innovation modelling tool that resulted from our 24-month research initiative. We also include descriptions of an ontology upon which the prototype is built and the design science approach through which it emerged. Keywords: Innovation, modelling, design science, design.
1.
Introduction
Innovation is increasingly regarded as an evolutionary, non-linear, and interactive process involving a focal organisation as well as actors and sources of knowledge from both inside (e.g. R&D, production, marketing, distribution, etc.) and outside (e.g. customers, suppliers, universities, research centres, etc.) its boundaries [1-3]. It is oftentimes viewed as a chaotic, unpredictable, and unstructured phenomenon, which is beyond management’s control [4]. We, in academia, have largely failed in our efforts to explain the phenomenon. For instance, in a recent review of the academic literature, Crossan and Apaydin [5] show how innovation research continues to be fragmented and lacks both a theoretical grounding and an empirical justification. The result is that innovation studies are inconclusive, inconsistent, and characterised by low levels of explanation [6]. Mohr [7] suggests that a major contributory factor to this situation is our insistence on adopting the organisation as the focus of our analysis. The organisation is too multi-dimensional and aggregate to have constant meaning within and across innovation studies [8]. Damanpour and Wischnevsky [9] recommend that innovation theory should recognize the substantive differences between organisations at the level of their approaches to innovation. Consistent with this advice, innovation models attempt to abstract from reality a set of features that are useful in describing, explaining, and predicting the reality of the innovation process in organisations [10]. We define the innovation model as a conceptual tool that expresses the logic by which
an organisation captures value from combinations of new and existing knowledge. But management has heretofore been unable to effectively capture, communicate, and share its innovation models. In addition, scholars have been unable to propose a standard approach to presenting the innovation models o f organisations [11]. Management literature is renowned for producing concepts and frameworks but few of these have been translated into software-based tools, even though they could bring considerable value to management [12]. Because of the limited cognitive abilities of the human mind and the complexity of the problem being addressed, management is likely to benefit from the support of tools for modelling the innovation processes of organisations. The objective of our study was to address this need by focusing on the research question of how to design an artefact to support practitioners in modelling the innovation processes of organisations. In this paper we describe a prototype of an innovation modelling tool that resulted from our research. Aristotle distinguished between four causes, each of which explains an artefact in a very different way but that belong together in definin g its nature [13]. Each of these four causes can be applied to the case of the modelling tool being proposed here and together they provide the structure for the remainder of this paper. We begin with the causa materialis, which is a description of the set of concepts and the ontology that provides the language to be used when modelling the innovation processes of organisations. Secondly, the causa efficiens is a description of the design science approach through which the prototype emerged. Thirdly, the causa formalis is a description of the prototype itself. Fourthly, the causa finalis is a description of the set of design principles derived from building the prototype. The paper finishes with a brief conclusion.
2.
The Conceptual Foundation for the Tool
In this section, we start by introducing a conceptualisation of the innovation process in organisations. We follow this by outlining a loose ontology for describing the conceptualisation. It is important to note the difference between an ontology and a model. An ontology is an agreed “set of concepts e.g. entities, attributes, processes, their definitions and their inter-relationships” used to represent a complex reality [14 p. 5]. On the other hand, a model is "a simplified description and representation of a complex entity or process" [12 p. 4]. In order for an innovation modelling tool to be truly effective, it should be built upon the foundation of the common ‘language’ offered by an ontology shared by a wider community. But this task is far from straightforward as the extant innovation literature has to date provided little continuity in its use of concepts and definitions [15]. A Conceptualisation of the Innovati on Process Innovation is the iterative process by which an organisation combines new and existing knowledge to create valuable new combinations [after: 16]. From a pragmatic perspective knowledge is only valuable to an organisation when it is put to some commercial use. The value of knowledge is, therefore, directly related to its commercialisability and this implies that knowledge further advanced in its jo urney towards market is more valuable than knowledge beginning the same journey. Here
we depict the journey of knowledge from its initial conception towards market as an innovation funnel – see Figure 1.
Fig 1. A Conceptual Framework of the Innovation Process [Source: 17]
Generation, selection, development, and commercialisation represent the evolutionary stages through which the knowledge passes – see Table 1. We do not intend implying a well-behaved single, orderly, and linear journey. While becoming somewhat more structured during its later stages, the journey is more often than not chaotic, unpredictable, and unstructured [4]. As the knowledge proceeds through the funnel it becomes increasingly constrained by different organisational factors such as goals, procedures, resources, etc. [18]. The journey is marked by dead-ends, re-births, and reversals as the knowledge is rejected, re-introduced, or reworked [19]. In this way new and existing knowledge enters and leaves the funnel at its various stages. Table 1. Stages of Knowledge Progression during Innovation [Source: 17] Generation Selection Development Commercialisation This generates ideas The pool of ideas The funded projects It ensures that from fragments of generated in the are developed into deliverables are knowledge coming previous stage is revenue-generating ‘where they need to together from funnelled into a deliverables. be when they’re different internal and smaller number of needed’ and markets external sources. funded projects. them intelligently.
For innovation in any organisation to prosper, the organisation must actively manage its innovation funnel through coordinating and integrating Actions and Resources. This is done through Controls, which periodically monitor the Results emerging from the end of the funnel (as well as the intermediate outputs from each stage within the funnel) and compare these with the Goals of the organisation before
deciding on changes to the Actions and the Resources. The Goals are the objectives of the organisation’s innovation efforts. Actions are the activities that seek out knowledge from various proximate and diverse sources in order to create value through new combinations. Actions are essentially the means through which knowledge is moved through the funnel towards market. But Actions are only effective when supported by sufficient Resources. Knowledge emerges from the funnel as Results, which are deliverables with commercial v alue e.g. products, processes, etc. We next turn to the academic literature in presenting an ontology for innovation modelling. An Ontology of the Innovati on Process The cornerstone of any ontological approach is a shared conceptual framework for modelling domain knowledge [14]. The ontology presented here is built around the five pillars of Goals, Resources, Actions, Controls, and Results from the conceptual framework in Figure 1. We further decompose these pillars into the building blocks that are necessary when representing the innovation models of organisations. While an organisation’s business model describes what value the organisation offers (or intends to offer) [12], its innovation model explains how the organisation creates (or will create) this value. We, therefore, suggest that the innovation goals of an organisation should be defined in terms of the product, customer interface, and infrastructure elements from the business model of the organisation – see Table 2. Table 2. Building Blocks of the Goals Pillar [Source: 17] Type Description Product Element This describes the value proposition of the organisation in terms of the goods and services it offers to customers. Customer This describes how the organisation interacts with its customers and Interface Element what kind of relationships it establishes with them. Infrastructure This describes what processes, resources, and partners are necessary to Element support these first two elements.
To foster innovation within organisations, resources must be distributed deliberately based on pre-defined goals [18]. Christensen [20 p. xvii-xviii] argues that “organizations successfully tackle opportunities when they have the resources to succeed, when their processes facilitate what needs to get done, and when their values allow them to give adequate priority to that particular opportunity in the face of all other demands that compete for the company’s resou rces”. We suggest that the innovation resources of an organisation should be defined in terms of the funding, human, facility, and tool resources required by the organization when acting to achieve its innovation goals – see Table 3. Table 3. Building Blocks of the Resources Pillar [Source: 17] Type Description Funding Adequate funding is clearly a critical input into the innovation process and funding may need to be designated to specific activities. Human People factors include the number and mix (with respect to their cosmopolitanism, propensity to innovate, skills, experience, and education) of people committed to the innovation tasks. M embers with higher levels of education and self-esteem from diverse backgrounds increase the effectiveness
Facilities
Tools
of innovation project teams. Facilities or physical resources is a broad category that ranges from buildings to computer equipment. Slack resources or unused capacity can in some cases be an important catalyst for innovation, whereby slack provides the opportunity for diversification, fosters a culture of experimentation, protects against the uncertainty of project failure, and allows failures to be absorbed. However, in other cases slack becomes synonymous with waste and is a cost that must be eliminated. Use of systems and tools is an important support for innovation in organisations. Tools can be of various sorts, including tools and techniques for promoting creativity, quality, etc.
The challenge for organisations is to use these resources in order to turn knowledge from both internal and external sources into exploitable knowledge [21]. But the ability of the organisation to do this depends on the proximity of the knowledge sources relative to the existing stock of knowledge in the organisation [22]. Territorial proximity is important as smaller distances facilitate more intense interactions, reduce institutional and culture differences, and thereby promote knowledge transfer especially of sticky tacit knowledge [22]. On the other hand, cognitive diversity is important in promoting novelty, which can be critical to innovation performance [22]. We suggest that the innovation actions of an organisation should be defined in terms of the different categories of search activities (defined by these dimensions of proximity) required by the organisation to achieve its innovation goals – see Table 4. Table 4. Building Blocks of the Actions Pillar [Source: 17] Type Description Internal New knowledge is generated either (1) intentionally by employees assigned the specific task of generating knowledge (e.g. through an R&D department or R&D projects) or (2) unintentionally by employees in going about their day-today activities (e.g. solving problems in day -to-day operations). Interactive New knowledge is generated by employees through direct (e.g. collaboration) and indirect (e.g. scanning) engagements with either (1) knowledge providers (e.g. private and public research institutes, and universities) or (2) other organisations (e.g. customers, suppliers, competitors, and consultancies). External New knowledge is generated through the acquisition of knowledge from the market place either (1) disembodied (e.g. from research organisations, training companies, consultancies, etc.) or (2) embodied e.g. machinery, equipment, software, business units, and organisations, as well as new recruits ) from assets.
Management controls can be broadly categorized into formal and informal controls [23]. Formal controls, which are mostly documented and are explicitly management authorised [24], influence behaviour through, for example, budgets, output targets, standards, policies, etc. On the other hand, informal controls, which are mostly unwritten and social controls [23], influence behaviour through organisational culture, norms, values, routines, etc. We suggest that the innovation controls of an organisation should be defined in terms of the different categories of formal and informal controls that are required by the organisation to achieve its innovation goals – see Figure 2.
Formal
Informal
Resources Process Results Allocations of financial Enforcement of plans, Enforcement of targets (e.g. budget, quotas, etc.), procedures, (e.g. output type human (e.g. skills, organisational product, process, etc., experience), facilities (e.g. structures, rules, volumes, quality, buildings, R&D lab), tools regulations, policies, efficiency goals), (e.g. equipment, software) and reward systems. statistical reports and material (e.g. raw materials) resources. Organisational traditions (stories, rituals, legends, ceremonies), commitment, norms, values, beliefs, and culture Fig 2. Building blocks of the Controls Pillar [Source: 17].
Innovation is not restricted to technological advances but instead can take place along any dimension of a business model [25]. One or more innovations are generally required in order for the organisation to achieve its innovation goals. For instance a new product that is technologically superior to those of competitors can still fail because it lacks an effective distribution or sales channel. We suggest that the innovation results of an organisation should be defined in terms of the different categories of innovation that are required by the organisation to achieve its innovation goals – see Table 5. Table 5. Building blocks of the Results Pillar [Source: 17] Type Description Offerings These are goods and services offered by the organisation and that are valued by its customers. Platform This is a set of common components, assembly methods or technologies from the organisation that serve as building blocks for a wider portfolio of offerings. Solutions This is the customized, integrated combination of offerings from the organisation that solves a customer problem. Customers This is the discovery by the organisation of new customer segments or the uncovering of unmet (and sometime unarticulated) customer needs in existing segments. Customer This includes everything a customer sees, hears, feels and in general Experience experiences while interacting with the organisation and its offerings. Value Capture This is the mechanism that the organisation uses to capture revenue streams from the value it creates. Processes These are the configurations of business activities that the organisation uses to conduct internal operations. Organisation This is the way in which the organisation is structured, its partnerships, and its employee roles and responsibilities. Supply Chain This is the sequence of agents, activities and resources required by the organisation to move its offerings from source to the customer. Points of These are the channels and the outlets that the organisation employs from Presence which its offerings can be bought or used by the customer. Networking This is the network through which the organisation and its offerings are connected to the customer. Brand This is the set of symbols, words or marks through which the organisation communicates a promise to the customer.
3.
The Approach to Designing the Tool
The practice of design and the science of design are both problem solving activities whose differences lie in their contributions to the body of design knowledge. The prime focus of design practice is artefact construction through applying existing knowledge, while design science aims at knowledge generation through artefact construction or observation [26]. When undertaking design research (which includes elements of both design practice and design science) abstract design knowledge should be seen as the research end result, while the creation of any instantiations based on it is an intermediary result [after: 27]. The creation of instantiations (which happens in the design practice element of the research) is an exploratory empirical part that justifies the abstract design knowledge (which emerges from the design science element of the research). In other words, design science receives its empirical grounding from design practice, but at the same time design practice receives it theoretical grounding from design science. The abstract design knowledge (generated from design science) should be applicable to a class of situations and audiences, whereas the situational design knowledge (generated from design practice) is particular to individual situations. These two types of knowledge are intrinsically linked in that abstract design knowledge is generalized and extracted from situational design knowledge and on the other side, abstract design knowledge may be adapted and applied to situational instances [27]. Design science contributes to the scientific endeavour by presenting this abstract knowledge in the form of design theories. Design science can be seen either as a preparatory activity before design practice is started, a continual activity partially integrated with the design practice, o r a concluding theoretical activity summarizing, evaluating and abstracting knowledge [27]. We, therefore, divide our approach into two distinct design activities separated by their degree of abstraction – see Figure 3. First, design science is focused on generating abstract knowledge to guide the design of an innovation modelling tool. The principal output from this design activity is the set of abstract design principles (presented as a design theory) for an innovation modelling tool. Second, design practice is focused on using abstract knowledge (embodied in the prototype of the innovation modelling tool) to design the innovation models of organisations. The principal output from this design activity is the situational knowledge generated from observing those using the tool and examining the innovation models they produce using the tool. In the case of this research, the design science and design practice activities are tightly integrated whereby output from one influenced the other. In other words, empirical results (from design practice) often inductively resulted in new principles and changes to the prototype (in design science), but theoretical deductions (in design science) in turn influenced situational activities (in design practice) through changes to the tool.
Fig 3. Our approach to design research
In Table 6 we outline briefly a sample of the methods through which the prototype has to date been evaluated. Table 6. Examples of Evaluations of the Innovation M odelling Tool Prototype Type Brief Description Purpose of Activity Work Ten two-hour sessions with a group of sixteen Exploratory design of Group postgraduate students from a M asters in Innovation the innovation programme. The tool was incrementally designed and modelling tool refined from week to week. Focus A 2-hour session with a group of fourteen Confirmatory Group postgraduate students from a M asters in Information evaluation of the Systems programme. The students were presented prototype. with the background to the tool and instructions for its use. They next observed a worked example before working in groups using the tool to represent innovation in Lego based on insights from a short video of the company. Case Using various sources of information (e.g. website To evaluate the ability Studies material, videos, documents, and interviews) twenty of the prototype to organisations (both local and global organisations, represent the such as Siemens, Nike, Lego, Apple, Adidas, innovation models of a M icrosoft, etc.) were represented using the prototype. broad range of The modelling was performed in some cases by the companies based on lead author of this paper but also postgraduate real life information. students.
4.
The Form of the Tool
In this section, we describe a prototype of the innovation modelling tool that hereafter we refer to as ‘The Canvas’. The Canvas is currently a paper-based prototype of what will afterwards be developed into a software-based tool. The Canvas is physically (and conceptually) divided into a number of levels and areas. The base canvas (refer Figure 4) is the first level and consists of three areas: the Goals, Results and Actions areas, which correspond to the identically named pillars of the innovation ontology. The Actions area is a matrix that is divided: vertically to describe the locus of the innovation activities - internal, interactive, and external activities; and horizontally to
describe the stage in which the innovation activities takes place – generation, selection, development, and commercialisation. We, therefore, end up with twelve cells that can be used to classify the innovation actions of an organisation. At the second and subsequent levels are one or more semi transparent canvas layers that are superimposed over the base canvas. The resources layer corresponds to the Resources pillar of the innovation ontology and describes the resources required to support the actions in each of the twelve cells. Likewise, the controls layer corresponds to the Controls pillar of the innovation ontology and describes the formal and informal controls applied to actions in each of the twelve cells in order to ensure that the innovation goals of the organisation are met. In this way the Canvas can be extended by any number of additional layers. For instance, a SWOT layer can be used to describe the strengths, weaknesses, opportunities, and threats for each of the twelve cells or for any other element (e.g. goals, results, etc.).
Fig 4. The Base Canvas Template
It can be surprising how the understanding of innovation in an organisation can differ so widely across stakeholders. This difference in perspective can make innovation modelling a difficult proposition but it also promotes creativity through the variety of perspectives that are brought to bear on arriving at an appropriate model. Innovation modelling requires a holistic look at the innovation logic of an organisation and it would, therefore, seem natural that innovation mod elling should be a team affair with each member articulating his or her personal thoughts as well as listening to and discussing the opinions of others. Obviously, the choice and number of persons participating depends on the size of the organisation and on the goals and ambitions of the modelling exercise. The Canvas is a hands -on tool that fosters both individual and group articulation and reflection through visual externalization of thoughts. The power of the Canvas originates from: its highly visual nat ure whereby the canvas is at the centre of the innovation modelling; as well as the simplicity of its use that involves positioning visual elements on the canvas. (See the next section for a
discussion of the main design principles that contribute to the effectiveness of the Canvas). Guidelines for Use of the Canvas The Canvas is printed out on a large surface so that a team can view the full canvas and the team members can start jointly sketching a model. The elements are presented using sticky notes, stickers and markers, each of which comes in different colours. A team member describes an element by writing a few choice words on a sticky note. The position of the sticky note on the canvas determines the element type (as a goal, result, action, resource or control). Use of colour and hand drawn lines supports the coding and linking of elements across the canvas. For example, in order to link the innovation actions necessary to achieve a certain innovation result, stickers of identical colour are added to the sticky notes corresponding to the related elements. In order to better understand the Canvas and its use, we now describe the steps involved in a typical modelling session whose aim it is to describe, analyse and improve the innovation model of an organisation. In Table 7 we describe step by step how this can be done. At the root of the relatively simple process is a more formal ontological approach to describing innovation models and the strict relationships between areas of the Canvas. Table 7. Steps in using the Canvas to model the innovation processes of organisations 1 Steps Description of Steps Kick off The first step is agreeing the composition of a multidisciplinary team with people from different areas and hierarchical levels of the organisation. The rules of engagement for the session are clearly outlined and discussed as are the definitions of key terms such as innovation and innovation model. Each individual must understand and accept the rules and especially those allowing each team member an opportunity to speak and ensuring other members listen. A typical session starts with a large blank template of the base canvas mounted on a wall or whiteboard. The structure of the base canvas is explained as well as the use of sticky notes, stickers, and markers to represent the elements of the model. Describe The members simply start identifying elements by writing on colour coded sticky the notes and adding the notes to the areas of the base canvas (or alternatively to the model border of the canvas in order to temporarily store them). As the session progresses the sticky notes can easily be moved, grouped or discarded. Sticky notes can also be grouped together or can be replaced by a more generic or refined sticky note. While there is no fixed sequence of steps to follow in completing the innovation model, we have found that some approaches do assist with speeding up the process while not unduly suppressing creativity. For instance, we suggest that the team starts with defining the innovation goals of the organisation before identifying the individual innovation results necessary to meet these goals. For each innovation result, the team should next identify the innovation actions necessary to make it a 1 Influenced by 28. Osterwalder, A., Business Model Manual-How to Describe and Improve your Business Model to Compete Better. 2009, Working paper, La Trobe University in M elbourne, Australia. … and … 29. Osterwalder, A. Methods for the Business Model Generation: how #bmgen and #custdev fit perfectly. 2011 [cited Apr 01, 2011]; Available from: http://www.businessmodelalchemist.com/2011/01/methods-for-the-business-modelgeneration-how-bmgen-and-custdev-fit-perfectly.html.
Assess the model
Improve the model
Test the model
reality. Finally, the team identifies the resources required by each innovation action and the controls to be applied to ensure the success of the action. We have also used a set of questions (superimposed over the base canvas) that we recommend team members ask themselves and their group in order to ensure that the base canvas is complete. Once there is a clear description and understanding of the innovation model, the next step is to assess it. This identifies if any element is missing or is ill-defined. One useful way to assess the innovation model is to use storytelling, which can involve team members telling stories from the perspective of different stakeholders e.g. customers, suppliers, competitors, etc. These stories involve the elements and their connections and can, therefore, show missing or unused elements, but also help in communicating the whole model to outsiders. Another way of assessing the innovation model is to undertake a SWOT analysis of each area in turn. This might be achieved by overlaying the areas with colour coded sticky notes e.g. red for weaknesses and green for strengths, etc. By now the team has already completed the two most important steps towards innovation model improvement – an understanding and an assessment of the current innovation model. The assessment of the innovation model should influence the directions through which the model might now be improved. The team must ask itself probing questions about how well the innovation model is aligned with the internal situation of the organisation (e.g. strategy, resources, etc.) but also with the external situation (e.g. market forces, etc.). At this stage it is important that the team does not quickly decide on one design but instead it considers as many alternative designs as possible before deciding to pursue a few in more detail. This phase encourages the team to test an innovation model by designing tests to validate the contents of the model. Each sticky note in the base canvas should be turned into a hypothesis and then tested. Hypotheses can be visually represented as sticky notes on a ‘hypothesis layer’ above the base canvas. Hypotheses are in turn converted into tests which are visually represented as sticky notes on a ‘test layer’ above the ‘hypothesis layer’. An example of a hypothesis might be that a t-shirt company could acquire all its t-shirts designs from outside the company and for no more than $3000 per design. There are likely to be many ways to test this hypothesis. For example, field trials can be performed at different levels of commission and the response of artists monitored at each monetary level. Alternatively focus groups could be arranged and the opinion of artists sought. When the team tests the innovation model in this way, it is likely it will lead to changes in the various areas and layers of the model before a final model is arrived at.
A Worked Example Based on the Canvas We will briefly present an example of the output from the second step – describing the model. The exemplar is produced from a short video 2 of an interview with the owners of the US-based company Threadless in which they discuss among other things how the company innovates. The model is not intended to be a complete and accurate representation of innovation in the company but instead illustrates how the Canvas was used in real time to represent the innovation process of the organisation
2 Web2ExpoSF 09: Jake Nickell and Jeffrey Kalmikoff, "A Conversation with the Founders of Threadless" (can be viewed at http://www.youtube.com/watch?v=tFXFe3AQy7M )
as outlined in the video (and without taking additional sources of information into account).
Fig 5. Base Canvas for Threadless
The base canvas is depicted in Figure 5 and it shows the innovation goals, results and activities of the company disclosed by the owners of the company. Threadless is an online apparel store run by skinnyCorp of Chicago, Illinois, since 2000. Its stated innovation goal is to produce highly artistic t-shirts from designs submitted by a community of artists. It downplays profit as the main driver of its business and instead the owners state that artists should be supported in producing art even when there is no discernable market for their output. In order to achieve its goal at least two innovation results are discussed. Firstly, there is the innovation surrounding individual t-shirts but also the innovation of the web platform that is necessary to support an online design proces s. Both of these results depend on undertaking many individual innovation actions that are disclosed in the interview. For instance, members of the Threadless community are enticed to submit t-shirt designs online, the designs are then put to a public online vote, and the winning designs are printed in limited batches and sold through an online store. The winning artists receive a cash prize as well as a store credit note. Likewise, the generation and selection of ideas for improving the web platform is largely driven by the observed and stated needs and desires of the community. The platform aims at creating an environment for nurturing creativity and that is supportive rather than competitive. Designers support each other by offering assistance and feedback on designs. The clustering of actions on the base canvas is interesting in highlighting the crowd sourcing nature of the business. For instance it can be seen that both the generation of innovation ideas and the selection of designs take place outside the boundaries of t he organisation and within the Threadless community of one million members. The result is that the company does not need to employ t-shirt designers despite handling 150-200 prospective t-shirt designs per day and selling 100,000 t-shirts per month.
Instead it secures the right to print t-shirts from its community members who design the t-shirts in return for prize money. It is also noticeable that little or nothing is disclosed about the process through which the ideas are developed into reality. For instance, we are told little about printing, storage, or delivery. There is also a lack of information on commercialisation activities, other than noting the importance of their website and newsletter in their marketing and sales activities. W ord of mouth generated from members of the community in popularising their own designs is also important to the success of the company. Reflecting on the model, we can also discern a distinct lack of collaboration with and acquisitions from third parties. The owners recognise this weakness and state that they ‘have not done a good job of determining who to form partnerships with’. Instead the company is built around a group of friends, who having no business experience developed it around an attitude of ‘why not’ and ‘trial and error’ rather than depending on outside help.
Fig 6. Resources Layer for Threadless
Next we extend the base canvas using layers. The resources layer and the controls layer are depicted in Figures 6 and 7 respectively and show the resources necessary for each action and the controls to be applied to each action to ensure that the innovation goals of the company are achieved. Very little information is provided on either aspect of the innovation process. The company appears to place little emphasis on management controls and instead adopts a laissez faire approach to internal actions and a facilitation approach to managing the community. The owners state that they are there to serve the community. The fact that much of the innovation process is located outside the boundaries of the company may go some way towards explaining the paucity of discussion about the internal resources assigned to the innovation process. Therefore, the power of the model is not just in representing the innovat ion process but also in highlighting what is missing. Next we discuss some of the main design principles that we have identified as contributing to the effectiveness of the prototype.
Fig 7. Controls Layer for Threadless
5.
Why Should the Tool Work?
During the last century, management was dominated by theories “embracing rational choice and decision-making as a primary modus operandi” [30 p. 156]. Managers are typically depicted as decision-makers who address a known problem by selecting from a discrete set of known solutions. The problems and solutions are always perfectly understood by the decision-maker, who uses rational deduction to identify the optimum solution for a situation. But these ass umptions are of little relevance to real-world organisations, where most problems are “ill-formulated, where the information is confusing, where there are many clients and decision makers with conflicting values, and where the ramifications in the whole sy stem are thoroughly confusing” [31 p. 15]. These wicked problems have no definitive solutions so that when a solution does exist it cannot be objectively evaluated as being right or wrong but at best it may be deemed to be more or less acceptable than alternative solutions [32]. Management must move beyond inspecting given circumstances and applying already well known solutions, to designing new approaches and creating new solutions [33]. Exclusion of design serves to hamper the role of human ingenuity and intelligence in ensuring the wellbeing of organisations [30]. Innovation modelling is an example of a wicked problem, which involves many diverse stakeholders who have different views on the problem and its solution. Management requires the support of tools that both display graphically the firm's current strategic position, as various stakeholders understand it, but also assist in the ongoing collaborative design of alternative routes to improving that position [34]. Management must be involved not only in the ongoing re-assessment, and improvement of an organisation’s current innovation model but also in the proactive designing and testing of a whole portfolio of potential innovation models. The first design principle, therefore, states that the
tool supports the user in designing and testing of innovation models (rather than being simply a decision making tool). The tool must encourage the designer (whether an expert or a novice) to explore as many different models as possible. When using the tool, it must be easy to try things out, and to backtrack when unsuccessful. This implies that the tools must be trustworthy so that users are comfortable trying things. The Role of Graphical Externalisations During the design process, the problem and its solution co-evolve whereby the designer must engage in solving the problem in order to reveal its full nature [35]. The process is iterative, progressing from internal representations (i.e. internalisations) to external representations (i.e. externalis ations) of thought and back again so that one interacts with the other [36]. Bruner [37 p. 23] states an externalization “produces a record of our mental efforts, one that is ‘outside us’ rather than vaguely ‘in memory’ ... It relieves us in some measure from the always difficult task of ‘thinking about our own thoughts’ while often accomplishing t he same end. It embodies our thoughts and intentions in a form more accessible to reflective efforts”. Externalisations reflect not only the thinking of the designer, but also simultaneously serves as a medium for solution development [33]. In addition, graphical externalisations offer a number of important cognitive benefits. Firstly, they decrease the cognitive stress on the scarce memory resources of the designer associated with simultaneously representing multiple problem componen ts [33, 3840]. Secondly, they can make use of the powerful visual system of humans through offering exploitable perceptual effects of a kind that are almost effortless for the designer to take advantage of during problem solving [41]. Thirdly, they are a way of making fleeting thoughts more permanent and making internal thoughts more accessible to others [42]. They provide an external record that does not rely on unreliable human memory but also they divorce ideas from specific individuals, thereby allowing a community to observe, comment on, and revise the ideas, and enact those revisions in further changes to the externalisation [34, 42]. Externalisations are, therefore, immensely important to the design activity [43-46]. The second design principle, therefore, states that the tool supports the user in producing innovation models in a graphical format. The tool focuses not on instructing the designer, but in providing the designer with a media for creating models. The tool provides the designer with a ‘canvas’ on which to work and th is challenges the creativity of the designer. The designer is encouraged to explore the canvas in order to design new innovation models. The Role of Talkback Design tools compensate for cognitive frailties by enabling users represent what they know using externalisations, which amplify the users’ design thinking [40]. The designer is engaged in a cycle of producing externalisations (such as sketches, mockups and memos), and deliberately and carefully reflecting on them in order to make sense out of what has been experienced and what is known [35, 46-48]. Schoen [48] differentiates between two reflection activities: reflection-in-action and reflection-onaction. The former denotes the reflective processes that take place while the designer is producing an externalisation, whereby the emerging material ‘talks back’ to the
designer, who simultaneously responds to the material by changing its representation. The latter denotes the reflective processes that take place after the designer produces an externalisation, whereby the resulting material again ‘talks back’ to the designer. A design tool can, therefore, support designers by, first, allowing them to articulate their thoughts in the form of an externalisation and, second, by amplifying the representational talkback of the externalisation so that they can reflect more effectively on the externalised thoughts. Amplification of representational talkback is concerned with two qualities of the externalisation: it must make it easy for designers to express what they need to express; and it must make it easier for the designer to perceive what has been represented [35]. The positioning of objects on a twodimensional space can be as powerful a means of representational talkback as, say, sketching [40, 49, 50]. First, the position as a state denotes the result of an action taken by the designer and when reflecting using this static trait, the designer is said to be reflecting-on-action [49]. For example, after drawing an object on a twodimensional space the designer may reflect on its size or colour. Second, the positioning as an action denotes what the designer is doing to reach a given state and when reflecting using this dynamic trait, the designer is said to be reflecting -in-action [49]. For example, while drawing an object on a two-dimensional space the designer may be reflecting on the size of the object relative to what it should be and may decide to enlarge (or shrink) it. It should be noted that positioning supports the two key qualities of reflective materials identified above. First, by directly manipulating the objects on the space, the designer can easily produce different visual representations. Secondly, by simply looking at the space, people can easily identify many visual properties from the space. The third design principle, therefore, states that the tool amplifies ‘talk back’ from the innovation models to the designer. The tool should not be intimidating, and should give designers (whether novice or expert) immediate confidence that they can succeed in their task. The Role of Constraints When using linguistic externalisations, designers may represent a domain in somewhat abstract terms that may even conceal from themselves and others their less than complete domain comprehension [39]. On the other hand the act of producing graphical externalisations often confronts the designer with his or her less than adequate domain comprehension since, unlike language, “graphics force a determinate representation that is severely limited in terms of the amount of abstraction that can be expressed” [39 p. 7]. This is true even when simply positioning objects on a two-dimensional space – the act carries a deeper meaning than simply ‘putting this there’ in the mind of the designer. The meaning of the space can be considered on a spectrum between two extremes: one extreme is that the tool imposes no meanings on the position of elements in the s pace; the other extreme is that the tool loads the position of elements in the space with pre-determined semantics [50]. Tools can, therefore, impose different degrees of constraint on the space. More unstructured tools (like a pencil and paper) impose little or no semantic constraints on the designer and they, thereby, provide a greater range of freedom to create elements as ideas come to mind. This results in externalisations that can be very idiosyncratic and that impedes their sharing and comparison, which can lead to ‘intellectual chaos’ among stakeholder [45]. On the other hand, highly structured tools (like a CAD program) are for the most part feature reach and impose more stringent semantic
constraints on the designer. This compels externalisations to abide by a common set of rules, thereby promoting their sharing and comparison. But constraints are also important for another reason. Left to their own devices, designers do not always use the most efficient representations of problems and their representations often impede problem solving [40]. This is why determining how to effectively represent problems is so important to problem-solving performance [40, 51]. The tool provided to problem solvers has a dramatic influence on their ability to choose an appropriate externalisation to solve a problem and the time taken to solve it [51]. For instance, Polich and Schwartz [52] observe how matrix representations of problems are especially effective for larger, more complex problems because of the ease of applying and storing the results of logical operations. The rationale for using tools to scaffold problem representations is artificial intelligence in reverse: “rather than having the computer simulate human intelligence, require the human to simulate the computer's unique intelligence and come to use it as part of their cognitive apparatus” [40 p. 370]. The forth design principle, therefore, states that the tool applies ontological constraints on the innovation models of the designer. The tool, thereby, constrains creativity by expecting the designer to adhere to rules when positioning his or her ideas on the canvas. The challenge for the designer is to represent the innovation model of an organisation on a limited space while satisfying various ontological and medium constraints. The Role of Collaboration The power of the unaided individual mind is highly overrated [35, 53]. Because complex problems require more knowledge than any individual is likely to possess, it is usually necessary for many stakeholders to participate, communicate, and collaborate with one other in order to satisfactorily arrive at a s olution [43]. But consensus is oftentimes unachievable, and instead the focus is on informed compromises across a symmetry of ignorance, which is the situation whereby each stakeholder possesses knowledge of a limited aspect of a potential solution [43]. Stakeholders must rise above the uncertainty of incomplete information and the ambiguity of competing interpretations to create a shared understanding of the problem (as well as a shared commitment to its possible solution) [32]. Each stakeholder is, therefore, a co-designer of the solution and not just a consumer of the final solution. Externalisations are critical to these social in teractions because groups have ‘no head’ and they support creativity based by providing a means for others to interact with, react to, negotiate around, and build upon an idea [43, 53]. Suthers [51] identifies three roles of externalisations that are unique to situations involving group in constructing and manipulating shared representations. First , they initiate negotiations of meaning among stakeholders so that individuals who wish to add to or modify the externalisation feel obliged to negotiate agreement with the other group members. The externalisations, thereby, contribute to a common language of understanding among members of the group [53]. Second, they support conversations through deixis, whereby components of the negotiated representations evoke in the minds of the group members rich meanings beyond the obviousness of the externalisation. These components can serve as an easy way to refer to the ideas previously developed. Third, externalisations are a reminder of common ground, whereby the negotiated representation serves as a group memory and reminds the group members of previous ideas and possibly serving as an agenda for further work.
The fifth design principle, therefore, states that the tool supports a collaborative approach to innovation modelling. Design is a dialectic process in which the goal is not to force solutions on stakeholders but instead to create an environment for dialog that results in solutions reached through compromise. Collaboration in innovation modelling depends on being able to quickly and cheaply develop modifiable and throw away models that assist stakeholders in creating shared understanding through engaging in a conversation with each other and with the models. The Role of Ease of Use and Ease of Modification But while design tools can be very powerful for the expert user, they can be less useful for the novice or occasional user who must first master basic functions before starting to represent their problem. The tools impede the creativity of the user who cannot simply start sketching ideas that come to mind but must follow a strict series of steps which hinders the flow of their thinking [54]. The tools, therefore, are less useful for novice users and particularly during the early stages of design where their ideas remain ill-formed and lack structure. The key is to find the right balance in the tool between enforcing controlled functionality and meta-model constraints, while at the same time giving the designer sufficient degrees of freedom [54]. The tool should make it easy for novices to get started (i.e. provide a low threshold) but also make it possible for experts to create advanced innovation models (i.e. provide a high ceiling). The tool should be facile and un-encumbering, so that both novice and expert users can try out different alternatives very quickly. The tool should be “self-revealing” so that it is clear to designers what needs to be done and how it should be done and as the designer advances in his or her experience with the tool, further levels of functionality should be revealed. When people are stressed or concentrating too much effort on how to use the tools to perform some task, then they will have less cognitive resources left over for finding creative solutions to their tasks [55]. In addition to their usability, tools should remain open to change by the designer. The provision of closed tools, in which the essential functionality is fixed when the tool is designed, is inadequate for coping with dynamic problem contexts [43]. Providing open systems that evolve at the hands of the users, thereby giving them the ability to extend the tools as they explore different problems and leverage new insights is important [43]. The sixth design principle, therefore, states that the tool must be easy to use and easy to modify. For instance, the tool should be extensible to incorporate other management tools, techniques and methods e.g. SWOT analysis, b alanced scorecards etc.
6.
A Concluding Remark on the Future of the Tool
We have noted a considerable improvement in the quality of the innovation models produced by novice users when using this prototype in comparison to more traditional pen and paper approaches. However, we suggest that further improvements are likely to accrue from developing the Canvas into a software-based tool. To paraphrase, Osterwalder and colleagues [12 p. 24] “specifying a set of innovation model elements and building blocks, as well as their relationships to each other, is like giving an innovation model designer a box of Lego stones. He can play around with these
stones and create completely new innovation models, limited only be his imagination and the pieces supplied”.
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