The creation of breakthrough concepts by design teams

14th Annual Conference of European Academy of Management, Valencia, Spain 1

The creation of breakthrough concepts by design teams

Thomas GILLIER* Grenoble Ecole de Management, Grenoble, France

Gerald PIAT R&D Division, Electricité de France (EDF), Paris, France

Akin Osman KAZAKCI Ecole Nationale Supérieure des Mines de Paris, Paris, France

*corresponding author: [email protected]

Abstract

How do design teams think in order to bring about breakthrough concepts? What are the main

underlying cognitive mechanisms at the front end of such creation processes? Empirical evidence

shows that firms often have difficulties breaking free from the conventional ideas surrounding

products. Although prior works in creativity emphasize the ideation process in the design teams,

little theoretical attention has been devoted to understand these teams’ approaches to think about

new ideas. Through the experimental analysis of ten design teams aiming to elaborate upon a

breakthrough concept of a movable and eco-friendly Antarctica museum, we examine the thought

processes used to follow or go beyond the existing museums. This research suggests that thinking

outside the box does not follow the two-steps funnel model in which idea generation is separated

from selection. Our results better support a process where breakthrough concepts result from the

continuous reconfiguration of known properties and unknown properties. Our findings indicate

that elaborating a breakthrough concept toward an “ecosystem of properties” approach (i.e.

connected network of solutions) outperforms the “primer concept fixation” (i.e. attachment to

first solutions) and “random exploration” approaches (i.e. free generation of many isolated

solutions). Managerial recommendations to assist design teams in pursuing an “ecosystem of

properties” approach are provided.

Keywords: design team, breakthrough concepts, new concept development, creative thinking, innovation, fuzzy front end, ideation


1. Introduction and research gap

How do design teams think in order to generate breakthrough concepts? What are the main

underlying cognitive mechanisms at the beginning of such creation processes? Today, in an

increasingly competitive context, organizations often try to create breakthrough concepts. These

are usually defined as ideas about new categories of products which could re-shape or break

current industrial paradigms, induce revolutionary changes in the structure of firms and provide

higher customer benefits compared to existing products (Ahuja et Morris Lampert 2001; C. M.

Christensen 1997; Danneels 2002; Leifer et al. 2001; Markides 2006; McDermott et O’Connor

2002; Stringer 2000). When comparing the creation process of incremental innovation which

follows predictable trajectories, the creation process of radical or disruptive innovations which

brings about unpredicted breakthrough concepts is often characterized as a more complex and

fuzzy process (Goldenberg, Lehmann, et Mazursky 2001; Khurana et Rosenthal 1998; Reid et de

Brentani 2004; V. Seidel 2007). The thinking processes to create breakthrough concepts are more

erratic, involving the unexpected connections of several pieces of knowledge that were not linked

before. This specific creation process is still not well understood, and it is not uncommon for

practitioners to feel confused and uncomfortable in this creation process (Assink 2006; Coyne,

Clifford, et Dye 2007; Goddard, Eccles, et Birkinshaw 2012; V. Seidel 2007).

In their search for distinction, innovative organizations often incorporate design teams who are

responsible for managing the front end of innovation by generating breakthrough concepts.

Although existing research in new product development (NPD) provides key findings for

developing breakthrough commercial products, little research investigates how designers think in

order to elaborate breakthrough concepts (Goldenberg, Lehmann, et Mazursky 2001; V. Seidel

2007). Research about how design teams think is still required as claimed by (Dahl 2011, 425) in

a recent JPIM special issue: “Research directed towards a better understanding of what enables

design team effectiveness is clearly needed (…) One approach to studying design teams would be

to seek a better understanding of the internal processes undertaken during the design function. As

one example, how design teams facilitate creativity in a group context would be interesting to

investigate. (p.425)” So, how do design teams think when they are invited to elaborate

breakthrough concepts that, at first, appear to be strange, unusual and ill-defined? How do design

teams reason for diverting from the norm and not being locked into traditional procedures?


Our contribution differs from the prior research on NPD along two dimensions.

First, existing research has extensively suggested both organizational factors such as motivation,

autonomy, process formalization or intra-organizational knowledge (Amabile 1996; Amabile et

al. 1996; Im, Montoya, et Workman 2013; Kim, Im, et Slater 2013) and creative techniques such

as brainstorming and analogical thinking (Dahl et Moreau 2002; Osborn 1953; Paulus et Nijstad

2003; Paulus 2013; Schirr 2012; Sutton et Hargadon 1996) increase the quantity and the quality

of the concepts generated by design teams. Rather than focusing on the external conditions that

increase the probability of success for innovative ideas for teams, this research aims to open the

black box of the ideation process to better understand the inner processes under which

breakthrough concepts are elaborated. We investigate how creative ideas emerge and how they

are welcomed, modified and formalized into a defined product.

Second, research on innovative design teams has gained a great deal of attention in the last

decade. In these studies, the generation of bright ideas is said not to come from single individuals

but to emerge from the interactions of ideas and knowledge from multiple actors (Hargadon et

Bechky 2006; Hargadon 1999; K. Sawyer 2008). Such interactions are proven to be innovative

mainly because of the analogical thinking that enable teams to build on each other's ideas and to

link their current problems, past solutions and prior experiences (B. T. Christensen et Schunn

2007; Dahl et Moreau 2002). Even if these interactions are often reported as important starting

points for the generation of new concepts, studies that explain how the interactions of thinking

processes influence the elaboration of preliminary breakthrough concepts at a fine-grain level are

still missing.

Our research proposes to investigate the elaboration of breakthrough concepts through the

comparison of experimental sessions conducted by ten design teams, each composed of three

professionals with experience in R&D activities. These sessions are part of an innovative real-life

projecti that aims to develop a breakthrough concept of a museum that immerses visitors in the

world of Antarctica while being eco-friendly and easily movable for exhibits all over the world.

In contrast to the current experimental studies about the ideation process, we use two important

methodological aspects to open the black box of ideation. First, the design brief is significantly

out of the ordinary and out of any known expertise areas immediately accessible: the participants


are placed in a situation where they have to create a novel conceptualization of what a museum

is. Then, a specific process-based metric is developed to distinguish whether design teams think

in conformity with current definitions of museums or in new directions. Thus, our research

methodology permits us to better identify and understand the “breaks” that are commonly

observed in breakthrough innovation thinking (V. Seidel 2007).

This research offers rich data and allows us to investigate two critical research questions: (1)

what are the different thinking approaches used by design teams during the elaboration of

breakthrough concepts? (2) Then, among these different approaches which are the best suited to

enhance breakthrough innovation? In order to answer this question, the approaches observed are

discussed and compared in respect to the quality of the final concepts produced. The final

concepts were assessed by a panel of thirteen experts with high experience in the domain of

museum and public events organizations.

In the next section, we will expand on what is currently known and expected regarding the

process of breakthrough concept elaboration in design teams.

2. Theoretical Model

2.1. The process of breakthrough concepts production

Although prior research in NPD has described the development, commercialization and diffusion

of breakthrough innovation in details, the research about ideation process is still claimed to be

scarce (Spanjol, Qualls, et Rosa 2011). According to (Page et Schirr 2008), ideation and

creativity topics represent only 5% of the 815 product innovation articles published between 1989

and 2004. Most existing research in this field is a collection of idea generation tools to help

people break free from conventional products and to challenge common assumptions (e.g.

brainstorming (Osborn 1953), brainsketching (Van Der Lugt 2002), active works with users

(Lilien et al. 2002; Nicholas, Ledwith, et Bessant 2013; Urban et Von Hippel 1988), TRIZ

methods (Altshuller, Shulyak, et Rodman 1999; Ilevbare, Probert, et Phaal 2013), Delphi

methods (Steinert 2009), unrelated stimuli (Hender et al. 2002; McFadzean 1998), open

innovation systems (Bayus 2012; Franke et Piller 2004; Poetz et Schreier 2012) or Concept-

Knowledge tools (Elmquist et Segrestin 2009; Gillier et al. 2010)).


All these different techniques point out that the breakthrough ideation process covers several

different thinking processes and approaches. Regarding the existing models of breakthrough

ideation itself, inspired by early studies in psychology, a body of research describes it as

“divergent thinking”, or individual and collective cognitive abilities that permit the generation of

a large set of ideas (Ames et Runco 2005; Guilford 1956; Williams 2004). In this perspective,

ideation for breakthrough concepts is viewed as a combinatorial process of ideas (Dahl et Moreau

2002; Kohn, Paulus, et Choi 2011). For (J.G. March 1991), such new associations may be

obtained through the wide exploration of new knowledge and competencies. (Kim, Im, et Slater

2013; Rietzschel, Nijstad, et Stroebe 2007) found that a deep exploration of heterogeneous

knowledge enhances the originality and novelty of ideas. In contrast, (Gabrielsson et Politis

2012) showed that the breadth rather than the depth of knowledge for functional work experience

favors the generation of new ideas. More fundamentally, some authors show that these creative

connections can be better interpreted using a design perspective (Dorst 2006; Hatchuel et Weil

2009; Simon 1969; Verganti 2009). In this perspective, final breakthrough concepts are explained

to come from the exploration of different design paths. A design path is defined as a successive

linkage of properties that can be more or less original. (McFadzean 1998) claimed that the

solutions proposed during ideation can be classified into a continuum ranging from “1- Paradigm

preserving ± where no elements or relationships are introduced. 2- Paradigm stretching ± where

either new elements are introduced or new relationships are conceived. In other words, the

problem space or paradigm boundary is stretched to enable group members to consider something

new. 3 - Paradigm breaking ± where both new elements and new relationships are introduced.

This occurs when the paradigm's boundary is completely broken by the participants.” (p133).

(Boden 2003) claimed that the higher level of creativity (“transformational creativity”) is

achieved because of the design of a new conceptual space (i.e. a new paradigm). For the author,

the “breaks” that characterize the “breakthrough” ideation process occur when the established

conceptual spaces are transformed into new ones. Quite similarly, (Hatchuel et Weil 2009;

Hatchuel et Weil 2003) model such conceptual shift as the expansion of concepts (i.e. unknown

propositions) and knowledge spaces (i.e. known propositions).

Formally, this first proposition is explored:


Proposition 1: The breakthrough ideation process is composed by a sequence of “known”

properties (i.e. existing knowledge about the current paradigm) and “unknown”

properties (i.e. ideas that go beyond the existing paradigm).

In summary, the elaboration of breakthrough concepts is usually defined as a creative design

process that radically changes the dominant logics and traditional beliefs of industries. Existing

studies in creativity and innovation management have extensively investigated the external

factors (techniques, appropriate environments…) to produce a large quantity of ideas with the

hope of obtaining a few great ones, but the process of ideation itself has been much less

investigated. Breakthrough ideation process is depicted as a design process that requires both the

involvement of “known” properties (i.e. elements that come from the existing paradigm) and

“unknown” properties (i.e. elements that go beyond the existing paradigm) (Hatchuel et Weil

2009; McFadzean 1998). However, little is known if different approaches exist to involve such

properties. The next section presents the literature findings regarding the characteristics and

challenges of design teams in breakthrough situations.

2.2. The dynamics of design teams during breakthrough concepts generation

To concur with (Dahl 2011), the existing research on innovative design teams is quite scarce and

mostly falls under the category of NPD team performance. In this perspective, multiple

organizational and contextual factors have been emphasized. Among other studies, innovative

NPD teams are most often those involving a collaborative leaderships style that permits a high

level of cohesiveness, open-mindedness, and high individual satisfaction and motivation

(Amabile et al. 1996; Jassawalla et Sashittal 2000; King et Anderson 1990; Sarin et McDermott

2003; Thamhain 2003; West 1990). The organic and autonomous NPD teams composed by

heterogeneous members increase their chance to be innovative (Keller 2001; Magni et al. 2013;

McDonough III 2000; Patanakul, Chen, et Lynn 2012). However, an important amount of

research in group brainstorming showed that not all teams are innovative. Specifically, it is

proven that the creativity of individuals outperforms when they work alone rather than when they

evolve in groups (Diehl et Stroebe 1987; Mullen, Johnson, et Salas 1991; Paulus 2013; Schirr

2012; Taylor, Berry, et Block 1958). (Hoegl et Parboteeah 2007) found that, in comparison to

single individuals, teams are better for elaborating and discussing ideas than for generating ideas.


Issues such as social loafing, free-riding and evaluation apprehension are pointed out (Eppler,

Hoffmann, et Bresciani 2011; Gallupe et al. 1992).

In contrast to experimental studies of brainstorming, several researchers try to understand how

teams innovatively think in real-life contexts, such as in product design firms like IDEO (Brown

2008; Kelley 2007; Sutton et Hargadon 1996). Those design teams are first characterized by their

“design thinking” competencies, i.e. a set of skills and methodologies to take into account

customers’ experiences and needs, to brainstorm new ideas and to develop early prototypes and

markets proofs (Brown 2008; Martin 2009; Victor Seidel et Fixson 2014; Sutton et Hargadon

1996). Those skills are reported to stimulate the imagination and the exchange of tacit knowledge

(Hargadon et Bechky 2006; Litchfield 2008; Mascitelli 2000; Schirr 2012). More particularly, in

these longitudinal studies, design teams are said to be innovative because of the ways the

participants interact with each other. (K. Sawyer 2008; R. K. Sawyer et DeZutter 2009) insisted

on the fact that breakthrough innovation does not come from a sudden flash of insights but it is

achieved through a participatory process that supports a sustaining “flow” of ideas. They

proposed some basic principles to follow, such as deep listening, building on each other or

provoking surprising questions. (Kohn, Paulus, et Choi 2011) found that ideas produced in

brainstorming are better when they are built on other ideas. In a qualitative study of 8 engineering

design organizations, (Hargadon 1999) showed that the interactions between designers often rely

on analogical thinking that permit the making of non-obvious connections between knowledge

from past problems or experiences and their current problems. During these interactions,

(Berchicci et Tucci 2010) highly stressed the capacity of design teams to build mental models and

shared belief systems. Often, this is a difficult task because breakthrough innovations may invoke

numerous instances for misunderstandings because of issues of ambiguity and equivocality, i.e.,

different interpretations may exist regarding the targeted market segments, the technologies

involved or the financial resources required. According to (Davison et Blackman 2005), teams

that are able to co-develop new ideas are those that have shared mental models that tolerate the

differences in points-of-view. Similarly, rather than searching for solutions that are convenient

for everyone concerned, (Badke-Schaub, Goldschmidt, et Meijer 2010) found that the most

innovative design teams do not look for agreement but instead confront their mindsets. However,


still much research is required to understand to what extent team members’ interactions

contribute to the elaboration of breakthrough innovation.

Finally, this second proposition is investigated:

Proposition 2: Breakthrough concepts are elaborated upon via an interactive process

through which team members generate properties by building on the properties of others

or their own.

In summary, the innovative design teams are usually said to pay a great deal of attention to how

they interact with each other. Design teams are required to both create new mental models and to

assure a certain level of cohesiveness between individuals. However, the relationships between

the process of elaboration of breakthrough concepts and the dynamics of design teams are still

not well investigated. The research proposes interesting frameworks to describe the trajectory of

concepts but the consequences on the collective reasoning is not investigated. Inversely, design

team interactions are well described but their impact on the trajectory of concepts is under

investigated. This provides the motivation for our research.

3. Research design and protocol for analysis

3.1.Overview of the research design methodology

This research is a comparison of ten innovative sessions conducted by ten different design teams,

each comprised of three people with experience in R&D activities. These sessions lasted one-

and-a-half hours each. These sessions are part of an innovative real-life project, supported by a

French cross industry innovation partnership, called MINATEC IDEAs Laboratory®, aiming to

elaborate an innovative concept of a museum that could be movable and eco-friendly, promoting

the experience of Antarctica. In 2012, MINATEC IDEAs Laboratory® decided to organize a

series of innovative workshops involving multiple external professional designers. The sessions

took place in a laboratory setting. All recorded data were analyzed following the principles of

verbal protocol analysis (or “thinking aloud protocol”) (Dunbar et Blanchette 2001; Ericsson et

Simon 1985; Green et Gilhooly 1996; Jaaskelainen 2010).

Our research goals were to explore how design teams manage their ideation processes and intra-

team relationships when they need to generate breakthrough concepts. In particular, we observed


the different approaches of exploration pursued by design teams and compared them with

quantitative and qualitative data. Contrary to prior research that mostly used case-study

methodologies for understanding group creativity at the firm level (Hargadon et Bechky 2006; V.

Seidel 2007; Sutton et Hargadon 1996), verbal protocol analysis permits tracking the team

cognitive process at a more fine-grained level. Historically developed in psychology and

cognitive sciences, this methodology gains popularity for studying innovation management (Dahl

et Moreau 2002; Dunbar 1995; Ruiz, Jain, et Grayson 2012; Van Der Lugt 2002; Ziamou, Gould,

et Venkatesh 2012). According to our knowledge, no prior studies have studied the process of

breakthrough concept ideation with such extensive data (i.e. studying the reasoning of ten design

teams during ten sessions, covering 15 hours total). The next two sections present our research

methodology regarding the context of the experiments, data collection and analytical process.

3.2. Data collection

3.2.1. Research protocol of the innovative sessions

A. Participants and formation of design teams

The 30 participants were all either engineering designers or industrial designers with an average

of 12 years of professional experience in R&D and innovation. Participants came from various

industrial sectors and held different positions at the moment of the experiments: 10 were

innovation managers, 11 engineering designers, 4 industrial designers, 3 industrial buyers and 2

B-to-B marketers. No participants had previous experience in the design of museum exhibits or in

the creation of important public social events. The participants were debriefed and compensated

for their participation.

B. Presentation of the design brief: a non-routine and breakthrough task

The ten design teams were given the assignment to further develop a breakthrough museum

concept that gives the visitor an immersive experience in an Antarctica-like world. The

participants were asked to elaborate both the form and functions of the museum (Luchs et Swan

2011). They were advised to design the architectural aspects and the possible museum activities.

The guidelines included the following:

• The museum aims to make people aware of the impending need to protect Antarctica.


• The museum is mobile – it is a touring museum that could be deployed anywhere in the

world, whatever the conditions.

• The museum is practical and easy to install and transport.

• The museum is eco-friendly as much as possible (ecological materials; energy harvesting

solutions…)

• The museum size is approximately 3600 m2.

This design brief was chosen for three main reasons. First, the theme of “museum” is simple and

easily appropriable for the subjects. Because museums are commonplace, all participants have

some experience with them. Second, this design brief is sufficiently open-ended; it offers the

opportunity to investigate how design teams think in very different ways about different domains

(architecture of the museum, visitors’ activities and experiences, business models, visitors’

management…). Thirdly, this design brief offers a great potential for disruptiveness since, by its

very definition, it is outside the scope of any known instance of its category. In prior

experimental studies, the design brief usually aims to create new products by an extension of a

known and existing product line (e.g. new SMS-based services for mobile phones (Magnusson

2009), additive feeding for babies (Poetz et Schreier 2012), a flexible-size tent for hiking (Badke-

Schaub, Goldschmidt, et Meijer 2010)) or through the re-design of a pre-existing product, such as

a re-designed bicycle rack (Dorst 2006). In contrast, the design task for the teams involves the

creation of a new category of product. The unusual dimensions of the design task (mobile

museum, eco-friendly…) challenge and go beyond the traditional definition of a museum. To a

certain extent, the participants are urged to deeply revise and break the classic identity and

common assumptions of what a “museum” is. It is significantly out of the ordinary and out of any

known expertise areas immediately accessible to them. So, how do people come to generate

descriptions of a system that they do not know? The fact that the design brief is too foreign to

instantly imagine what the final concept can be, design teams cannot use a rule-based approach

but need considerable effort for imaginative and innovative design. This shift in their thinking

enables us to truly study how design teams cope when breaking from paradigms. These two

criteria are essential for studying the “foolishness” of teams (James G. March 2006) and detecting

the controlling parameters used by the design teams in order to not get lost while proceeding in

the dark.


C. Organization of the design sessions

Each of the ten design sessions was run individually. In order to analyze how the design teams

naturally reason, they worked in total autonomy without any helps from facilitators. At the start

of each session, the design task was given to the design team. They were informed that they had

one hour and thirty minutes to formulate one single concept for an innovative museum. The

design teams were asked to summarize their concept on an A3 sheet of paper with sketches, user

scenarios, texts and motto. All experiments were launched in the same large room; table, white

paper, and pencils were provided. Participants did not have any access to external documents (no

computer, no internet connections, no books, no phone…). Participants were free to use

whichever innovative techniques they wanted. Basically, they all adopted an unstructured form of

brainstorming. At the end of each session, the participants were asked to present their concept in

ten minutes to one of the authors. In order to cover all the aspects of the design proposals and to

provide reliable and comparable qualitative data between the ten designs, a semi-structured guide

was used (see Appendix 1). All the experiments and interviews were video- and audio-recorded;

the ten final concepts were collected. Finally, because teams varied in their ability to sketch their

concepts, and this was outside the scope of research, a professional designer polished the ten final

concepts in order to avoid bias during their evaluation. In order to faithfully respect the concepts

proposed by the ten design teams, the professional designer was closely supervised and informed

of the initial sketches and semi-opened interviews.

3.2.2. Research protocol of the ratings of the final concepts

The 10 final concepts were then assessed by a panel of 14 professionals – 6 of them were experts

in museums (2 directors of museums, 2 curators, 1 public programmer and 1 exhibition designer)

and 8 of them were specialized in the organization of public events (1 director and 7 project

managers) (see Appendix 2). Judges were asked to rate the 10 final concepts with 3 criteria

according to a five level Likert scale: 1/ “novelty” compared to the existing museums – all kinds

of museum could be considered (from old-fashioned museum to innovative one (planetarium,

3D-relief movies…)); 2/ “feasibility” in terms of how simple it would be to implement it –

economically and technically; 3/ “value” for the visitors, or the possible benefits for users to visit

such a place. The score of one judge was not taken into account in the study because some scores

were not provided. Although many criteria can be used to evaluate new concepts, these 3 criteria


are the most frequently used in idea evaluation processes (Magnusson 2009; Poetz et Schreier

2012). All judges were blind to the research. In order to increase the reliability of judges, the

evaluation process was divided into 3 steps according to an adapted version of Delphi techniques.

In the first step, the rating criteria were presented and discussed by the judges. This aims to

reduce the possible differences in the interpretation of the 3 criteria. Then, the final sketches of

the 10 final concepts were presented and each judge rated independently. Finally, the results of

ratings were discussed in an unstructured form by the judges who could modify (or not) their

initial grading.

Inter-rater reliability between the judges was measured using the inter-judge agreement reliability

formula, called the Proportional Reduction Loss (PRL) of Rust and Cooil (1994). PRL is proven

to be of superior performance to several other reliability approaches for qualitative or quantitative

data (cronbach’s alpha, cohen’s k…) because PRL does not allow reliability to appear inflated

(Rust et Cooil 1994). The applicability of this statistic formula has been successfully tested by

(Cabra and Joniak, 2008) for the assessment of innovative products and services. The inter-rater

reliability was calculated for each of the criteria during both the first and second round of

evaluation: PRL-novelty {2nd=.74;1st=.71}; PRL-feasibility {2nd=.74;1st=.71}; PRL-value

{2nd=0.70;1st = .71}. All statistics show PRL ≥ 0.7 and so the internal consistency is largely

acceptable. The results also show that raters had a high degree of agreement among them from

the first evaluation round on. The Delphi method used during the second evaluation enabled

raters to improve, to a small extent, their joint agreement, but, overall, the raters’ judgments were

confirmed rather than modified.

3.3. Data analysis protocol of the experimental sessions

3.3.1. Coding and analysis of the verbal protocols: process-measures metrics

For analyzing and tracking the evolution of design team reasoning, we follow the principles of

verbal protocol analysis (Dunbar et Blanchette 2001; Dunbar 1995; Ericsson et Simon 1985;

Nisbett et Wilson 1977). Within the verbal protocol analysis approach, the participants are asked

to say out loud what she or he is thinking while carrying out their innovative design process.

Such research methodology has been extensively used in several domains for understanding the

mind (decision making, creativity, learning, knowledge acquisition…) and the team dynamics


(communication, team mental model…). In innovation, this methodology has been particularly

used to investigate the role of analogical thinking of innovative people and scientists (B. T.

Christensen et Schunn 2007; Kalogerakis, Lüthje, et Herstatt 2010), and consumers’ experiences

and their learning process when faced with technological innovations (Ruiz, Jain, et Grayson

2012; Ziamou, Gould, et Venkatesh 2012). Such methodology is appropriate to fully investigate

real-time situations. It also permits to avoid biases of interpretation after the experiment.

The conversations and the different interactions between the participants of each team were

recorded and transcribed. Information regarding the identification of the speaker and the specific

actions and reactions of participants (drawing, handling objects, jokes, laughing, mime…) were

integrated into the transcripts. In total, the ten transcripts covering 15 hours of video were used in

the present data analysis. The degree of verbalization varies between teams, and the number of

words per team ranges from 12,500 to 23,000 words.

A. Identification of the design properties

We define the final concept as a conceptual entity composed by a sequence of design properties

(Hatchuel et Weil 2009; Hatchuel et Weil 2003). Regarding our context, basically, four broad

categories of design properties were expressed by the different teams: the organization of the

museum (i.e., “how the museum concept works”) – the nature of the exhibition (i.e. “what the

museum concept does”) - the architecture of the museum (“how the museum concept is built”)

and miscellaneous. Each category includes design properties such as functions (e.g., visitor

activities, mode of transports…), technical elements (e.g. video, rooms…) or visual aesthetics

(e.g., size, shape, and decoration). Although such categorization was not taken as an exhaustive

list, it helped the authors identify and code the design properties (see Appendix 3 for examples of

design properties).

On average, the design properties represented 18.3% of the total number of words per transcript

and each design team discussed a single design property between three and four times during the

session. Participants spent the rest of their discussion dealing with other activities such as

problem framing, team coordination or team socialization. In total, the ten design teams

generated 822 design properties (S.D. = 20.61).


B. Codification of the design properties: unknown or known

All the design properties were then either coded as “known” or “unknown”. Adapting the

framework of (McFadzean 1998), “known” properties refer to those that preserve the current

paradigm of museums by not introducing any new elements or relationships to the domain of

museum, contrary to an “unknown” property that breaks the current paradigm by looking at

something in an entirely new way. More precisely, a design property was considered to be

“known” if it could be obviously and easily observed in existing or past museums. In this case,

the design property can be viewed as being part of the knowledge or past experience of at least

one member of the team. All types of museum (scientific, artistic, cultural, or historical) were

considered – note that, a specific attention was given to the website of the major museum

dedicated to Antarctica, the museum of the Arctic and Antarctic in St. Petersburg (Russia). For

instance, the design property “including a map of the southern hemisphere” was assigned as

“known” because it already exists in the St. Petersburg museum. The design property “discounted

cost of tickets for children” was also a “known” property because it appears in most museums.

These known properties can be interpreted as the participants’ current knowledge and

experiences of museums. Because the participants were not specialized in museums, the known

properties were mostly pieces of common and established knowledge.

In the cases where the design property was not usually encountered in museums, the design

properties were judged as “unknown”. For instance, the design properties “a museum that is a

zeppelin” or “museum with ice footsteps sound effects” fall into this category. The degree of

feasibility or the value delivered by the design properties was not taken into account. These

unknown properties came from the imaginations of the design team members; they are ideas that

were unproven or unrealized in the domain of a museum.

Among the 822 design properties reported, there were 510 unknown properties (62%, SD=16.06)

and 312 known properties (38%, SD=9.32).

C. Inter-rater reliability measures

Identifying concepts in free-form text is not a simple task. However, (Chiu et Salustri 2012)

found that humans are able to identify and agree upon concepts. In order to increase the scientific

rigor of this research, our coding procedure followed the indications provided by (Chiu et Salustri

2012). Two of the three authours completed the coding of all the data independently and without


communicating. Identified design properties were indicated by highlighting each time they

appeared; the two coders individually named each design property with their time of appearance

and the names of the creators. For coding, the software Atlas Ti (version 6.2, www.atlasti.com)

was used. Afterwards, the two coders analyzed and compared their coding segments side-by-side

in order to validate (or not) their identification. The Percentage Agreement (PA) between the two

coders for identifying the design properties was calculated for each team. While this method does

not exclude agreements that occurred by chance, it is simple and appropriate for exploratory

conditions. For each transcript, the PA was calculated. The meaning of design properties was

discussed thanks to the names which were provided by the two coders. All the differences in the

interpretations were resolved between the two coders. The overall average PA was 0.77, which

shows that the identification of design properties can thus be considered acceptable and

satisfactory because it reaches common thresholds (> .7). Then, for each property, the two coders

judged if the property was “known” or “unknown”. Inter-rater reliability was measured for the

full set of design properties. The PRL for each team reached a satisfactory level (> 0.7), and

overall, the PRL was 0.75.

D. Understanding the elaboration of concepts through linkography

Once the different design properties were identified and coded, an analysis of the relationships

between them were made by two of the authors. As previously said, we define a concept (or idea)

as a conceptual entity composed by a sequence of design properties. This analysis permits a

better view of how the different concepts were elaborated. For doing this, the linkography

methodology was used (Goldschmidt et Weil 1998; Goldschmidt 1995). We refer to previous

works in design literature to detail the original methodology (Kan et Gero 2008; Perry et

Krippendorff 2013). Tracking the links between the ways in which participants interact

innovatively (Van Der Lugt 2002). For each design property, we examined its possible linkages

with one or more previous design properties. If a design property was evoked without any

references to other design properties, no links were reported. The careful study of the verbal

protocols and video recordings permitted the links with explicit evidence. In total, 1279 links

were identified with a standard deviation of 45.32. Basically, we noticed links between design

properties in two frequent situations. The first identified when a design property was used to

modify or better elaborate one or more past design properties (“backward links”). We observed


that design teams frequently build several design properties around one topic (e.g. providing a

sensation of cold, simulation of a storm, walking on the ice, turning down the temperature…). A

second frequent case occurred when participants used design properties to jump to a totally

different idea (“forward links”). The following scenario illustrates this kind of situation. After

reading the design brief, participants were thinking about possible solutions for designing a

mobile and easy-to-install museum when one participant came up with the idea that the museum

could be “some barges” (design property A). After some discussion, participants decided that this

solution was not an appropriate one because the museum could only be installed in cities with

waterways (a river, sea…). Another participant proposed that the museum could “fly as an

airship” (design property B).

Finally, the Linkographer software1 was used to obtain a link diagram for each of the ten teams.

(Figure 1 gives an example of the established links over the timeline of the session for team #1;

Appendix 4 represents all diagrams). The right axis represents the set of design properties and the

“V” shapes are the connections between them. Once the links diagrams were completed, three

kinds of indicators were then used to better analyze the results.

Self-link index

This index was originally provided by (Van Der Lugt 2002). In our case, the self-link index is the

ratio of the links that the members make with their own prior properties, in relation to the total

number of links made. The self-link index indicates to what extent members interact when

generating properties. Thus, a design team with a low self-link index is a team that generates

properties by building on each member’s properties.

Link density

The notion of link density (or “index density” (Goldschmidt 1995)) indicates how the design

properties are connected with each other. It is calculated by dividing the number of links between

design properties by the total number of design properties. Thus, design teams with a high level

1 version 1.1. https://sites.google.com/a/linkographer.com/linkographer


of link density can be interpreted as design teams that adopt a holistic mindset. Inversely, the

teams that encompass a reductionist mindset (the generation of single ideas, no deep investigation

of previous ideas…) have a low level of link density. From there, we developed three specific

measures: the unknown link density, the known link density and the mixed link density.

The unknown link density measures how teams develop the connections between their unknown

properties. It informs us how the current paradigm boundary of a museum is broken. It is

calculated by the ratio between the number of links between unknown properties and the number

of unknown properties. A high level means that the teams have the ability to string uncommon

(unusual) design properties together. Such teams succeed to build a coherent and understandable

imaginative world involving new objects or new situations. Usually, these design teams are

comfortable with navigating between “strange” or even inappropriate ideas; the reality and the

feasibility aspects are not taken into account.

The known link density measures how much teams develop links between their known properties

(i.e. the ratio between the number of links among the set of known properties and the number of

known properties). A high level of known link density means that teams create concepts by the

integration of elements coming from the current dominant design. In this case, the design process

preserves the current paradigm of a museum.

If unknown link density implies a break in paradigm and known links density evokes paradigm

preservation, the mixed link density can be viewed as an intermediate level and labeled as

paradigm stretching (McFadzean 1998). It is calculated by the ratio between the number of links

between unknown properties and known properties and the number of design properties. It

provides information about how closely unknown and known design properties are connected

together. Typically, in our study, this indicator reveals how teams rely on their current knowledge

in the domain of “museum” for creating unknown concepts.

Cluster

Our second family of indicators is built around the notion of clusters (Goldschmidt 1995). A

cluster is a series of successive design properties that explore same specific issues. In our

experiment, a cluster was obtained by the formation of three consecutive links. A cluster

indicates how much the design teams continuously focused on the development of partial

solutions. The visual investigation of clusters permits us to attest whether teams focused on a


connected set of design properties or not (cf. Figure 1). Three kinds of clusters were defined: the

forward clusters, the backward clusters and the double clusters. The forward clusters are a

structured set of consecutive design properties that are used by design teams to enrich a solution

or used as the basis for generating new ones. The Backward clusters are used to revisit and refine

previous design properties, and double clusters are simultaneously forward and back-linked

clusters. Double clusters are made of design properties that are useful both to modify existing

ones and generate new ones. See (Bilda et Gero 2008) for further details.

Figure 1. A simplified example of a linkography diagram for team #1

4. Presentation of the results In the following section, we will first, in sub-section 4.1, give a short synthesis of our main

findings. We found that three main approaches were used by the design teams to elaborate

breakthrough concepts: “primer concept fixation” (teams #1, #9 and #10), “random exploration”

(teams #4 and #8), and “ecosystem of properties” (teams #3, #5, #6, #2 and #7) (see Figure 2).

According to the final concept ratings, design teams that use the last approaches received the best

scores (see Table 1). Then, in sub-section 4.2., we deeply analyzed to what extend the ecosystem

approach differs from the two other approaches in terms of the generation of known and

unknown properties (proposition 1) and how team members interacted to link the properties

(proposition 2).

Name of the design property **known or unknown **

links

backward cluster

forward cluster

double cluster


4.1. Description of three approaches of breakthrough concepts elaboration

4.1.1. Primer concept fixation approach: attachment to early properties

Teams # 1, #9 and #10 conducted a primer concept fixation approach. For these teams, the

linkographs show a much higher density of links at the beginning of the sessions than at the end

of the sessions. It means that these teams generate more backward clusters than forward clusters.

These teams tend to create a “heavy” property at the beginning of the session and they never

abandon it. Most of the design properties generated in the middle or at the end of the sessions are

linked to the primer property. The generated design properties are used only for reinforcing and

better elaborating the primer property. We noted that even if the primer property induced

difficulties or design problems, the design teams did not discard it. They did not start to think in

new directions but they persisted in making it work. Such an approach seems to not be

appropriated for breakthrough concepts: team #10 and #1 strongly lie below the average of the

three criteria. They scored lowest (hereafter referred to as the “less innovative teams”).

Alternatively, team #9 is ranked 7th since this teams provides an original concept, but it is hardly

feasible and provides poor value for the visitors.

4.1.2. Random exploration approach: generating concepts randomly

Teams #4 and #8 adopt a Random exploration approach. The link density and the number of

clusters are the lowest score compared to other teams. Even if these teams generated design

properties, they were disconnected from each other. These teams may have great ability to

brainstorm, but unfortunately, their results are produced in isolation. Team #4 is the team that

generates the least number of properties and the least number of citations per properties. Once a

property is generated, it is abandoned. In contrast, team #8 produced a very high rate of known

properties. During their work, they acknowledged that they were too focused on their current

knowledge: “we are really stuck, we are always thinking about what we know, and maybe we

need to innovate and to invent things that do not exist in reality” (45min.). The final concepts are

rated quite medium/low by the judges.

4.1.3. Ecosystem approach: integrating properties into a platform

Teams #2, #3, #5, #6 and #7 used what we label an ecosystem of properties. These teams

produced interdependent and entangled clusters of design properties. The density of links is high


and, in average, there are a balanced number of backward and forward clusters. Teams #2 and #7

constituted two or three ecosystems of properties and the teams #3, #5 and #6 succeeded to create

only one single ecosystem of properties. Clusters created by teams #3, #5 and #6 are specific

because they are backward, forward and double clusters. It means that these teams proposed

properties which enabled them to reconnect with prior properties and to stimulate the creation of

later properties.

The judges’ ratings show that creating one single ecosystem of properties was the most

appropriate approach for breakthrough concept elaboration: teams #3 and #6 scored highest

(hereafter referred as the “highly innovative teams”). Team #3 is clearly the highest innovative

team regarding the three criteria of novelty, feasibility and value. Team #6 scores very good

results on the three criteria. Teams #2 and #5 propose solutions that are not really original but

they are feasible and of value for visitors. In contrast, the final concept proposed by team #7 is

original but it is not very feasible and provides poor value for the visitors.


The figure below illustrates the three approaches:

exploration approaches Teams

Average final

scores

Average # of

forward clusters

Average # of

backward clusters

Average # of

double cluster

Illustrative process

prime concept fixation

#1, #9,

#10

low

2.3

5.3

1.3

Random exploration

#4, #8

Low-Medium

1

2.5

0.5

ecosystem of

properties

#2, #7

High

medium

4

4

1

#3,#5,

#6

High

4

4

2

Fig. 2 Toward a typology of team exploration approaches

Exploration approaches

Novelty1

Feasibility2

Value for visitors3

Mean score (final rank)

team #1 primer concept fixation 2.69

2.92

2.62

2.74 (10)

team #2 ecosystem of properties 3.00 4.08 3.31 3.46 (4)


team #4 random exploration 3.62 3.15 2.77 3.18 (8)




team #8 random exploration 2.77 4.08 3.46 3.44 (5)

team #9 primer concept fixation 3.92 2.77 3.00 3.23 (7)

team #10 primer concept fixation 2.54 3.15 2.69 2.79 (9) Note: novelty, feasibility and value score was graded with a 5-level Likert scale (1=poor; 5=very good). The final ranking was found using the mean between the 3 scores. No weighting was used (novelty, feasibility and value scores). 1mean=3.40 ; S.D.=0.66 2mean=3.57 ; S.D.=0.59 3mean=3.15 ; S.D.=0.50

Table 1. Ratings of the final concepts

property

backward link

forward

legend ::


4.2. Lessons from the ecosystem approach

4.2.1. Elaborating breakthrough concepts is not generating solutions: producing a lot of

unknown properties is not a promise of success

Contrary to the random exploration approach, the ecosystem approach highlights the fact that

breakthrough concept elaboration does not require the generation of a large number of ideas.

Interestingly, this result strongly contradicts the common assumption that considers fluency

(skills to generate a high number of ideas generated) as a key factor of creativity (Osborn 1953).

Our data does not confirm that more ideas lead to better ideas – as suggested by other authors in

creativity who show that quantity does not always breeds quality (Osborn 1953, 131; Rietzschel,

Nijstad, et Stroebe 2007; Yang 2009). We found no significant correlations between the quantity

of properties generated and the three evaluation criteria. Indeed, if team #6 generated the highest

number of design properties (125 design properties, ranking 2nd), the team that proposed the most

breakthrough final concepts (team #3) was also the one that generated a low number of

properties. In comparison with the random exploration approach, the ecosystem approach does

not direct design teams to generate solutions but to think in more rational ways.

Although we found that generating a large number of unknown properties increases the novelty

of the final concepts (r = .077, p < .05, n=822) and that design teams naturally talk much more

and much more often about unknown properties than about the known properties (respectively, r

=.107; r = .138; p < .01; n=822). We found that the number of unknown properties does not

necessarily impact the feasibility and the value of the final concepts. Thus, a design team can

generate a high amount of unknown properties with a low overall score (e.g. team #10, ranking 9)

or inversely, a design team can generate a low amount of unknown properties with a high overall

score (e.g. team #3, ranking 1). More precisely, we found that what is important for breakthrough

concept elaboration is not the quantity of properties but rather the proportion between the number

of unknown properties and the number of known properties. Our empirical analysis suggests an

optimum ratio of about 65%. When too few unknown properties are generated in comparison to

known properties (<65%), like the less innovative teams (team #1 and team #10), teams stay

fixed around the usual dominant paradigms of museums. To the contrary, if too many unknown

properties are generated in comparison to known properties (>65%), teams create final concepts

that do not make sense for the judges.


Differing from the primer concept fixation approach, the ecosystem approach increases the

possibility of design teams generating useful properties. Indeed, our results show that the

production of design properties is required during all of the design process, and the timing of

generation has differing impacts on the three criteria. Firstly, the unknown properties produced

early in the process seem to have a higher impact on the novelty of the final concepts than the

unknown properties produced late in the process (correlation between time of unknown

properties emergence and novelty r= -.103; p < .05, n=512). Secondly, the properties that are

generated late in the process positively impact the feasibility score of the final concept

(correlation between time of properties emergence and feasibility r= -.08; p < .05, n=822). The

design properties generated at the end of the sessions can reinforce the feasibility aspect of the

final concept, but the properties can be unknown or known properties. Finally, the known

properties generated late in the process increase the value score better than the known properties

proposed earlier in the process (correlation between time of known properties emergence and

feasibility

r = -.13; p < .05, n=310).

nb of design properties

citation average

Average score

(final rank)

known property1

unknown property2 known property3 unknown property4

team #1 34 31 5 7,19 2.74 (10)

team #2 29 39 4,85 4,41 3.46 (4)

team #3 21 39 3,32 5,49 4.31 (1)

team #4 21 36 1,9 2,22 3.18 (8)

team #5 21 74 2,15 2,62 3.46 (3)

team #6 46 79 2,91 2,57 3.72 (2)

team #7 32 55 2,93 3,82 3.38 (6)

team #8 44 51 2,66 3,49 3.44 (5)

team #9 26 59 2,15 4,34 3.23 (7)

team #10 38 47 3,15 2,31 2.79 (9)

1 mean=31.20 S.D.= 9.32

2 mean=51.44 S.D.=16.06

3 mean= 3.10 S.D.=1.06

4 mean= 3.85 S.D.=1.59

Table 2. Number and citation average of known and unknown properties per team

4.2.2. Linking unknown properties in order to create a collective “imaginary” world

While our results show that the quantity of properties is not crucial, the ability of design teams to

make links between their properties is a much more important factor for elaborating breakthrough


concepts. Compared with the design teams that make either no links between their properties

(random exploration approach) or only backward links with the first properties (primer concept

fixation approach), the design teams that follow an ecosystem approach focuses much more on

establishing links between their properties rather than generating properties individually.

Indeed, our results significantly show that the mean score of the ten teams increases with the link

density (r = .657, p < .05, n=10). The more a property is linked to other properties, the more it

increases the novelty score (r = .155; p=.000; n=821), the feasibility score (r=.089; p < .05;

n=822) and the value score (r = .155; p= .000; n=821) of the final concepts.

More precisely, our result shows that not all links are equal. Indeed, to generate properties by

building on prior unknown properties is particularly effective. In fact, the design teams who link

properties with prior unknown properties increase the novelty score (r=.212; p=.000; n=822), the

feasibility score (r=.092; p<.01; n=822) and the value score (r=.177; p=.000; n=822) of the final

concepts. Opposingly, one property that is built on known properties has a negative impact on the

novelty score of the final concept (r= -.084; p<.01; n=822) and no significant impact on the other

scores. We also notice that linking several successive unknown properties may provoke some

confusion in the design teams. Indeed, in such situation, the design teams may realize how much

their creativity challenges their common frame of reference as illustrated by team #2:

− [designer 1] : We can imagine that… − [designer 2] : ooh… wait a moment. Finally, I am not sure we are still designing a

museum. The word “museum” is confusing. For me, this is no more a museum. − [designer 3] : yeah, I agree. We should stop using the word “museum”, let’s name it the

“storm-arium”

Analyzing the results of the extreme design teams, we found that the less innovative teams are

often stuck by known design properties. Although the highly innovative teams also generated

known properties, they did not use them for generating unknown properties, and this is supported

in the analysis of the number of times that design properties are cited and mobilized during the

sessions. Although the frequency of discussion around the design properties is quite similar (609

citations for the high-innovative and 624 citations for the low-innovative), the highly innovative

teams tend to focus their attention much more on the unknown properties than the less innovative

teams. On average, the citation of unknown properties represents 71.26 % for highly innovative

teams and 59.45% for less innovative teams (χ2 < .001). According to our observations, the highly


innovative design teams used known design properties mostly for sharing the main issues of the

topic. Metaphorically speaking, the known properties enabled them only to agree about “what is

the box to think outside”. Thus, the design teams were then more able to monitor whether their

thinking was breakthrough or not. However, more research is required to validate this hypothesis.

Another result concerns between whom the links are established. We found that when designers

“built on each other”, it had a positive impact on the mean score of the final concepts (r = .073; p

< .05; n =822). Contrarily, when designers built upon their own ideas, it was negatively

correlated to the mean score of the final concepts (r = -.076; p < .05; n =822). This analysis is

qualitatively confirmed by counting the talk turnovers in the design teams. The highly innovative

teams involve the participation of the three designers whereas the discussions in the less

innovative teams are often monopolized by one designer.

mean score (rank)

total links1 link density2 known link density3

unknown link

density4

mixed link

density5 self-link

index6

team #1 2.74 (10) 113 1.73 .76 1.35 .68 .59 team #2 3.46 (4) 99 1.44 .51 1.13 .57 .48 team #3 4.31 (1) 154 2.53 .52 2.41 .78 .29 team #4 3.18 (8) 60 1.04 .43 1.1 .19 .36 team #5 3.46 (3) 143 1.46 .25 1.36 .34 .37 team #6 3.72 (2) 226 1.8 .58 1.66 .54 n.d. team #7 3.38 (6) 163 1.85 .53 1.33 .82 .51 team #8 3.44 (5) 112 1.18 .66 .76 .46 .43 team #9 3.23 (7) 104 1.22 .15 1.1 .42 .45

team #10 2.79 (9) 105 1.24 .53 .98 .46 .38

1mean=127 S.D.=45.7

2mean=1.55 S.D.= .44

3mean= .49 S.D.=.18

4mean=1.31 ; S.D.= .45

5mean= .53 ; S.D.= .20

6mean= 0.39 S.D.= .09

Table 3. Linkography results

4.2.3. Economy of thought and inventive design thinking: evaluating the “fit” of properties

Our result also shows that the ecosystem approach used by the most innovative team (team #6

and team #3) enable them to economize mental resources. In fact, this team did not have to

produce a lot of design properties to be inventive. They proposed very few design properties

compared to other groups but they exploited them efficiently. Once a property was generated, this


team tried to directly make links with previous properties. This winning approach enabled them

to increase the degree of cohesiveness and limit the number of abandoned design properties.

As a consequence, we also observed that this approach had an impact on how the design team

evaluated the different design properties. Basically, once a design property was generated, we

observed two kinds of reactions in the other teams. A first common attitude was to evaluate each

design property individually. This evaluation was either made immediately after the generation of

each design property or at the end of the session. In general, design properties were evaluated

according to their novelty or feasibility aspects; quite frequently, participants disagreed and long

discussions are required to resolve the conflicts reach compromise. Team #3 and #5 evaluated

design properties in a radically different way. The design properties were not evaluated according

to their novelty or feasibility but rather, team members evaluated whether the design properties

“fit” each other. This team did not evaluate design properties in a linear way, but it rather

followed an evolutionary process where design properties appeared and disappeared according to

their fitness to the overall concept. They did not evaluate the properties at the end, nor did they

choose which were the best and worst properties to keep in the final concept. Rather, the

ecosystem of properties was designed is progressively by re-forming and modifying step-by-step

the generated design properties. The evaluation of the design properties were made in more

“natural” way in the sense that they did not necessarily choose the most original or feasible

properties. Instead, they evaluated whether each design property “fit” the overall ecosystem.

5. Conclusion, implications and further research

5.1. Synthesis and theoretical implications

The results presented in this article indicate that, behind the generic term of ideation process,

design teams can at least follow three different approaches when generating ideas: “primer

concept fixation”, “random exploration” and “ecosystem of properties”. According to our results,

the last approach is the most effective one to develop breakthrough concepts. The ecosystem

approach incorporates three main features. The design teams (1) generate both unknown and

known properties, not just a large number of unknown properties, (2) and single structure their

thinking by simultaneously generating and linking properties instead of generating disparate


properties, and (3) do not evaluate the properties individually toward the classic criteria

(feasibility, novelty, value…) but rather evaluate properties during the generation phase by

collectively discussing the “fit” between the properties.

At a theoretical level, our research invites us to reconsider the fuzzy front end that is discussed in

the current models for the development of breakthrough concepts by the generation of numerous

ideas in the hope of selecting the most satisfying ones. In comparison with this ideas generation-

selection model, our results suggest that the process of elaboration of breakthrough concepts is

better depicted by design models where the final concept is not viewed as a single unit but as an

aggregation of several properties. The identification of known and unknown properties shows

that breakthrough concepts do not involve one single break but they are the consequence of

several linked breaks.

Furthermore, an important contribution concerns the research methods. Although the existing

creativity and innovation litterature often argues that the specific cognitive processes involved in

creative thinking need to be investigated through the creative process itself rather than the

outcome, research protocols and process-based metrics are still missing. In this research, our

codification schemes (the unknown and known properties) and the different measurement indices

developed (number of properties, link density, self-index) now permit an improved apprehension

of the creative process.

5.2. Managerial implications

Regarding the managerial implications, one main question remains: if design teams follow the

three approaches, what can organizations do to push their design teams toward the best one (i.e.

the ecosystem approach)?

Some answers may be found regarding the role of facilitators and the appropriate organization of

such creative sessions. Existing literature usually recommends that managers follow the funnel

model which calls for the generation of a large and various set of ideas, and then, the evaluation

and selection of the best ones. In this perspective, research on organizational creativity often

recommends having two contrasted climates for the generation and the selection of ideas: an open

and safe environment where participants fell free to generate ideas as much as possible, and, a

more dissident atmosphere where each idea and opinion can be discussed and confronted


(Amabile et al. 1996). Contrary to that, our research proposes to combine the two activities

through a compositional design process where links between properties are reinforced. This

suggests that facilitators need to possess new skills and responsibilities. Rather than stimulating

and energizing people to produce a large quantity of various ideas, the facilitators should devote

more time to coordinate the ecosystems of properties. For instance, it may require a person to

help insure that participants produce an appropriate ratio of unknown and known properties (65%

according to our empirical result). In order to achieve such a ratio, even the least novel ideas (i.e.

the known properties) have to be shared and discussed. Furthermore, our results also confirm the

importance of people expanding on the ideas of others (Kohn et Smith 2011; Kohn, Paulus, et

Choi 2011). However, our research offers additional clarifications. For instance, the main reasons

for this primary reason of such is not to increase the number of ideas but it rather aims to

continuously discuss the possible links and compare the ideas as soon as they are generated.

Consequently, the facilitators may have the responsibility incite the confrontation of opposite

point of preventing opposing points-of-views until the right moment and managing creative. Note

that the necessity to encourage cognitive conflicts (Badke-Schaub, Goldschmidt, et Meijer 2010).

This contradicts contradict one of the most common brainstorming rules (“never criticize ideas”)

(Osborn 1953). A second clarification is that participants do not have to expand on all kinds of

ideas: the facilitators must be able to differentiate the known and the unknown ideas in order to

favor only the links between the unknown ones. Finally, rather than delaying the evaluation of

ideas at the end of the session, the facilitator must encourage participants to shape an ecosystem

of ideas by measuring the fit and the coherence between their ideas. This objective must be

clearly communicated to participants: the objective is no more to obtain a ranked list of many

ideas but it is to design well-articulated, linked networks of ideas.

5.3. Generalizability and further research

The findings are based on an experimental study of ten professional design teams committed to

one hour and thirty minutes of work. Although we collected more in comparison to other studies,

future research is encouraged to conduct experimentations in other contexts. For instance,

observing the process of elaboration of breakthrough concepts in even more naturalistic settings

and over the entire life of a project would allow researchers to better know if the three approaches

presented here differ and if the ecosystem approach is also the one that is the final concepts are


most efficiently integrated into an organization. Alternatively, conducting other laboratory

experiments could be helpful to better know how critical factors such as socialization aspects,

personality traits or techniques used impact the different approaches. Other research limitations

can also be identified. In our research, a preliminary briefing was given to the design teams.

However, the design of such briefings could be considered as a creative design process in itself.

We may thus hypothesize that such initial activities may influence the approaches that are

mobilized by the design teams afterwards. Regarding the methodology, our research protocol

enables us to quickly follow the design thinking but some additional coding could be relevant.

For instance, this research does not specifically examine major creative cognitive processes, such

as analogizing, in detail. Therefore, we notice that design teams use different kinds of heuristics

to generate unknown properties and to establish links. For instance, they often tend to “inverse”

or “virtualize” prior properties. Finally, our research emphasizes the importance of building

properties on the unknown properties of others. This result extends existing results found in

brainstorming literature (Bayus 2012; Kohn et Smith 2011; Osborn 1953). In this research, we

provide supplementary findings by showing that links between properties are not equal: if

building on unknown properties impacts the quality of the final concepts, the inverse is not true

Further but further theoretical research on the exchange process of concepts and knowledge are

required to better explain such results.


APPENDICES

• What is the external architecture of your concept? What do people see when they are

outside?

• How do they enter in the museum? How does the exhibition begin? What do visitors feel

in the entrance?

• What do the visitors do inside the museum? What are the main activities proposed?

• Can you imagine a use-case for your concept?

• Can you draw a plan to view the organization of the museum? How is it organized?

• How do you make people aware of the needs to protect the Antarctic?

• Is your museum mobile? How can it be opened anywhere in the world, no matter the

conditions? How do you install and transport it?

• What about the eco-friendly issue?

• Do you have any other comments? Is your sketch a good picture of your museum?

Appendix 1 – Semi-structured guide used at the end of each session


the organization of the museum (i.e. “how the museum concept works”)

MUSEUM WITH THEMATIC

ROOMS

DARK ROOM FOR MOVIES

AGORA

“a room where you can learn the history of Antarctica, another about animals and nature and another about scientific experimentations”

“museum with rooms that are sufficiently dark to watch movies correctly”

“Let’s have a main room where people can meet and chat each-other, a kind of Agora”

the nature of the exhibition (i.e. “what the museum concept does”)

MOVIES PROJECTION ON

WALL

ICE-FLOOR MOVING BY BOAT

“Imagine you walk in a corridor with projection of movies on the wall”

“Yes, I think that it could be nice to walk on a strange ground. It could be a slippery surface with ice”

“ What is really fun, it is that the visitors enter inside the museum with a boat like Conquistadors

the architecture of the museum (“how the museum concept is built”)

CIRCUS TENT TRANSLUCENT ROOF

ENERGY HARVESTING

“we can have canvas like in the circus”

« A roof where you can see outside, a translucent roof, for the lights…”

« We didn’t speak about the ground…we need to have something special. For instance, a ground that harvests the people energy when walking »

Appendix 2 – Examples of design properties and verbatim


Appendix 3 – A breakthrough final concept (left) and a less innovative final concept (right) – (illustration made by Etienne GIORGETTI)

Appendix 4 – overview of linkography diagrams


Appendix 5 – Average evolution of the design properties for the ten design teams

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i This project is supported by MINATEC IDEAs Laboratory®, a French cross-industry innovation partnership.

The creation of breakthrough concepts by design teams

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