Creative crowds and wise screen teams are needed to accelerate customer centric innovation

Christian Brix Tillegreen MSc. Management of Innovation & Business Development Copenhagen Business School
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Creative crowds and wise screen teams are needed to accelerate
customer-centric innovation
A case study of an internal online ideation at Novozymes
Copenhagen Business School
MSc. Management of Innovation & Business Development
January - 2014
Christian Brix Tillegreen (021187-2179)
-
Supervisor: Jörg Claussen
Dept. of Innovation and Organizational Economics

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Std. Pages: 68 (STUs: 129.430)
Executive Summary
Development of innovative solutions which meet market needs timely and accurately is becoming more
and more critical for companies in order to stay competitive. With the advent of the digital
collaboration age crowdsourcing offers a novel approach to conceive and co-develop innovative
solution concepts online. The implicit attractiveness of crowdsourcing lies in the expectation of an
acceleration effect: while traditional communication of market needs and ensuing solution development
involves lengthy, error-prone communication processes, crowdsourcing opens the possibility to engage
all critical business functions simultaneously and in a real-time fashion. Thus, market needs could be
shared instantaneously across the organization and turned into solutions concepts faster and more
accurately through cross-functional concept development.
The present thesis investigates certain success factors which need to be in place in order to further
strengthen the role of crowdsourcing as an innovation accelerator in the Danish biotech company
Novozymes. Earlier research on an internal crowdsourcing campaign in Novozymes had already
indicated three key success factors: (1.) diversity of crowd composition, (2.) absorptive capacity and
capability of the idea-receiving organization and (3.) a culture permissive to new ideas and innovation
(Lauto et al., 2013).
The present thesis now focusses on a more recent dataset, recorded from a subsequent internal digital
campaign, called ‘New Claims for Detergents’. Here the focus of attention lied with particular
capabilities of the crowd and the screen team, respectively. ‘Crowd wisdom’ was investigated as the
crowd’s capability to create a sufficiently large number of quality ideas, to co-develop these through
cross-functional discussion as well as their collective receptiveness to novelty and to market needs as
articulated in the presented ideas. The ‘screen team’s wisdom’, on the other hand, related to the
professional scrutiny of this expert group and their cognitive capability of gaining new insights from
the presented ideas and their discussion by the crowd.

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With regards the crowd’s wisdom it was found that the present crowd of 105 employees was highly
engaged in sharing and actively discussing 76 diverse ideas, many of which featured novelty and
market needs. Moreover, idea novelty, articulation of market needs and cross-functional discussions
where significant predictors of high dot-vote scores as awarded by the crowd. Interestingly, the screen
team’s perception of the same ideas and discussions was distinctively different: novelty, market needs
and cross-functional discussions were not correlated with the screen team scores. This was interpreted
as the result of the team’s critical scrutiny for idea consistency and in particular for supporting
evidence. Furthermore, this 5-person team took the ad hoc decision to go beyond their originally stated
objective of identifying a couple of trophy ideas (‘idea hunting’) because they became intrigued by
emerging idea-connections during their meticulous work with the idea-universe generated by the
crowd. They conceived a systematic approach to cluster and organize all ideas which lead to a
‘strategic’ idea map. This was an unintended product of their collective cognitive process and enabled
new angles of discussion in their management circles.
While it is too early to conclude whether this serendipitously arising cognition process can form the
foundation of a new repeatable ‘crowd-strategizing’ protocol, it seems very unlikely that the condensed
insights of the screen team would have been possible without the spontaneous, collaborative mass-
creativity of the crowd.
It is discussed whether it is desirable to push innovation burden from the screen team onto the crowd
(e.g. by demanding better idea consistency and evidence), because one has to consider the inherent risk
of negatively impacting the very crowd-creativity without which the screen team cannot unfold its
potential of insight creation and ‘wisdom’.
The major finding of this thesis is thus that desirable capabilities and behaviors of crowd and screen
team are highly distinctive but complementary; they are interdependent and indispensable to enable
accelerated innovation in the context of Novozymes.
It follows that sustainable use of crowd-sourcing as an innovation accelerator in Novozymes must pay
special attention to ensure that innovator crowds stay creative and engaged but also to muster critical,
yet open-minded screen teams. The value proposition to these expert teams must be that facing the

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creative, ‘noisy’ chaos of the crowd can have significant paybacks in the form of new maps of insights
which could be much more intriguing than ‘just’ a couple of new good ideas.
Table of Content
1. Introduction...................................................................................................................................... 7
1.1 Problem statement ..........................................................................................................................................9
1.2 Research hypotheses.....................................................................................................................................11
1.3 Research relevance .......................................................................................................................................15
1.4 Disposition....................................................................................................................................................16
2. Literature review ........................................................................................................................... 17
2.1 Innovation – in the science-based corporation .............................................................................................18
2.2 Crowdsourcing .............................................................................................................................................19
2.2.1 Assembling the “wise crowd” ................................................................................................ 20
2.2.2 Crowdsourcing in the corporation ......................................................................................... 21
2.3 Collaboration modes.....................................................................................................................................25
3. Company and case description..................................................................................................... 28
3.1 Description of Novozymes ...........................................................................................................................28
3.2 Innovation in Novozymes.............................................................................................................................30
3.3 Innovation processes in Novozymes ............................................................................................................31
3.4 Case: New Claims for Detergent Enzymes ..................................................................................................32
3.4.1 The process of “New Claims for Detergents”........................................................................ 33
3.4.2 Scope and plan........................................................................................................................ 33
3.4.3 Mobilization and composition the crowd ............................................................................... 34
3.4.4 The online ideation phase....................................................................................................... 34
3.4.5 Screening and selection of ideas............................................................................................. 35
4. Methodology................................................................................................................................... 37
4.1 Research approach and design......................................................................................................................37

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4.1.1 Data collection........................................................................................................................ 38
4.1.2 Qualitative data ...................................................................................................................... 39
4.1.3 Quantitative data .................................................................................................................... 40
4.2 Two-fold analysis of crowd versus screen team...........................................................................................43
4.3 Limitations....................................................................................................................................................44
5. Analysis........................................................................................................................................... 45
5.1 Analysis part 1: Descriptive analysis and distribution of ideas...................................................................46
5.1.1 Outcome of “New claims for detergent”................................................................................ 46
5.1.2 Ideas with cross-functional involvement: ............................................................................... 47
5.1.3 Novelty of ideas: ..................................................................................................................... 49
5.1.4 Clustering of the ideas............................................................................................................ 50
5.1.5 Responsiveness to market needs:............................................................................................ 51
5.1.6 Screen team evaluation: ......................................................................................................... 54
5.2 Conclusion of analysis part 1: ......................................................................................................................56
5.3 Analysis part 2..............................................................................................................................................58
5.3.1 Putting the numbers into relation........................................................................................... 58
6. Discussion........................................................................................................................................... 60
6.1. Revisiting the research hypotheses..............................................................................................................60
6.2 The crowd‘s excitement about the presented ideas was not shared by the screen team...............................62
6.2 The screen team’s perspective......................................................................................................................63
6.3 From singular ideas to ‘strategic idea landscapes’ .......................................................................................65
7. Conclusions and Perspectives - Moving from idea-hunting to ‘crowd-strategizing’ .............. 68
7.1 Further Research...........................................................................................................................................71
Bibliography .......................................................................................................................................... 72
Appendices..................................................................................................Error! Bookmark not defined.
Appendix 1: ........................................................................................................Error! Bookmark not defined.

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List of Figures and Tables
Figure 1; Disposition of the thesis .......................................................................................................... 16
Figure 2; the layers of crowds................................................................................................................. 22
Figure 3; Modes of Collaboration........................................................................................................... 26
Figure 4; Novozymes organization......................................................................................................... 29
Figure 5; Process of online ideation........................................................................................................ 33
Figure 6; Composition of the Crowd ...................................................................................................... 47
Figure 7; Distribution of new ideas ........................................................................................................ 49
Figure 8; Comprehensive idea map ........................................................................................................ 50
Figure 9; Statistical comparison of screen team scores given on commercial and technical criteria ..... 55
Table 1; Table of hypotheses .................................................................................................................. 11
Table 2; Pros and Cons of Crowdsourcing ............................................................................................. 23
Table 3; Total outcome of the dataset:.................................................................................................... 47
Table 4; Distribution of cross functionally discussed ideas.................................................................... 48
Table 5; Origin of the Top 25 ideas ........................................................................................................ 49
Table 7; Distribution of ideas with market articulation.......................................................................... 54
Table 8; Regression model of the screen team score and the crowd score:............................................ 59
Table 9; Summary of findings and hypotheses acceptance .................................................................... 62

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1. Introduction
“…You can’t ask customers what they want and then try to give that to them. By the time you get it
built, they’ll want something new…” - This quote by Steve Jobs is a great point of departure for this
thesis. The ability to develop new innovations that customers actually want is one of the single hardest
things to do for a modern corporation. Especially in times where technology and markets are changing
by the day, in relation to Moors law (Moore, 1965), the rapid development in production and new
market spaces are forcing companies to innovate faster and to come up with new ideas they can deliver
to the market more rapidly than the competitor.
One of the greatest challenges for innovation in modern corporations is the organization’s ability to
listen to the ‘market needs’ and to develop solutions which meet the customer’s needs accurately,
timely and cost competitively. In essence, this capability equals accelerated innovation, which should
give companies a substantial competitive advantage.
Looking at Danish biotech company Novozymes, the challenge of accelerating innovation is put high
on the agenda by top management. In April 2013 the new CEO, Peder Holk Nielsen, addressed this
challenge in a press release video about the new strategy in Novozymes in which he focusses on
customer-centric innovation as a new ambition for Novozymes:
“…The first focus area of the new leadership-team is on growth, it is going to be, bringing innovation
quicker, faster, from labs, from our research through business development and to our customers. Get
this process to work faster and to get more delivered to our customers, this is focus area number
one…” - Peder Holk Nielsen, CEO of Novozymes, interview from Spark TV 02/04/20131
.
This thesis is set out to investigate how Novozymes handles this challenge by using digital tools and
internal crowdsourcing in their goal to deliver innovation faster to their customer, and more accurate to
the market needs.
Listening, understanding and responding to the market needs with competitive innovations is difficult
because of the organizational complexity: even the most skilled sales representatives and technical
1
http://www.novozymes.tv/video/7631519/peder-holk-nielsens-view-on.

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service experts may not understand what the customer really wants and the reports they forward to the
other parts of the organization may dilute or even distort the essence of the customer’s need. As a
result, R&D more often than not may end up developing the wrong products too late. A partial solution
to this challenge lies in establishing multidisciplinary account teams, made up of sales reps, customer
service, supply chain and R&D experts. Still, many account teams may find themselves challenged in
lacking the necessary diversity of skills and insights – which they naturally will attempt to mitigate by
engaging into a dialogue with their cross-functional networks. Undoubtedly, such dialogue increases a
given account team’s chances to kick start the right innovation activities but the underlying problem of
lacking diversity still lingers because account teams will in most cases try to get their answers and
insights from their ‘usual suspects’ – i.e. the expert circles which the habitually consult and collaborate
with. Consequently, innovation will follow the established patterns of failures and successes. The
advent of collaborative online ideation tools which combine elements of traditional ideation and social
media seem to offer a more fundamental solution to the problem of limited diversity. Idea campaigns
can be designed in such a way that very large and very diverse crowds are composed of members
across all functions of the business system, all markets, segments and hierarchy levels. Naturally,
customers can be part of such ideations as well.
In a sense, online ideations facilitate a ‘level playing field’ discussion because everybody’s’ ‘voice’ is
equal and is being ‘heard’ by everybody else, and everybody’s dot-votes count the same. Thus,
differences in hierarchy are – at least partially – abolished. Hence, the anticipation is that in such an
arena there is a much higher chance that the actual market needs are being heard in an undistorted
fashion and responded to in a meaningful way by a motivated crowd of creative participants.
Novozymes has been using collaborative online ideations since 2011 in a successful manner and
routinely across business divisions. The first online ideation in Novozymes was studied thoroughly by
Lauto et al (2013). The main findings were that online ideations are a potent tool to boost front-end
innovation through digital collaboration.
The research subject for this thesis is to give a descriptive analysis of one particular Novozymes online
ideation campaign called ‘New Claims for Detergent’ and to investigate how the ‘market needs’ are

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being perceived in the articulated ideas and in the crowd discussion. One of the most tantalizing
questions which this thesis tries to answer is whether there is significant evidence that the crowd
responds to market needs with innovative solutions.
“…Innovation has nothing to do with how many R&D dollars you have. When Apple came up with the
Mac, IBM was spending at least 100 times more on R&D. It's not about money. It's about the people
you have, how you're led, and how much you get it…” Steve Jobs quoted in "TIME digital 50"
in TIME digital archive (1999)
The overall objective of this thesis is not to arrive at general conclusions or validations about
crowdsourcing as such rather its aim is a more in-depth investigation of a single case on how internal
crowdsourcing can accelerate the innovation process in a global company. By applying newly gained
knowledge in the most appropriate way, and thereby pose some unambiguous propositions and
managerial recommendations for future acceleration of innovation within Novozymes.
1.1 Problem statement
The internal crowdsourcing exercise ‘New Claims for Detergents’ which is the subject of this thesis
was novel in Novozymes in the sense that it aimed at create new concepts in response to market needs
as articulated in the ideas from Sales and Technical Service participants. This was in contrast to earlier
internal crowdsourcing exercises which were mostly conducted within the R&D community
exclusively, with no or little representation of customer-facing employees. The crowd of ‘New Claims
for Detergents’ thus consisted of employees from Sales, Technical Service, Marketing and R&D,
spanning across the entire value creation chain of Novozymes. Participants were partially known
experts within the field (enzymes for detergent) and partially ‘unusual suspects’ i.e. people without an
expertise in detergent enzymes but with a high potential to contribute with good ideas and energy to the
cause. Typically, newcomers with a rising star reputation working in other business divisions were
chosen to complement the ‘usual suspects’. Diversity was also present in terms of geographical
location and tenure of participants. The dataset used for this thesis represents a digital ideation
campaign which lasted approx. 14 days, leading to 74 ideas and over 200 comments, produced by 105
employees from worldwide locations. The limitation however was that the ideation did not involve

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customers directly but that representatives from Sales and Tech Service functioned as ‘proxies’. This
thesis will investigate the following aspects in order to answer the posted research questions:
The presented thesis studies the abilities of online ideation as a tool for idea generation filtering as well
as deeper insight creation into idea clusters, which extends beyond identification of singular winner
ideas. As argued by a number of scholars and researchers the ability of crowdsourcing to turn ideas into
innovations and new products it is difficult to measure or even predict (Poetz and Schreier, 2012).
The purpose of this thesis is therefore to contribute to the existing literature with empirical evidence
from the case of “New Claims for detergent”. The exploratory nature of the research has led to an
investigation of the effects of inventor diversity on the development of ideas. The question examined is
thus:
Which role does the ‘wisdom’ of the crowd and the screen team play in internal crowdsourcing as a
tool to accelerate a corporation’s response to market needs?
In order to answer this central research question the analysis will go into investigation of nine different
hypotheses as stated below. The examination of these research hypotheses will elucidate the different
conditions which need to be fulfilled in order to conduct online ideations successfully – which, in turn,
is expected to accelerate customer centric innovation processes. Based upon the results and managerial
recommendations will be proposed to aid the company’s ongoing exploration of opportunities within
crowdsourcing. It is thus hoped that this study makes a contribution to enhance and to optimize future
customer-centric innovation processes.

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1.2 Research hypotheses
In order to answer the question whether the crowd and the screen team in the present campaign can be
considered as ‘wise’ and thus having the potential to accelerate customer centric innovation, nine
hypotheses are stated which are clustered in four categories (Table 1). In the context of this thesis a
wise crowd is expected to:
(1.) Come up with a large proportion of high-quality ideas in relation to the stated challenge
(hypotheses 1-3)
(2.) Present a substantial proportion of novel ideas which are considered as having a high innovation
potential by crowd and screen team alike (hypotheses 4 and 5)
(3.) Present a substantial proportion of ideas which trigger cross-functional discussions which serve to
develop these ideas in a collaborative fashion. Such ideas are ranked highly by crowd and screen
team, respectively (hypotheses 6 and 7)
(4.) Propose a substantial fraction of ideas which contain a clear articulation of market needs.
Furthermore, such ideas are ranked highly by crowd and screen team alike (hypotheses 8 and 9).
Table 1; Table of hypotheses
Hypothesis
category
Hypo-
thesis
no.
Hypothesis text Criteria for
acceptance
Method of
testing
Idea
quality
1 Ideas of high quality are characterized by a substantial length
of text in order to articulate thoughts of a certain complexity.
Such ideas represent the majority in the present idea
population.
75% of all ideas
contain more
than 420
characters
Descriptive
statistics
2 Ideas of high quality draw multiple comments from the crowd
and constitute the majority in the present idea population.
75% of all ideas
are associated
with at least 2
comments
Descriptive
statistics
3 Ideas of high quality contain supporting references to internal
or external sources, which is the case for a substantial fraction
of present ideas.
25% of all ideas
contain
references
Descriptive
statistics

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Idea
Novelty
4 Novel ideas represent a substantial fraction of the idea
population
25% of all ideas
were judged as
novel by the
screen team
Descriptive
statistics
5 Novelty is a predictor of high idea ranking. This is expressed
by significant correlation of novelty with idea scores, as given
by the crowd and the screen team, respectively
p<0.05 Regression
analysis
Idea co-
creation
6 Ideas with cross-functional comments constitute a substantial
proportion of the entire idea population
25% of all ideas
are associated
with cross-
functional
comments
Descriptive
statistics
7 Co-creation is a predictor of high idea ranking. This is
expressed by significant correlation of cross-functional
discussion with idea scores from crowd and screen team,
respectively.
P<0.05 Regression
analysis
Responsiv
eness to
market
needs
8 Ideas which contain a clear articulation of market needs
constitute a substantial proportion of all ideas
25% of all ideas
contain market
needs
articulation
Descriptive
statistics
9 Ideas with market needs articulation are ranked highly. This is
expressed by significant correlation of ‘market need
articulation’ with idea scores from crowd and screen team,
respectively.
P<0.05 Regression
analysis
The reasoning behind picking the various acceptance criteria for the nine stated hypotheses is explained
in the following: arbitrary criteria were used for all hypotheses which were tested by descriptive
analysis (hypotheses no. 1-4, 6 and 8). Statistical significance (p>0.05) was the acceptance criterion
when testing hypotheses no. 5, 7 and 9.
Hypothesis 1: ‘Substantial length’ is arbitrarily set to mean 420 characters of text based on the
communication principle in the social medium Twitter: in Twitter a post is limited to 140 characters,
but the immense global success of the medium proves empirically and beyond doubt that 140
characters comprise an information ‘package’ or ‘string’ whose length is fully sufficient to express a
virtually unlimited number of reasonably complex thoughts. In the context of this thesis it is further
inferred that a well-articulated idea consists of at least three such Twitter-length statements (3 x 140 =

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420): first statement pertaining to background or context of the presented idea, second statement
embodying the problem at hand and the third statement describing the proposition of its solution.
‘Majority of ideas’ is defined to mean 75% of all ideas posted.
Hypothesis 2: It is assumed that the number of comments which an idea triggers is likely to be an
indicator of content relevance and thereby of idea quality. It is reasoned that ideas with no comments
are most irrelevant since nobody in the crowd was enticed to respond to them. Ideas which drew only
one comment are equally likely to have a no or only very low relevance since singular comments may
just be an expression of spontaneous (dis)approval. Thus, the minimum number of comments which an
idea needs to draw from a crowd to be considered relevant – at least to some extent – is two. Two
comments per idea are viewed as a minimum threshold for relevance: an initial idea is sufficiently
relevant to trigger somebodies comment and somebody else adds another comment which contributes
an additional perspective to the idea or the first comment. One could also say that one idea and two
ensuing comments constitute a set of minimum requirements to justify the use of the term ‘idea
discussion’.
Hypothesis 3: References to internal or external sources are a strong indicator of idea quality since
references support ideas with ‘reason to believe’ originating from others than the idea proposer. The
crowd of the investigated campaign consisted of 50% participants from R&D. It is therefore argued that
since R&D employees (i.e. scientists) are to a high degree used to reference their results and
conclusions, it would be fair to expect that at least half of all ideas coming from R&D should contain
some kind of reference. This would correspond to a reference incidence of 25%, assuming an equal
distribution of idea generation frequency across all business functions involved.
Hypothesis 4: Idea novelty is a key feature of innovation and in the present campaign the screen team
scored all 74 ideas as being novel or not after the campaign was concluded (Appendix 2). The re-
combinatorial nature of ideas, as described by Schumpeter (1939), Nonaka (1994) and others, implies
that one can expect novel ideas to constitute a substantial but still minor percentage of all idea posted.
In the context of an earlier Novozymes crowdsourcing exercise by Lauto et al, (2013) found that only
about 25% of all posted ideas could be considered as novel. Based on this evidence the acceptance
criteria for using the term ‘wise crowd’ is equally being set to 25% for the idea population studied here.

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Hypothesis 5: Novelty is also expected to be correlated positively with idea ranking. In the present
dataset two such rankings exist: the idea ranking of the crowd, as expressed by the sum of dot-votes per
idea, and the idea ranking of the screen team, recorded as the summed quantitative scores on six pre-set
screening criteria for each idea (appendix 1). Regression analysis was used to answer the question
whether there was a statistically significant (p< 0.05) correlation between the crowd and screen team
perceptions of idea novelty as expressed.
Hypothesis 6: Co-creation and collaboration are argued to be important – if not critical - conditions of
innovation since different modules of knowledge and insight are recombined into something new
(Keller, 2001). The crowd on ‘New Claims’ was cross-functional in its composition. Participants came
from R&D, Sales, Marketing and Technical Service. It is arbitrarily expected that at least 25% of all
ideas led to discussions between participants from different business functions. The reason why this
threshold was not set higher is the notion that most employees from the implied functions were not
used to have collaborative idea discussions with each other – but were faced with such a ‘challenge’ for
the first time.
Hypothesis 7: Cross-functional discussion is viewed as an indicator of collaboration on a given idea in
the framework of the online process. Different perspectives and information are added to the initial idea
which is expected to increase the innovation potential of the idea. It is therefore hypothesized that such
ideas receive higher scores from crowd and screen team alike. Regression analysis was used to answer
this question and significance level was set to be a p-value of p < 0.05.
Hypothesis 8: In order to accelerate customer centric innovation, crowds need to be responsive to
market needs. In the context of this thesis, market needs are indirectly articulated in the ideas posted by
the various participants. A classification scheme was established to score the ‘intensity’ and ‘clarity’ of
market need articulation as explained in Analysis 5.1.5. Since 50% of all crowd participants came from
customer-facing business functions such as Sales, Technical Service and Marketing it is expected that
at least 25% of all ideas are categorized as containing a market need.

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Hypothesis 9: It is hypothesized that a ‘wise crowd’ shows a significant (p<0.05) response to ideas with
market need articulations, as expressed through high dot-vote scores. It is also expected that the screen
team shares this perspective, as expressed through high scores on the pre-set criteria as detailed in
Appendix 1.
1.3 Research relevance
Since the internet has made it possible to connect individuals in larger groups to interact in online
communities, there has been done extensive research in the area of crowdsourcing. In the resent years
special focus has been put on the question on the maximal productive size of crowds, and which role
crowd diversity plays in the innovation performance, (Soukhoroukova, 2012). However, there seems to
be a need for a deeper perspective and analysis of what crowds actually express online and how
constructions of new ideas emerge in such campaigns. The current literature seems elusive on this
point.
Recent work by Poetz and Schreier, (2012) is one of the few articles which c deals with research
related to the present thesis. However, the focus in their study was more on the differences between the
external versus the internal crowd performance. The research is especially narrowed down to a single
company’s internal performance and how the generated ideas are related to the external world. It is also
more focused on the screening process by senior experts versus the crowd’s opinion. The analysis of
the participant’s ideas is relevant both in terms of strategic implications for the company and for the
academic world, since such ‘crowd insights’ or ‘wisdom’ has not been investigated systematically
before.
The relevance of this thesis lies in its findings around how wise an internal crowd actually is and what
unconsciously happens in the aftermath of the screening process. Especially in terms of ‘crowd-
strategizing’ a novel cognitive step process was performed by the screen team which according to
present search efforts has not been described previously in such form.
In the sense of future crowdsourcing exercises in Novozymes, this thesis will be used as a key element
in designing new opportunities with this field. The finding conclusion will also be presented to
Innovation Management for recommendations in driving customer-centric innovation forward.

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1.4 Disposition
This figure illustrates the deposition which the thesis is built on and gives the reader a better
perspective of what is done to answer and conducting the research.
Figure 1; Disposition of the thesis

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2. Literature review
This chapter will provide a view of some of the recent and most relevant research literature on
crowdsourcing and idea management in large corporations. The research, done in this field has been
focusing to a large extend on external crowdsourcing (Howe, 2006). In the recent years, quantity has
been the main research object, in terms of maximizing the crowd. However, the latest research
investigates how to measure and manage the ideas coming in from the crowd (Soukhoroukova, 2012).
The object of this part of the thesis is also to identify the how the literature reflects upon theory of
assembling and using a crowd to achieve business objectives. The notion of using internal
crowdsourcing in a strategic context is not something that has been documented that much, in contrast
to a lot of theory regarding large open source and external crowdsourcing (Flynn, et al., 2003).
In order to discuss the research hypotheses, it is necessary to create some clarification on how
crowdsourcing has been presented and used to generate new ideas and solutions for the past decade.
The measurement of a “wise crowd” and “picking the right idea” are two very different things, and
seen as two different ways of executing an ideation. A winning idea can easily be a pure technology
focused solution, however in this thesis, the theory is focused on the way companies built and structure
their crowd in order to use the generated outcome in a strategic matter and identity knowledge and
market gaps.
The provided starting point is to give an understanding for what types of arguments that can be stated
in order to justify online idea campaigns inside companies, and to give a perspective to narrow down
some related and specific research. This enables the thesis to draw on some of the relevant theory to
answer the research questions around the use of internal crowdsourcing in Novozymes.
The following chapter will start with a short explanatory approach of where the need for a collaborative
environment has emerged from in science based companies. Then the chapter goes into the core of the
literature around crowdsourcing and explains the theoretical benefits and barriers and other
implications of crowdsourcing.

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2.1 Innovation – in the science-based corporation
In the text “The evolution of Science-based business: innovating on how we innovate” by Gary Pisano
from 2010, it is argued that traditionally, science has always been connected to innovation and thereby
states that it has its home in the R&D part of the organization. However Pisano explains that in the later
20th
century, innovation systems starts to change in the area of the emerging bioscience and
biotechnology industry; especially in the way science and business are connected. A shift emerged in
the way science moves more efficiently from the laboratory to the commercial market (Pisano, 2010).
“…even DuPont, by the 1980s, was asking its research laboratories to focus more on the commercial
needs of the existing businesses (Hounshell and Smith, 1989)…”
Now, this seemed like a good way to get R&D aligned with the rest of the organization and an easy
way to commercialize new innovations fast to the market, but Pisano argues that there is an important
attribute that lies in the iterative nature of R&D, “…time horizons to resolve fundamental uncertainty
can be quite long. Thus, not only might the financial costs of exploration be high, but critical technical
uncertainties may not be easily or quickly resolvable early in the development process.” This is not a
new challenge of the science-based business field, it is more a question of the knowledge base that the
companies now operates in, which now is changing in an extreme pace.
In areas where the underlying science is more mature, knowledge is often modular. That is, with deeper
understanding comes knowledge about fundamental “building blocks” and how those interact (Ibid).
Collaborative software is an example of how R&D can break down problems into module components,
for example in the idea generation phase. In sense there could be different “pieces” but their boundaries
are not clearly defined. How one thing affects the other may not be well understood at all (ibid). Pisano
calls this “the integration problem” and relates it to the argument made more than 50 years ago by
Schumpeter (1939), where he found that breakthrough innovation is the result of recombination and
integration of existing bodies of knowledge (Fleming, 2001). Many empirical studies have confirmed
that Schumpeter was right in his observations (ibid).

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2.2 Crowdsourcing
Crowdsourcing is a recent approach to increase the amount of ideas, solution and turn them into
innovations in companies. The ideology behind crowdsourcing is to use the wisdom of many
individuals instead of relying on only a few experts (Surowiecki, 2004). The literature uses various
terms to describe related phenomena, such as peer production, collaborative systems, community
systems, collective intelligence, crowd wisdom and mass collaboration (Flynn, et al. 2003). Other terms
often used in the literature include consumer co-creation (Simula, et al. 2012), open innovation
(Chesbrough, 2011), user innovation (von Hippel, 2005), collaborative innovation (Soukhoroukova,
2012), customer driven (Schreier, 2011) and used-generated content (Hine and Kapeleris, 2006). In this
thesis the use of the term “crowdsourcing” is set to describe idea and innovation generation.
Crowdsourcing is still on its raising in the corporate world, however the concept of crowdsourcing is
very known in world around, basically it is a way for someone; a person, department, group,
government, company, even countries, to source a question, problem, issues or new ideas and get
feedback from a wide number of people; the crowd. In history we see a lot of different examples.
Boudreau and Lakhani (2011) mention an example from the 15th century where authorities in Florence
presented an open invitation for everyone to participate in designing what would be the world’s widest
and tallest dome for the city’s new cathedral.
One of the largest and most well-known examples of crowdsourcing is Wikipedia.org; this website is
used all over the world and is developed by a global crowd to create free access to knowledge. The size
of knowledge shared on Wikipedia is a great example of how powerful the internet can be in sharing
and generate knowledge (Surowiecki, 2004).
In the literature around crowdsourcing, the notion of “wisdom of the crowd” is very often discussed.
The wisdom of the crowd refers to “the discovery that the aggregate of a set of proposed solutions from
a group of individuals performs better than the majority of individual solutions.” (Yi et al., 2012:452).

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2.2.1 Assembling the “wise crowd”
In crowdsourcing the single most important variable that needs to be in place and function correctly if
of cause the crowd itself. Surowiecki, 2004 suggest a framework whereas it is necessary to break down
the advantages of crowdsourcing into three types of “wisdom of crowds”, which is classified as the
following (Surowiecki, 2004):
Cognition
In order for a crowd to be cognitional, it needs to be highly capable of intellectual thinking and
understand the information processing aspect of the problem proposed. Secondly the crowd needs to
have some understanding of market judgment, which Surowiecki argues can be much quicker and more
reliable, in producing ideas that will succeed in the evaluation process. The crowd is also less subject to
political or managerial powers than the discussions of experts or in knowledge heavy committees
(ibid).
Coordination
In order for a crowd to be classified as a coordinating crowd their behavioral approach needs to be
clearly focusing around optimizing the utilization of the ideas co-development aspects. Surowiecki uses
examples from experimental economics, however it is also takes in the cultural aspect in terms of how
common understanding within a culture allows remarkably accurate judgments about specific reactions
of other members of the culture (ibid)
Cooperation
The cooperative aspect of assembling the “wise crowd” refers to how crowds can form networks and
connections of trust without a central system controlling their behavior or directly enforcing their
compliance.
Furthermore Surowiecki argues that, not every crowd is rational and can create wise decisions, (e.g.
group of investors in a stock market bubble), so he describe these following four criteria you need to
form a wise crowd, in order to create a rational crowd and not end up with an irrational crowd
(Surowiecki, 2004).:

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Table 2; Criteria of a wise crowd
(Source: Surowiecki, 2004)
Surowiecki argues however, that failure of crowd intelligence is still a risk. In some cases the crowd
can come up with bad judgment. He argues that the crowd cognition or cooperation failed because the
participants of the crowd were too conscious of the opinions of others and instead of thinking
differently started to emulate and conform each other’s ideas (ibid). He asserts that when the decision
making environment is not set up to accept the crowd, then the benefits of individual and private
knowledge is lost and not articulated. This can lead to the crowd only being able to be as beneficial as
its smartest member, instead of perform better as a crowd (ibid).
2.2.2 Crowdsourcing in the corporation
The preceding chapter has defined and discovered which factors are important to secure when using
crowdsourcing as a tool. Now it is wanted to give a deeper understanding of crowdsourcing within the
corporation, and look at some of the literature around internal crowdsourcing. In the text of Simula
et.al, 2012, is it explained that the potential of crowdsourcing in relation to creating new ideas and
innovations in a business-to-business environment is starting to get quite popular, because
crowdsourcing is lowering the cost and shortening the product development cycles. One of the key
value propositions of crowdsourcing is that it enables companies to collect ideas from large groups and
manage review them, instead of using time and money on sourcing from few experts (Simula et.al,
2012) .
In order to get an understanding of how crowdsourcing can be handled by companies I will use the
framework provided by Simula et.al, 2012. This framework consists of four layers of participants,
which a company can use when initiating an exercise for crowdsourcing. The framework builds on the
traditional stakeholder theory (Freeman et. al. 2004). It is argued that this model can be applied to a

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number of industries, but in this thesis the research will mainly be used in the context of internal
crowds.
Figure 2; the layers of crowds
(Scource: Simula et. al., 2012).
In the center of the figure (3) are the employees of the company, this is also were most companies
begin when using crowdsourcing in the business-to-business sector, however after some practices,
proof of concept and success cases, the company can start to move into the external layers, consisted of
trusted partner (also known as “value-chain partners”) by Freeman et al., 2004. The next layer consists
of a specific crowd, including people with certain skills, knowledge and expertise or other pre-
qualifications; it can also refer to a community of like-minded individuals (Simula et. al., 2012).
The third and last layer is the general crowd which can consist of everyone, who has an interest in the
scope of the campaign, even competitors can participate here.
However in this case study it is necessary to return back to the first layer, and focus on the internal
crowd, within the boundaries of the company. The other layers are outside the scope of this thesis.
According to (Simula et. al., 2012) Companies usually target internal idea ideation at all employees, in
order to increase serendipity, which is seen as a good thing. Howe, 2008, argues that the best solutions
often emerge from crowds that are the least likely to come up with the right way of solving the raised
problem. Internal crowdsourcing has been practiced by many companies. One example is the

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pharmaceutical giant Eli Lilly, who have demonstrated the value of harnessing the knowledge of
employees to solve problem and idea generate throughout the organization. However Boudreau and
Lakhani 2011, points out that company culture can be a major issue in the pursuit of getting a crowd to
be innovative thinking, they state that one of the most important motivators is to provide the crowd
with the sense that their ideas are valued by management. When designing an online ideation Simula et.
al. 2012 argues that it needs to be done in such a way that they can be unambiguously understood by
the crowd. In order to engage people in crowdsourcing and to make them contribute, people need to
understand the context of the idea and have the right frame of mind (ibid). They argue that feedback
also plays a major role in the perception of how the crowd will react to the requested task; “There is
also the risk that people may want to develop their ideas by themselves and are not ready to share or
that they think their idea is not good enough to post. Another challenge related to engaging users is the
importance of feedback: not receiving feedback may have a negative impact on future participation of
the contributor. Similarly, the informant in Gamma pointed out that if people think that the internal
idea service is meant for R&D people only, there may be less interest in participating.”(Ibid).
Simula et. al. 2012 gathers their findings in a table of advantages and benefits and some of
consequences and barriers in using internal crowds in crowdsourcing. These thoughts of internal
crowdsourcing will be taken into consideration in the discussion part of this thesis.
Table 2; Pros and Cons of Crowdsourcing
(Source: Simula et. al. 2012.)

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To get a bit deeper into the research related more specific to my study of internal crowdsourcing, I will
draw on the recent published article, “The Value of Crowdsourcing: Can Users Really Compete with
Professionals in Generation New Product Ideas?” By Poetz and Schreier (2012). They make an
analysis of a case company that crowd source ideas for new products, and ask a crowd of 100+
participants. 52 were internal employees and the remaining 51 were customers. The participants where
posting ideas through the company website. The ideas were screened and evaluated by the company’s
CEO and the Head of R&D, blinded of whether the source of the ideas originally came from inside the
company or outside. The study is similar to my research in terms of measuring the ideas and where the
ideas come from. Poetz and Schreier set the following research question; how attractive are new
product ideas generated by users through a crowdsourcing process compared with new product ideas
generated by a firm’s professionals? In this study it is stated that the professional engineers out
performed in the creation of feasible ideas, in contrast to the ideas generated outside R&D, in the case
the, which the authors conclude to be an indication that the professional crowd is much more focused
on deliver ideas that is easier to realize in technical terms, whereas the ideas from costumers are
focused on the commercial benefits for the end-user, but lacks of realistic deliverables and timeframes
for actual execution to the market. Whereas the authors draw on the theory from Chesbrough (2011),
that they need to reinvent the formulated question to a specific problem-solver, crowd or group.

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2.3 Collaboration modes
In the article by Pisano and Verganti, 2009, called “Which kind of collaboration is the right on for
you?” the authors go through different types of crowdsourcing strategies. They are districting between
open and closed networks, and between a hierarchical and a flat governance approach. This very simple
framework is effective in order to get an overview of where the strategic decisions should be made
when initiating a crowdsourcing exercise. The text especially discusses which types of crowds
companies should invite and focusing on who will carry the “Innovation burden”, meaning screening
and filtering ideas etc.
They argue that the cost of searching and screening ideas increases when a company engages with
larger crowds than the internal capacity can carry. However the advantages of having an open network
could result in attracting a large number of problem solvers and consequently a huge number of ideas
generated. The text argues that in this scenario the company doesn’t have to identify either the best
knowledge domain or the most appropriate experts in those domains (Ibid). The company doesn’t even
have to know the contributor, but the text argues that it can be dangerous as well, if the knowledge
domain is a sensitive business area, where you don’t want to have competitors or others who can use
the information provided to other winnings. The authors note that often interesting innovative solutions
can emerge from contributors the company never has imagined could come up with good ideas.
Open modes, however, have their disadvantages. An interesting notion is that, they are not as effective
as closed approaches in identifying and attracting the best participants. Pisano and Verganti argue this
is because as the number of participants increases, the likelihood that a participant’s solution will be
selected (especially for an ambiguous problem) decreases (ibid).
“…Open modes are effective only under certain conditions. First, it must be possible to evaluate
proposed solutions at a low cost. Sometimes the screening process is extremely cheap and fast…”
“…In other cases, though, the only way to find out whether an idea is worth pursuing is through
expensive and time-consuming experiments, and you’ll want to consider fewer (but better) ideas. The
only way to do that is to invite contributions from the problem solvers that you think will have the best
chance of providing good ideas. That is, to opt for a closed mode…” (ibid)

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The figure (4) shows the four basic modes of collaboration: a closed and hierarchical network (an elite
circle), an open and hierarchical network (an innovation mall), an open and flat network (an innovation
community), and a closed and flat network (a consortium).
(Source: Pisano and Verganti, 2009)
Figure 3; Modes of Collaboration
Elite circle: In this mode one company selects the crowd, the screening process is also made by the
company and they define the problem and choose the solution. Pisano and Verganti argue that this
mode is appropriate when the company know the knowledge domain and can determine from where the
best solution to the problem are likely to emerge from. In this mode the chosen experts play a major
role and the company needs to have the capabilities to pick them internally. The owners of the ideation
do the evaluation and screening process and only they evaluate the proposed ideas.
Innovation Mall: In this scenario there is still one company that posts a problem, but here anyone can
propose the solution, and the company chooses the best idea. This mode of collaboration enables the
company to get ideas from many parties, and the best ideas can come from unexpected sources. Here
Pisano and Verganti suggest that the consequences of missing out on a good idea, as in the elite circle,
are limited in this scenario due to the self-selection of participant. Thus if the problem is small or
narrow it can be broken down into smaller sessions, to resolve it faster. In this mode the evaluation

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process is spread over the different parties involved and thereby enables the company to screen the
ideas fairly cheaply.
Innovation Community: In this mode anybody can propose problems, suggest solution and decide
which solutions are best and put into use. This is the most open approach, this mode is properly most
appropriate when the company needs ideas from many different parties. The company can’t own the
intellectual property underlying the solutions, and needs to take this into consideration these
collaboration modes are therefor often seen in open source software projects e.g. Linux or Apache.
Consortium: The authors suggest that this collaboration mode is like a private club, where participants
jointly select the problem and co-create the chosen solution and decide together how to conduct the
further assessment of the winning ideas. This mode is appropriate when the company, like in the Elite
circle, knows the knowledge domain and knows where the best solutions are most likely to emerge.
Once again the importance of having the right experts is a key factor in this mode and the company
needs to have the capabilities to pick them; however the experts need to have “share power” over the
decision making in the selection of winning ideas. Pisano and Verganti also suggest that all the
expertise of all participants is needed in this mode in order to harness the true innovation. In this mode
there can be different ways of sharing the intellectual property, e.g. a co-owned patent or royalties to
the owners of the winning ideas.
Choosing a collaboration mode involves more than understanding the trade-offs. A company must take
into account its strategy for building and capturing value. And as the strategy evolves, the right mode
of collaboration might change, too. (ibid)

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3. Company and case description
In this chapter I will give a short presentation of the case company; Novozymes and provide an
overview of how the company innovate, more specific how Novozymes handle ideas and how the
Innovation Department manage it. In the last part of this chapter I will provide a deeper understanding
of the chosen idea campaign; “New Claims for Detergent”. That later will be analyzed.
3.1 Description of Novozymes
Novozymes is the world leader in Bioinnovation and Biosolutions. Novozymes' core business is
industrial enzymes, microorganisms, and biopharmaceutical ingredients. Their goal is to help their
customers (such as P&G, Unilever, Nestle, PepsiCo) to achieve more efficient product and process
solutions to save energy, raw materials, and reduce waste. The pursued result is higher quality, lower
costs, and a better environment. The biological solutions are used in the production of numerous
products such as biofuels, detergents, food, and animal feed. (NZ1)
Novozymes has over 6000 employees globally, working in Research and Development (R&D),
Production, Sales, Marketing, Technical Service and general administration. The company has a
portfolio of over 700 products, used in 130 countries. The company is quoted on NASDAQ OMX
Copenhagen A/S (NZ2).
The company is performing well financially. Over the past 10 Years, during which the company has
been operating separately from its sister company Novo Nordisk, Novozymes has achieved an annual
sales CAGR of 8% (NZ1). Due to the rapid development within their fields of technology and the
company’s ability to innovate, as well as strong secular trends such as sustainability, chemical
replacement, and energy security, Novozymes today aims to increase their business by more than 10%
a year. On top of this lies the opportunity within cellulosic biofuels. Turning agricultural waste into
sugars for the production of biofuels and other chemicals is a very interesting opportunity for
Novozymes. However, due to the uncertainties associated with the timing and scope of this
opportunity, it is not yet included in their long-term sales growth ambition (The Novozymes Report
2010). In 2012 Novozymes achieved a turnover of 11 billion DKK, and had a net profit of 1,6 billion
DKK. (NZ1)

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Business areas
Novozymes’ business consists of two segments: Enzyme Business and BioBusiness. Enzyme Business
is engaged in development, production and distribution of enzymes, this currently accounts for more
than 90% of sales, while BioBusiness generates the remaining 10%.
Detergent enzymes
These enzymes are used in laundry and dishwashing detergents. In the process of washing clothes,
certain enzymes break down water-insoluble stains into water-soluble molecules that can be rinsed
away by the wash water.
Technical enzymes
Technical enzymes are used, among other things, in the transformation of starch into different kinds of
sugars. This functionality is used in the starch and fuel industries. By 2014 the company expects that
the enzymes will make it possible to produce advanced biofuel from certain agricultural residues in
large-scale production. Technical enzymes are also used for many other applications, for example
leather and textile treatment and forest product industries.
Food enzymes
Enzymes enhance quality or production efficiency in the production of food products such as bread,
wine, juice, beer, noodles, alcohol, and pasta.
Figure 4; Novozymes organization

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Feed enzymes
Adding enzymes to animal feed increases the nutritional value of the feed and improves phosphorus
absorption in the animals. This leads to faster growth of the animals and improves the environment as
less phosphorus is released via manure. (NZ2)
Microorganisms
Novozymes’ beneficial microorganisms are used in industrial and municipal wastewater treatment, as
well as in the cleaning of surfaces such as carpet, concrete, drain lines, and septic tanks in industrial
and household applications. Beneficial microorganisms are also at work in aquaculture and agricultural
applications.
Biopharmaceutical ingredients
Biopharmaceutical ingredients are proteins and other biological substances used in the pharmaceutical
industry. The proteins replace proteins from humans and animals that have traditionally been used and
have posed the risk of transferring disease. The industrial proteins do not pose this risk and offer further
advantages such as cost savings, process performance, consistency, and compliance. (NZ2)
3.2 Innovation in Novozymes
Innovation plays an important role for Novozymes as a business. In the industry of biotechnology, it is
vital to stay ahead of competition all the time through new innovations, and to protect technologies and
business areas’ extensive use of patents (NZ1). Novozymes currently holds over 6000 patents, and is
filing about 150 new patents per year (NZ1). In comparison, Novozymes’ largest competitor, the
Danish company DANISCO (now acquired by DuPont) holds about 2500 patents and is filing about
50-70 per year. This is why Novozymes can call themselves the world leader of bio-innovation.
Novozymes puts a lot of focus on R&D. In 2010, 14 % of turnover was invested in their research and
development department, which is located at 8 different sites around the world and employs 800
researchers. Within this department a great amount of attention is directed at promoting collaboration
across geographical boarders, and the sharing of knowledge through both face-to-face contact and
databases (NZ1). Innovation by Novozymes typically takes place in close collaboration with their
customers. Partnerships play an important role in Novozymes’ business model. While developing a
new biological solution, Novozymes works closely with their partner in order to optimize their

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technology’s functionality for application in the customers’ products or processes (NZ1). When it
comes to commercialization, Novozymes role is generally to deliver the technology, while their
partners take it to the market. Because of this, Novozymes is extremely dependent on strong
partnerships (NZ1)
3.3 Innovation processes in Novozymes
Face-to-face ideation is a common and fairly used tool within the company to brainstorm on new
concepts and to come up with new ideas on specific topic. These workshops a usually administered and
facilitated by the Innovation Office and has been focusing a lot on technical solution and are mainly
exercised in the R&D community. Since R&D are located around the world these face-to-face ideation
are heavily costly to arrange and typically scientist are flown in from different destinations, which
means the budget goes mostly to travelling expenses.
Another tool idea generation tool is the Idea Web (an idea suggestion box on the companies Intranet),
which is also administered by the Innovation Office. This idea box is open to all members of R&D and
Business Development. The Idea Web is open 24/7 all year around. The Innovation Office is
responsible for screening and forwards the posted ideas to an expert within the field. If the idea is good
the idea will be taking into further assessment in R&D. However the screening process is not optimal
and can often lead to very little feedback to the idea submitter. On the positive side it can be argued
that the Idea Web is good place to showcase your idea to senior experts, especially if the submitter is
new in Novozymes or perhaps located far away from Head Quarters in Denmark.
For maturing ideas and concepts, is the initiative called RIC (Radical Innovation Catalyst) which goal
is to mature and initiate good ideas coming out i.e. Ideations and turn them into projects. The RIC
community is built on volunteer allocations, to make sure that employees are choosing the projects they
really believe in. The Innovation Office is responsible to gather the ideas and run a website where
employees can read about the new projects and decide if they would like to get involved.
With a need for more and faster acceleration of innovation processes and a more customer-centric
approach, demanded from Senior Management, the Online Ideation tool is set out to be the right
process to fulfill the wanted goal of new projects delivered faster to customer. The Innovation Office is
thereby on a journey to test out internal crowdsourcing as the right method to achieve this goal. The
R&D Management initiated this strategy in late 2011, and has now slow but steady grown into a

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concept, that today is a well-known tool for idea management in all of Novozymes. Since 2011
Novozymes has performed 17 online ideation campaigns, most of them with a technology and product
innovation focus, thus drawing heavily on R&D crowds. The parameters of success for internal
crowdsourcing were analyzed and described by Lauto et al (2013). Diversity, absorptive capacity and a
culture permissive to innovation were the most important ones.
3.4 Case: New Claims for Detergent Enzymes
The overall goal of the online Ideation; “New Claims for Detergent“ was to identify new ideas that
could boost the growing area and core business of enzymes for detergents and solutions that could lead
to new claims for Novozymes’ customers. In the term of “new claims”, it is meant that customers can
use the provided technology as sustainable “green” claim of e.g. washing powder without harsh
chemicals or claiming ‘whiter than white, or ‘total stain removal at 45C’ and similar Some of the
largest detergent manufactures and marketers are customers at Novozymes (e.g. Procter & Gamble and
Unilever) today. In order to keep sales growing to these customers, Novozymes needed to come up
with more ideas on creating enzymatic solutions that they can be used to sell consumer products and
use “green” claims. By applying an internal online idea generation tool, it became possible to crowd-
source ideas from around the organization, in that way the tool combined the classical ideation process
with an online community, with selected employees from around the world. The online platform was
provided by an external consultancy firm NOSCO2
, who has been offering the online platform to
Novozymes for a little over a year at this point. The collaborative online ideation process was designed
together with the Innovation Office at Novozymes and consultants from NOSCO.
2
Website: nos.co

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3.4.1 The process of “New Claims for Detergents”
The process was divided into different phases to make sure that the ideas would make it through the
whole campaign and at the same time to mobilize the crowd in order to get the most out of their
creativity.
Figure 5; Process of online ideation
3.4.2 Scope and plan
The online ideation aimed at identifying problematic stains and possible solutions with focus on
Americas and EMEA markets. The division called Household Care, who is responsible for developing
the detergent business within Novozymes, was the main sponsor of the campaign. Household Care had
just developed a new strategy called “Triple 20”, with the goal of triple the turnover within Household
Care by 2020, one of the way of succeeding this was to identify, develop and launch 5 new laundry
claim enzymes similar to the very successful Mannaway3
, which sells for close to 250 million DKK a
year. Basically the challenge was formulated to find “the next big thing” within the detergent business
area. This meant also that this online ideation had a very high priority in Novozymes and participants
were allowed to spent time on the online platform.
3
A big blockbuster Novozymes product within detergent
- Problem statement
- Source / Solve?
- Admin Team
- Process template,
- Communication plan
- Timeline
- Screen team
- Crowd
Execute
communication
plan:
- Purpose
- Expectations
- Process
- Timing
- Incentives
- Feedback
- Support
Posting
& discussing
by crowd
Screen team
5 ideas /solutions
1 week 1 week 1 week 2 weeks 2 days 1-3 months
Assessment of
winning ideas

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3.4.3 Mobilization and composition the crowd
Assembling and construction of the crowd was steered by the Innovation Office in close collaboration
with the Household Care group, and started to handpick the different scientist who was specialized
within enzymes for detergent, and the unusual scientist how could possibly come with a creative angle
on ideas. The campaign was also the first cross-functional online ideation done in Novozymes. 50% of
the crowd was origin in R&D and the other 50% was coming from departments of Marketing, Sales
and Technical Service.
This was done in order to get a more diverse and a more customer oriented approach to the ideas, and
not only conceive ideas heavily focused on technological propositions.
3.4.4 The online ideation phase
The online phase was planned to run for 2 weeks and the 105 invited participants was able to submit an
unlimited amount of ideas and comments. The online ideation was kicked off with a conference call
where the participants were briefed by Nosco’s consultant and the scope was clarified and questions
from the crowd would be answered.
The participants were given profile with name and picture on the platform delivered from Nosco, the
profile was typical profile as seen on social media. The platforms had a few features, where participants
could follow, share, vote, and comment on ideas. The platform was designed to give a simple overview
of the idea flow and showcase which ideas were most popular at the given moment.
When posting an idea the crowd was kindly asked to write the idea with a short description and attach
documents and other relevant items. The participants was presented by a short summary of what the
screen team was looking for in an “good” idea and what types of criteria an idea needed to contain and
address in order to succeed in the evaluation process. These were the criteria set up by the screen team;
 A stain which is problematic for many consumers
 Future stains based on market trends within food compositions
 An enzyme which there is reason to believe will be able to remove the problematic stain(s)
 Completely new idea for an enzyme
 An enzyme solution with high technical probability

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In order to get the crowd motivated and get started on posting good ideas, the Innovation office and
screen team announced that they would nominate the most novel idea and the author(s) would then be
awarded with prizes. The winner would also be appraised as an innovator within Novozymes. In order
get ideas flowing fast, was the most active person also awarded, the person with the best arguments for
choice of a given stain (to develop an enzyme to remove) and the best team/department (most active
and best argues) would also be awarded. This should generate internal competition and help the idea
flow, since it is critical to get the first few ideas posted on the platform.
3.4.5 Screening and selection of ideas
The phase after the idea generation had ended the phase of screening and selection of the winning ideas
started. The online ideation had generated 74 ideas, and had over 200 comments from the 105
participants. Furthermore the crowd had been voting on the ideas, they believe the most in. The screen
team was now set out to identify the ideas that had the most potential and which ones should go into
further assessment in R&D.
The screen team was a diverse group with senior managers and directors from R&D, Marketing and
Business Development.
Title Department Tenure with Novozymes Tenure with NZ Detergents
Senior Director Innovation Office 15 years none
Senior Manager Detergent Marketing 12 years 5
Senior Manager Business Development 7 years 7
Dept. Manager Detergent R&D 10 years 10
Senior Manger Technical Service 8 years none
The point is to show the diversity of the screen team, its high level of knowledge as expressed by
tenure but also to illustrate that none of these people had direct decision or resource power in
detergents as such their findings to Management in Detergents were on a pure recommendation for
discussion and decision basis.

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The screening process was conducted over 2 days where the team reviewed all of the ideas and gave
them ratings in the different criteria’s chosen for this campaign. Each of the screen team members need
to score the idea within 6 overall criteria from a score of 1-5, where 5 was the highest score. Following
meetings was held where the screen team would present their scores and discuss which ideas they liked
the most.
The first three criteria’s was related to the commercial/market needs in the idea, and the last three
criteria were related to technical probability. The screen team did extended background check on the
idea both with internal and external databases, and could only score the ideas high if there were found
written evidence that the idea could be realized. (For a closer look at the specific criteria please see
Appendix 1)
Once the screen team had previewed all of the ideas and scored them, the top 5 ideas was picked and
presented to the crowd as the winning ideas. The ideas would then go into further assessment and
matured for development in R&D.
The description above of “New claims for detergent” has provided an overview of the process and
designs the idea campaign. The following chapter will present the methodological approach to the
research in this thesis.

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4. Methodology
In the pursuit to answer the research questions that have been proposed in this thesis, the research is set
in the perspective of social constructivism in order to address the exploratory and explanatory findings
and thereby put it in an abductive 4
design to seek evidence and conclusions.
Since this thesis is a single case study of an online ideation within Novozymes, the investigation and
research have been focusing around a number of data sources, mainly quantitative data have been
collected and analyzed, but also qualitative data have been collected to reach a point of in-depth and
understanding in the process around the investigated online ideation.
In the following section, it will be explained how the different data sources have been collected and
made it possible to create a foundation for the research approach and develop new knowledge
regarding internal crowdsourcing as a tool for creating new ideas.
4.1 Research approach and design
In line with the structure described above the method applied in this case study will approach the
conduction of the case study method proposed by Yin (1994).
According to Yin (1994) a “case study is an empirical inquiry that
- Investigates a contemporary phenomenon within its real-life context, especially when
- The boundaries between phenomenon and context are not clearly evident” (Yin, 1994; 13).
Further the “case study inquiry:
- Copes with the technically distinctive situation in which there will be many more variables of
interest than data points, and as one result relies on multiple sources of evidence, with data
needing to converge in a triangulating fashion, and as another result.
4
Abduction is a form of logical inference that goes from observation to a hypothesis that accounts for the reliable data
(observation) and seeks to explain relevant evidence.

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- Benefits from the prior development of theoretical propositions to guide data collection and
analysis.” (ibid.).
The analysis includes internal sources from Novozymes, in order to come up with a conclusion
followed by managerial recommendations. The case study approach fulfills the characteristics above
and the empirical data does as well since it is chosen to base the analysis on both qualitative and
quantitative data, which is recognized as evidence in this approach (Yin, 1994).
There are certain areas where the case study deviates from the method proposed by Yin (1994). The
data is conducting on an embedded single case study due to the fact that it is analyzed as a subunit that
contributes to Novozymes overall innovation strategy; e.g. the industry trends, competitors, core
competencies and different resources has not been analyzed due to the scope of the thesis.
Analyzing an embedded unit as an online ideation require that the analysis of the embedded unit
contribute to the “major interest of the study”, which the analysis does by going in-depth with internal
factors to get a broader picture of how Novozymes internal resources could be used in a future
approach of crowdsourcing exercises (Yin, 1994; 120).
4.1.1 Data collection
The gathering of information and data has been extensive. As explained above, the exploratory
approach of the research has led to a process where the data has been collected from different sources,
and continually been revisited and made it possible to get new insight. Since the online ideation was
held in October 2012, and the data for this thesis have been collected in April/May 2013, it has been
important to read through all the ideas and do semi-constructed interviews with members of the screen
team and some face-to-face meetings, in order to get a understanding of what the organizational
conditions was at the time for the campaign and to understand how the screen team evaluated and
researched the ideas submitted.
This has enabled the study to retrieve both qualitative and quantitative date, which has led to an in-
depth understanding of the research inquiry. The method for collecting the data, has been through
exported excel spreadsheets from the Nosco platform, secondly the screen team evaluation documents
has been collected and analyzed, questions and clarifying of the documents has been asked to the

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screen team, this has been done through semi-constructed interviews. The last data source has been the
employee database at Novozymes, in order to get the job titles and departments of the participants of
the online ideation and thereby classify and label them to the ideas they were involved in.
The analysis in this thesis are mainly focused around the quantitative data collection; although it is held
that without the qualitative data collection, the development of hypotheses and the subsequent
development of discussions and conclusions could not have been completed. The specific data
collections – qualitative and quantitative – will be presented in the following sections.
4.1.2 Qualitative data
As presented in the Chapter 3 – Company and Case description the qualitative data collection is
primarily conducted in explanatory manner to get an understanding of what happened in the idea
campaign. This made it possible to get a deeper understanding of what the process ideation was and to
figure out the reflections behind the “New claims for detergents” was, here were a number of
qualitative collections applied. This included observations of the full idea campaign process,
thoroughly read-through of all the ideas and exploratory interviews with some of the screen team
members. A transcript of the specific arguments of the ideas from the screen team, has also been
collected, to identify what kind of background research the screen team had conducted when scoring
the ideas from there different parameters.
The deliberations and assumptions in this thesis were built on a method of triangulation drawing on (i)
theory, (ii) qualitative and (iii) quantitative data. “Triangulation refers to the use of different data
collection techniques within one study in Order to ensure that the data are telling you what you think
they are telling you.” (Saunders et al, 2007:139). The qualitative data collections are therefore
important in the methodological considerations although the quantitative data are the dominant in this
thesis’ analysis.

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4.1.3 Quantitative data
The quantitative data is the dominating part of the analysis in this thesis. The data has been collected
from multiple sources. The exported data from the idea platform, delivered by Nosco has mainly been
the primarily source of data. The export options in Nosco’ platform was either a PDF file or an Excel
spreadsheet, the PDF file is a full extracts of all the ideas posted in the campaign, whereas the Excel
Spreadsheet contained the following information; (i) ideas ID; (ii) number of comments; (iii) number of
crowd votes and thereby the crowd score; and (iv) submitters name. However, these data points doesn’t
completely fulfill the wanted data to answer the developed hypotheses, it was important to create even
more data point in order to provide the regression models to identify if there were any correlations and
statistical evidence. The following variables were thereby developed; (v) functional involvement in the
idea; (vi) idea depth; (vii) comments depth; (viii) market needs articulated in idea; (ix) cross-functional
involvement.
The functional involvement in an idea (v) includes the functions or department that commented or
submitted a specific idea, divided into Sales, Marketing, Technical Service and R&D. The idea depth
(vii) and comments depth (viii) was measured from calculating the characters written in the ideas, in
order to see if the ideas with many words were rated higher than the ones with short descriptions. The
market needs articulation was measured out from a reading through the ideas, this variable was score
from a scale of 1-3, 3 being the highest in terms of market needs articulation, this meant that the ideas
was classified to see if the crowd was responsive to the market. The cross-functional (ix) variable was
measured out from a binomial setting, if more than one department was involved in the idea, the idea
was scored with the number of 1, and if only one department was involved it was scored with a 0. .
The second source of data was collected from the screen team, who had ranked all the 74 ideas after the
online ideation had ended. It was thereby possible to use the screen team spreadsheets to develop
further variables; (STi5
) screen team total idea score; (STii) novelty of idea; (STiii) commercial
probability; and (STiv) technical probability. All of these multiple collections of quantitative data
enabled the analyses in this thesis. The statistical measurements of data involve calculations of means
5
ST= Screen Team

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and standard deviations. Furthermore, it will be presented as contingency tables of various variables to
test for statistical significance using simple regression models tested in the software program called
STATA6
. The dependent and independent variables will examined in the analysis, and will be
explained in the following sections.
4.1.3.1 Dependent variables
The regression models provided in the analysis contains two different dependent variables; the
regression models have been calculated with the same independent variables in order to see if there was
any significant evidence and relations between the two dependent variables.
The first dependent variable is the final screen team score; this variable is set as the dependent variable
to identify how the screen team interpreted the ideas. The - screen team score is therefor set up to be
the indicator for how the ideas performed throughout the online ideation.
The second dependent variable was the - crowd score, this variable is set up to be an indicator for how
the crowd interpreted the ideas and makes is possible to identify the “wisdom of the crowd”. The crowd
rated the ideas from 1-5 stars, this shows clearly which ideas the crowd believed the most in.
The indicator in the statistical analyses is therefore the screen team score and the crowd score. The
mission of the indicator, as described by De Solla Price (1978), is to find the simplest pattern in the
data at hand, and then look for more complex patterns, which modify the first. The more complex
patterns in the dataset are thus the correlation between the crowd and the screen team, and will enable
to identify if the “wisdom of the crowd” is present in this ideation. The independent variables will be
explained in the following section.
6
http://www.stata.com/

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4.1.3.2 Independent variables
In the regression models provided, the independent variables are based on the different department’s
performance in the online ideation and on which type of ideas posted, in terms of novelty and market
articulation. In order to answer the developed hypotheses and to investigate if there are any correlations
between the crowd choices and the screen team choices on the quality of ideas. The chosen
independent variables were: (i) New idea to Novozymes; (ii) market articulation; (iii) cross-functional
involvement; and (iv) origin of ideas (Divided of the departments, Marketing, Sales, Technical Service
and R&D). (i) The New idea to Novozymes variable refers to how new ideas posted in the ideation are
correlated to the depended variables, this variable is discovered from analyzing the ideas and is put into
a binary score of 1 or 0, when it was ranked as a new ideas. (ii) The market articulation variable refers
to how strong the market or customer needs are articulated in the ideas. This variable is used to get a
perspective of how the market is interpreted in the online ideation, and will be used as a parameter to
figure out how the clear the market is represented. The variable is created by an read through of the all
the ideas, and is score every time a sign of market is articulated inside the idea or in the discussion
affiliated to the idea. (iii) The cross-functional variable refers to the combinations of participant
involved in the ideas. This is measured by looking at how and from which departments the ideas and its
comments are represented from. The variable is also made as a binary, whereas 1 if there are more than
one department involved in the idea, and 0 if it is only one department posting and discussing the idea.
This variable is used to give a perspective on how collaborative the crowd was, and how the different
departments interacted in developing new concepts and to see if they could understand their different
professional languages. (iv) The origin of ideas variable is created to track where the ideas are coming
from, this variable is developed by tracking from which department the idea submitter is, and thereby
figure out from where the ideas origin from. The four departments are divided into there each own
variable to be able to run regression model on them. This variable is used to figure out how the Crowd
and Screen Team looks at ideas coming from the different eras of Novozymes, and to test if the
strength of quality in the departments in there submission of ideas in the campaign.
To test the hypotheses these variables are put in to the analyses to gain the insights on how the different
conditions had any relevance in developing, evaluation and choosing the winning top-5 ideas.

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4.2 Two-fold analysis of crowd versus screen team
Since the analysis is created by qualitative and quantitative data, the following analysis will be two-
folded. By two-folded it is meant that the data received from the screen team is generated in a
descripted way and gives a clear insight on how the ideas were evaluated together with the semi-
constructed interviews. The screen team, spreadsheet has also provided a deep insight to what the 5
members were focusing on, and gives a clear perspective of what the more in-depth knowledge has
provided in the final evaluation.
The quantitative data has been retrieved and modeled in a way that gives insight on how the crowd and
the screen team acted purely based on data during the ideation. The regression models provided in the
analysis are thereby made only to give a perspective of how the different ideas were conceived and
how they were generated and evaluated.
The analysis will therefore be based on the different types of information, and in order to answer the
research question, the final conclusion will be based on some of the assumptions made on top of the
findings from the two-fold investigation of data.
The way the analysis is created is based on the relevant findings from the data and later put into
discussion to give a more accurate answer on how Novozymes can use crowdsourcing in their pursuit
of customer-centric innovation.
The two-folded approach, enables the thesis to get a more realistic outcome, and makes it possible to
generate some more executable managerial recommendations.
However, a single case thesis will also have its limitations; the following section will explain the
different limitations will come into play, and argue why this thesis not can be used as a general
objective case study.

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4.3 Limitations
Conducting a single case study requires that it is a critical case, extreme or unique case, or a revelatory
case in order to be able to generalize from (Yin, 1994; 39). The case study does not test directly any
specific theory, nor is it an extreme or unique case and the type of case has not been inaccessible to
prior studies. This means that the analysis cannot be used for any generalization or base precedence for
other biotech companies with the same challenges. The analysis, conclusion and recommendations are
aimed at Novozymes’ specific strategy and the output of this case study is only valid when used by
Novozymes. However in light of the strategic managerial recommendations it is in some sense,
possible to apply to other similar companies if the internal setup is ready and mature for online idea
generation. The single case study has it limitation especially when it comes to objectivity and
generalization conclusions.
A huge limitation, when investigating an internal crowdsourcing exercise, focused on the market needs,
is of cause that there have been no external parties involved at all. It would have been a lot easier to
answer the research question, if a customer or a partner had been involved in the online ideation.
The empirical data consists of both qualitative and quantitative data however the dominant use of
quantitative data in the analyses has its implications. The quantitative data is used to simplify the
answer to the research question. The Analysis is arguably limited by the use of quantitative data in that
it fails to capture the complex nature of the situation. The case for quantitative data analyses is,
however, that it reduces the interpretative and subjective elements in the research inquiry. Through the
use of quantitative data, it is possible to support the found assumptions by testing of hypotheses to a
greater extent than with qualitative data. The information available in the data set has inevitably guided
the research. That is to say, the limitations of data have resulted in the chosen variables; other variables
could have been examined had different data been available. The four chosen independent variables,
however, are consistent with the suggestions from theory presented in the Literature Review and it is
therefore believed that they are important diversity dimensions in relation to the scope of this thesis.

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