Design Thinking as a Set of Attitudes and Behaviours

behaviour of its practitioners (Michlewski, 2008). Brown (2009) states that within an organisation, conceptualising design thinking as a set of shared attitudes allows a company to create and shape a continuous culture of innovation. In a variety of free teaching resources (e.g. d.school, 2016) the d.school at Stanford University has popularised a set of six principles which have often been used to describe the behavioural component of design thinking in practice. These attitudes should not be thought of as static

properties, but instead be viewed as dynamic principles which are shaped by one’s own experiences (Goldman et al., 2012; Kolko, 2015). Goldman et al.

(2012) therefore refer to the development of these attitudes as continuous

“mindshifts” which occur during the practice of design thinking and not as a static “mindsets”.

In the following paragraphs the six attitudes introduced by the d.school are briefly summarised based on their available teaching materials (d.school, 2016) and the description provided by Doorley and Witthoft (2012). A seventh attitude (“abductive reasoning”) was added based on the arguments of

several other authors (e.g. Collins, 2013; Dorst, 2011; Liedtka, 2000, 2015;

Martin, 2004, 2009; Penaluna et al., 2014; Scott et al., 2016; Tynan et al., 2016 forthcoming).

Focus on Human Values

Although, many different definitions of design thinking have been put forward, most authors agree that it is a human-centred activity (Brown, 2008, 2009;

Grots & Pratschke, 2009; Kelley & Kelley, 2013; Kelley & Littman, 2001, 2006; Leifer & Steinert, 2011; Liedtka, 2015; Rodgers, 2013; Tynan et al., 2016 forthcoming; von Thienen et al., 2011). This means that the insights developed through the interactions with potential users of a product or service and other stakeholders should guide and shape the decision-making process within a project (Doorley & Witthoft, 2012). Prioritising these insights

will significantly increase the chances for future success of a novel concept (Keinz & Prügl, 2010; Liedtka & Mintzberg, 2006).

Be Mindful of the Process

Several authors have proposed various process models for design thinking (e.g. Brown, 2008, 2009; d.school, 2016; Design Council, n.d.; Grots &

Pratschke, 2009; Huber et al., 2014; Kelley & Kelley, 2013; Kelley & Littman, 2001; Liedtka & Ogilvie, 2011; Meinel & Leifer, 2011; Stickdorn, 2010). These models should not be seen as prescriptive step-by-step instructions, but rather as sets of connected activities (Brown, 2008, 2009). Using such models enables a team to break down their project into more manageable tasks (Ho, 2001), which allows the team to increase its focus on individual activities, while still being aware of the larger context of the project (Doorley

& Witthoft, 2012). Several current process models will be further elaborated in Section 2.2.6.

Collaborate Across Boundaries

As previously stated in Section 2.2.4, design thinking is a team-based activity which benefits from having multiple disciplines and points of view

represented within a team (Alves et al., 2006; Brown, 2008; Fischer, 2000;

Kelley & Kelley, 2013; Kelley & Littman, 2001, 2006; Lockwood, 2010b;

Lojacono & Zaccai, 2004; von Thienen et al., 2011). To turn a diverse group of individuals into a working team requires each team member to collaborate across disciplinary and hierarchical boundaries (Doorley & Witthoft, 2012;

Kelley & Littman, 2006). Being aware and actively managing collaboration tends to lead to a “cross-pollination” of domains and ideas (Kelley & Littman, 2006) and an overall increased performance of an innovation team (Kayes et al., 2005; Nakui et al., 2011).

Bias toward Action

As Doorley and Witthoft (2012) explain, teams should stress reflective action over contemplation in a design thinking project. Active experimentation

provides a great way to uncover new insights and directions (Brown, 2008, 2009; Dow et al., 2012; Dow & Klemmer, 2011; Goldman et al., 2012; Leifer

& Steinert, 2011). Reflecting on how such new findings were discovered and what this means for a project will accelerate the learning process within a team and increase its innovation capabilities overall (Brown, 2009; Dow et al., 2012; Kelley & Littman, 2001; Leifer & Steinert, 2011).

Embrace Experimentation

Effective design thinking teams turn implicit thoughts and ideas into tangible objects and prototypes throughout a project (Doorley & Witthoft, 2012; Meinel

& Leifer, 2011). Conceptualising and constructing low-resolution prototypes with varying foci, which can be tested with potential users, enables a team to gain a deeper understanding of underlying problems and user needs

(Skogstad & Leifer, 2011). This decreases the chance of investing in ideas which do not show a sufficient market potential (Brown, 2009; Dow et al., 2012; Skogstad & Leifer, 2011). Learning through low-resolution prototyping allows a team to continually make progress without over-investing resources (Doorley & Witthoft, 2012).

Show Don’t Tell

In design thinking, ideas should be conveyed through details rather than speculation (Doorley & Witthoft, 2012). Visualisation therefore plays a key role in communicating thoughts, ideas, and the vision of a project (Liedtka, 2015). The goal is to create sharable experiences and gain empathy through sharing rich stories as an addition to the gathered factual information

(d.school, 2016; Doorley & Witthoft, 2012). This will aid in creating a shared understanding within the team (Fischer, 2000; Gilson & Shalley, 2004;

Kleinsmann et al., 2010; Welsh & Dehler, 2012).

Abductive Reasoning

Traditionally, two modes of reasoning are distinguished. Whereas in inductive logic, phenomena are proven through observation and measurement,

deductive logic focuses on proving something through reasoning (Liedtka, 2000). Several authors have proposed that design thinking heavily relies on abductive logic, as a third way of reasoning (Collins, 2013; Dorst, 2011;

Leavy, 2010; Liedtka, 2015; Martin, 2005; Scott et al., 2016; Tynan et al., 2016 forthcoming). Abductive reasoning is concerned with envisioning new phenomena without having definitive proof for its existence. Liedtka and Ogilvie therefore call it the logic of “what might be” (Liedtka, 2011; Liedtka &

Ogilvie, 2011). An attitude of abductive reasoning allows a team to think creatively about new solutions (Penaluna et al., 2014). Often, such creative speculations cannot be determined logically (Liedtka, 2000), but can only be iteratively tested through user feedback gathered via low-resolution

prototypes.