Design Creativity 2010 part 11 ppt

In this paper the strength of metaphoricity is a function of feature similarity between its target and source entities, as well as the domain dissimilarity between the two entities..

An Approach to Measuring Metaphoricity of Creative Design

Hung-Hsiang Wang and Jung-Hsuan Chan

National Taipei University of Technology, Taiwan

Abstract Metaphor is central to design creativity as it

involves processes of discovering two different objects

which are similar in some perspectives and combining them

together into a new and meaningful one This study argues

the degree to which a design contains metaphor is a good

indicator of the creativity of the design In this paper the

strength of metaphoricity is a function of feature similarity

between its target and source entities, as well as the domain

dissimilarity between the two entities The situation of

metaphoricity is the salience imbalance of the similar

features between of its target and source entities To test the

argument, five award winners of various well-known

creativity-oriented design competitions are accordingly

presented to twenty-six design students to assess the

metaphoricity strength and saturation, and the creativity on a

subjective base Results reveal the creativity has significantly

positive relation between the object similarity and

metaphoricity saturation

Keywords: design creativity, metaphor, similarity, industrial

design

1 Introduction

Metaphor is not only a style in speech and writing but

a resourceful method of human’s thinking in daily life

(Lakoff and Johnson ,1999) It has been thought the

kernel ability of creativity that helps us to creatively

put two things from two different domains together

into a new one (Seitz, 1997; Ricoeur, 1981)

Metaphors have long been recognized to play an

important role in industrial design (Hey and Agogino,

2007) Metaphor is often used at earlier stages of

conceptual design to solve problems or interpret

meaning in a creative way The conceptual design

starting with an initial design goal, through ideation,

evaluation, and finalization can be seen as a process of

defining a target, searching sources to construct pairs

of the target and sources as alternatives, and selecting

a satisfactory one from these alternatives

Although there are some metaphor theories based

on similarity measures, few of them have been applied

to the area of design creativity Moreover, empirical

evidence supporting the positive relation between

design metaphor and design creativity has rarely been reported Therefore, this study aims to provide empirical evidence regarding design metaphor measures and its implications to design creativity Quantitative results of questionnaires for assessing design metaphor and creativity are presented following

a short literature review

2 Metaphoricity in Design

2.1 Metaphorical Design

Metaphors are represented by the form “A is B”, where B is called the source of the metaphor, and A is the target Metaphor can be used for understanding of

an unknown situation A in terms of one familiar thing

B (Gentner 1983; Gentner and Markman, 1997; Novick, 1988; Vosniadou, 1989; Ortony, 1993) Interpretation of a metaphor is a process of discovering which features of the source may be valid and useful to understanding the target To construct such a metaphor, one needs to find out the source B that is similar to the target A in some perspectives but dissimilar to each other in terms of membership of certain categories The similarity maintains a reasonable mapping from the source B to the target A, while the dissimilarity promises an unusual mapping The search for sources is thus described as a mapping

of the target and sources based on their common features As long as the mapping is reasonable but unusual to a certain degree, the conceptual design is said to be creative in terms of the processes or the products A creative design is identical to a both new and meaningful design

Take Alessi's Anna G corkscrew, designed by Alessandro Mendini for example The main goal of this project is to design a new object that belongs to the target domain of wing corkscrew As it has the salient feature of two wing-like levers, it also called an angel corkscrew or butterfly corkscrew Thus, dancers are selected as the source domain, and a female

dancer’s body elements that are similar to the parts of

a wing corkscrew are identified On one hand, that the

similarity between the wing corkscrew and an dancing

woman make a reasonable mapping The pairs of their

similar features include (1) the handle of the corkscrew

and the head of the angle, (2) the two levers and the

two arms, (3) the rack and pinion connecting the levers

to the body and the puff shoulder lace dress, (4) the

motion in which the levers are raised as the worm is

twisted into the cork, and the action of the angle’s

raising arms while dancing, as well as (5) the smooth

motion of pushing down the levers to draw the cork

from the bottle and the elegant putting down arms On

the other hand, the similarity between human dancers

to tools in household use is so low that the mapping is

unusual As a result, we can say Anna G corkscrew is

a creative product because of good metaphor

Fig 1 Anna G corkscrew (left) and the woman in puff

shoulder lace dress (right) (adapted from

http://www.alessi.com and

http://www.costumediscounters.com/womens-costumes,

respectively)

2.2 Metaphor and Design Creativity

Metaphor is a very useful tool in creativity, not only in

designing creative interface for effective and efficient

use, but also in dreaming up both new and valuable

ideas Creativity enables designers to transcend

conventional knowledge domain so as to investigate

new ideas and concepts which may lead to creative

solutions As a metaphorical design is typically based

on a reasonable and unusable mapping from source

domain to target domain to represent some

distinctiveness and meaningfulness, it has importance

in design creativity Statistically assessing the

metaphors used by students in design creativity,

Casakin (2006, 2007) determines synthesis of design

solutions is the stronger factor of the use of metaphors,

whereas metaphors play an important role in design

creativity

Use of metaphors can contribute to designers’ (1)

productivity of, meaningful, interpretable and relevant

ideas, (2) rarity of the ideas, and (3)

comprehensiveness of the ideas These aspects are

respectively associated to the three dimensions:

fluency, originality, and elaboration used to assess

divergent thinking and other problem-solving skills in Torrance Tests of Creative Thinking, developed by Torrance (1974) Furthermore, retrieving concepts from metaphors demands creative thinking Effective and efficient indexing and retrieving the source objects that are similar to the target object, but belong to the domains that are dissimilar to the target domain are obviously related to fluency, flexibility, and originality

in design creativity Successful combination and adaptation of the features of the target and source objects are apparently associated with originality and elaboration

3 Measuring Metaphoricity

In this paper, the metaphoricity of a design is measured by the similarity between the target object and the source object, the dissimilarity between the target domain and the source domain, and the salience imbalance of the common features of the target object and the source object The followings describe these three factors

3.1 Object Similarity

Similarity plays an important role in human perception (Goldstone, 1999; Kovecses, 2002; Tversky, 1977) Similarity measure used to quantify the degree of resemblance between a pair of cases (Liao, Zhang and Mount, 1998) There are many models of similarity measurement The most common method in geometric (or spatial) models is an inverse measure of Euclidean distance This method is suitable for continuous variables, though limited for discrete ones

However, similarity measures are commonly used for discrete features (Everitt et al., 2001) For real data sets, it is more common to see both continuous and discrete features at the same time In other word, a database often contains such types of variables as binary, nominal, ordinal, interval, and ratio A more powerful method is to use a weighted formula to combine their effects A method for measuring mixed variables is proposed by Gower (1971) and extended

by Kaufman and Rousseeuw (1990) The similarity measure for objects x and y with d features with mixed data (also called d-dimensional mixed data) is defined

as









d

d

/ ) S (

= y)

where Si indicates the similarity for the i-th feature

(also called variable) between the two objects, and δi

is Gower's General Similarity Coefficient

The coefficient δi is usually 1 or 0 depending upon

whether or not the comparison is valid for the i-th

feature If differential variable weights are specified, it

is the weight of the i-th feature, or it is 0 if the

comparison is not valid That is, if the weight of any

feature is zero, then the feature is effectively ignored

for the calculation of proximities Note that the effect

of the denominator





d

i

i

1



is to divide the sum of the similarity scores by the

number of variables; or if variable weights have been

specified, by the sum of their weights

Calculation of the component similarity Si is

various with discrete and continuous variables For the

discrete variables (including binary), Si is assigned to

either 1 if xi = yi, or 0 if xi ≠ yi For the continuous

variables, Si is obtained by using the normalized

city-block distance as

Si = 1 - |xi - yi| / Ri (2)

where Ri is the range of the i-th feature over the two

objects

Again, take Anna G for example In the

two-dimensional mixed data as shown in Table 1, the target

is Anna G corkscrew, denoted by x, and the source is

the female dancer, denoted by y

Table 1 Mixed variables for the target and source objects of

Anna G

Feature

Discrete (structural) Continuous

(behavioral)

Target

object

x yes yes yes 0.9 1.0

Source

object

y yes yes yes 0.8 0.7

Coefficient δ i 1 1 1 2 2

Note: Each i-th feature denotes as the followings

1: a head-like part attached to the top of body,

2: two arms-like parts attached to shoulder,

3: puff-shoulder-like shape on each shoulder,

4: rotating the head-like part while raising two arms,

5: smooth pushing down two arms-like parts two arms

For simplicity, let’s assume that behavioral features such as rotating are twice as important as structural features such as having arms Thus, the weights of the former are given by 2, while that of the latter 1 Furthermore, the behavioral and structural features are treated as continuous and discrete features, respectively The range of each continuous feature is given by 1 Thus, the similarity measurement is obtained as

S (x, y)=(1×1+1×1+1×1+2×0.9+2×0.7) / (1+1+1+2+2)

= 0.89

3.2 Domain Dissimilarity

In addition to the similarity between the target and source objects, the dissimilarity between the target and source domains also plays an important role in metaphorical design Winner (1985) suggests a good metaphor have a sufficiently long distance (i.e., higher dissimilarity) between the domains to which the target and source objects correspondingly belong Casakin (2005) points out that the degree of difficulty to establish a metaphor is mainly determined by how remote the source is from the target Michalko (2001) also determines a positive relationship between the probability of inspiring new concepts by metaphors and the domain dissimilarity

This study measures the distance between the two classes or categories of which the target and source objects are members, respectively, to obtain the domain dissimilarity For the target, Industrial and Business Taxonomy, developed by Ministry of Economic Affairs of Taiwan, is a practical domain classification For example, the classes can be home accessories, 3C-electronics, transportation, fashion, and sport and entertainment In contrast, the source domains are much more diverse They may range from nature to artificial, from creature to non-creature, or from tangible to intangible classes

The domain taxonomy seems to be arranged in a hierarchical structure, which is typically organized by supertype-subtype relationships In such an inheritance relationship, the subtype by definition has the same features as the supertype plus one or more additional features For example, corkscrew is a subtype of wine accessory, but not every wine accessory is a corkscrew Hence, a type must satisfy more features to

be a subtype than to be a supertype Theoretically the domain dissimilarity can be computed not only by the inversed similarity, but also by the depth and width of the supertype-subtype relationships

Sometimes it is hardly to consider such relationships because of the difficulty of specifying the consistent supertype of the target and source objects For instance, the supertype of wing corkscrew could

be corkscrew, wine accessory, tool, or to the extreme,

thing Likewise, the female dancer could be the

subtype of female, human being, mammal, animal, or

to the extreme, thing, too At this moment, it is more

or less uncertain to decide which hierarchical level of

supertypes However, given that the supertypes of

target and source objects have not recognized yet, we

can judge the domain dissimilarity by simply

estimating the distance between the undecided

supertypes without naming them or specifying their

detailed features For example, we can assess this

domain dissimilarity by giving a value, 0.9, for the

dissimilarity between the category of wing corkscrew

and the category of female dancer

3.3 Salience Imbalance

Besides similarity of objects and dissimilarity of

domains in metaphors, the salience (i.e., significance)

of the common features between the target and source

objects plays an important role On the basis of

Tversky’s (1977) notion, Ortony (1979) thinks the

imbalance, denoted by I(x, y), in salience levels of

matching features of the two objects is a principal

source of metaphoricity Given that the feature sets of

the target object x and the source object y are A and B,

respectively The salience imbalance of x and y,

denoted by I(x, y), is expressed as a linear function of

the measures of their common features, and is given by

where (A∩B) represents the of common features of x

and y, ƒA and ƒB represent measures of salience based

on the values in A and B respectively, and g is some,

probably additive, function

Ortony (1979) suggests that a convenient way of

conceptualizing this imbalance is to visualize the

features of x and y as a list with the most salient

features at the top Then salience imbalance can be

thought of as the degree of slope from features in B to

features in A, and can be characterized, to a first

approximation, by considering the combined effect of

the difference in salience between the matching

features for x and for y together with the (independent)

degree of salience in each, as in Equation (3)

Using the concept of salience imbalance, Ortony et

al (1985) classify four types of similarity into literal

similarity, metaphorical similarity (including simile),

anomalous similarity, and reversed metaphorical

similarity If the common feature salience is both high

in the target and source objects, the similarity is literal

For example, the two objects may be almost identical,

or one of the objects is obviously the explanation of

the other On the contrary, if it is both low in the target

and source objects, similarity is anomalous because such a resemblance is too trivial If the salience is high

in the source object, but low in the target object, the similarity is metaphorical In contrast, if the salience is low in the source object, but high in the target object,

it is called reversed metaphorical similarity

This classification can be represented in diagonal arrow lines from the salience ranking of source features to that of target features as shown in Table 2, developed by Wang and Liao (2009) This diagram of salience imbalance analysis allow us to (1) list as many features of the target object and the source object in salient order, respectively, (2) link the pair of two similar features by drawing an arrow line from the source feature to the similar target feature, and (3) assign the degree of similarity between the two objects

on the linking lines As the slope of these linking lines describes the degree of metaphorical similarity, this diagram is a useful tool of questionnaires for the subjects to depict their responses about metaphoricity

Table 2 Diagram for salience imbalance (adapted from

Wang and Liao, 2009)

For representing the difference between the target and source objects, an exaggerative but reasonable way to deal with the salience ranking is required This study considers the law of diminishing marginal utility to convert the salience ranking into a non-linear decreasing sequence as salience weighting There are many popular decreasing sequences, such as 1/n, 1/2

n-1, and n2 (n=1, 2, 3,…), used for ranking transform Wang and Chou (2010) compare the exaggerative effects of the three sequences and conclude that the decreasing sequence, 1/n, is superior to the others For the object x with d features, the i-th feature’s normalized salience is given as





i

w

1

) / 1 ( / ) / 1

Target object x Source object y Features

of x Salience in x Salience in y Features of y

xi Higher wi Higher wi yj

Literal

Anomaly

Metaphor Reversed Metaphor

For example, the sequence of ranking salience, 1, 1/2,

1/3, 1/4, 1/5, is normalized into the sequence of rating

salience 0.438, 0.219, 0.146, 0.109, 0.088

Furthermore, Wang and Chou (2010) propose a

practical way to determine the feature salience

imbalance of the target object x and the source object y

as

d

1

i

d 1 i

xi yi i

xi

(w

=

y)

where Si is the similarity of the i-th feature of the

target object x and the source object y Si can be

obtained, as the equation (2), but not limited to this

method

By adding two features to the data in Table 1, let

create Table 3 for demonstrating how to calculate I(x,

y) Given that we consider only features of the target

and source objects, in which only five features are

similar (Si >0), and the rest are absolutely dissimilar

(Si =0) The normalized salience values converted

from salience rankings are shown in Table 3 Thus, the

salience imbalance of the objects x and y is computed

as

I(x, y) =

(0.257×1+0.097×1+0.052×1+0.032×0.9+0.022×0.7) /

0.46 = 0.450/ 0.46 = 0.978 > 0

Table 3 Mixed variables for the target and source objects of

Anna G

Feature

Similarity S i 1 1 1 0.9 0.7 0 0

y 1 2 3 4 5 6 7

Salience: w x 129 096 077 064 055 386 193

w y 386 193 129 096 077 064 055

w y–w x .257 097 052 032 022

3.4 Metaphoricity

As previously described, the salience imbalance, I(x,

y), is practical for identifying whether or not an object

is a member of metaphorical design For a typical

metaphorical design, its salience imbalance value is

supposed to be as greater than 0 as possible Also, the

metaphoricity strength, T(x, y), of a design can be

thought as a function of the feature similarity and

domain dissimilarity between the target and the source

This study defines it as T(x, y)= (α×S(x, y)+β×D(x, y))/ (α+β) (6) where α, β are the weights for the feature similarity

and domain dissimilarity, respectively (α+β≠0)

By the definition, the design example shown in Tables 1 and 3 is a significantly typical metaphor This study calls this characteristic “saturation” The metaphoricity is extremely saturated, because the salience imbalance, I(x, y), is 0.978 Moreover, this well-saturated metaphorical design is significantly of strength, for T(x, y), is 0.895 (= (0.89+ 0.9)/ 2), given α= β= 1

4 Example and Testing

To determine the relation between the metaphoricity and creativity of designs, this study chooses five metaphorical products as the stimuli for testing, in a fashion of purposive sampling The stimuli are chosen from five international competitions: International Forum (iF) concept award; red dot Design Award- concept; Good Design Award (G-Mark); International Design Excellence Award (IDEA) and Taiwan International Design Competition- students (TID), as displayed in Table 4 Participants of this test are twenty-six industrial design students of National Taipei University of Technology

Table 4 Five stimuli for testing

Aroma Humidifier

humidifier potted

plant

G-Mark

mouse

jelly IDEA

sharpener

red dot

Pebble Eraser

eraser pebble TID

Zipper Speaker

speaker zipper iF

First, the materials shown in Table 4 are presented to

each participant He or she is requested to complete the

following stages for each design:

1 List the top-seven salient features of target and

source respectively

2 Specify the salience rankings for the seven

target features and the seven source features

respectively

3 Link up the pairs of similar features by

drawing an arrow line from the source feature

to the similar target feature

4 Put the degree of the similarity on each line

(ranging from 0 to1)

5 Determine the degree of the dissimilarity

between the target domain and source domain

(ranging from 0 to1)

6 Determine the degree of overall creativity of

this stimuli (ranging from 0 to1)

The metaphoricity of all the five stimuli is

measured by using the raw data acquired in the above

stages For convenient reason, the strength constants α

and β, and the coefficient δi for each feature are set as

1 As the space is limited, let’s merely take one of the

participant’s responses on Zipper Speaker for example

Table 5 shows how the diagram of salience

imbalance analysis is applied This participant

identifies the top-seven features of the speaker, but has

some difficulty on the sixth and seventh features of the

zipper Although only top-five features of the zipper

are listed, the normalized salience used is still based on

seven features without any difficulty, for it is

impossible to have a pair including the sixth or the

seventh features The participant then draws arrow

lines to connect the common features, and put the

similarity value of each pair of common feature on the

corresponding line The similarity between a speaker

and a zipper is thus obtained as

S(speaker, zipper)= (0.4+0.8+0.5+0.7) / 4 = 0.6

Since the participant gives the degree of the dissimilarity between the target domain and source domain, D(speaker, zipper), as 0.8 Consequently, the metaphoricity strength is obtained as

T(speaker, zipper) = (S(speaker, zipper)+D(speaker, zipper))/ 2= (0.6+0.8)/ 2= 0.7

The summation of salience imbalance differences

is computed as

(wy–wx)= (0.386–0.129)+(0.193–0.096)+(0.096– 0.055)+(0.077–0.193)= 0.279

The feature salience imbalance of the Zipper Speaker is then calculated as

I(speaker, zipper)= ((0.386–0.129)×0.4+(0.193–0.096)

×0.8+(0.096–0.055)×0.7+ (0.077–0.193)×0.5) / 0.279= 0.54 > 0

Table 6 presents results of measuring object similarity, domain dissimilarity, metaphoricity strength, salience imbalance(metaphoricity saturation), and creativity for each stimulus In general, the relation between the metaphoricity strength and the creativity is intermediately positive (r=0.65) Nevertheless, the correlation coefficient of the domain dissimilarity and the creativity is rather low (r=0.08), whereas the correlation coefficient of the object similarity and the creativity is significantly high (r=0.90)

The implications are two-fold First, this relation between is by no means a perfect linear correlation, if the measurement of domain dissimilarity is applicable The metaphoricity strength becomes much more undecided than this study predicts Having got this point firmly recognized, in our short study the weight

of the feature similarity, α, and the weight of the domain dissimilarity β should not be set to 1 Alternatively, we can turn to only consider the object similarity instead of the metaphoricity strength

Table 5 Diagram of salience imbalance analysis for Zipper Speaker

Features (Normalized) Salience (Normalized) Salience Features

Broadcast music (0.386) 1 1 Control opening/closure

Rotating-button (0.193) 2 2 Moving up and down

Control volume (0.129) 3 3 Jagged parts

On/off (0.096) 4 4 Two in one

Square box (0.077) 5 5 Pull ring

Couple (0.055) 7 7 -

0.4 0.8 0.5 0.7

Second, it might be too abstract for the participants to

learn what the target domain and source domain of an

object are Perhaps, determining the supertype of a

subtype, or the class of an object, is not as

straightforward as determining the features of the

subtype, or the features of an object In the test, a few

participants ask for clear definition or exemplars, when

they are requested to describe these two domains for

each stimulus The above two points remain to be

proved in further investigations

Table 6 Results of metaphoricity and creativity

measurements

Title

Object

Similarity

Domain Dissimilar

Metapho -ricity Strength

Metapho-ricity Saturation

Creativity

Aroma

Humidifier

0.78 0.46 0.48 0.45 0.75

Jellyclick 0.75 0.32 0.55 0.78 0.79

Pebble

Eraser

0.54 0.36 0.46 0.26 0.55

Zipper

Speaker

0.71 0.46 0.45 0.31 0.62

In contrast, the metaphoricity saturation (i.e., salience

imbalance) and the creativity have considerable

positive relation (r=0.88) The correlation coefficient

is as high as that of the object similarity and the

creativity (r=0.90) This represents that the two factors

can be used as an alternative indicator of creativity of

designs

5 Conclusion

This research has proposed a feature-based approach

to measuring metaphoricity of designs, including

measures of the object similarity, domain dissimilarity,

and salience imbalance The strength of metaphoricity

is defined as a function of feature similarity between

its target and source entities, as well as the domain

dissimilarity between the two entities The saturation

of metaphoricity is the salience imbalance of the

similar features between of its target and source

entities To test the argument, five award winners of

various well-known creativity-oriented design

competitions are accordingly presented to twenty-six

design students to assess the metaphoricity strength

and saturation, and the creativity on a subjective base

Results reveal the creativity has significantly positive relation between the object similarity and metaphoricity saturation In this sense, creative designers are those who learn how to maximize the similarity between the target and source objects, the dissimilarity between the target and source domains, and the salience imbalance, in order to create both new and meaningful solutions Nonetheless, relation between the creativity and the domain dissimilarity might not be a perfect linear correlation, which is much more uncertain than predicted The strength of metaphoricity remains to be determined in further studies The limitation of this method is that features

of target and source may hard to indicate by general participants with non-design background, and participants with different culture would evaluate metaphorical design differently To sum up, metaphoricity measures have potential to develop alternative tool for assessing the creativity of designs

Acknowledgements

We wish to thank National Science Council, Taiwan, ROC, for their generous financial assistance under Grant NSC 99-2221-E027 -084

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Interrelations between Motivation, Creativity and Emotions in Design

Thinking Processes – An Empirical Study Based on Regulatory Focus

Theory

Madeleine Kröper1,2, Doris Fay2, Tilmann Lindberg1 and Christoph Meinel1

1 Hasso Plattner Institute at University of Potsdam, Germany

2 University of Potsdam, Germany

Abstract Design thinking, here defined as a team-based

innovation method, helps to deal with complex design

problems by sustaining in-depth learning processes on

problem perception and diverse solution paths To carry out

design thinking processes successfully, motivation is a

central psychological aspect to ensure creativity of the

project outcome In this paper, we ask how motivation is

affected by the design thinking process and how it is related

to team member’s emotions throughout the process We

adopted regulatory focus theory to conceptualize

motivational variables Experience Sampling Method within

a field study with two samples was used, investigating

people’s motivation of setting and approaching goals

throughout real-life design projects that used design

thinking Results of this study show that the different phases

carried out in design thinking processes significantly impact

motivation and emotions of the members of a design team

Keywords: Design Thinking, Design Thinking Processes,

Motivation, Creativity, Emotions, Teams, Regulatory Focus

Theory

1 Introduction

In the broadest sense, design thinking refers to the

“study of cognitive processes that are manifested in

design action” (Cross, Dorst and Roozenburg, 1992)

Practitioners as well as scholars in various disciplines

have long been interested in understanding the

cognitive processes that underlie design activities

Early research trying to unravel the thought processes

in design activities studied how outstanding designers

approach problems and develop creative solution

concepts (e.g Lawson, 2006; Cross, 2007) This

research has initiated an extensive scientific discourse

on the exploration and analysis of cognitive strategies

that carry the generation, synthesis and creative

transformation of divergent knowledge within design

processes (e.g Nagai and Noguchi, 2003; Owen,

2007) Identified design strategies have been

reinterpreted as normative guidelines for design projects and creative problem solving in general (Lindberg, Noweski and Meinel, 2010) In this context, design thinking has been translated into a holistic framework moving beyond designers’ professional domains and it has since been gradually applied to various disciplines and fields of innovation

in both academia and business (Beckman and Barry, 2007; Brown, 2008; Dunne and Martin, 2006)

The fundamental principle underlying design thinking is that design problems and solutions are explored in parallel in consideration of different stakeholder perspectives (Cross, 2007; Lawson, 2006) Design problems are regarded as made up of exogenous stakeholder perspectives (the user’s, the client’s, the engineer’s, the manufacturer’s, the law-maker’s, etc.) that finally decide about the solution’s viability (Dorst, 2006) Dealing with a design problem’s complexity is therefore a matter of negotiation between different and probably conflicting perspectives, so that design processes are regarded as a

“reflective conversation with the situation” (Schön, 1983) Design thinking thus supports all activities relevant for accessing the diverse knowledge and multiple perspectives that reside in the different stakeholders in order to use them for inspiration; and it facilitates the creative transformation of the knowledge base into new concepts

The specific problem solving patterns in design thinking are rather determined by heuristic and situational reasoning than by analytical and rationalist thinking Furthermore, instead of external standards for evaluating the quality of design outcomes, design thinking asks for developing those standards within the process Therefore, design thinking assigns strong responsibility for deciding and evaluating how to proceed in a design process to the design team itself (that is what knowledge should be grasped and what concepts and designs should be elaborated) As a result, design thinking process models cannot be more

than a framework of suggestions that help design

teams to go through their own learning and creativity

processes

Against that background, we assume that team

motivation plays a decisive role in putting those

suggestions into practice We therefore seek to find out

how motivation is affected by the different phases of

the design thinking processes; this will enable us to

better understand team creativity We also explore

whether motivation and emotions in design thinking

processes are interrelated, as both concepts show

strong interdependencies (Ryan, 2007) To deal with

these questions, we draw upon a conceptionalization of

motivation offered by regulatory focus theory (Higgins,

1997; 1998) We conducted a study using the

Experience Sampling Method with design teams

Design teams adopted design thinking methodology;

they worked in two German IT companies In the

following, we present the conceptual and theoretical

foundations and develop this study’s hypotheses

1.1 Design Thinking Process Model

This study draws on a comprehensive design thinking

process model that has been formalized at the

Hasso-Plattner-School of Design at Stanford (US) and the

HPI School of Design Thinking in Potsdam

(Germany) It distinguishes six phases (Plattner,

Meinel and Weinberg, 2009): understand, in which a

design team is asked to build up general expertise

about a design problem, to identify stakeholders and

contexts of usage for further examination; observe, in

which the design team goes into the field and gathers

widespread insights and develops empathy for the

stakeholders of the design problem; synthesis/point of

view, in which the collected insights are summarized,

shared in the team, and compiled in a framework of

viewpoints on the design problem; ideate, in which –

based on the lessons learned so far – ideas and

concepts are created (for instance by brainstorming

techniques) and roughly sketched out; prototyping, in

which ideas and concepts are turned in tangible

representations allowing to generate genuine feedback

from users and other stakeholders; and test, in which

this feedback is collected and processed for further

refinements and revisions As Figure 1 shows, these

phases are not placed in a linear sequence, but are

highly iterative Therefore, the responsibility for the

decision on when to move into which phase and how

to get through an entire design process lies with the

design team The model is complemented by a set of

rules that communicates a certain mind-set towards

creative design Rules emphasize 1) the readiness to

explore seemingly odd paths as well (instead of going

rashly for the obvious things) and 2) acting generally

quickly, experimentally, and iteratively Those rules are in particular: “fail often and early”; “defer judgement” and “encourage wild ideas” (cf Osborn, 1953)

Fig 1 Iterative design thinking process (Plattner, Meinel

and Weinberg ,2009)

1.2 Regulatory Focus Theory and Creative Performance

We draw on regulatory focus theory to explore motivation in design thinking (Higgins, 1997; 1998) This theory presupposes that human motivation serves

to satisfy the two basic needs of approaching pleasure and avoiding pain (hedonic principle) The theory suggests that these desired hedonic end-states are reached through self-regulatory processes, which refer

to the processes by which people seek to align themselves with appropriate goals or standards (Crowe and Higgins, 1997) Two distinct types of regulatory systems, called promotion and prevention focus, drive this process of self-regulation The promotion focus

has a desired end-state as reference value, focusing

individuals on goals they long for and is induced by nurturance needs, ideals and rewards (gain/no-gain situations) The prevention focus, conversely, has an

undesired end-state as reference value, motivating

individuals to avoid damages or unpleasant situations This focus is induced by security needs, duties and the fear of punishment (non-loss/ loss situations) It is assumed that the promotion focus represents the “ideal self“, that is a person’s wishes, hopes, and aspirations, while the prevention focus represents the “ought self“, which includes a person’s obligations, duties, and responsibilities (Higgins, 1997) Both foci influence people’s perception, behavior, performance, and emotions (Förster and Higgins, 2005) The theory distinguishes furthermore between chronic and momentary foci Individuals differ in their chronic tendency to be promotion and prevention oriented; furthermore, signals and stimuli of any type of situation also activate the promotion and/or prevention focus (Higgins, 1998; Crowe and Higgins, 1997) Thus, process feedback, task instructions or goal framing has a significant impact on the two dimensions of regulatory focus (Idson, Liberman, and Higgins, 2004; Higgins, Shah, and Friedman, 1997)