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UNIVERSITY OF EASTERN FINLAND Faculty of Social Sciences and Business Studies Business School

MITIGATING COGNITIVE BIASES IN DECISION-MAKING BY DESIGN THINKING METHODOLOGY: DESIGNERS’ VIEWS

Master’s thesis International Business and Sales Management

Mikki Mustonen (234990) May 2017

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ABSTRACT UNIVERSITY OF EASTERN FINLAND Faculty of Social Sciences and Business Studies Master's Program in International Business and Sales Management MUSTONEN, MIKKI P.: Mitigating Cognitive Biases in Decision-making by Design Thinking methodology: Designers’ views. Kognitiivisten vinoumien lieventäminen päätöksenteossa Design Thinking -metodologialla: Suunnittelijoiden näkemyksiä Master’s Thesis: 78 p. Instructor: Professor, PhD Andreas Fürst May 2017

Key words: Decision-making, heuristics, cognitive biases, design thinking In addition to its key role in business, decision-making affects greatly our everyday lives. If you think about your life, business organizations or relationships from the point of view of decision-making, you might notice that both experience and outcome of the journey are determined by the decisions made. The decision-making, and especially the flaws in it, is the phenomenon this paper studies. As design process is also a series of decisions, I inspect this phenomenon from the perspective of design, or more precisely from the point of view of design thinking. The literature review consists of two main research fields: well-studied area of heuristics and cognitive biases in decision-making, and much less-studied field of design thinking. The intersection of these two, the issue this study views, is even more narrowly studied. Cognitive biases are referring to these errors in decision-making and judgement people are vulnerable to. Design thinking, “a way how designers think”, as a proposed remedy for these biases is the topic this thesis addresses. The approach for this study was qualitative. Ten designers were interviewed, which provided data which was treated inductively and analyzed with qualitative content analysis. Overall, the designers saw these biases as problems present in their design work, and the patterns were noticed about the means and methods they suggested to mitigate the biases discussed. The results of the analysis were compared with the previous propositions about bias mitigation by the means of design thinking. At the end of study the researcher presents his suggestions for mitigating cognitive biases in decision-making.

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TIIVISTELMÄ ITÄ-SUOMEN YLIOPISTO Yhteiskuntatieteiden ja kauppatieteiden tiedekunta Kauppatieteiden laitos International Business and Sales Management MUSTONEN, MIKKI P.: Mitigating Cognitive Biases in Decision-making by Design Thinking methodology: Designers’ views. Kognitiivisten vinoumien lieventäminen päätöksenteossa Design Thinking -metodologialla: Suunnittelijoiden näkemyksiä Pro Gradu -tutkielma: 78 s. Ohjaaja: Professori, PhD Andreas Fürst Toukokuu 2017

Avainsanat: Päätöksenteko, heuristiikat, kognitiiviset vinoumat, design thinking -metodologia Päätöksenteko on keskeinen ilmiö sekä liiketoiminnassa että jokapäiväisessä elämässämme. Voikin väittää, että tehdyt päätökset määrittävät sekä lopputulemaa että matkan aikaista kokemusta niin yritysorganisaatioissa kuin elämässä ylipäätään. Tämä tutkielma käsittelee päätöksenteossa esiintyviä kognitiivisia vinoumia ja niiden lieventämistä design thinking -metodologian näkökulmasta. Kirjallisuuskatsaus koostuu kahdesta osiosta: laajalti tutkitusta päätöksentekopsykologian tutkimuskentästä heuristiikkojen ja kognitiivisten vinoumien osalta, sekä niukasti tutkitusta design thinking -metodologiasta. Näiden kahden hyvin vähän tutkittu kohtaamispiste on aihe, jota tämä gradu käsittelee. Kognitiivisilla vinoumilla viitataan järjestelmällisesti esiintyviin inhimillisiin virheisiin päätöksenteossa. Design thinking -metodologia, eli ajatteluprosessi tai -keino, jolla suunnittelijat tunnistavat ja ratkaisevat ongelmia, on puolestaan aiemmassa kirjallisuudessa ehdotettu keino näiden mahdollisten virheiden oikaisemiseen. Data kerättiin haastattelemalla kymmentä suunnittelijaa, ja se analysoitiin käyttämällä laadullista sisällön analyysiä ja induktiivista lähestymistapaa. Kaiken kaikkiaan suunnittelijat näkivät kognitiiviset vinoumat haasteina suunnittelutyössään. Päätöksenteon parantamiseksi havaittiin keinoja, joilla suunnittelijat pyrkivät ehkäisemään tutkimuksessa käsiteltyjen kognitiivisten vinoumien negatiivisia vaikutuksia suunnitteluprosesseihinsa. Analyysin pohjalta saatuja tuloksia verrataan aiemmassa kirjallisuudessa ehdotettuihin keinoihin lieventää kyseisiä kognitiivisia vinoumia, ja tutkielman lopussa tutkija esittää omat ehdotuksensa niiden lieventämiseksi.

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TABLE OF CONTENTS 1. INTRODUCTION 7

1.1. RELEVANCE OF THE TOPIC AND BACKGROUND 7

1.2. RESEARCH GAP 9

1.3. GOAL OF THE THESIS 9

1.4. STRUCTURE OF THE THESIS 10

2. LITERATURE REVIEW 11

2.1. COGNITIVE BIASES IN DECISION-MAKING 11

2.1.1. Decision-making research 11

2.1.2. Decision-making psychology 12

2.1.3. Heuristics and cognitive biases 13

2.1.4. Mitigation of cognitive biases 14

2.1.5. Cognitive biases dealt in this study 16

2.1.5.1. Projection bias 17

2.1.5.2. Egocentric empathy gap 18

2.1.5.3. Hot/cold gap 18

2.1.5.4. Focusing illusion 18

2.1.5.5. Say/do gap 19

2.1.5.6. The planning fallacy 19

2.1.5.7. Hypothesis confirmation bias 19

2.1.5.8. The endowment effect 20

2.1.5.9. The availability bias 20

2.2. DESIGN THINKING 21

2.2.1. Design thinking as a phenomenon 21

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2.2.2. Phases and tools of the design thinking process 23

2.2.2.1. Tools used through design process 24

2.2.2.2. Stage I: Tools for data gathering about user needs 24

2.2.2.3. Stage II: Tools for idea generation 25

2.2.2.4. Stage III: Tools for testing 25

2.3. THEORETICAL FRAMEWORK 26

3. METHODOLOGY 27

3.1. RESEARCH APPROACH 27

3.2. DATA AND ITS COLLECTION 27

3.3. DATA ANALYSIS 32

4. ANALYSIS AND RESULTS 42

4.1. DESIGNERS’ VIEWS OF HOW TO MITIGATE COGNITIVE BIASES DURING THE DESIGN PROCESS 42

4.1.1. Overview of the categories appearing in the data 42

4.1.2. Designers’ suggestions to mitigate the Projection Bias 43

4.1.3. Designers’ suggestions to mitigate the Egocentric Empathy Gap 46

4.1.4. Designers’ suggestions to mitigate the Hot/Cold Gap 48

4.1.5. Designers’ suggestions to mitigate the Focusing Illusion 50

4.1.6. Designers’ suggestions to mitigate the Say/Do Gap 53

4.1.7. Designers’ suggestions to mitigate the Planning Fallacy 55

4.1.8. Designers’ suggestions to mitigate the Hypothesis Confirmation Bias 57

4.1.9. Designers’ suggestions to mitigate the Endowment Effect 59

4.1.10. Designers’ suggestions to mitigate the Availability Bias 61

4.2. SUMMARY OF RESULTS 63

5. DISCUSSION AND CONCLUSIONS 64

5.1. KEY FINDINGS AND THEIR SIGNIFICANCE 64

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5.2. ASSESSMENT, THEORETICAL IMPLICATIONS AND FUTURE RESEARCH POTENTIAL 69

5.3. PRACTICAL IMPLICATIONS 71

REFERENCES 73

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1. INTRODUCTION

1.1. RELEVANCE OF THE TOPIC AND BACKGROUND

In our daily lives we humans use various kind of heuristics to fasten our information processing

and decision-making, and often also to make it possible to come up with a decision in the

circumstances of uncertainty. However, even though more often than not those heuristics,

shortcuts for the judgement and decisions, are mostly correct and beneficial for us, at times they

cause us to believe that something false is true and make us to base decisions on flawed

understanding. Those errors in our decision-making are called cognitive biases; and

unfortunately, we humans are very prone to these systematic errors in judgement and

decision-making in situations of uncertainty (Kahneman, 2011). Tversky and Kahneman (1975)

remind that heuristics, i.e. this our tendency to jump to conclusions and judge things based on

very little information, are often quite useful: in everyday life they allow us to make decisions

fast and to be confident enough to take the actions needed. In trivial things this is mostly a

positive thing when the risks are acceptable, but it can be disastrous when the stakes are really

high. However, it is good to recall that even though this study basically discusses the flaws

caused by intuition, one should never underestimate the power of the intuition and the intuitive

interpretation based on the unconscious mind that stores most of the information out of our reach

(Freud, 1915).

So, in many situations it would be beneficial for us to find out ways how to mitigate or reduce

the flaws in our decision-making. Cognitive biases can be mitigated for example if we are able to

recognize the situations in which they are likely to occur (Tversky & Kahneman, 1975), and/or if

we are able to find other ways to reduce the effect of these biases, or the risk of falling into these

mental traps. One suggested perspective for bias reduction is presented in Liedtka’s article

(2015), in which she proposed that making use of the design thinking could help decision-makers

to mitigate the effect of cognitive biases affecting the decision-making, especially in the

situations where the innovation is goal.

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Design thinking refers to the idea of design as a thought process (Brown & Katz, 2011), i.e. the

way how designers think while trying to understand problems and solve them. Venkatesh et al.

(2012) explain it as ‘design as a state-of-mind’, which is an analytical and creative process

engaging a person to experiment, prototype, gather feedback and re-design (Razzouk & Shute,

2012). The link between design thinking and decision-making is based on the idea of viewing

design process as a series of decisions. While designing a new solution the designer continually

makes decisions (Dym et al., 2005) regarding to the problem, the needs to address, the features

or aspects to include and not to include in the solution, and so on. During this process a great

number of decisions needs to be done, and viewed from the decision-making perspective, those

decisions as a set form the creative outcome of design process. And to improve that outcome of

creative problem solving, designers have for ages created and developed methods and tools to

success even better in their task. Due this designers’ advanced way of understanding and solving

problems, the design thinking has become a hot topic within the business context, and as Brown

& Katz (2011) stated, “design has become too important to left just for designers”.

When I viewed design as an actively evolving decision-making process, I thought that I wanted

to study more how design thinking methodology could be used to use tackle the flaws in

decision-making. I reasoned that to gather understanding about the means how the thinking

process behind the curtains of design work could help us to tackle these errors in

decision-making, I need to collect data about designers’ views how to mitigate the cognitive

biases harming their design work. I had a great opportunity to interview experts of a large

Finnish IT agency, a market leader in Finland in designing and creating digital services. As great

Herbert A. Simon and William G. Chase (1973) argued after studying chess masters, the

expertise is built up in one’s long-term memory during thousands of hours of practice as “a vast

repertoire of patterns and associated plausible moves”. Because the experts I interviewed had

acquired experience in design for 5 to 20 years, I reasoned that during those thousands of hours

of practice, they have formed some interesting ways to understand the problems at hands better

and to come up with superior solutions, i.e. to end up with right decisions made during the design

process. Those ‘plausible moves’ for defining and solving problems, located in their long-term

memory, I tried to understand by interviewing them. This made it possible to search for means

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how designers are addressing these potential flaws in thinking and decision-making harming

their design process.

It would have been even better if I had an opportunity and skills to study in higher detail their

decision-making process during the design process, but my experience in the research methods

for inspecting decision-making from the perspective of psychology were not deep enough to

conduct such study. Therefore I thought that by discussing about their views and feelings about

how they handle or would handle, the possible failures in their decision-making, I could

understand if there are something that should be studied more deeply. So, this thesis is not a

study to test Liedtka’s (2015) propositions about design thinking’s impact on cognitive biases,

but rather to find out what kind of methods, tools or principles the designers I interviewed use to

tackle the cognitive biases she studied in her article. Thus, this thesis adds up into the continuum

of the discussion and hopefully raises new ideas to research in the future.

1.2. RESEARCH GAP

Mitigation of cognitive biases has been studied from many perspectives and in many fields

(described more detailed in the chapter 2.1), also from the aspect of design thinking. However,

the design thinking aspect lacks a study with data gathered from designers’ views about

mitigating cognitive biases. As Liedtka (2015) put it, her propositions about the possibilities of

design thinking in bias reduction are mere starting points for the discussions and more study

about the subject is needed. I reasoned that as a continuum to her paper about the issue, I could

contribute into this branch of decision-making research by trying to understand how

design-professionals mitigate, or would mitigate, the biases harming their work.

1.3. GOAL OF THE THESIS

My goal for this thesis is to understand how designers address the possible risks of cognitive

biases sabotaging their work. Therefore my research question is: How designers approach the

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mitigation of cognitive biases during the design process? I will reflect the findings of my study

against Liedtka’s propositions, and as a result of my master's thesis I will come up with

suggestions of how the design thinking methods, and possibly other methods also, could mitigate

cognitive biases.

1.4. STRUCTURE OF THE THESIS

In this thesis I will first review the literature regarding decision-making, more specifically about

the heuristics and cognitive biases and the means how to mitigate biases in decision-making.

After that, the quite narrowly studied field of design thinking is discussed. In the third chapter I

explain how I conducted the empirical part of this study. It should provide a reader a clear

picture of how I collected the data, how I structured it later on and which were the means used to

analyze the data to get answers for the research question. In fourth chapter I present the results of

my qualitative content analysis. In chapter five, I sum up my research and key findings and

compare them to the previous literature. Finally, I assess my study and reason how all this could

help people to make better decisions.

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2. LITERATURE REVIEW

Chapter 2 gives a reader an update about the subject; what and from which perspectives these

phenomena have been studied, and therefore clarifies the issues this thesis is addressing. The

model used for structuring the previous literature is hierarchical, i.e. the diving into a subject is

started from wider perspective and ends up with a more specific definition about the issue

discussed. This chapter aims to link individual aspects discussed into the bigger picture of this

thesis.

2.1. COGNITIVE BIASES IN DECISION-MAKING

This section present the previous decision-making psychology research from the point of view of

heuristics and cognitive biases in decision-making. First, the approaches for studying the

decision-making are briefly overviewed in the chapter 2.1.1. and the main approach within this

study is selected. In the chapter 2.1.2. the decision-making psychology is positioned in the

research field of psychology and the major approaches for studying decision-making from the

perspective of psychology are reviewed. After that the heuristics and cognitive biases are

described as a main level, and then the specific biases studied in this paper are presented. Finally,

I will introduce various aspects that previous literature has suggested as remedies for cognitive

biases. This, this chapter naturally flows from the wide perspective of studying decision-making

to the suggested mitigation strategies and methods for cognitive biases in the previous literature

within the context of cognitive psychology.

2.1.1. Decision-making research

Decision-making generally means choosing one option of many available ones (Edwards, 1954).

Even though our minds tend to do it quickly and automatically (Kahneman, 2011), the process of

making decisions is often very difficult because of uncertainty and possible conflicts (Shafir et

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al., 1993). Burthold (2009) describes decision-making as “a cognitive process of making a

selective judgement and choice”, which refers to psychological aspect of decision-making. In

addition to psychological perspective, decision-making is also an object of research in the field

of neuroscience (Eagleman, 2016). While the perspective of decision-making psychology is

more focused on what is happening in human cognition and behaviour, the decision-making

studies from the perspective of neuroscience, a branch of biology research, is trying to

understand what is actually happening within the nervous system from the point of view of

decision-making, for instance by neuroimaging the neurons of the brain while making decisions

(Sanfey et al., 2003). As a link between the neuroscience and the psychology lies

neuropsychology, which studies how our nervous system affects our cognition and behaviour by

linking the nervous activity, cognition and behaviour. All this said, this thesis views

decision-making from the point of view of the psychology.

2.1.2. Decision-making psychology

Psychology is a field of science that aims to offer us tools to answer questions such as “why we

are who we are, and why we do what we do” (Bonior, 2016). The research areas of psychology

can be sorted in many ways, and the area we are viewing in this study is decision-making

psychology from the research branch cognitive psychology. Within the field of cognitive

psychology, the decision-making can also be inspected from various perspectives, such as from

rational decision-making theories in business organization context (e.g., Kahneman & Tversky,

1979; Simon, 1959; Simon, 1979); entrepreneurial decision-making (e.g., Shepherd et al., 2015);

ethical decision-making in organizations (e.g., Jones, 1991; Loe et al., 2000); clinical

decision-making viewing the diagnosis making and decisions about treatment plans (e.g.,

Burthold, 2009; Croskerry, 2013); criminal justice system (e.g., Roberts and Murray, 2013); and

from the point of view of emotional research (e.g., Lerner et al., 2015). Also, a quite a new

branch of research is to view how making use of the data available could benefit organizations in

decision-making (e.g., Janssen et al., 2017). In this thesis, I focus on rational organizational

decision-making, and more accurately on the flaws in rational decision-making and judgement,

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called cognitive biases (Kahneman and Tversky, 1972), often occurred by our intuitive way of

thinking, acting and understanding the reality – i.e. because of the heuristics (Kahneman, 2011).

2.1.3. Heuristics and cognitive biases

Heuristic in the context of decision-making psychology refers to the mental shortcut, which is “a

simple procedure that helps us to find adequate, though often imperfect, answers to difficult

questions” (Kahneman, 2011). Because this study deals with data gathered from people

designing digital services, it is good to clarify that on the other hand in the context of

human-computer interaction the term heuristics is used in heuristic evaluations, which is a

usability to inspection method to search problems in the usability of a digital service or a product

by following a set of usability principles, i.e. heuristics (Nielsen, 1994). Thus, this study views

heuristics from the point of view of decision-making psychology, as shortcuts for our decisions.

Tversky & Kahneman (1973) recall that often cognitive heuristics, mental shortcuts allowing

humans to perform more efficient, are useful, but at times they lead to remarkable systematic

errors, cognitive biases, in thinking. They add that the heuristics are very economical and usually

effective, so the existence of heuristics is mainly a positive thing. However, at times the

consequences of relying on intuitive decision-making in the circumstances of high uncertainty

are negative and drive one to make harm-causing irrational decisions. Hallihan et al. (2012) put it

as that these cognitive heuristics are unconscious models for humans to make our information

processing more efficient, and that these heuristics cause cognitive biases, natural flaws in

human information processing making us to end up with imperfect reasoning. It is good to

remember that even though the heuristics at times cause us troubles, generally they are very

beneficial by allowing us to go through everyday situations very efficient.

So, our minds are prone to see things in the erroneous ways and often we have no idea that our

thinking is or was flawed, not even speaking about noticing and understanding the reasons

behind these errors. Croskerry (2013) stated, that because of cognitive biases, logical fallacies,

false assumptions and other reasoning failures, it seems that our daily lives are filled with flawed

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thinking. As Kahneman (2011) put it: “We can be blind to the obvious, and we are also blind to

our blindness.” The next chapter presents suggested means to mitigate the cognitive biases.

2.1.4. Mitigation of cognitive biases

Cognitive bias mitigation, also referred as debiasing, is a “procedure for eliminating biases from

cognitive strategies of a decision-maker” (Arnott, 2006). In practice the discussion around the

mitigation of cognitive biases addresses the means to reduce the effect of these flaws in thinking.

Kahneman (2011) noted that even though he has been working with this subject for decades, his

increased ability to prevent biases from happening is mostly limited to recognizing the possible

situations in which they are likely to occur. If the nobel laureate who has been working with this

subject for decades does not feel comfortable with the controlling his flaws in thinking, we

should not either.

Wilson and Brekke (1994) also underline the difficulty of avoiding biases, and the very

challenging nature of getting rid the bias when the mind is affected by false belief. Earlier

Tversky and Kahneman (1974) stated that it is possible to recognize the situations in which these

biases are likely to happen, and therefore try to make corrections to avoid these thinking errors.

Few years later they (Kahneman & Tversky, 1977) discussed about the overconfidence bias, a

bias called as ‘planning fallacy’ in this thesis, and the difficulty to overcome it. As a cure for it,

they suggested reference class forecasting, which means looking for similar past situations or

cases and predicting the future by looking at them and at their outcomes (Kahneman & Tversky,

1977). Other suggested means to mitigate biases are such as: repetitive training with educational

games designed for bias mitigation training (Dunbar et al., 2014), single training intervention

(Morewedge et al., 2015), documentation of rationale of one’s judgements (Cushing & Ahlawat,

1996), high level of metacognition, i.e. one’s awareness of his or her mental processes (Wilson

& Brekke, 1994) and seeking out objective perspectives and other viewpoints (Kahneman, 2011;

Lieberman et al., 2014).

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The contexts in which the mitigation of biases have been mostly studied are: clinical

decision-making (e.g., Croskerry et al., 2013a; Croskerry et al., 2013b), auditing (e.g., Guiral et

al., 2015; Cushing & Ahlawat, 1996), criminal justice system (e.g., Burke, 2006), the training

situations with debiasing tools such as serious games (e.g., Dunbar et al., 2014; Morewedge et al,

2015) and design (e.g. Hallihan et al., 2012; Liedtka, 2015).

Design is the context in which this study inspects cognitive biases and their mitigation. Hallihan

et al. (2012) found out in their first study, that confirmation bias, which is called in this thesis as

hypothesis confirmation bias, is present during the concept generation. The results of their

second study suggest that decision matrices are effective tools in reducing that bias while

evaluating the concept. Jeanne Liedtka in her article in 2015 chose a wider angle to approach

bias mitigation with means of design. She proposed that design thinking as an end-to-end process

could mitigate a set of cognitive biases, that are introduced in the next subchapter. Later, in the

chapter 5 the findings of this study are reflect against the propositions Liedtka (2015) presented

in her article about bias mitigation from design thinking perspective:

“Proposition 1: By insisting on the collection of deep data on customers’ concerns and

perspectives as central in the need finding stage, design thinking mitigates the effects of the

projection, egocentric empathy, focusing, and hot/cold biases.

Proposition 2: By improving decision-makers’ ability to better imagine the experiences of others

in the need finding stage, design thinking mitigates the effects of the projection, egocentric

empathy, focusing, and hot/cold biases.

Proposition 3: By insisting that innovation tasks be carried out by diverse, multifunctional

teams, design thinking mitigates the effects of the projection, egocentric empathy, focusing, and

hot/cold biases.

Proposition 4: By using qualitative methodologies and prototyping tools, design thinking

improves customers’ ability to identify and assess their own needs, mitigating the effects of the

say/do bias.

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Proposition 5: By using methods that do not rely on users’ ability to diagnose their own

preferences, design thinking mitigates the effects of the say/do gap.

Proposition 6: By teaching decision-makers how to be better hypothesis testers, design thinking

mitigates the effects of the planning fallacy, confirmation, endowment, and availability biases. It

does this by insisting that they prototype, surface unarticulated assumptions, and actively seek

disconfirming data.

Proposition 7: By insisting that decision-makers work with multiple options, design thinking

mitigates the effects of the planning fallacy, the hypothesis confirmation bias, and the

endowment effect.

Proposition 8: By insisting that decision-makers conduct and reflect on the results of

marketplace experiments, design thinking mitigates the effects of category 3 biases.”

(Liedtka, 2015)

2.1.5. Cognitive biases dealt in this study

For over half a century psychologists have conducted hundreds of empirical studies about

heuristics and cognitive biases and recognized an impressive list of various flaws in thinking

(Hilbert, 2012). As a framework for the set of cognitive biases inspected in this study from

design thinking perspective, I use the structure of Liedtka’s (2015) compilation of the set of nine

well-documented cognitive biases that she stated as biases worth exploring more deeply from the

point of view of design thinking. The nine biases she viewed are: the projection bias, the

egocentric empathy gap, the hot/cold gap, the focusing illusion, the say/do gap, the planning

fallacy, the hypothesis confirmation bias, the endowment effect, and the availability bias. Some

of these phenomena are called with different names by different researchers, such as the

endowment effect as loss aversion bias and planning fallacy optimism bias by Blumenthal-Barby

& Krieger (2015). Even though there are differences in the naming of the biases between

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researches, this thesis uses the ones Liedtka chose to use for this set of biases. Following

subchapters provide brief of the biases discussed.

Table 1: Cognitive biases discussed in Liedtka’s study.

Cognitive bias Description Innovation consequences 1. Projection bias Projection past into future Failure to generate novel ideas

2. Egocentric empathy gap

Projection of own preferences onto others Failure to generate value-creating ideas

3. Hot/cold gap Current state colors assessment of future state

Undervaluing or overvaluing ideas

4. Focusing illusion Overemphasis on particular elements Failure to generate a broad range of ideas

5. Say/do gap Inability to accurately describe own preferences

Inability to accurately articulate and assess future wants and needs

6. Planning fallacy Overoptimism Overcommitment to inferior ideas

7. Hypothesis confirmation bias

Look for confirmation of hypothesis Disconfirming data missed

8. Endowment effect

Attachment to first solutions Reduction in options considered

9. Availability bias Preference for what can be easily imagined

Undervaluing of more novel ideas

Source: Liedtka, 2015

2.1.5.1. Projection bias

Humans' tendency to predict future too confidently based on the current status of things or

knowledge is called projection bias (Loewenstein & Angner, 2003). This kind of behavior results

in future predictions that are too biased toward the present (Liedtka, 2015). This bias makes

creating new things often complicated, and at times makes it very difficult to come up with new

ideas and to find ways to solve problems more efficiently by more innovative solutions. And

because we tend to think future as a projection of the present moment, it is very natural to

intuitively believe that most things will stay same.

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2.1.5.2. Egocentric empathy gap

Just as it sounds, this bias is a phenomenon something to do with one’s ego and the impaired

ability to understand other’s perspectives. Egocentric empathy gap causes a human to

overestimate the similarity of one’s and other parties’ values and understanding of a situation

(Van Boven et al., 2000). This can cause one to come up with new ideas that serve the values of

a creator, but not the values of those who these new ideas meant to serve (Liedtka, 2015). When

one is designing a new solution for other parties to fulfill their needs and to solve their problems,

this bias is causing serious problems that need to be tackled. In the context of organizational

decision-making this bias may weaken the position of the people under the influence of the

decision made.

2.1.5.3. Hot/cold gap

Hot/Cold gap considers how the change in decision-maker’s emotional state affects the valuation

of a thing, for example how hunger increases the immediate preference for food (Loewenstein &

Angner, 2003), which, depending on the moment, often differs greatly from the stable, or

normal, preference. Liedtka (2015) argues that this phenomenon impedes the accuracy of

predicting how others will react for an idea. While designing experiences this may become an

issue if some need is overemphasized because of the designer’s current emotional state. This

could be seen for instance in putting too much emphasis on the simplicity of a solution at the

expense of content, because the person designing it happens to be tired or in hurry.

2.1.5.4. Focusing illusion

Focusing illusion makes us overvalue things under our attention at a specific moment, and at the

same time to undervalue other things, aspects, factors or ideas that are not getting our attention.

Loewenstein and Angner (2003) describes focusing illusion – also called as focalism (by Wilson

et al., 2000) – as phenomena which causes human to exaggerate one factor or aspect over the

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others, when the attention is focused on it. Kahneman (2011) describes focusing illusion in one

sentence: “Nothing in life is as important as you think it is when you are thinking about it.”

2.1.5.5. Say/do gap

Say/do gap refers to human’s low reliability to predict one’s future behaviour (Liedtka, 2015).

Fellman (1999) brings up that people often say different things that they do, which makes for

example surveys a problematic way to gather reliable data. Reliability of one’s statements is

especially low in the situation in which the person had to forecast his or her need for a new

product or new modification of a current product (Morwitz et al., 2007). In other words, people

have difficulties to say what they think, want or would do. Within the design context the say/do

gap is occurring in the mind of customer or user.

2.1.5.6. The planning fallacy

Planning fallacy has to do with our unconscious will to see future as a set of positive events.

Armor & Taylor already in 1998 stated that “people's expectations about their personal futures

are positive and often unrealistic is one of the most robust and reliable findings in the study of

the psychology of prediction” – over 200 empirical studies about unrealistic optimism had been

documented and demonstrated (Armor & Taylor, 1998). Kahneman (2011) describes the

planning fallacy as plans and forecasts that are really unrealistic and close to the best-case

scenarios, and which could been improved with consulting analytics or statistics of similar kind

of cases. In our daily lives this bias tricks our thinking towards neglecting possible negative

events and basing our plans on falsely positive image of the future.

2.1.5.7. Hypothesis confirmation bias

A hypothesis confirmation bias, called as confirmation bias by Kahneman (2011), causes people

to search for data that is in line with the beliefs they currently hold (Kahneman, 2011).

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Kahneman adds that this bias makes us uncritically accept suggestions and exaggerations of

extreme and improbable events. In their study in 1978 Snyder and Swann found out that each

participants in the study planned to test the hypotheses provided by searching for behavioral

evidence that would confirm their hypotheses. Ditto and Lopez (1992) stated that the information

consistent with a preferred conclusion is reviewed less critically than information inconsistent

with it, and thus more supportive data is needed for one to come up with a not-preferred

conclusion. This bias makes objective decision-making very difficult by driving us to neglect

disconfirming data (Liedtka, 2015).

2.1.5.8. The endowment effect

Endowing effect is a phenomena caused by loss aversion, a factor of human mind that makes us

to feel the fear of loss of $100 more intense than the hope of gaining $150 (Kahneman, 2011).

Because of the endowment effect, we feel pain of giving up things we own (Kahneman et al.,

1991). This, from the point of view of design thinking is driving us to attach to the first solution

and reduce the options considered (Liedtka, 2015). In our everyday lives this biases is present

when we unintentionally tend to hang on decisions, relationships or other things in which we

have invested time or money.

2.1.5.9. The availability bias

Availability in this context is an ease with which instances of a various category come to mind

(Tversky & Kahneman, 1975). Availability defines how large we think those categories are,

which, as an automatic thinking process, is called availability heuristic. Because the easiness to

come up with instances is not correlated with the category size in reality, it causes us to fail in

judging size of various categories and in their relation to other categories. For example one may

think his or her input in house cleaning (category 1) is larger than the spouse’s input (category

2), i.e. number of times he or she cleans the house (Kahneman, 2011). From the perspective of

designer, it is a costly mistake to overemphasize the need for a specific feature or piece of

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content, just because he or she overestimates the portion of the audience needing it. Or for

marketer the availability bias can cause difficulties while pondering the marketing message that

would create the largest impact on the audience.

2.2. DESIGN THINKING

In this chapter I introduce previous literature about design thinking from the perspective of its

ability to mitigate cognitive biases, as proposed by Liedtka (2015). Because she emphasized that

design thinking as an end-to-end process could mitigate the biases, the weight in this chapter is

to describe the nature of design thinking and phases within it, and not the specific tools seen as

included in design thinking methodology. In the next subchapter I will present the main thoughts

and characteristics about design thinking. The subchapter 2.2.2. introduces different phases of

design thinking process and the outline of tools seen central to the various phases. The purpose

of this chapter is to make reader familiar with main ideas of design thinking, the approach

chosen for studying cognitive bias mitigation, and also more familiar with the context from

which I gathered the data.

2.2.1. Design thinking as a phenomenon

There is not any generally accepted definition for design thinking yet (Liedtka, 2015). Even

though design itself has been studied for ages, the way of thinking behind it has gained its

popularity during only in recent years, mostly by practitioners. In his book “Sciences of the

Artificial” in 1969, Herbert A. Simon first time referred to design as a way of thinking. Design

thinking as a term appeared first in 1987 in a book authored by Peter Rowe (Liedtka, 2015). He

used the term mainly in the context of architectural design and did not capture its current

meaning which is used in the business environment (Liedtka, 2015). As a thought process, which

is the current way this term is now widely used, design thinking is attributed to the Californian

design company IDEO, and more specifically to its founder David Kelley and its current CEO

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Tim Brown (checked from www.ideo.com 15th the May in 2017). Brown and Katz (2011)

described the goal of design thinking is to help to improve people’s lives with services and

products that stem from insights shaped out of real-life observations. They expressed the reason

for the use of design thinking in other than design context also by stating that “design has

become too important to left just for designers”.

In practice the core idea of design thinking is that the way how designers approach the problems

and a search for the solutions, including also the methods used to design various products and

services, can be used as strategies for creative problem solving in other contexts and

environments also. One description refers design thinking as a natural human activity, a creative

and analytical process, that guides a person to experiment, create prototypes, gather feedback and

based on all that, to re-design (Razzouk & Shute, 2012). Tim Brown, the CEO of IDEO, has

defined design thinking as “a discipline that uses the designer’s sensibility and methods to match

people’s needs with what is technologically feasible and what a viable business strategy can

convert into customer value and market opportunity” (Brown, 2008). Shapira et al. (2017) put it

that design thinking extends the power of design to the ‘designer within each of us’. When it is

seen as a thought process, it is clear that design thinking is not just a set of different tools and

methods, but a way of thinking, or in other words: “design as a state-of-mind” (Venkatesh et al.,

2012).

Shapira et al. (2017) concluded that design thinking is human-centered, research-based,

optimistic, multidisciplinary and collaborative by its nature. Teal (2010) emphasizes the

non-linear nature of design thinking. He mentioned that when designing is conducted as a linear

process, the creativity may be reduced due the possible dilution of intuitiveness and variance.

That flexibility provided by non-linear nature is a needed in the process, and Razzouk & Shute

(2012) added, that the designers tend to change their goals and constraints as they design. The

flexibility of the process allows one to take risks, failure and learn during the process. Beckman

& Barry (2007) referred to this by inspecting the relationship of innovation and learning cycle,

the learning is a main driver of design. From the point of view of people and skills, the design

thinking process can be developed based on divergent thinking (Choi & Kim, 2017). When the

http://www.ideo.com/

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wanted outcome of the designing process is an improved or a totally new way to solve problem,

divergent thinking is a vital asset for filling the pool of possibilities. Choi and Kim put it that

divergent thinking provides an access to produce various alternatives for a problem.

Owen (2007) noted that creative people tend to work in two different ways, either find and

discover things or make something new. In design thinking process the both ways are present. A

person needs to discover how the users or customers behave, feel and think, and on top of that

understanding, one is able to define and solve their problems. Design thinking process has been

divided based on these main phases and the next subchapter discusses more precisely about these

stages and key tools within them.

2.2.2. Phases and tools of the design thinking process

The aim of this subchapter is not to go through all the methods and tools used by designers, but

rather to provide an outlook of design thinking methodology to make it easier to understand the

results of this study. Liedtka (2015) stated, that all the descriptions she reviewed for her study

about design thinking process emphasized the iterative cycles of the three stages she presented in

her paper. For this reason, and for the sake of consistency, as a structure for the phases of design

process and as the allocation of the design tools between these phases, I follow Liedtka’s view.

There are three phases in her model of design thinking process from practical perspective (see

the table 2 below). First phase, or stage, is the exploratory phase, in which the focus is on data

gathering to understand the users and to identify their needs to understand the problem. The

second stages is meant for idea generation, followed by third phase of prototyping and testing.

The purpose is to repeat the iterative cycle of the stages and go back-and-forth when needed, as

the design thinking process is iterative at its nature. (Liedtka, 2015)

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Table 2: Design thinking tools per stages I-III of design thinking process, as Liedtka (2015) described.

Stage I: Data gathering about used needs

Stage II: Idea generation Stage III: Testing

Ethnographic research techniques, such as: - Participant observation - Job to be done - Journey mapping

Collaborative sense-making and ideation tools

Prototyping and various testing approaches such as: - Assumption testing - Field experiments

Visualization and co-creation (used throughout the process)

2.2.2.1. Tools used through design process

Visualization refers simply to various means to express things visually. In this context it can

mean visualizing things such as ideas, services, products, processes and business models. Liedka

(2015) explains that the use of narrative or visual imagery in its different forms are at the core of

visualization. Visualization is a great method to communicate complicated things and also to get

a better overview of one’s own thoughts and ideas, and their interrelationships, about the issue in

the works.

Liedtka (2015) describes cocreation as a principle which means that emphasis is on collaborative

teams that are functionally diverse. She adds that in the context of design thinking it also

“incorporates techniques that engage users in generating, developing, and testing new ideas”.

Those techniques can be for example various workshops or self-reflecting tools to understand the

user and its problem more thoroughly.

2.2.2.2. Stage I: Tools for data gathering about user needs

This stage of data gathering includes various techniques of ethnographic research such as

observation, ‘job to be done’ (JTBD) and journey mapping (Liedtka, 2015). The core meaning

of using these tools or techniques is to understand the user or customer better and the real

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problem lying behind. Participation observation refers simply to various observation techniques

that help a designer to interpret human behaviour, in actual context preferably. The main idea in

the job to be done framework is to find out the real need, or ‘job’, behind a need or behaviour of

a human. For instance, if a person goes to a store and buys a hedge trimmer, but the real job is

not to trim trees or bushes, but rather to keep the trees and bushes in a beautiful shape and

condition. Therefore, it could be easier for the customer to get a tree that does not grow after

reaching a certain shape. In turn, journey mapping, or customer journey mapping, is a widely

used tool which helps one to understand and improve the customer experience and the flow

between touchpoints, i.e. a moments when a customer interacts with a company (Homburg et al.,

2015).

2.2.2.3. Stage II: Tools for idea generation

During this stage the designer may use various thinking and ideation tools to accelerate the

ideation and to clarify thoughts and concepts in head. Liedtka (2015) mentions that using

collaborative sense-making techniques facilitates the process of drawing insights from the

ethnographic data gathered, and helps to create a “common mind” within the team. She adds that

collaborative ideation tools, such as brainstorming and various techniques to develop concepts,

are beneficial when a team needs to create hypotheses about potential solutions and

opportunities.

2.2.2.4. Stage III: Tools for testing

The third phase is to test possible solutions with various prototyping and field experiment

techniques. Prototyping, and also the field experiments, are meant to test hypotheses i.e. current

assumptions about things such as the problem, solution or target audience (Liedtka, 2015).

Prototyping from the perspective of design thinking means rather opportunity to learn and

communicate, than just to display or test a solution (Liedtka, 2015). She adds that prototypes aim

to improve the content and accuracy of feedback of conversations. In addition to the common

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meaning for word prototype as a ‘test version’ of a product or service, prototypes can be for

example user scenarios, experience journeys and illustrations of business or service concepts that

help to communicate the idea.

2.3. THEORETICAL FRAMEWORK

Theoretical framework for this study is based on the literature reviewed above. The framework

guides the structure of the research, but the most emphasis is put on the research question. As

mentioned in the introduction chapter, the goal of this thesis is to raise new ideas in the narrowly

researched branch started by Liedtka (2015) by searching insights from data about how designers

are dealing with the cognitive biases discussed in this study. The phenomenon inspected in this

study is the mitigation of cognitive biases occurring in the decision-making by design thinking

methodology.

The main theories of this research are the prior findings and understanding about organizational

decision-making, more precisely how the heuristics at times cause flaws, i.e. cognitive biases, in

our thinking negatively affecting the decision-making and judgement processes. Against the data

gathered from designers from bias mitigation aspect, I will reflect the design thinking processes

and various parts of it. This idea was presented by Liedtka (2015) in her article “Perspective:

Linking Design Thinking with Innovation Outcomes through Cognitive Bias Reduction”.

However, the purpose of this study is not to test the propositions suggested by Liedtka, because

the research setting of this study does not allow it, and in addition to her propositions, I want to

study the data gathered from the practitioners and try to understand if there are other means how

designers mitigate these biases.

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3. METHODOLOGY

3.1. RESEARCH APPROACH

The epistemological perspective in this thesis, and in my thinking, is closest to the critical

realism, which understands the world as observable but very complex whole, about which we,

humans, create our own views and beliefs. I see the social reality of each human separately from

the observable world and its phenomena. However, I do not find it necessary to dive too deep

into this pondering, but rather to focus on the research questions and the most promising and

reachable ways how to get decent understanding of the issue at hand. Therefore I could conclude

that my philosophical aspect for conducting this research is near the pragmatic. (Saunders et al.,

2007).

This study is a qualitative research and the data is collected by semi-structured interviews. My

initial research approach is combination of inductive and deductive, because I formed the

interview questions based on the the biases that Liedtka (2015) proposed, but I am trying to

inductively find from the data the actual ways how designers try to mitigate those biases. I

analyze the data inductively because I want to give room for the new ideas to emerge (Saunders

et al., 2007). However, for the reason that I am more or less familiar with various design

methods and design thinking process, I find it impossible to be 100% objective with the data, so I

feel that the approach for analyzing it is also a mixture of inductive and deductive approach,

even though I would prefer completely inductively approach if it was possible.

3.2. DATA AND ITS COLLECTION

The fit between the group from which I collected the data and the decision-making comes from

the nature of design. In practice the outcome of design is a set of decisions which are made

during the design process (Dym et al., 2005). Because of this aspect and the propositions made

by Liedtka (2015), I found designers an interesting group to study the decision-making and its

flaws. For the reason that the main psychologists from the field of decision-making heuristics

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and cognitive biases have stated (Tversky & Kahneman, 1974; Kahneman, 2011) that humans

are able to be aware of the situations in which the biases occur, I reasoned that by discussing

with designers about those situations I could be able to gather understanding about their means to

address those possible problems.

I collected data for this study by interviewing designers from the point of view of cognitive

biases in decision-making. Saunders et al. (2007) pointed that it is might be easier to get answers

from interviewees than questionnaires, especially when the topic is interesting and relevant to

their work. They also mention that it is widely accepted fact that the interview will be most

advantageous approach to obtain data in the circumstances where the questions are complex.

Because this thesis studies the errors in decision-making, which I see as a complex issue to

discuss, I reasoned that conducting interviews would be the most suitable method to understand

how designers try to mitigate the flaws in thinking while making decisions while designing.

Saunders et al. (2007) also mentioned that semi-structured and in-depth interviews would

possibly lead the discussion into the unpredictable areas which were not on the interviewer’s

mind beforehand. I wanted to be able to ask clarifying questions if something seemed worth

exploring more, and give the opportunity for the interview to have more depth if the situation

lead to it, so the strictly structured questions were not a suitable method for data gathering. On

the other hand, I wanted to have a clear focus in the discussion to obtain data that is possible to

arrange based on the bias discussed, which would have been problematic with unstructured

interviews (Saunders et al. 2007). Therefore the structure for my interviews followed the

procedure of semi-structured interviews, which made it also possible to

I got an access to interview 10 designers from a Finnish IT company designing and building

complex data-driven digital services. Designers had various backgrounds but at the moment all

of them were working with digital services. I chose the interviewees based on the nature of their

work: designing digital services; and on the amount of experience they had: +5 years of design

experience. From table 3 you can find characteristics of each expert interviewed.

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Table 3: Gender, design experience, main role and industry of the interviewees.

Gender Design experience Main role Industry

Expert 1 Male 8 years Business Design IT / Digital services

Expert 2 Male 10 years UX Design IT / Digital services

Expert 3 Female 5 years Service Design IT / Digital services



Expert 6 Male 15 years Service Design IT / Digital services



Expert 9 Female 20 years Visual Design IT / Digital services


After weighting few alternatives how to structure my questions, I ended up to form nine

semi-structured questions, one for each cognitive bias discussed in this study that could be

mitigated by design thinking methods. As Saunders et al. (2007) guided, I grounded my

questions to the real-life experiences of my participants. Questions were also stripped off any

jargon and divided into clear specific questions, rather than longer ones, which often include two

or more questions at once. The questions were basically in the following form: “How would you

or other designers make sure, or should make sure, that ‘situation X’ will not happen during the

design process?”. The full set of the main questions is presented in the table 4.

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Table 4: The basic structure of the interviews (translated from Finnish to English)

1. In a situation where the target is to find new ways to solve a problem, how would you

and/or other designers make sure, or should make sure, that the new design does not lean too

much on the previous versions, or the things that have already been done, but rather try to find

new and innovative ways to solve it and create something totally new? (Protection bias)

2. How would you and/or other designers make sure, or should make sure, that during the

design process you do not automatically suggest that the taste and/or preferences of the target

audience is very similar or similar with your taste and preferences? (Egocentric empathy gap)

3. How would you and/or other designers make sure, or should make sure, that the current

state of your mind does not heavily guide the evaluation of the future potential of the solution

or design on the table? (Hot/cold gap)

4. How would you and/or other designers make sure, or should make sure, that you do not

invest scarce resources on only one element or aspect by the cost of other as important or even

more important ones? (Focusing illusion)

5. How would you and/or other designers make sure, or should make sure, that you do not

suggest that what the target audiences says is exactly what they mean, feel or would do?

(Say/do gap)

6. How would you and/or other designers prevent, or should prevent, the overconfidence and

unfounded optimism while evaluating the future potential of a design or an aspect of the

solution at hand? (Planning fallacy)

7. How would you and/or other designers make sure, or should make sure, that while gathering

feedback and evidence about the quality or functionality of the design you prefer, you are able

to treat both, the positive and negative signals, with same weight and attention? (Hypothesis

confirmation bias)

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8. How would you and/or other designers prevent, or should prevent, the situation from

happening, in which you ‘fall in love’ with the solution in hand that you have already worked

with, and invest too much scarce resources into it, without looking at other options enough?

(Endowment effect)

9. How would you and/or other designers make sure, or should make sure, that you give

enough weight and attention also to the options or thoughts that are difficult to imagine, so

which are metaphorically ‘quite far away as thoughts’, but you are still able to somehow get a

grip on them? (Availability bias)

Before interviews I gave a brief summary about the subject I am studying. Most of the

interviewees were familiar with the idea of cognitive biases already and all them easily got the

clue about the issue we were discussing. While asking the permission to interview the experts, I

told them the goal of my study: to understand how designers tend to mitigate cognitive biases

during their designing work. In the beginning of interviews I described the nature of questions,

which in practice meant that I told them that each question handles one bias and the purpose is to

understand how would the interviewee or other designers make sure that some unintended

situation does not happen.

Saunders et al. (2007) added that the interviewee or respondent bias is something which needs

also to be taken into the account. They defined this bias as a challenge which can make

interviewees not to answer certain questions, or only partial reveal the real picture of the

situation regarding to the issue I was studying. During my interviews this phenomenon was a

possible risk, because describing their design processes and possible failures in their work could

potentially make interviewees feel that it could lead them to ‘socially undesirable’ situation in

the eyes of other co-workers or an employer. My plan to reduce this bias was to first make sure

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that interviewees understand that the interviews will be anonymous and I will not reveal

information that could help to link answers to specific persons.

During the interviews I made lots of notes, even though I also audio-recorded the interviews. It

allowed me to write down my first reactions for their answers. I thought that during the interview

situation my reactions and the ideas emerged will be different than the ones emerging while

reading the transcribes. In addition to the actual transcribed interviews, I included contextual

data, such as background information of the respondent and the interview settings, and also the

notes within the data. As a result of interviews, I had 5 hours and 13 minutes of audio data, each

interview lasting from 18 to 42 minutes. After transcribing the audio data, I had 65 pages of

transcribed data (font Times New Roman / font size 12).

3.3. DATA ANALYSIS

In this thesis, the analysis method is qualitative content analysis. Because the objective of this

thesis is to understand various, not predetermined, ways how designers are addressing the flaws

in human thinking while designing, I decided to approach the data inductively. For the

comparison; inductive approach allows new insights to arise from the data, while on the other

hand by analyzing data deductively the target for the search is locked on the predetermined

factors (Saunders et al., 2007). I coded the data by a common data-driven strategy for qualitative

content analysis; by adapting coding from grounded theory (Schreier, 2012). Schreier explains

that this open coding strategy is great for discovering concepts in data, which was the main

requirement the coding strategy: it needed to make it possible to find out unpredictable ways

how designers are addressing the mitigation of the biases discussed. She continues that the open

coding, which is used in this thesis, starts with conceptualizing the data, followed by defining the

categories, and finally by developing the categories to achieve hierarchical order between (main)

categories and subcategories.

In practice, this meant that I coded the data using open coding method. Before getting into

coding, I read the data several times to gather a good overview and understanding of the data, as

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Schreier (2012) and Saunders et al. (2007) suggested. The problematic issue in my inductive

approach was that due my interests and work I do, I am familiar with design thinking and

designing methods, so I needed to put extra effort to not to lean too much towards design

thinking methodology while coding. Basically it meant that every time I added a code similar to

the common designing method, I double checked if the interviewee was really talking about that,

or did I just wrote the familiar and easy to imagine option as a code.

The conceptualizing phase, i.e. the first round of my coding, formed 390 data-based codes. Of

the total number of 390 codes, 283 were unique. Next I read my data through again two times

and checked that I had been systematic with codes. I had to make some fixes to my codes, such

as changing a code “prototyping” to “testing” if the interviewee did not talk exactly about

creating a prototype and testing it, but rather just about testing a released solution.

After first round of coding, I started to define categories for the codes to end up with a number of

categories that are manageable from the point of view of analyzing the data. Even though the

design thinking and the way how designers work had central position in my study, I did not

categorize the codes based on the design thinking phases or methodology, but from the point of

view of general utilization. In practice it meant that the design thinking methodology limited in

no ways the formulation of categories. After many rounds of reshaping the subcategories I had

30 subcategories specified in the table 5.

Table 5: Subcategories for the codes (A-Z order)

Analytics

Audience’s self reflecting tools

Back-and-forth process

Benchmarking

Changing fresh designers

Decentralized decision-making

Documenting the decisions

Exposure to new things

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Feedback from peers

Frameworks for thinking

Future focus

Improving the self-awareness

Instantly writing down ideas

Interviewing

Iterative development process

Loose schedule

Multi-disciplinary teams

MVP-principles

Necessity of multiple options

Observing

Project management tools

Prototyping

Removing restrictions

Secondary research

Sense-making tools

Structured decision-making

Surveys

Testing

Visualization

Working in teams

After I had a good set of (sub)categories, I started the category developing phase (Schreier,

2012) to form main categories out of the subcategories. I went through number of different

categorizing options. I forced me to use number of different point of views for finding

commonalities between the subcategories to be able to openly view the data. This helped me to

35

weigh many ways to link different subcategories within each others. After few rounds of ideation

and categorizing, I created main categories based on the idea on what aspect the decision-maker

should focus on if willing to mitigate the harm caused by the cognitive bias discussed. The main

categories for my analyzing shaped into the following six forms: understanding the audience,

project management, decision-making principles, thinking and ideation tools, teamwork and

self-development. The naming and hierarchy does not follow the form of design thinking

methodology, because I wanted to see if I am able to find something new around the Liedtka’s

(2015) propositions. So, from 283 initial data-based unique codes I created 30 subcategories,

which ended up under six (6) main categories in the hierarchy of categories, as seen in table 6.

Table 6: Hierarchy of subcategories and main categories

Subcategories (30) → Main categories (6)


Decision-making principles Decentralized decision-making


Structured decision-making

Exposure to new things Self-development


Feedback from peers

Teamwork Multi-disciplinary teams

Working in teams

Analytics

Understanding the audience

Observing

Prototyping

Secondary research

Surveys

Testing


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Interviewing

Benchmarking

Thinking and ideation tools


Future focus



Sense-making tools

Visualization

Back-and-forth process

Project management


MVP-principles

Loose schedule



In the table 7 are example citations for each subcategory.

Table 7: Categories, subcategories and citations from transcribed interviews

Category Subcategory Example citation

Decision- making principles


Interviewee 1: “..That you have to make up more than one option. Very often the first sit-down session is that we shoot all our ideas into the paper, so we get our heads clean of those. That’s because often those first ideas are very likely those very obvious ones.”

Decentralized decision- making

Interviewee 3: “..Well I think that you should increase the number of people making decision. So that you get more people evaluating the decision and making the call.”

37


Interviewee 1: “..One should strive for a situation in which the design decisions are documented. In a way that ‘why this was done and what is the hypothesis behind it’. Then you are later able to return into that and think again why this kind of decision has been made. ”

Structured decision- making

Interviewee 3: “It would be good to keep decicion-making criterias in your mind, or reflect the decisions into the pre-defined goals and create a structured decision out of that process.”

Self- development

Exposure to new things

Interviewee 9: “You have to listen things and be interested to explore new things so that you can come up with your own ideas.”


Interviewee 6: “..all things like that are practically based on self-awareness. That of course your mental state affect in those things, but if a person is enough aware of self, he can understand the underlying reasons for those things or states of mind; is it a feeling that is affecting right here, or is it a fact that is guiding our decision.”

Teamwork Feedback from peers

Interviewee 9: “..I think it is the herd (a design team in this context) that makes you learn… you won’t develop if you just decide by yourself and love your ideas. Very often I fall in love with something, and then a peer says that, ‘hey, that is really nice and cool, but this is not the place to use it so let’s use it later, okay?’. You really need some another person to review your stuff. For example you never should do your CV or portfolio by yourself. “

Multi- disciplinary teams

Interviewee 8: “..when there is something concrete to discuss about, the next step is to start pondering new sides of that solution or a thing in a multi-disciplinary team. The wider is the range of skills and knowledge who is designing that, the more likely is that the group is able to come up with

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optional point of views. In a way that ‘hey has anyone thought that how about this and that side in this solution are we going to arrange?’ I mean that as much as possible diversity should be involved in the process.”

Working in teams

Interviewee 6: “I would say that if the team is really small, there is a huge risk that a hypomanic designer drives whole team straight to the wall. Often in a team there are many kind of persons and it is good to cast as wide range of personalities as possible … so that there are variety of personalities in early enough in the process so you can reflect your own thoughts based on that.”


Analytics Interviewee 2: “..if you can test them and reveal the results based on numbers that which works and which does not.”

Observing Interviewee 8: “..basic observation and a kind of working habit observations which allows to come up with realizations and spot moments in which there would be need for new kind of doing things.”

Prototyping Interviewee 9: “..and rather use prototypes. If you just ask ‘would you want this?’ of course they say yes, but in reality would they use it? If you don’t have a prototype, they cannot give you a real answer.”

Secondary research

Interviewee 1: “I read a lot about what that company or employees write on internet. And of course the annual report needs to be read through so you know what they have on the table in a big picture.”

Surveys Interviewee 5: “... via email a short survey or just go to a street or shopping mall to ask people about it..”

Testing Interviewee 3: “..And testing the solution of course.

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If it is possible to test with larger audience, that is at least a good way to shoot down poor solutions...”


Interviewee 8: “..or different kind of methods which allows the members of audience to report their own daily-lives during a specific time span … designers should prefer methods allowing them to ‘get under the skin’ of a person who is observated”

Interviewing Interviewee 9: “... end-users needs to be interviewed. Sometimes they do now know, only think they know … and the group interviewed should as diverse and large as possible.”

Thinking and ideation tools

Benchmarking Interviewee 5: “... you could go through some other things, like what similar things have been done, or through some very different kind of solutions.”


Interviewee 6: “One example is that you involve the users and business, and then try to forget totally the platform on what you are designing for, and decide it later.”

Future focus Interviewee 10: “..so we go through things like ‘how this could make your life easier’, and NOT discussing based on first looking the old solution and asking the things which were not working in that… it kind of guides you to think away from the current solution.”


Interviewee 6: “... if you get some good stuff, it is good to immediately write them down somewhere, in phone for example. At times these kind of thoughts or ideas appear, and they also tend to disappear.


Interviewee 1: “And of course this kind of thing which is used sometimes even accidentally, is making use of randomness in ideation. In a way that you just pick some really random stuff and force yourself to think from that point of view. This helps

40

you to trick your thinking process when you your mind to do some really weird things.”

Sense- making tools

Interviewee 8: “.. all kind of flowcharts which helps you think how to move between elements of service … building service journeys to help you identify critical touchpoints … also visualizing and drawing different kind of blueprints helps you to understand the whole picture of the situation and gives you some perspective..”

Visualization Interviewee 1: “I believe that a hand is like a continuous extension for your thinking. Always just write and sketch ideas when they appear.”

Project management

Back-and- forth process

Interviewee 4: “... I have noticed that at times it is good to let your work rest for few days and then look it again. Then you maybe notice something from fresh perspectives colored with new ideas.”


Interviewee 2: “Pilots and iterative development process prevents the situation in which the final solution is filled with biased thoughts.”

MVP- principles

Interviewee 10: “I think that kind of MVP-thinking helps when you have some service with many layers or parts. So you do not keep on twitching some parts, but rather create a whole pipe through the service so you create a kind of minimun version of the service.”

Loose schedule

Interviewee 1: “..if possible, you should not plan a project in a way that you have to create it in one session, but rather in a way that you will have some time between the sessions. It helps you the develop the idea in your head during the process. You have opportunity to forget the whole thing and them look it again more critically.”

41


Interviewee 2: “..by prioritising things. You need to have a clear picture about which things are important in this project or service. Based on that you should rank them and decide how to use your time on them.”


Interviewee 7: “I would maybe assign totally new guys to do it. I would not go through very specifically about what has been done and what there is at the moment, but rather to give them brief about the future, or about the current challenges of the users…”

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4. ANALYSIS AND RESULTS

4.1. DESIGNERS’ VIEWS OF HOW TO MITIGATE COGNITIVE BIASES DURING THE DESIGN PROCESS

In this section I go through the findings raised during many rounds of analyzing the data

gathered from interviews. As mentioned before, the method for approaching the data was more

inductive than deductive. Therefore, many of the findings about the ways to improve the quality

of decision-making is not from the field of design studies, nor design thinking. This made the

process less straight-forward, but I believe it was a good trade-off for the fresher results I

managed to come up with. This chapter is divided into chapters about each cognitive bias studied

in this thesis. The quotations from the interviewees are used to increase the reliability of results.

This chapter is doing its best to answer my research question: How designers approach the

mitigation of cognitive biases during the design process?

4.1.1. Overview of the categories appearing in the data

In this section I introduce the big picture of different weights of the categories emerged from the

data, divided into nine sections based on the bias discussed. The table 8 provides an overview of

the appearance of the categories in the data. Each category contains from two to eight different

instruments or principles, i.e. subcategories. As mentioned in the chapter 3, the categories are

sorted based on the nature and purpose of the instruments. As the table 8 shows, some of the

biases were addressed by interviewees by various methods or principles. For example the

methods suggested for mitigating Hot/cold gap were very colorful; each main category were

present in 30-50% of interviews. On the other hand, when discussing about ways to address the

Say/do gap, every interviewee mentioned methods from the customer understanding category,

and only mention emerged from other categories altogether.

43

Table 8: Overview of the categories emerged from the data per each data section

Self-development

Project managem

ent

Teamwork

Thinking and

ideation tools

Decision-making

principles


In total

Projection bias 3 2 3 5 0 7 20

Egocentric empathy gap

5 0 0 3 0 10 18

Hot/cold gap 3 3 2 2 3 5 18

Focusing illusion 1 8 3 3 0 2 15

Say/do gap 0 1 0 0 0 10 11

Planning fallacy 3 5 5 2 1 5 21

Hypothesis confirmation bias

3 0 8 4 2 3 20

Endowment effect 0 5 6 1 7 4 23

Availability bias 1 1 6 7 0 1 16

In total 19 25 33 27 13 47 162

4.1.2. Designers’ suggestions to mitigate the Projection Bias

The aim of this section is to answer the question: “By which means designers mitigate the

Projection bias during the design process?” The projection bias is a error in thinking, which

practically makes people to project the past and current state of things into the future. On other

words, it impedes the development of novel ideas by projecting one’s past into the attempts to

imagine the future (Liedtka, 2015). In the context of design, it makes it more difficult to come up

with new ideas for solving problems, because the mind is prone to replicate familiar things.

The answers from the interviewees were very colorful, and the most common categories

appearing in this section of data were ‘understanding the audience’ (70% of respondents) and

‘thinking and ideation tools’ (50%). The rest of categories were equally present (20-30%), except

44

of the ‘decision-making principles’, which was totally absent in the data dealing with projection

bias.

Table 9: Categories emerged from the data dealing with Projection Bias

Self-development

Project management

Teamwork Thinking and Ideation

tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes Yes

Expert 2 Yes

Expert 3 Yes

Expert 4 Yes Yes

Expert 5 Yes Yes Yes

Expert 6 Yes Yes


Expert 8 Yes

Expert 9 Yes Yes


In total 3 2 3 5 0 7

The most common subcategory in this data section was the ‘interviewing’, a subcategory of a

main category ‘understanding the audience’. Half of the interviewees mentioned interviewing as

a good way to prevent from projecting the current state of things into the future. And interesting

mixture was especially conducting interviews from the point of view of future. In a way that

guide the discussion into the future while interviewing the users or customers. These two

quotations held in the both subcategories; ‘interviewing’ and ‘future focus’:

45

“Well maybe in that situation one should attempt to make more interviews and try to

forget the current state of things. You just have to get over the current way of doing

things. (Interviewee 7)

“..so we go through (while interviewing users) things like ‘how this could make your life

easier’, and NOT discussing based on first looking the old solution and asking the things

which were not working in that… it kind of guides you to think away from the current

solution. Quite often just adjusting the way how you set your questions can help you to

guide interviewees not to discuss about the current state but imagine the future.”

(Interviewee 10)

‘Benchmarking’ and using various ‘frameworks for thinking’ were also methods that were used

by designers in terms to prevent projection bias from harming the design process. Benchmarking

was used to find new ideas from fresh perspectives from other industries and various thinking

models and frameworks were tools that helped designers to get out of the common thinking zone

and start playing with ideas and one’s own mind. Both of the instruments are subcategories of

the main category ‘thinking and ideation tools’.

“And of course this kind of thing which is used sometimes even accidentally, is making

use of randomness in ideation. In a way that you just pick some really random stuff and

force yourself to think from that point of view. This helps you to trick your thinking

process when you your mind to do some really weird things.” (Interviewee 1)

“I use a lot of benchmarking, and not just from the same industry but rather try to think

things from new perspectives. For instance if I design for the hotel industry, I do not look

what the competitors are doing, but rather how, for example, a car dealer is handling this

issue. Or how they are taking care of a washing service or something.” (Interviewee 5)

Also working and ideation in ‘multi-disciplinary teams’ arose in the discussions for a one

method to not to project the present into the future.

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“Multi-disciplinary designing could help in that. In a way that there are not only

designers around the table. It is the best way to go if there are people from various

functions and tasks working together around the same table.“ (Interviewee 4)

4.1.3. Designers’ suggestions to mitigate the Egocentric Empathy Gap

This section is introducing the methods the designers’ suggested for mitigating the Egocentric

Empathy Gap. This bias, egocentric empathy gap, makes it more difficult to be sensitive to the

other people’s needs and preferences by making us humans to overestimate the similarity of our

tastes, i.e. preferences, and the tastes of the other people.

Experts suggested instruments from three category to help to tackle egocentric empathy gap. All

of them mentioned tools or principles from the ‘understanding the audience’ category, half

described also the methods in ‘self-development’ category and 30% of interviewees came up

with means within the ‘thinking and ideation tools’ category as a cure for this bias.

Table 10: Instrument categories emerged from the data dealing with Egocentric Empathy gap

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes Yes

Expert 2 Yes

Expert 3 Yes Yes


Expert 5 Yes Yes

Expert 6 Yes Yes

Expert 7 Yes

Expert 8 Yes

47

Expert 9 Yes Yes

Expert 10 Yes Yes

In total 5 0 0 3 0 10

Subcategory ‘interviewing’ of the category ‘understanding the audience’ was the most common

instrument mentioned in this data section. Most of interviewees emphasized that conducting the

interviews is a must for understanding the audience.

“...user-centered designing is the thing. That you interview, interview and interview

before you even start to design. And when you start designing you continually gather

feedback. Starting from the very first rough sketches, one should go to discuss with end

users.” (Interviewee 7)

The second most common subcategory to emerge in this part of data was ‘improving the

self-awareness” of a category ‘self-development’. Half of the answers in a way or another made

it clear that while designing one needs to be very aware of self and one’s role in the process.

“I try to externalize myself away from the situation. I mean that I attempt consciously

NOT to think how I would do this. This is the basic point. I try to avoid using phrases like

‘I think this should be done like this’ as arguments while thinking about the solution.”

(Interviewee 1)

‘Analytics’ about users’ the real usage of digital services was also a point which was mentioned

as a method to mitigate this bias. In this context analytics basically means the quantitative data,

such as user behaviour within an app or a webpage.

“Some real statistics, not just beliefs. And then to make sure that the design is based on

the real statistics… Often when you ask something from the target audience, it can be

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totally different than the reality … One should based everything on the real data.”

(Interviewee 3)

One answer contained both the ‘improving the self-awareness’ and the ‘analytics’ subcategories.

“In a live service analytics etc cursor-statistics and all like that are good for this.

Designer should never assume that the service will be used like him/her. It is often the

wrong way. We need to accept that people won’t use it in the same way. (Interviewee 4)

4.1.4. Designers’ suggestions to mitigate the Hot/Cold Gap

This subchapter explains how the designers in my sample tend to mitigate the cognitive bias

called hot/cold gap. This flaw in our thinking very powerfully colors our evaluations about the

issue while being in various states of minds. A good example of this bias is a study in which

people systematically evaluated their happiness to be much higher if they ‘happen’ to found a

coin, which was purposely set in place by researcher, just before taking part into the happiness

study. They were ‘feeling lucky’, which colored very strongly and systematically their image of

their own happiness. In this context this bias may drive designers to emphasize wrong aspects

and over- or underestimate the potential of different solutions, depending on the current state of

the mind.

The interviewees mentioned quite colorful range of various possibilities which they use, or

would use, to mitigate the hot/cold gap. Not really any category dominated in this section of

data.

Table 11: Categories emerged from the data dealing with Hot/Cold gap

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience


49

Expert 2 Yes Yes

Expert 3 Yes Yes

Expert 4 Yes Yes


Expert 6 Yes

Expert 7 Yes

Expert 8 Yes Yes

Expert 9 Yes

Expert 10 Yes

In total 3 3 2 2 3 5

The instruments in the category ‘decision-making principles’ were seen by some as quite

remarkable way to mitigate the cognitive biases occurring during the design process. The

‘structured decision-making’ and ‘documenting the decisions’ were methods present in this piece

of data handling the hot/cold gap. Also methods ‘loose schedule’ and ‘back-and-forth process’ of

the ‘project management’ category were mentioned in many answers.

In one answer three subcategories were present; ‘documenting the decisions’, ‘loose schedule’

and ‘back-and-forth process’:

“..One should strive for a situation in which the design decisions are documented. In a

way that ‘why this was done and what is the hypothesis behind it’. Then you are later

able to return into that and think again why this kind of decision has been made … this

depends a lot on the case; is there time to test things and think? Or is it a case in which

everything needs to be done immediately with hell of a hurry. ” (Interviewee 1)

A decision-making principle ‘structured decision-making’ was mentioned few times in this set of

data. In this quotation it meant criterias which are used for reflecting decisions against them.

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“It would be good to keep decision-making criterias in your mind, or reflect the decisions

into the pre-defined goals and create a structured decision out of that process.”

(Interviewee 3)

Back-forth-process was also suggested for the cure for hot/cold gap.

“... I have noticed that at times it is good to let your work rest for few days and then look

it again. Then you maybe notice something from fresh perspectives colored with new

ideas. … Maybe like to notice things like ‘am I too excited about this’ so maybe one

should look it again after some time has passed. Or ‘am I just so pissed off about this that

I should re-think this later on.’ etc. ” (Interviewee 4)

4.1.5. Designers’ suggestions to mitigate the Focusing Illusion

In this subchapter the main findings according to focusing illusion are presented. The focusing

illusion, or focalism, is also a phenomena studied in the field of psychology. This flaw in

thinking processes causes human to overestimate one aspect or factor at the expense of the others

(Liedtka, 2015). In practice it guides us, humans, to focus too much in one detail or only few

details while not giving enough attention to the other parts of the whole. In daily lives this bias is

responsible for making people to waste their time in the things with minor importance or

effectiveness.

The most common category in this set of data about focusing illusion was the ‘project

management’ with the occurrence of 80% of the interviews. Also the ‘teamwork’ and the

‘thinking and ideation tools’ were categories with the highest presence. In few answers it became

clear that that this bias is occurring quite often actually.

“When you are working with some project this is always quite difficult, because you can

not avoid of getting excited of some aspect…” (Interviewee 1)

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“Often this happens easily when we work together with the customer. They might have a

view that some aspect is really important, and then we easily get stuck polishing it up.”

(Interviewee 3)

Table 12: Categories that emerged from the data dealing with Focusing Illusion

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes Yes

Expert 2 Yes

Expert 3 Yes


Expert 5 Yes

Expert 6 Yes Yes

Expert 7 Yes Yes

Expert 8 Yes Yes

Expert 9 Yes Yes

Expert 10 Yes

In total 1 8 3 3 0 2

Of the ‘project management’ category, the most common subcategories were various ‘project

management tools’, such as prioritizing, todo-lists and time management.

“Basic todo-list, even a simple one. And to prioritize things within it...” (Interviewee 5)

“That you have deadlines and you need to get something out on some specific date. That

helps a lot to streamline things.” (Interviewee 3)

52

From the ‘project management category’ also, the ‘MVP-principles’, referring to minimum

viable product (MVP), was mentioned often. In practice those MVP-principles mean that one

should not rush and create the full solution immediately, but rather first to make a one with only

the most crucial features or aspects to please the user or customer, and test how that

hypothesis-based minimum viable solution really performs in the market (Ries, 2011).

“And it is just that this so called minimum solution thinking guides one to not to focus too

much in some details.” (Interviewee 3)

“I think that kind of MVP-thinking helps when you have some service with many layers

or parts. So you do not keep on twitching some parts, but rather create a whole pipe

through the service so you create a kind of minimum version of the service. By doing that

you kinda avoid the situation in which you get stuck to tweaking some menu icon when

you have the whole pipeline though the service on your design table.” (Interviewee 10)

Teamwork was also seen as a cure for this bias.

“If you work in some design team or as a pair with other designer, one is in responsible

role. The accountable one takes care that we do right things. In practical level, both are

responsible and one immediately informs other of it is noticed that there has been focused

on wrong things.” (Interviewee 1)

The subcategory of ‘sense-making tools’ of the ‘thinking and ideation tools’ main category got

mentioned by three experts. In the answer below the ‘sense-making tools’ was combined with

the ‘visualization’.

“..and kinda draw in your mind, or with some tool, some effects about how solving some

thing affects the other thing, and what needs to be done before it is able to move forward

to handle some issue. These are like some kind of frameworks for thinking which needs to

be flexible because things affect each others and schedules change and there are those

things that you can affect, and also those things which you can not affect. And those

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things are really important to make clear to yourself during the design process.”

(Interviewee 6)

4.1.6. Designers’ suggestions to mitigate the Say/Do Gap

This sections clarifies the data gathered from the data about the say/do gap. Say/do gap simply

refers to the difference between what people say about their actions, and how they really act. The

distribution of the categories in this set of data was very heavily weighted on the ‘understanding

the audience’ category.

Table 13: Categories that emerged from the data dealing with Say/Do Gap

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes

Expert 2 Yes

Expert 3 Yes

Expert 4 Yes

Expert 5 Yes Yes

Expert 6 Yes

Expert 7 Yes

Expert 8 Yes

Expert 9 Yes

Expert 10 Yes

In total 0 1 0 0 0 10

54

Over the half of interviewees suggested methods referring to the subcategory ‘analytics’ as a

remedy for this bias.

“Well in this case the quantitative data gets more important, because often people really

say one thing and do another. So different tools for that which measure how people

behave in reality. In online-services and digital services you can easily find the tools to

see what people really do. In this case I would emphasize the significance of the

quantitative data.” (Interviewee 7)

The second most common subcategory with appearance of four in the interviews was

‘observing’, which in practice means observing the users or customers, preferably in their natural

environment. It might mean observing the usage of some product, or the user during the service

process, or even observing how people act in their daily lives in the work or home for instance.

In the next quotation the expert 3 emphasizes the in-context observation within the environment

she had been designing for.

“The customer observations are really central in this. So you go and do things also by

yourself. In one project in noticed that I should had gone immediately to the customer site

and do those things by myself. Then I later thought that damn, I should have done this

much earlier…. So I really mean that the designer has to go there and do those things

also.” (Interviewee 3)

In some answers the ‘analytics’ and the ‘observing’ were both suggested. They were seen as

complementary remedies for tackling the harms caused by say/do gap.

“...all kind of data and analytics, I mean what the market reveals about the behaviour of

humans. For example what the web analytics tell about the stage in which happens the

conversion or in which the people go to another site. Kinda all of the data-driven design,

so we would have the real data on which we can base our understanding about how

people behave, and with the empathic research methods we can deepen the information

with the aspect of why the people behave like that.” (Interviewee 8)

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4.1.7. Designers’ suggestions to mitigate the Planning Fallacy

This section handles my interpretation of the data regarding the planning fallacy. This chapter

aims to answer the question “How designers try, or would try, to mitigate the harm caused by

the planning fallacy in their designing work?”. The planning fallacy, an enjoyable trap to fall

into, is caused basically by the overconfidence and unrealistic and really rosy picture of the

future, which make people to systematically view the future as a series of mostly positive events,

even though they describe the past as a balanced set of negative and positive events (Armor &

Taylor, 2015). In this set of data, the three most common categories were ‘project management’,

‘teamwork’, and ‘understanding the audience’. Some interviewees saw this bias as a quite tricky

one and important issue to discuss.

“This bias is really really often within the designers and the customers who we are

designing for … This occurs very often as a phenomena, and it is really dangerous.”

(Interviewee 3)

“That is a really good question. I would say that if the team is really small, there is a

huge risk that a hypomanic designer drives whole team straight to the wall.”

(Interviewee 6)

Table 14: Categories that emerged from the data dealing with Planning Fallacy

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes Yes Yes Yes

Expert 2 Yes Yes


Expert 4 Yes

Expert 5 Yes

Expert 6 Yes Yes

Expert 7 Yes Yes

56


Expert 9 Yes

Expert 10 Yes Yes

In total 3 5 5 2 1 5

The subcategory ‘MVP-principles’ of the ‘project management’ category was among the most

common ones in this data section. Interviewees saw that building the minimum viable product or

solution and test it in market or with real users or customers help to straighten possible over-rosy

views of the solution or idea at hand.

“This has to do with the project model. … MVP-principles make it easier. At first some

kind of hypothesis is formed, for instance in the project X we thought we needed a feature

Y… Then we created the first light version which helped us to validate it in a way that

‘yea this is a thing that they really need and they want to use it’... ” (Interviewee 2)

The subcategory ‘analytics’ of the category ‘understanding the audience’ was also suggested as a

good option to tackle planning fallacy.

“Analytics is a really good tool for this … if a solution is used by users or customers; and

because we have integrated some analytics tool, for example Google Analytics, into it;

we can monitor the real usage and see if it really provides any value to someone.”

(Interviewee 10)

Working with other people and gathering ‘feedback from peers’ were also seen as a remedy for

mitigating the planning fallacy.

“One should lay his vision open to criticism of other experts, also internally and

externally with customers.” (Interviewee 1)

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4.1.8. Designers’ suggestions to mitigate the Hypothesis Confirmation Bias

Here are presented the results from analyzing the piece of data dealing with the designers’ means

to mitigate the hypothesis confirmation bias. Hypothesis confirmation bias causes

decision-makers to systematically seek for confirmative data for their preferred alternatives

(Liedtka, 2015). This bias causes us to unconsciously come up with favorable explanations that

support our point of view of an issue, or arguments that are leaned towards the alternatives that

we, for one reason or another, prefer. The data revealed that the experts in my data sample raised

possible remedies mostly from the ‘teamwork’ category. Also the ‘thinking and ideation tools’

and ‘self-development’ were ones that were quite common categories in the interviews.

Table 15: Categories that emerged from the data dealing with Hypothesis Confirmation Bias

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience




Expert 4 Yes

Expert 5 Yes Yes

Expert 6 Yes Yes

Expert 7 Yes

Expert 8 Yes Yes

Expert 9 Yes

Expert 10 Yes

In total 3 0 8 4 2 3

58

The most common subcategories in this part of data were ‘working in teams’ and ‘feedback from

peers’, both from the ‘teamwork’ category. Interviewees suggested that by gathering feedback

and working with other people, the risk of falling in this cognitive trap could be mitigated.

“In this it helps also if there are more people. Within the team or then ask external

feedback from colleagues.” (Interviewee 1)

“.. just go and ask feedback from very different kind of people. That is at times difficult

for designer to reveal what he or she has done, but one just needs to be brave enough to

ask the feedback.” (Interviewee 5)

“...it is the team and and sparring. And it does not even need to be a team, it can be a

mentor also. Just need to have someone who can spar you.” (Interviewee 9)

Ideas regarding the ‘improving self-awareness’ subcategory emerged also quite often from this

data.

“In principle the designer should be able to do it; the mindset needs to be kind of a type

that one understands that ‘this is only my opinion, I do not know anything about this, this

needs to be tested’. It should make it already.” (Interviewee 2)

Various ‘frameworks for thinking’, including different games to play with one’s mind were also

present in this section of data.

“.. to force the mind to kinda turn around the thought in your head to new positions and

perspectives. Consciously look for positive aspects of an idea, and consciously search for

a possible show-stopper. I mean, like consciously change the mindset to bomb the idea

from various angles.” (Interviewee 1)

“... to write imaginary negative sensational headlines for newspapers that will published

one year in the future, if we continue like this. I mean like try to imagine and come up

with something really negative that has been happened because we acted as we

planned.” (Interviewee 5)

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4.1.9. Designers’ suggestions to mitigate the Endowment Effect

In this subchapter the results regarding the endowment effect are presented. The endowment

effect is a phenomena which causes humans systematically feel more pain for losing something

they already have, compared to the pleasure of getting something new in their hands. ‘Loss

aversion’ makes it more painful to give up something in hand than getting a new solution

(Kahneman, 2011). In this context it could mean, for example, a solution, an idea or an iteration

of a design. In practice this makes decision-makers to become attached to options they have been

working with or to something they already have. The three most common categories in this piece

of data were ‘decision-making principles’, ‘teamwork’ and ‘project management’.

Table 16: Categories that emerged from the data dealing with Endowment Effect

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes Yes





Expert 6 Yes Yes

Expert 7 Yes


Expert 9 Yes

Expert 10 Yes

In total 0 5 6 1 7 4

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The most commonly occurred subcategory was ‘necessity of multiple options’. The experts

suggested that when you work with multiple options, it could be less likely for one to fall into

this thinking flaw.

“Work in a way that it is a must to come up with at least three options or directions in

very short amount of time. If you work fast it might prevent from becoming attached with

any options. And very important thing is that one should make sure that each of the three

options gets equal amount of attention and time. It is like if one would draw three

pictures, one could really carefully draw the first and come up with detailed version, and

then create the second and third by just sketching quickly. It is important to give enough

time for all options.”

One interviewee suggested that the ‘necessity of multiple options’ should be combined with

‘teamwork’ aspect.

“At times it could be a good thing to have a different team creating the alternative

options if it is a large project. If same guys create all the alternatives, it is very likely that

they have the number one idea, and the other alternatives are poorly made by purpose. …

then they can easily sell the idea they prefer.” (Interviewee 6)

‘Project management tools’ were also seen as a cure for this cognitive bias.

“It is good to have a structure for the design process. For example using design sprints,

that you have a week rhythm. So you remember early enough to expose the idea for early

testing. So that it is completely impossible to even get into the point that you have been

stuck for four weeks with one thing. You need to speed up the cycle.” (Interviewee 8)

The subcategory ‘project management tools’ was combined with the ‘teamwork’ aspect in one

answer:

“The design process should be faster. Kinda do more things together - yea, you still have

‘peace’ to make the design - but in very early phase to systematically expose the design

for other people.” (Interviewee 3)

61

4.1.10. Designers’ suggestions to mitigate the Availability Bias

In this subchapter the findings regarding to the availability bias are presented. In practice the

availability heuristic makes humans to assess the importance of an issue by the ease of imaging

it, i.e. retrieving it from memory (Kahneman, 2011). Availability bias caused by the availability

heuristic makes us overvalue options that are easier to imagine, and undervalue alternatives that

are more difficult or more complicated to view within one’s mind. This bias is responsible for

people’s propensity to come up more likely with incremental solutions than disruptive ones

(Liedtka, 2015). The distribution of categories emerging in the data was very clearly weighed on

‘teamwork’ and ‘thinking and ideation tools’.

Table 17: Categories that emerged from the data dealing with Availability Bias

Self-development

Project management


tools

Decision-making

principles

Understanding the

audience

Expert 1 Yes

Expert 2 Yes

Expert 3 Yes Yes

Expert 4 Yes

Expert 5 Yes Yes

Expert 6 Yes

Expert 7 Yes

Expert 8 Yes Yes


Expert 10 Yes Yes

In total 1 1 6 7 0 1

62

Working in ‘multi-disciplinary teams’ was seen as a great way to get out of the habit of coming

up with very obvious solutions that are easy to imagine. Some interviewees thought that it could

help to interact with people from various backgrounds.

“..when there is something concrete to discuss about, the next step is to start pondering

new sides of that solution or a thing in a multi-disciplinary team. The wider is the range

of skills and knowledge who is designing that, the more likely is that the group is able to

come up with optional point of views. In a way that ‘hey has anyone thought that how

about this and that side in this solution are we going to arrange?’ I mean that as much as

possible diversity should be involved in the process.” (Interviewee 8)

“For me it has helped a lot that there are these multi-disciplinary things. If you for

example think about the way how innovations happen and when we talk about disruption,

you’ll notice that innovations in specific industry raise often from some other industry by

other actors. Often because they are able to view the thing from external perspective. It is

very difficult if we have some model or habit how to see and understand things. That

thing needs to let go.” (Interviewee 4)

Another popular subcategory in this section of data was ‘visualization’. Many of the experts

interviewed thought that visualizing ideas and thoughts could help in getting a grip of them,

which would allow them to give more weight and attention also to the ideas harder to imagine.

“I think it is needed to just start to sketching ideas into visible form … like if you have

even some very distant idea or thought, a seed of an idea, you could just start to make

some rough drawings of it and how it could work. That could be the way to clarify it.”

(Interviewee 7)

“I believe that a hand is like a continuous extension for your thinking. Always just write

and sketch ideas when they appear.” (Interviewee 1)

63

4.2. SUMMARY OF RESULTS

In the table 15 the categories that were found in 50% or more of the answers per bias are

highlighted. This gives an overview of which the means of mitigating biases that were presented

most often per each biases discussed. The most mentioned categories for mitigating projection

bias were ‘understanding the audience’ and ‘thinking and ideation tools’; for empathy gap

‘self-development’ and ‘understanding the audience’; for hot/cold gap ‘understanding the

audience’; for focusing illusion ‘project management’; for say/do gap ‘understanding the

audience’; for planning fallacy ‘project management’, ‘teamwork’ and ‘understanding the

audience’; for hypothesis confirmation bias ‘teamwork’; for endowment effect ‘decision-making

principles’, ‘teamwork’, and ‘project management’; and for availability bias ‘thinking and

ideation tools’ and ‘teamwork’.

Table 18: Overview of results: occurrence of the main categories per bias. 50% or more highlighted.

Self-development

Project managemen

t

Teamwork Thinking and ideation

tools

Decision-making

principles

Understanding the

audience

Projection bias 3 2 3 5 0 7


5 0 0 3 0 10

Hot/cold gap 3 3 2 2 3 5

Focusing illusion 1 8 3 3 0 2

Say/do gap 0 1 0 0 0 10

Planning fallacy 3 5 5 2 1 5


3 0 8 4 2 3

Endowment effect 0 5 6 1 7 4

Availability bias 1 1 6 7 0 1

64

5. DISCUSSION AND CONCLUSIONS

5.1. KEY FINDINGS AND THEIR SIGNIFICANCE

It was interesting to see that in some cases experts used very different tools to tackle the same

possible pitfalls in their design work. So, the range of tools used was quite wide and colorful. I

also noticed a “design method trend” for each expert, which was seen as methods and

instruments one is preferring in his or her designing work to try to mitigate various cognitive

biases occurring during the design process.

The categories ‘understanding the audience’ and ‘teamwork’, which are close to the aspects

proposed by Liedtka (2015) by different names, were the most common categories in my data.

Accordingly with my goal, and as the analyzing approach made it possible, the tools and

methods I found, were not limited within design thinking methodology. In addition to the

propositions suggested by Liedtka (2015), I was able to find new possible ways to tackle

cognitive biases. The other aspects than design thinking methodology were mostly within the

‘decision-making principles’, ‘self-development’ and ‘project management’ categories.

Regarding to these aspects other than design thinking, Cushing & Ahlawat (1996) had found also

that documentation of rationales for one’s decision had mitigating effect on cognitive biases.

Hallihan et al. (2012) in turn suggested that hypothesis confirmation bias could be mitigated by

using decision matrices. As the presence of the subcategory ‘improving self-awareness’ in my

data suggests, Wilson & Brekke (1994) proposed earlier that high level of metacognition, i.e.

one’s high awareness of his or her mental processes, could help to mitigate cognitive biases.

For the reason, that the idea of studying the effect of design thinking on the bias mitigation is a

new and very scarce approach, the most comparison between my results and the previous

literature is focused on the propositions in Liedtka’s paper (2015). Because the size of my

sample is relatively small, I use as a criteria for my results and her propositions to be in line,

when in half or more (highlighted in the tables below) interviews the expert mentioned the

means related to the methods Liedtka proposed as a remedy for the bias discussed. It is

65

appropriate to emphasize, that even though the experts during the interviews had not intuitively

mentioned a method Liedka had proposed, it does not mean that those methods do not mitigate

the bias. It can for example mean that they just did not mention that for reason or another, or that

the method actually mitigates the bias, but they are not aware of that happening. All this said, the

meaning for this comparison is just to raise new ideas, and not try to disconfirm or confirm

Liedtka’s propositions.

As her first proposition suggested, over half of interviewees mentioned means referring to the

collecting data about customers’ concerns and perspectives as the cure for the projection bias,

egocentric empathy gap and hot/cold gap (see the table 19 below). However only 2 of 10

recipients referred to these means while discussing about mitigating focusing illusions; they

rather preferred a means related to the project management, such as todo-lists, deadlines,

prioritizing and conducting the project by following MVP-principles.

Table 19: Liedtka’s 1st proposition and my results regarding to projection bias, egocentric empathy gap, hot/cold gap and focusing illusion

Proposition 1: By insisting on the collection of deep data on customers’ concerns and perspectives as central in the need finding stage, design thinking mitigates the effects of the projection, egocentric empathy, focusing, and hot/cold biases. (Liedtka, 2015)

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience



5 0 0 3 0 10



66

Her second propositions about using storytelling and metaphor to enhance decision-makers

abilities to imagine others’ experiences (Liedtka, 2015), which in my data is named as

‘visualization’, was in line with designers’ views only in respect of projection bias.

Table 20: Liedtka’s 2nd proposition and my results regarding to projection bias, egocentric empathy gap, hot/cold gap and focusing illusion

Proposition 2: By improving decision-makers’ ability to better imagine the experiences of others in the need-finding stage, design thinking mitigates the effects of the projection, egocentric empathy, focusing, and hot/cold biases. (Liedtka, 2015)

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience



5 0 0 3 0 10



The majority of the interviewees of my study did not mention the same methods as in the

proposition 3 by Liedtka (2015) about diverse and multifunctional teams, so the this proposition

was not line with my findings.

Table 21: Liedtka’s 3rd proposition and my results regarding to projection bias, egocentric empathy gap, hot/cold gap and focusing illusion

Proposition 3: By insisting that innovation tasks be carried out by diverse, multifunctional teams, design thinking mitigates the effects of the projection, egocentric empathy, focusing, and hot/cold biases.

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience

67



5 0 0 3 0 10



This propositions 4 and 5 were perfectly in line with the data I gathered about designers’ views

about mitigating say/do gap.

Table 22: Liedtka’s 4th and 5th propositions and my results regarding say/do gap.

“Proposition 4: By using qualitative methodologies and prototyping tools, design thinking improves customers’ ability to identify and assess their own needs, mitigating the effects of the say/do bias.” (Liedtka, 2015)

“Proposition 5: By using methods that do not rely on users’ ability to diagnose their own preferences, design thinking mitigates the effects of the say/do gap.” (Liedtka, 2015)

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience

Say/do gap 0 1 0 0 0 10

For some parts, the 6th proposition were in line with the data I gathered. The data regarding to

planning fallacy included few mentions about MVP-principles and prototyping, but in the big

picture, the teamwork and multidisciplinary teams were the most common aspects mentioned in

respect of these biases. I think that by comparing for example this proposition 6 with the data I

gathered about the same biases, it is looks that the grouping of biases she did in her paper,

resulted in too wide propositions addressing too many different biases at once. The biases have

so diverse main causes that it might be complicated to find a tools or methods that mitigate many

of them at the same time.

68

Table 23: Liedtka’s 6th proposition and my results regarding to planning fallacy, hypothesis confirmation bias, endowment effect and availability bias

“Proposition 6: By teaching decision-makers how to be better hypothesis testers, design thinking mitigates the effects of the planning fallacy, confirmation, endowment, and availability biases. It does this by insisting that they prototype, surface unarticulated assumptions, and actively seek disconfirming data.” (Liedtka, 2015)

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience



3 0 8 4 2 3



The seventh proposition was in line with my data only in respect of endowment effect: 70% of

interviewees suggested working with multiple options as a cure for that bias. However, only 10%

mentioned that as a cure for planning fallacy and 20% for hypothesis confirmation bias. Again,

teamwork was seen as a good remedy for each of the biases discussed.


“Proposition 7: By insisting that decision-makers work with multiple options, design thinking mitigates the effects of the planning fallacy, the hypothesis confirmation bias, and the endowment effect.” (Liedtka, 2015)

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience



3 0 8 4 2 3


69

Her last proposition suggested that by conducting and reflecting market experiments, designers

could mitigate the biases presented in table 25. For planning fallacy three experts mentioned

methods related to market experiments, such as analytics (understanding the audience) and

MVP-principles (project management). For hypothesis confirmation bias only two; for

endowment effect only 20% also; and for availability bias none of them. This proposition was

not in line with the results of this study.


“Proposition 8: By insisting that decision-makers conduct and reflect on the results of marketplace experiments, design thinking mitigates the effects of the planning fallacy, the hypothesis confirmation bias, and the endowment effect.” (Liedtka, 2015)

Self-development

Project managemen

t


tools

Decision-making

principles

Understanding the

audience



3 0 8 4 2 3



5.2. ASSESSMENT, THEORETICAL IMPLICATIONS AND FUTURE RESEARCH POTENTIAL

The data for the study is gathered from experienced people who solve problems in their daily

work by design thinking methodology, and for this reasons I believe the data is relevant and

provides a good overview of ways how the designers working with digital services are

addressing the cognitive biases in their work. The approach to the data was an issue that I

pondered a lot. It could have been easier to test strictly the impact of the design thinking

70

methodology on cognitive biases with more deductive approach, but I chose to handle the data

inductively, because I found it much more exciting to have a chance that the data reveals

something new and interesting. And that actually happened, so the choice was successful. As a

research setting, it would have provided more reliable results if I had expertise to study actual

decision-making processes within the context, but my experience in the research methods for

inspecting decision-making from the perspective of psychology were not deep enough to conduct

such study. One limitation of this study might be, that I knew the interviewees before, and

therefore it might be more difficult to act objectively in the interview situation. However, I see

the benefits of trust more remarkable than possible disadvantages of knowing interviewees

beforehand.

My results based on designers’ views about the issues suggest that at least regarding to the biases

discussed in this study, the mitigating effect of design thinking on cognitive biases should be

inspected on a bias basis, not as a group of biases as Liedtka (2015) proposed. However, as

Liedtka suggested, the findings of this study strengthen the proposition that design thinking

could be valuable methodology to mitigate the cognitive biases harming decision-making, and

the aspect should be studied more thoroughly. Therefore to the research area of decision-making

psychology this study provides a solid reasoning to start studying the means how designers

address the cognitive biases harming their decision-making. For the reason that the results of this

research present a fresh look into the designers’ thinking from a fresh perspective, the emerging

branch of design thinking research could use this study as a base for questioning the

completeness of design thinking methodology. Could some of the methods or principles that

were found in the data be the missing aspects of design thinking? Some of the suggestions by

designers were clearly used in their design work to define and solve wicked problems, but not

yet seen as a part of design thinking methodology.

My propositions with provide a good starting point for someone with deeper experience in

conducting decision-making experiments to test them with more strict research setting. It could

be also good idea to take a step back and study more specifically how designers actually see

these biases and their effect on the ability to define and solve problems. One interesting research

71

would be to study the effectiveness of my propositions within a case company and to find out

whether a systematic use of suggested tools and principles would prevent the negative effects of

cognitive biases. Therefore, further studies should be conducted on this matter.

5.3. PRACTICAL IMPLICATIONS

I believe that almost any kind of organization could benefit from educating decision-makers

about the principles how designers define and solve problems. And while a growing number of

companies have some kind of design know-how within their personnel, spreading those skills in

the organization could help to come up with better decisions related to other than design-related

wicked problems also. In the table 26 are my propositions to mitigate cognitive biases based on

the insights gathered from designers.

Table 26: My propositions based on designers’ views for mitigating the nine biases discussed

1. Focus on the deep understanding of the audience and make use of thinking and ideation tools to mitigate the projection bias.

2. Attempt to thoroughly understand the preferences and concerns of the audience and find ways to improve self-awareness to mitigate the egocentric empathy gap.

3. Deep understanding of the audience mitigates the hot/cold bias.

4. Structured project management tools and principles mitigate the focusing illusion.

5. Use analytics and conduct observations to mitigate the say/do gap.

6. Project management framework, getting better understanding the audience and working in teams mitigate the planning fallacy.

7. Teamwork mitigates the hypothesis confirmation bias.

8. Leaning on decision-making principles, teamwork and making use of project management principles and tools mitigate the endowment effect.

9. The use of thinking and ideation tools and multi-disciplinary teamwork help to mitigate the availability bias.

72

Managers would benefit of going through the propositions presented above and reflecting them

on the decisions they need, or should, make in their day-to-day work. It could be also refreshing

to view the work as a series of decisions, and then zoom into the most recurring decision-making

situations to ponder which biases are likely to affect the quality of decision. This would be more

effective if one had achieved a good overview of the common flaws in decision-making. All this

said, paying attention to the decision-making process is superior idea when the stakes are high.

And even though the heuristics fasten our decision-making, and the intuition can be seen as a

gateway to the unconscious mind, it is good to remember that “we can be blind to the obvious,

and we are also blind to our blindness” (Kahneman, 2011).

73

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