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This article analyzes two different strategies that both aim at creating innovative design or business concepts based on a user-centered approach: design thinking and lean startup. Both approaches involve customers, potential users, or other stakeholders into their development process. Although there are significant differences in both strategies, there are also several similarities in methodology and process design. This article compares process models for lean startup and design thinking and highlights the specific differences and similarities, based on a structured literature review. As a result specific modifications of both strategies are suggested. This article contributes to a better understanding of both—design thinking and lean startup, and it may help to improve either of the two strategies to foster innovative concepts.
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Mueller, Roland & Thoring, Katja (2012). Design Thinking vs. Lean Startup: A comparison of two user-driven innovation strategies.
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Roland M. MUELLER*a and Katja THORINGb
aBerlin School of Economics and Law; bAnhalt University of Applied Sciences
This article analyzes two different strategies that both aim at creating innovative design or business concepts
based on a user-centered approach: design thinking and lean startup. Both approaches involve customers,
potential users, or other stakeholders into their development process. Although there are significant differences in
both strategies, there are also several similarities in methodology and process design. This article compares
process models for lean startup and design thinking and highlights the specific differences and similarities, based
on a structured literature review. As a result specific modifications of both strategies are suggested. This article
contributes to a better understanding of bothdesign thinking and lean startup, and it may help to improve either
of the two strategies to foster innovative concepts.
Keywords: Design Thinking, Lean Startup, User-Driven Innovation
Lean principles were developed in the early seventies by Toyota in Japan, called lean
manufacturing, to optimize production processes (Womack, 2003). The idea of lean
principles is to make the production process more efficient by reducing any sort of waste in
the process—this could mean either the reduction of resources (human or material) or the
elimination of needless or redundant activities or expenses, like the reduction of storage
space. This strategy revolutionized production processes in the automotive industry. By now,
lean principles have become also important for general management, and other disciplines
like IT development, which make use of lean concepts but transfer them also to non-
manufacturing contexts. One example is “lean startup” (Ries, 2011)—an innovation method
for startup companies that claims that the most efficient innovation is the one for which there
is an actual demand by the users. Or put in other words: the biggest waste is creating a
product or service that nobody needs. This concept is highly relevant for any strategy or
method that aims at creating innovations.
The term “lean startup” was developed in the IT industry for software startups, but is more
and more commonly used also for other sorts of innovation projects in other disciplines
(Ries, 2011). A startup is defined as “a human institution designed to create new products
and services under conditions of extreme uncertainty” (Ries, 2011, p. 8). Therefore not all
new companies are classified as a startup and on the other hand also an established
* Department of Information Systems | Berlin School of Economics and Law
Badensche Str. 52 | 10825 Berlin | Germany
Roland M. Mueller and Katja Thoring
department in a big company could be a startup. Lean startup evolved from the “customer
development” method (Blank, 2006). The idea behind these methods is, that in addition to a
process for “product development”, a startup also needs a process for “customer
development” to find and understand the customers. This leads to developing solutions
based on a user-centered approach and adapting to customer needs. Within this article, we
mainly use the term “lean startup” instead of “customer development”, to highlight the lean
aspects of the method. The aim of lean startup is to build a continuous feedback loop with
customers during product development cycles (Maurya, 2012). It tries to test the core
business assumptions early in the product development process, sometimes even before
any product is built at all.
Another user-driven innovation strategy that has become more and more popular during
the last decades is “design thinking”. Based on designerly methods and principles, this
strategy was developed by the design consultancy IDEO in the late 90s (Kelley & Littman,
2001). Although it is not referring to lean principles, the main idea behind it is similar: it tries
to identify user needs in order to create appropriate solutions.
Similar to lean startup, design thinking is also focusing on users or customers. Based on a
user-centered approach with multi-disciplinary teams, it aims at solving complex (wicked)
problems (Buchanan, 1992; Rittel, 1972) and at generating innovative solutions. Design
thinking makes use of extensive user research, feedback loops and iteration cycles. It is
becoming more and more popular among business schools (e.g. the Rotman School of
Management (Martin, 2009)), and it is applied in R&D departments of companies to foster
This paper provides a structured analysis and comparison of the two innovation
strategies—lean startup and design thinking—with the goal to identify potentials to enrich
either of the two by merging or adapting specific parts or aspects.
The article is structured as follows: The first section presents an extensive literature
review that also provides short introductions of both, lean startup and design thinking, and
which is then used as a basis for a comparison of the two strategies. The different
characteristics are summarized in a structured framework, highlighting similarities, gaps, and
differences in naming conventions of both strategies. In conclusion we suggest some
modifications and intersections of the two processes, in order to reveal potential to enrich
either of the two.
For re-engineering the two strategies, we analyze two types of data sources about lean
startup and design thinking: 1) published literature and case studies, and 2) process models
for the two different processes. We are aware that design thinking as well as lean startup are
not just processes but consist also of tacit elements, like practices, experiences, specific
mind-sets, and company cultures (Thoring & Müller, 2011a). These intangible elements are
important and not everything in both methods can be made explicit and reduced to a
process description. However, we think that a detailed comparison of the process steps is
still useful to better understand both innovation approaches.
The insights from these two data sources, such as similarities and differences, are then
summarized in a structured framework, which can be found in Table 1.
First, we analyze relevant literature and published case studies for both strategies (e.g.
Blank (2006), Blank & Dorf (2012), Brown (2008), Brown (2009), Cooper & Vlaskovits
(2010), Kelley & Littman (2001), Kelley & Littman (2005), Kolko (2011), Martin (2009),
Design thinking vs. Lean Startup
Maurya (2012), Plattner, Meinel & Leifer (2011), Plattner, Meinel & Weinberg (2009), Ries
(2011), Sims (2011), and Thoring & Müller (2011a, 2011b, 2011c)). The literature review
reveals that the two communities of lean startup and design thinking do not interact and cite
each other very often. They use similar methods and tools, but have developed different
names for them. This reveals potential for learning from each other strategy.
As the second step, we compare the two strategies based on process models. However, for
both methods there is not one defined process model available. Moreover, the descriptions
of the processes are often informal and there exist various versions of the process because
of adjustments and further developments. Therefore we use different types of process
models: We compare two abstract models—a design thinking process model by Plattner et
al. (2009) and the “lean learning cycle” (Ries, 2011), see Figure 1. These abstracted models
allow for the comparison of the two strategies on a meta level: the number of process steps,
order, alignment, labeling, frequency, and direction of the different activities can be checked
against each other.
Figure 1: Comparison of abstracted process models for design thinking (left) and lean startup (right). (Plattner
et al., 2009; Ries, 2011),
Both process models make use of six process steps. The most significant difference is that
the lean learning cycle is arranged in a circular form, while the design thinking process is
arranged in a linear way. This might suggest that design thinking should be executed in
subsequent steps, while lean startup appears to be more flexible. Unlike the design thinking
process, which begins with the “Understand” phase, the lean learning cycle has no clear
beginning or ending—the circular alignment of the steps suggests that they are supposed to
be executed in a continuous and repeatedly manner.
The goal of the build-measure-learn cycle is learning (Ries, 2011). What is built is based
on a problem or solution hypothesis. The test of a hypothesis is therefore the intended
learning step. For testing the hypothesis, appropriate metrics must be defined (measure
step). For generating these metrics and then test the hypothesis, an experiment has to be
designed (build step). Therefore the build-measure-learn cycle could also be regarded as a
classical scientific hypothesis-metric-experiment cycle that starts with the learning goal
(theory or hypothesis) and ends with an experiment (prototype) to test the hypothesis.
When comparing the individual steps of both processes, some interesting similarities
become obvious: e.g. “learn” in lean startup could be interpreted as “understand” or as “point
of view” in design thinking. “Build” in lean startup might be similar to “prototype” in design
thinking. And “measure” in lean startup can either be “observe” or “test” in design thinking.
This is in-line with the before-mentioned assumption that the lean learning cycle could start
at any step of the process model.
Roland M. Mueller and Katja Thoring
And finally, the lean learning cycle might be applied to different levels of a project. On a
meta-level, it could be applied to the entire process, and on a micro-level, it could be applied
to specific details (like the color of a signup button). That means, it is possible to zoom into
sub-processes and execute the lean learning cycle also for smaller design decisions. The
design thinking process model, however, seems to be only applicable to the entire problem;
not to specific sub-problems.
In addition to these abstract process models, two more detailed process models along with
the related process descriptions are compared: a process model for design thinking based
on method engineering by Thoring and Müller (2011b), and a process model of lean startup
by Cooper and Vlaskovits (2010), see Figures 2a and 2b. These detailed process models
along with the descriptions provided by the respective authors allow for a content-related
comparison of the two strategies: What is happening within each specific step, what kind of
methods and tools are used, and what is the outcome of each step?
Understand Observe
Interview Observation
Point of View
Storytelling Clustering
Brainstorming Clustering
Prototyping Test
Facts Insights Clustered
Ideas on
Ideas on
Negative Feedback on Prototype
Negative Feedback on Concept
Negative Feedback on Problem Definition
Negative Feedback on User Needs
Text Photos Videos
Journey Scenario
Model Role
Video Graphic
Negative Feedback on Design Challenge
Figure 2a. Detailed process model for design thinking (Thoring & Müller, 2011b).
Figure 2b. Detailed process model for lean startup (Cooper & Vlaskovits, 2010).
The model of the design thinking process (Figure 2a) describes the six steps of the process
and the iteration loops that result from the last step 'test'. Notably about this process is that it
does not start with an idea, but with a problem or a question, instead. Usually the ideas are
developed within the process, in the fourth step 'ideation'. Before that, there is an extensive
Design thinking vs. Lean Startup
focus on the research, where 'understand' means secondary research and 'observe' means
user research. Here, design thinking makes use of research methods from other disciplines
such as ethnographic methods and other qualitative methodology. The acquired knowledge
is then condensed into a sort of micro-theory about the problem or the user needs, the 'point
of view' (POV) that is afterwards used to develop solution concepts in the 'ideation' step. It is
here where innovative ideas are developed that aim at solving that previously identified
problem or address the users’ needs. The selected idea is then visualized or built
('prototype') in order to test it and gather feedback from prospective users ('test'). According
to the feedback the concept is iterated, by returning to one of the previous steps. See
(Thoring & Müller, 2011b) for a more detailed description of the design thinking process.
Figure 2b shows a process model, adapted from the four steps of the “customer
development” process. Lean startup is a trademark by Eric Ries and combines customer
development with ideas of agile software development, lean management (Womack, 2003),
and open source software (Ries, 2011). Since there is no explicit process model for lean
startup, we refer to the customer development process, which consists of four steps:
‘customer discovery’, ‘customer validation’, ‘customer creation’, and ‘company building’
(Blank, 2006). In the customer discovery phase, the founders discover the appropriate
customer group and market segment and validate if the product solves a problem for the
customer group. This phase tries to find indications of a so-called ‘problem-solution fit’. The
goal is to discover a customer problem and to test if the problem is worth solving (Blank,
2006). Central to this is finding the minimal set of features for solving the core problem: the
so-called Minimal Viable Product (MVP). An MVP “is that version of the product that enables
a full turn of the build-measure-learn loop with minimum amount of effort []” (Ries, 2011, p.
77). In early stages of the process, this can be tested and feedback of potential customers
can be gathered with e.g. minimal landing pages, paper-prototypes, or early working
prototypes. In the customer validation phase it will be checked if the market is saleable and
large enough for a viable business (Cooper & Vlaskovits, 2010). The goal is to find some
validation of a ‘product-market fit’ and to answer the question if the developed product is
something that people want (Maurya, 2012). A product-market fit means that 1) the
customer is willing to pay for the product, 2) there is an economically viable way to acquire
customers, and 3) the market is large enough for the business (Cooper & Vlaskovits, 2010).
After this step, the innovation is validated. The company creation phase is concerned with
building a scalable business through a repeatable sales and marketing roadmap (Cooper &
Vlaskovits, 2010). In the company building phase, departments and business processes are
defined to support scale (Blank, 2006).
The following section presents a detailed comparison of both innovation strategies, based on
the aforementioned data sources (related literature and case studies, and process models).
Table 1 provides an overview and comparison of the important aspects in design thinking
and lean startup. We compare the general goals and the specific focus of both methods, the
approaches, methods, specific process steps, as well as the respective target groups. More
detailed descriptions of the respective similarities and differences of both strategies are
provided in the two following sections.
Roland M. Mueller and Katja Thoring
Design thinking
Scope, Focus
General innovations
Solve wicked problems
Fail early to succeed sooner
Yes (“Iteration”)
Ideation is part of the process,
solutions are generated in the process
Qualitative Methods
Strong focus: elaborated ethnographic
methods, user research, observations,
Not a focus
Business Model
Not a focus
Adaption of
Not a focus
Typical Methods
Shadowing, Qualitative Interview,
Paper Prototyping, Brainstorming (with
specific rules), Synthesis, etc.
Hypothesis Testing
Not a focus
Prototype Testing
Rapid iteration
Target Group
Users (usually end users, sometimes
other stakeholders)
Table 1. Comparison of important aspects of design thinking and lean startup
Innovation Focus: Both concepts have the same goal, which is to foster innovations. Hence,
we first take a look at innovations in general. Other than an invention, an innovation is not
only something new, but it also proves to be economically viable, technically feasible, and
therefore it is successful in the market. Brown (2009, p. 19) describes three criteria for
successful innovations. According to this, an idea must be desirable, viable, and feasible
(see Figure 3). Many companies focus too much on the latter two—they start either with a
new technological invention, or with a business model, but forget to consider the user’s view.
Many of these concepts fail, because the developed products do not solve an actual
problem for the user. Those products are not desirable—nobody really needs or wants them,
and hence nobody is going to buy them.
Figure 3. Criteria for a Successful Innovation, adapted from Brown (2009)
Design thinking vs. Lean Startup
User-centered Approach: Both, design thinking and lean startup, take the perspective of the
users and other stakeholders into account and focus on extensive user testing in order to
improve their respective concepts.
Test Prototypes: Both concepts try to gather user feedback in early stages of the process, in
order not to waste lots of resources by building something that nobody wants. Rough
prototypes (Buchenau & Suri, 2000; Coyette, Kieffer, & Vanderdonckt, 2007; Walker,
Takayama, & Landay, 2002), which can be used for user testing, are a significant similarity
of both strategies.
Rapid Iteration: For both strategies, the solution and the problem are quite unclear in the
beginning. Both teams work under extreme uncertainty, and the developed prototypes
undergo extensive iteration within the process. ‘Fail early to succeed sooner’ is the credo of
design thinking, while lean startup describes the ‘fail fast’ concept. Both means, that the
sooner you realize an idea is not working, the faster you can update it and retest it, which in
fact saves time and money. Lean startup emphasizes the importance of small batch sizes to
improve “the speed at which startup find validated learning” (Ries, 2011, p. 188).
Scope: While lean startup is mainly targeting at startups, design thinking is seeking for
innovations in general (that could then be turned into startups or be utilized somehow else).
Project Initiation: The initial business idea in lean startup is already there from the beginning.
It is then tested to check its validity, and can therefore be changed considerably during the
project. In design thinking, however, the project starts with a challenge, not with an idea.
Typical for design thinking is the goal to solve a so-called wicked problem (Buchanan, 1992;
Rittel, 1972), which means that the solution may be quite ambiguous. The problem is not
defined until an extensive phase of user and secondary research has been conducted, and
the ideas are then generated during the process.
User Research: Design thinking is focusing on extensive user research in the beginning of
the project. For this inductive approach it makes use e.g. of ethnographic methods (Kelley &
Littman, 2005). In lean startup, however, the use of qualitative research methods is not as
elaborate. The project starts with a product vision of the founders.
Synthesis: Design thinking suggests several sophisticated methods for synthesizing insights
from the user research (Kolko, 2011). Among these frameworks are ‘Personas’, ‘2-Axis
Mappings’, ‘User Journeys’, or ‘Causal Maps’. They help to align the researched information
in a qualitative way, in order to condense them into a so-called ‘Point of View’—a kind of
micro theory about the user needs, which determines the further direction of the process.
Lean startup does not work with synthesis methods and/or qualitative frameworks.
Customers, Users, and Stakeholders: The name of the Customer Development method
(which also applies to lean startup) already indicates one of its unique characteristics: To
develop its own customers means to find out who might be the early adopters or lead users
(Hippel, 1994; Lilien, Morrison, Searls, Sonnack, & Von Hippel, 2002), and what kind of
problems they might have that could be solved by the suggested product. Unlike classical
‘product development’ which pretends to know the problem and searches for a (technical)
solution to solve this problem, in Customer Development the customer problem that should
be solved is not fixed but can be changed and discovered. However, the starting point in
lean startup and Customer Development is normally a business idea. Also in design thinking
there is no preconceived user problem. However, the process starts with extensive
ethnographic user research before any ideas are generated. Lean startup and customer
development distinguish between different types of customers (‘users’, ‘influencers’,
‘recommenders’, ‘economic buyers’, and ‘decision makers’) (Maurya, 2012) and market
Roland M. Mueller and Katja Thoring
types (‘new markets’, ‘existing markets’, and ‘re-segmented existing markets’) (Blank, 2006).
Design thinking only refers to ‘users’, which usually means ‘end users’ or sometimes
‘stakeholders’ and does not use any market typology.
Ideation: Design thinking makes extensive use of classical ideation techniques, borrowed
from other creative disciplines, to generate ideas (for example brainstorming and
brainwriting). Since lean startup usually starts with a business idea, no ideation techniques
are explicitly applied.
Iteration/Pivoting: Both strategies identified the need to modify ideas or prototypes according
to user feedback. ‘Iteration’ in design thinking starts usually after the ‘testing’ step, towards
the end of the whole process, and is performed on the prototype. In lean startup, however,
‘pivoting’ could be applied much earlier. Even early hypotheses are tested, not only the
prototyped idea. Therefore it is possible to determine whether a specific assumption about
the problem or user need is correct or not, even before a prototype is created. This might
save a lot of time, and resources. In design thinking it may happen, that this insight comes
not until the end of the process so that the process has to start over from scratch.
Adaption of deployments: Lean startup has adapted the concept of the andon cord of the
Toyota production system. In Toyota, the andon cord will stop the whole assembly line in
case of a quality problem (“Stop the production so that the production never has to stop”)
(Ries, 2011, p. 227). The equivalent to the assembly line in software development is
continuous deployment, which pushes code changes automatically into production. This
reduces the cycle time and therefore increases the learning speed. However, even with unit
tests that check for errors, unexpected problems might occur. For analyzing problems, lean
startup promotes the “five whys” method (Ries, 2011, p. 229). It asks not only for a reason
of a problem, but also for the reasons behind the reasons. Then proportionally investments
in all these reasons are made. This will help to learn from mistakes and accelerate or
decelerate the speed of new deployments.
Quantitative Evaluation: Lean startup is using metric-based evaluation techniques. There are
several suggestions of how hypotheses can be tested in a quantitative way (e.g. evaluating
the customer acquisition costs by minimal landing pages at a small scale), and there are
checklists for product-market fit and MVP definitions (Blank, 2006). Ries (2011) presents
“innovation accounting” to measure the progress in validated learning. He warns against
“vanity metrics” and defines actionable metrics that are linked to the specific business
models. He distinguishes between three “engines of growth” (viral, sticky, and paid) and
suggests metrics for each of them. For the measurement of the effectiveness of design
solutions often split-test experiments (A/B test) are used. For understanding the longitudinal
effect of a design decision on the metrics, cohort-based analyses are suggested. Design
thinking does not suggest such metric-based evaluation techniques.
Business Model: Lean startup makes use of Osterwalder’s Business Model methodology
(Osterwalder & Pigneur, 2010) that helps to systematically align stakeholders (partners,
customers), value propositions, required resources, cost and revenue structure, channels,
etc. for a startup business model. The business model elements of the canvas are
considered as hypotheses that must be tested as early as possible (Blank & Dorf, 2012).
Maurya suggests an adapted business model framework called ‘lean canvas’ (Maurya,
2012). Design thinking does not suggest such a focus on the business model of an idea.
Qualitative Evaluation: Design thinking uses elaborated qualitative evaluation techniques.
Testing and user feedback are mainly gathered through qualitative interviews and
ethnographic methods. Even though also in lean startup open interviews are used, there is
not such a focus on qualitative data. Also the methods to conduct and evaluate these
qualitative research methods are not as developed as in design thinking.
Design thinking vs. Lean Startup
The literature review revealed that, even though both communities have similar goals, they
do not cite and refer much to each other. This shows an opportunity for learning from each
other method. Each strategy has its specific target group. It is not suggested to interchange
both strategies arbitrarily, since they both focus on specific requirements. If someone has
already a business idea that he/she wants to bring on the market, then lean startup might be
the right choice. Design thinking, on the other hand, is the better strategy if you are still
looking for the right business idea for founding a company, or if the user problem is still very
vague. Still, we believe that both strategies could benefit from each other, since they both
involve specific features that the respective other strategy is not considering, but that might
be helpful, though. To improve either of the two, the following adaptations are suggested:
There is potential to improve the design thinking process by converging the two strategies in
terms of the iteration. Pivoting as it is practiced in lean startup seems to be a promising
opportunity to strengthen the design thinking process. This means to implement feedback
testing and iteration loops earlier in the process, even before there is a prototype. This could
happen for example after the Point of View or after Ideation. The testing of early problem
hypotheses, that can be falsified or validated, might save time and resources, and could
result in a better output of successful project results.
Moreover, it is suggested to implement metric-based evaluation techniques as they are
commonly used in lean startup. For example, testing in design thinking is mostly performed
qualitatively in the analyzed literature. Therefore, checklists or specific test environments
that allow for quantitative measuring of user feedback (such as landing page design, smoke-
test, etc.) should be implemented in the design thinking process.
Also, it is suggested to develop a business model in addition to the prototype, to validate the
viability of the concept.
Unlike design thinking, lean startup does not describe specifically how customer input could
be collected. Qualitative research methods—e.g. ethnographic methods—could be applied
to improve the definition of the targeted customers and to identify their needs and problems.
Similarly, we suggest adapting the synthesis methods from design thinking. Structured
frameworks or the generation of a qualitative persona might help lean startup to better
understand and develop their customers and their respective needs and problems. Both
should be scheduled at the beginning of the process.
Lean startup could also benefit from the use of ideation techniques, as they are applied in
design thinking, to develop concept variations. Although lean startup usually starts with a
concrete business idea, it might be helpful to use structured ideation methods to iterate that
idea within the process, specifically before the problem-solution fit is achieved.
Consequently, pivoting should be applied earlier (already on the initial concept). And
finally, qualitative feedback evaluation, such as qualitative user interviews, could be
implemented in the pivoting steps, in addition to the metric-based evaluation techniques.
Based on the analysis of the two data sources (literature review and process model
comparison), as well as on the before mentioned ideas to improve both strategies, a more
radical merging of both processes suggests itself. As a consequence, we propose an
interlaced process model that combines the main aspects of both innovation strategies,
which we call “lean design thinking”. This suggested adaptation of the two methods
combines the most promising aspects of both strategies and addresses the identified gaps.
Roland M. Mueller and Katja Thoring
Figure 4 shows this model of lean design thinking, highlighting the respective aspects,
adapted from the two original processes.
For example, the first steps of the design thinking process (understand, observe, point of
view, ideation) are maintained, prototyping is merged with customer discovery from lean
startup (adding aspects like business model generation or funnel proposition), and customer
validation from lean startup are added to the end of the process. Testing should be executed
after each step, instead of only once at the end of the process, as it is proposed in design
thinking, and it should involve both—qualitative and metric testing methods.
Figure 4. Suggested model of “lean design thinking: Adaption and merging of promising aspects of both
innovation strategies.
Creativity and innovative processes can be understood using the evolutionary metaphor
(Campbell, 1960; Thoring & Müller, 2011c). The creativity of evolutionary processes can be
explained by the combination of generation (variation) and selection of ideas (Simonton,
1999). The previous analysis showed that design thinking has advantages in the generative
step (ideation). Even though both processes emphasize the importance of testing, in lean
startup the selection of ideas based on quantitative metrics is more rigorous. Because in
innovation, generation and selection of ideas are both important, the interlaced “lean design
thinking” process, which combines the strengths of both methods, seems promising.
The work presented in the article may contribute to a better understanding of both—design
thinking and lean startup, and it may help entrepreneurs and intrapreneurs to utilize either of
the two strategies for improving their innovation projects. Practitioners from both fields can
use it as a source of inspiration to enrich their innovation strategies by adopting the identified
relevant tools and methods of the respective other strategy. For entrepreneurs, innovators,
and startups who may want to develop high-tech innovations, it provides a more complete
view on innovation strategies in general. For researchers, this article provides an analytical
deconstruction of both methods through method engineering, including a comparison, a
mapping of both methods, and the identification of gaps, differences and intersections.
Educators who may want to teach one of the two methods will also benefit from the detailed
analysis. And finally, the article highlights the relevance of innovation strategies in general
for management, business innovation, and user-centered design.
Design thinking vs. Lean Startup
We rely our analysis and suggestions mainly on the mentioned literature and published
process models. This may not reflect the actual application of the respective processes in
practice. It might be that e.g. qualitative ethnographic methods are already well established
in lean startup, or that the business model is already addressed in design thinking projects,
but since this is not yet explicitly defined in the respective process models and descriptions,
these questions warrant further research. Also, we did not analyze the tacit elements of both
strategies, such as specific mind-sets, team constellations, or company culture. The
influence of such intangible aspects needs further research.
The presented process model of “lean design thinking” is intended as a first step towards a
better cooperation between the two communities of design thinking and lean startup, with
the goal to adapt and merge interesting approaches of both strategies. Future work will
include the application of the suggested process model in a case study, in order to validate
its advantages over the separately applied individual processes, as well as structured
interviews with practitioners from both communities to analyze the actual application of both
methods in practice.
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... Many of the lean product development methodologies presented in the literature are either quite abstract or are not well-suited to some product categories (e.g. medical devices for which a minimum viable product cannot be rapidly deployed) (Baines et al., 2006;Pease, Dean, and Van Bossuyt, 2014;Mueller and Thoring, 2012). Hence, in this paper, we present an educational case in lean early-stage design for LMIC markets through the development of a novel DFU risk assessment tool. ...
... The repeated divergence-convergence creates space for creative thought in an otherwise linear process (Howard, Culley, and Dekoninck, 2008). In the latter stages of this process, we employ rapid, iterative, experimental prototyping, which is a common feature of lean approaches (Mueller and Thoring, 2012). ...
... Despite the benefits of a lean design process to product development for LMIC markets, the literature on this subject is sparse. Many of the published lean design methodologies are vague and/or not applicable to some important product categories, such as medical devices (Baines et al., 2006;Mueller and Thoring, 2012). The only methodology proposed for product development specifically for LMICs that we are aware of is that of Pease, Dean, and Van Bossuyt (2014). ...
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Interest in applying a “lean” philosophy to design has been slowly growing in recent years, but there are still few broadly applicable methodologies and illustrative cases published in the literature to guide lean design processes. Lean approaches promise cost reduction and increased product value, which could be particularly beneficial in product development for low- and middle-income country markets, where value demands are high. We use the clinical need of efficient diabetic foot risk assessment in lowresource healthcare settings to present an example of lean early-stage design of a medical device. The background of existing medical literature and commercial products is intentionally leveraged throughout the design process to streamline development and minimize the need for independent validation of product strategies and design features. Our approach resulted in an efficient design process that generated a novel, purely mechanical plantar pressure evaluation device that can indicate high risk of diabetic foot ulcer in resource-constrained settings. This case provides a practical example of how design processes can be adapted to be leaner where there are large gains from minimizing design cycle time and cost.
... However, these processes typically do not address the FFE or the commercialization stage . Design thinking, on the other hand, typically focuses on the FFE and does not address the commercialization stage as well, but often ends with a prototype (Mueller & Thoring, 2012). By contrast, lean startup focuses on the commercialization stage; more specifically on customer development and business integration (Ries, 2011). ...
... Combining design thinking with agile approaches has been suggested for software engineering (Corral & Fronza, 2018) and data modelling (O'Driscoll, 2016). Some authors have tried to integrate lean startup and design thinking and suggested a combination of both methods (Koen, 2015;Lichtenthaler, 2020;Mueller & Thoring, 2012). Others call for better integration of agile concepts into the design thinking process to address the entire innovation process (Micheli et al., 2019), which is what we aim for with the work presented in this chapter. ...
... Future studies will have to apply and validate the process model through empirical studies or action research. NOTE 1. Parts of this chapter are based on two previously presented conference papers (Mueller & Thoring, 2012;Thoring & Müller, 2011). ...
... Introduced largely by Steve Blank in the book titled The Lean Start-up, authored by Eric Ries in 2011. Lean start-up is a reference for most software entrepreneurs (Yang et al., 2019) which is increasingly being used to describe various types of innovation development in other areas (Roland and Katja, 2012). The lean start-up methodology, inspired by Toyota's lean manufacturing principles, creates value for customers and eliminates waste during the development phase. ...
Purpose The purpose of this study is to answer the following questions. What kind of entrepreneurial identities do students have that motivate them to choose either of the entrepreneurship course and university-based incubator? How do students involve in the entrepreneurship ecosystem at university based on their entrepreneurial identity? Design/methodology/approach For this study, the author began to gather information using previous knowledge and any aspect of a work, namely, from the literature review to represent interpretive syntheses of the meaning-making literature review addressing the research question. Findings This study suggests what happens to entrepreneur students from academia and the reason that they end up in one of the two aforementioned paradigms. This paper aims to underpin the issue of how various entrepreneurial identities of students cause substantial contributing factors in forming such entrepreneurial activities at university and throughout the entire innovation ecosystem. Research limitations/implications Almost all of the content of the entrepreneurship education (EE) courses and incubator training is oriented towards consensual entrepreneurship methods, in accordance with entrepreneurship education. Although the core contents of the EE courses and university-based incubators’ training are the same, the outcomes are quite different. Originality/value This study considers the students’ entrepreneurial identities with a focus on their point of view that led them to end up in one of the two common entrepreneurship resources at universities: the EE course and entrepreneurial activities related to university-based incubators.
... Therefore, Lean Start-up provides a suitable effectual vehicle for impact measurement & management in new ventures. One key advantage is that the focus on hypothesis development and quantitative testing in Lean Start-up [45] can be linked to esSROI, including the data gathering that is necessary to validate and substantiate impact [37]. In this paper, we aim to explore and answer what approaches can be employed to effectively measure the comprehensive sustainability impact of pre-seed business models in the very early phases. ...
In this paper, we present an early-stage Sustainable Return on Investment (esSROI) measurement tool to evaluate the impact of early-stage business models. The main objective for developing the tool is to capture the holistic sustainability-related impacts of the incubation process from start-ups already during the conceptualization and pre-seed phases. An early, holistic impression of sustainable potential offers many opportunities to iteratively improve the degree of potential sustainable impact. The scope of designing and alternating business models is the widest early in the process before narrowing it down. This very early application differentiates esSROI from other tools that are used later in seed phases. Applying the tool in the conceptualization phase might make it even more usable already among (student) teams before incubation/acceleration. The quantitative measurement tool esSROI consists of a questionnaire design and follows a triangulation and long-term approach that includes three measurement points that capture the iterative progress. A preliminary study has been conducted in 2022 and shows that the tool is easy to use and accepted by founders.
... Combining elements of Lean and design thinking is not just a suggestion for accelerators; others have noted the potential synergies of these approaches (Müller & Thoring , 2012). ...
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The study aims to explore the role of business startup accelerators by Selecting, Mentorship, and Fund parameters in launching successful enterprises in Gaza Strip from entrepreneurs' perspectives. To achieve the aim of this study, a descriptive-analytical approach was followed. To collect data mainly, a self-designed questionnaire was distributed to the targeted population of the study, in addition, to the use of previous studies, books, reports, papers, and documents from trusted websites as secondary resources. The study population was (150) consisted of Gaza Sky Geeks beneficiaries which is the only business accelerator in the Gaza Strip. Regression analysis indicated that all three dimensions (Selecting, Mentorship, and Fund) have positive and significant effects on launching successful enterprises in Gaza Strip. The most important recommendations were related to governments which need to play a fundamental role in preparing a suitable ecosystem for entrepreneurs in Palestine and especially in Gaza Strip to achieve benefits related to economic growth generally. Also, universities and other academic institutions are recommended to play their role in providing graduates with an entrepreneurship culture. In addition to coordination between business incubators and business accelerators. The study recommended business startup accelerators in Gaza Strip to put vital selection criteria. Also, to continue enhancing mentorship, by paying much attention to legal mentorship. Moreover, to put in consideration helps entrepreneurs to get enough funds enabling them to concentrate on their startups. Business startup accelerators in Gaza Strip are recommended to specify success criteria and update them continuously, to follow the progress of startups and treat any weaknesses before exacerbating. Keywords: (Accelerator – Startup – Selection - Mentorship – Fund)
... Big data has become a core theme in information systems research and practice [1][2][3]. Traditional manufacturers must deal with disruptive changes in their businesses in an environment of growing volatility of external influences, shorter product lifecycles, increasing global competition, and exponential technological progress [4][5][6]. ...
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To remain competitive, companies must decide on new, desirable products. This can be achieved by integrating insights how customers use a product into the process of deciding on a new product. Currently, this process is primarily based on market research that can only reveal the intention of consumers. Through the digitization of products, companies have access to large amounts of customer data that allow the application of data analytics methods. We provide a taxonomy of artificial intelligence, machine learning and data analysis, so that the notion of data analytics can be defined. Thus, the terms customer usage data, as well as a generic, five-stage product decision process (PDP) are defined and differentiated from consumer data and the product development process. Eventually, we show which data analytics methods on customer usage data can be used in order to tackle current challenges within the PDP. We incorporate the results of our structured literature review by connecting selected examples to our concept of the PDP. Our insights help to apply the proper data analytics methods in the PDP and hereby address the interplay between product decision and product development. Finally, future research directions for data analytics methods on customer usage data are put forward.
... With its promise to quickly design programs that are uniquely responsive to users' needs, human-centered design (HCD) is a burgeoning area in the global health field. HCD researchers and practitioners seek to iterate and "fail fast to succeed sooner" in their design and iteration of solutions (Thoring and Müller, 2011;Müller and Thoring, 2012;Brown, 2013). Altman et al. (2018) note that this philosophy of rapid failure can pose a tension in the health field, where the stakes of failure are high. ...
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Much of the methodological literature on rapid qualitative analysis describes processes used by a relatively small number of researchers focusing on one study site and using rapid analysis to replace a traditional analytical approach. In this paper, we describe the experiences of a transnational research consortium integrating both rapid and traditional qualitative analysis approaches to develop social theory while also informing program design. Research was conducted by the Innovations for Choice and Autonomy (ICAN) consortium, which seeks to understand how self-injection of the contraceptive subcutaneous depot medroxyprogesterone acetate (DMPA-SC) can be implemented in a way that best meets women's needs, as defined by women themselves. Consortium members are based in Kenya, Uganda, Malawi, Nigeria, and the United States. Data for the ICAN study was collected in all four countries in sub-Saharan Africa. In order to both illuminate social phenomena across study sites and inform the program design component of the study, researchers developed tools meant to gather both in-depth information about women's contraceptive decision-making and data targeted specifically to program design during the formative qualitative phase of the study. Using these two bodies of data, researchers then simultaneously conducted both a traditional qualitative and rapid analysis to meet multiple study objectives. To complete the traditional analysis, researchers coded interview transcripts and kept analytical memos, while also drawing on data collected by tools developed for the rapid analysis. Rapid analysis consisted of simultaneously collecting data and reviewing notes developed specifically for this analysis. We conclude that integrating traditional and rapid qualitative analysis enabled us to meet the needs of a complex transnational study with the added benefit of grounding our program design work in more robust primary data than normally is available for studies using a human-centered design approach to intervention development. However, the realities of conducting a multi-faceted study across multiple countries and contexts made truly “rapid” analysis challenging.
Conference Paper
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Increasingly university programmes are introducing a range of experiential learning based programmes to support students to develop their entrepreneurial competencies during their time at university. This paper describes how the University of Galway is utilising the Entrepreneurial Potential and Innovation Competences Tool (EPIC) to track the changes in student self-reported competencies having participated in one of its flagship student entrepreneurial programmes. Based in Ideaslab, the university’s Human Centre Design Studio, the approach used is experiential, design centric and informed by D.School, Stanford. Initial findings from the EPIC surveys completed by 23 students are reported. These data are part of a university wide initiative and further data will be collected over the next three semesters. In so doing, we hope to add to the body of knowledge concerning the utility of this approach to measuring the changes in entrepreneurial competencies following participating in a university based entrepreneurial learning activity.
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En el presente artículo se va a utilizar la técnica Desing Thinking para solucionar y evaluar el problema de la selección de métricas de calidad de un software, considerando algunas técnicas para la recopilación de las ideas que puedan dar soporte a la elaboración de una propuesta de solución al problema que en este caso es el aseguramiento de la calidad basadas en las métricas de evaluación definidas en los estándares actuales conocidos. Todo esto es posible gracias al apoyo de herramientas web que proporcionan plantillas para abarcar las fases del Design Thinking. También se hace el detalle de las fases empezando por la fase de Empatizar seguido de Definir, Idear, Prototipar, Testear y finalmente llegando a los resultados y las conclusiones encontradas luego de todo el proceso del Design Thinking. Se espera que los resultados ayuden a la construcción de software de calidad y sean utilizados en un proyecto real y en sus diferentes etapas haciendo que el software tenga éxito en el mercado.
Conference Paper
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Young, agile, innovative are some adjectives that can be used to describe startups. Known to disrupt and change markets they are also early adopters of cutting-edge practices. However, startups do have a very low success rate and a major reason is the lack of funds. Startups operate with limited assets and visibility on profit margins. Investors secure their investment based on the reputation and credit worthiness of the startup founders or valuation methods. A pivotal question is, are there missing variables in these valuation approaches? The answer is, 'Sustainability'. Today there is a need for greater accountability on sustainability in businesses and sustainable funding too is increasing. Startups with a sustainable approach can be more attractive to investors. This paper proposes startups to adapt a sustainable first approach not only in meeting societal demands but also to enhance the startup's value. Sustainable valuation will help founders and investors alike in identifying long-term benefits of a startup's sustainable approach.
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Abstract Only critical distancing let Godard value the premonitory potential of film, and at the same time, point out the incapacity to register the realness that it generated. The filmmaker has always believed that “film is prophetic, it predicts and announces things”, and that this condition corresponds to its essence as a record. Rancière in a lucid reading of Film History says that for Godard: “film is responsible for not filming fields in their time; great for filming them before their time and guilty for not knowing how to recognize them”. This article begins with these clear political propositions, as well as proposing some variations.
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How does man know anything and, in particular, how can we account for creative thought? Campbell posits 2 major conditions: mechanisms which produce wide and frequent variation (an inductive, trial and error, fluency of ideas) and criteria for the selection of the inductive given (the critical function). The ramifications of this perspective are explored in terms of organic evolution and human history, and in terms of psychology and epistemology. This exposition is offered as a pretheoretical model.
“Everybody loves an innovation, an idea that sells.“ But how do we arrive at such ideas that sell? And is it possible to learn how to become an innovator? Over the years Design Thinking – a program originally developed in the engineering department of Stanford University and offered by the two D-schools at the Hasso Plattner Institutes in Stanford and in Potsdam – has proved to be really successful in educating innovators. It blends an end-user focus with multidisciplinary collaboration and iterative improvement to produce innovative products, systems, and services. Design Thinking creates a vibrant interactive environment that promotes learning through rapid conceptual prototyping. In 2008, the HPI-Stanford Design Thinking Research Program was initiated, a venture that encourages multidisciplinary teams to investigate various phenomena of innovation in its technical, business, and human aspects. The researchers are guided by two general questions: 1. What are people really thinking and doing when they are engaged in creative design innovation? How can new frameworks, tools, systems, and methods augment, capture, and reuse successful practices? 2. What is the impact on technology, business, and human performance when design thinking is practiced? How do the tools, systems, and methods really work to get the innovation you want when you want it? How do they fail? In this book, the researchers take a system’s view that begins with a demand for deep, evidence-based understanding of design thinking phenomena. They continue with an exploration of tools which can help improve the adaptive expertise needed for design thinking. The final part of the book concerns design thinking in information technology and its relevance for business process modeling and agile software development, i.e. real world creation and deployment of products, services, and enterprise systems.
As the world deals with increasing complexity-in issues of sustainability, finance, culture, and technology-business and governments are searching for a form of problem solving that can deal with the unprecedented levels of ambiguity and chaos. Traditional "linear thinking" has been disparaged by the popular media as being inadequate for dealing with the global economic crisis. Standard forms of marketing and product development have been rejected by businesses who need to find a way to stay competitive in a global economy. Yet little has been offered as an alternative. It is not enough to demand that someone "be more innovative" without giving him the tools to succeed. Design synthesis is a way of thinking about complicated, multifaceted problems of this scale with a repeatable degree of success. Design synthesis methods can be applied in business, with the goal of producing new and compelling products and services, and they can be applied in government, with the goal of changing culture and bettering society. In both contexts, however, there is a need for speed and for aggressive action. This text is immediately relevant, and is more relevant than ever, as we acknowledge and continually reference a feeling of an impending and massive change. Simply, this text is intended to act as a practitioner's guide to exposing the magic of design.