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

specialeMaster%20Final%20version.doc#_Toc378580251


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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

1http://www.novozymes.tv/video/7631519/peder-holk-nielsens-view-on.

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

http://www.time.com/time/digital/digital50/08.html

http://www.time.com/time/digital/digital50/08.html


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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

NL?

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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5. Analysis

In the analysis the findings will be presented from the investigation of the “New Claims for Detergent”

online ideation data set. This will be presented in an analysis that is formed into two main parts. The

first part of the analysis will provide an overview of the distribution of ideas and how the participants

are interacting and to what extent they are sharing their insight, regarding their occupation in the

organization. The analysis will then look more closely into the articulation of the ideas, and with the

use of regression analysis try to explore the patterns of the market needs articulated in the ideas

generated. This will lead me to test my hypotheses that are provided in the section Problem statement.

The empirical findings investigated in this analysis will be discussed in the next chapter and will enable

me to answer my research question.

In the first part of the analysis it will be examined how the outcome and distribution of the ideas to

show the patterns of what the crowd produced in the idea campaign. Secondly, the screen team data set

will be examined, to show which criteria and what background the ideas was rated on. By analyzing

the output from the campaign, it will be possible to draw some assumptions on what was going on in

the online ideation, during and after. The data set retrieved from the screen team, and their comments

are used in descripted manner to help get a perspective on how the evaluation process was conducted.

This will lead to the second part of the analysis.

In the second part of the analysis, the data will be put into relation and be examined to find the

implications of market needs articulated in the ideas, and how this relates to the perception of the

crowd and the screen team. There will be provided a regression model of the data analyzed and

afterwards used to illustrate the results to answer the hypotheses on the possibility that internal ideation

can enhance the acceleration of innovation processes in Novozymes, and which role the crowd and the

screen team are taking on. In order to provide a deeper analysis of the implications of an internal online

idea generation, with a cross-functional crowd and a diverse screen team the following discussion will

put the findings in a broader strategic perspective. The following chapter will enable a thorough

discussion of the empirical findings and results, and put these into relation to the theoretical research

provided in the Literature review.


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5.1 Analysis part 1: Descriptive analysis and distribution of ideas

In this part of the analysis the quantitative outcome will investigated of the idea campaign “New claims

for detergents”. This will be done in a descriptive matter to get a deeper understanding of the

distribution of ideas, comments, and how the crowd interacts with one another. The provided

descriptive statistics and the given examples of conversation between the different departments will be

used in order to describe the way e.g. R&D communicates with Sales. Secondly the screen team data

set will be provided and analyzed to identify the different parameters of evaluation criteria. The

presented outcome of the idea campaign is the full data from the crowd consisting of 105 participants;

this is created to give an overview of how the ideas were generated. The 74 generated ideas will be

examined based on novelty, functionality, market needs articulated and how the ideas are developed in

a cross-functional manner. The second part of this first analysis will focus on the perception of the

screen team and how they determine “winning ideas”. This will also be a more in-depth analysis on

how the screen team evaluate and rate the novelty of ideas in relation to how the crowd rates them. Due

to the scope of this thesis, the activity, time spend on the campaign, diversity of tenure within

Novozymes, or the age, gender or location will not be investigated.

5.1.1 Outcome of “New claims for detergent”

In order to measure, (i) idea quality; (ii) novelty of ideas; and (iii) cross-functional co-development of

ideas, it is needed to go into the ideas generated and classify the different parameters. This will be

explained further in the following section. The screen team evaluation will be presented afterwards, so

it can be put into the provided regression model in the second part of the analysis.

The table (3) below shows the total outcome of the dataset from the campaign, in sense of number of

descriptive counts of from the ideas generated. The table is set up in order to identify hypothesis 1 and

2, with regards to the number of characters and references in the ideas. The table also shows the total

outcome of ideas and the metadata of the generated outcome in terms of; comments, submitters, votes,

number of novelty, market needs articulated and cross-functional co-development of ideas. The

different data point will be further analyzed in the following sections, in order to answer the hypothesis

in discussion chapter.


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Table 3; Total outcome of the dataset:

Totals of the dataset No.

Total no. of participants 105

Total no. of ideas 74

Total no. of idea submitters 53

Total no. of comments 200

Total no. of votes 744

Total no. of ideas w. Cross-functional involvement 38

Total no. of novel ideas to Novozymes 27

Total no. of idea w. market needs articulated 33

Total number of references 14

Total numbers of ideas with more than 420 characters 60

5.1.2 Ideas with cross-functional involvement:

In the Chapter Company and Case, it is presented how the crowd was composed:

Figure 6; Composition of the Crowd

This section goes into detail about the composition and the distribution of the cross-functional ideas

that was co-developed by the crowd. The crowd consisted to 50% of R&D employees and 50% Sales &

Tech Service and Marketing.(Sales and Tech Service reporting to the same executive Vice President):

the cross-functional composition of the crowd was chosen to enhance the idea development in a more

cross functional way. By cross-functional involvement is meant e.g. that if a participant coming from

R&D submits an idea and it is only R&D colleagues that comments on the idea, this is classified as

R&D ideas only. The table provided is an overview of how the idea with cross-functional involvement

was distributed in the campaign. It also shows how the cross-functional ideas performed in the

evaluation of the screen team.


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Table 4; Distribution of cross functionally discussed ideas

Distribution of cross-functional ideas Total ideas: Cross-func. Top 25 ideas

R&D 34 9 3

Sales 17 9 1

Marketing 11 9 3

Technical Service 12 11 4

Total: 74 38 11

R&D submitted 34 ideas in all, out of these 34 only 9 ideas had cross-functional involvement in the

comments within the ideas. 3 of the ideas with cross-functional involvement got rated in the top 25

ideas by the screen team.

Sales submitted 17 ideas in all, out of these 17 ideas, 9 ideas had cross-functional involvement. 1 idea

with cross-functional involvement got rated in the top 25 ideas by the screen team.

Marketing submitted 11 ideas in all, out of these 11 ideas, 9 ideas had cross-functional involvement. 3

ideas with cross-functional involvement got rated in the top 25 ideas by the screen team.

Technical Service submitted 12 ideas in all, out of these 12 ideas, 11 ideas had cross-functional

involvement. 4 ideas with cross-functional involvement got rated in the top 25 ideas by the screen

team.

The results of the cross-functional involvement shows that even though R&D posted most ideas there

was “only” 9 ideas with involvement from other departments and 4 ideas in the top 25. Ideas from

Technical Service had 11 out 12 ideas with cross-functional involvement, and 4 ideas in the top 25.

This could suggest that ideas from Technical Service should be the superior in posting ideas that

creates cross-functional involvement. But if we take a closer look at the involvement of the ideas

posted another picture emerges. Out of the 38 cross-functional ideas R&D was involved in 35 of the

ideas In relation to the others where Sales was involved in 13 ideas, Marketing was involved in 21

ideas, Technical Service involved in 14 ideas. So it is clear that R&D is heavily active in the comments

of the ideas, whereas an idea coming from Technical Service was more likely to get discussed or

commented by others.


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Table 5; Origin of the Top 25 ideas

Where idea origin from No. Of ideas in Top 25

R&D 15

Sales 4

Marketing 2

Technical Service 4

Total 25

The implications of these findings will be discussed later in the Chapter Discussion.

5.1.3 Novelty of ideas:

One of the criteria the screen team was set up to evaluate was the novelty of an idea. The novelty of an

idea means that, the idea needs to be new to Novozymes. This could be a new technology, or a new

way of solving detergents problems, or just an incremental improvement to the existing process.

Each of the five screen team members was asked to score the novelty of an idea on a scale from 1-5, 5;

being a completely new idea and unexplored to Novozymes, 1; for being an incremental improvement

of a known concept or technology. All five screen team members was not obliged to rate all of the

ideas individually, so some of the ideas was only rated by some of the Screen team members.

If the novelty sum rose over a score of 10 points, this is classified as a new idea to Novozymes, since

this is where the screen team agrees that this is a fairly new idea. Figure 8 displays the distribution of

the novelty sum of all the ideas generated. 27 ideas were rated higher than the 10 point mark. That

correspond to around 37% of the posted ideas was new to Novozymes.

Figure 7; Distribution of new ideas


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5.1.4 Clustering of the ideas

The initiative to cluster and order all ideas generated by the crowd was taken spontaneously by the

screen team who got intrigued by emerging connections between the presented ideas. the original

overview table produced by them is reproduced in Appendix 3. It needs to be stated already here that

this ad hoc clustering exercise performed by the screen team was not according to the originally

planned protocol of the campaign, as detailed further in the discussion chapter.

In connection with the present thesis research the original two-dimensional idea-chart was expanded to

contain a third dimension which was the final screen team score (figure 8). After analyzing the ideas it

was possible to illustrate graphically how the clustering of the ideas shaped a three-dimenional map.

This enabled the analysis to demonstrate what an idea should contain to be rated high in the evaluation:

Figure 8; Comprehensive idea map

The entire idea population depicted as a three-dimensional plot according to customer ideas addressed (x-axis),

proposed enzyme technology/enzyme class (z-axis) and accumulated score (y-axis)


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The figure above shows how the ideas look like when they are clustered together with the proposed

technology and the customer issue addressed within the idea. The way this analysis is done is by taken

the average of the screen team score in each cluster to see which one performed the best. It is very clear

that the ideas containing the technology “deglycosylation of coloured compound through glycosyl

transferase” is by far the best rewarded when combined with the customer issues of stains from “fruits

and vegetables” and “Tea, coffee and wine” where the averages score is 102 from the screen team.

These ideas also went into further assessment after the idea campaign.

The second best rewarded area is the “Oxidoreductase” combined with the issues of “grass” stains with

an average score of 94. This suggests that Novozymes should look more into these areas since it is of

high priority and there is a lot of knowledge being shared around these clusters. However what it also

reveals is that if an idea is lacking a specific technology, or lacking the addressing of a concrete

customer issue, then it is rated very low. It shows that the ideas that do not address a specific

technology or a specific customer, scores an average of 41.5 which is almost 60% lower than the top

ideas.

5.1.5 Responsiveness to market needs:

In order to examine the market needs articulated in the posted ideas, the ideas have been examined

through to identify and classify where the market needs are articulated, the following section shows the

classification of ideas and put into 3 categories.

Category 1: No market needs articulated at all.

Category 2: A market, trend, competitor or consumers is mentioned.

Category 3: A specific customer “name” or a specific customer request is mentioned.

To explain in detail what is meant by “market needs articulation”, the following provided is a few

examples from the online ideation in the category of 2 and 3:

Idea 20: …Arabic gum is added as 0.8-2.4% into 3 of Chinese national stains (sebum, protein, carbon

black stains) as sticker and emulsifier. it is also a popular worldwide food ingredient used in e.g.

cheese, cream ,processed fruits etc. Arabic degrading enzyme like mannaway to mannose could largely

improve the stain removal for food enzymes as well as great help to Chinese specific stains and sales in

China…


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Comments: … We have been discussing that a claim enzyme may have a hard time in the Chinese

market - but I guess that providing cleaning benefits on several of the Chinese national stains may be

the way into the Chinese market? ....

This example is in category 2, because it resonate to a market, in this case the Chinese market. The

comment supports the idea and comes with some insights on how the market situation is in this market,

and states that this could be a way for Novozymes to open up into this market where they not currently

having any business.

Idea 44: …Big soaper companies like P&G, Unilever and Henkel have divisions involved in making

colored pens or could quickly acquire them. Why not co-develop a family friendly marker system where

enzymes in the washing powder will remove any of the colors in the marker pen set? You could put a

special seal on the pens showing they are part of the "color be gone" system…

Comments: …By mixing these basic colors together, you get all the colors that a desktop printer can

make (well maybe we need black too). I think that we should get both Novozymes and P&G experts to

work the problem…

Comments: … We can include pens yes - How many pens does P&G and Novozymes give out? That is

the place to start. That is how Post-it notes started. What a great gimmick to explain to customers the

power of our enzymes…

This example is scored in category 3, because the idea is built on some direct customers business where

Novozymes could go in and contribute with some technology, in this case some of the biggest

detergents sellers in the world (Unilever, P&G). The comments responses in a great fashion, especially

the first comment suggest Novozymes should go into closer collaboration with the partner P&G. The

second comment goes a bit further and suggests that Novozymes creates a gimmick to explain the

power of the enzymes within the product.

Idea 46: …Improve swimming pool cleaning process (to degradate body grease, sun protector base) in

a more sustainable way by applying enzymes to replace chemicals…


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Comments: …We already received 2 customer’s request on this sense in Latin America, important

idea in my point of view…

This example is in category 3 as well, because the idea takes steam from an existing problem, and

provides the technical solution, but especially the comment that Novozymes in Latin America already

have received directly indications of customer needs is important for the idea. This is a clear example

of that sharing ideas can lead to insight from elsewhere in the organization.

Idea 56: …Most of the fabrics on the market now are mixed fabrics of polyester and Cotton. However

many technical clothes like sport equipment are made of polyester only. There are no solutions on the

market for improving the feel of these fabrics and our cellulases have no effect on polyester. Esterases

have already been proven to perform fabric modification on polyesters and a combination of esterases

with cellulases has shown great effects on mixed fabrics...

Comments: …We have positive experience with Cutinase in this area. RD did work around 2004-5 if I

remember right and the idea is currently part of a surface modification project running with P&G…

Comments: … We have received a lot of customer’s request on that. Great idea…

In this example the idea is very much focusing on the technology, however the first comment directs

the attention to a project already running with a customer (P&G), that might give a clearer

understanding of if there have been developed more in-depth research that could benefit the idea. The

second comment, makes the idea even more interesting, by sharing that there have been received a lot

of direct customer request to this problem. This makes the idea qualify into category 3.

This example is taken directly from the outcome of the idea campaign, there are a lot more, so these are

just some few examples of how the classification of the ideas into the categories. It is however hard to

draw a specific line whether when ideas end up in a category 2 or 3, thus the selection and

categorization of the chosen ideas is made in together with internal experts from the Innovation Office

in Novozymes, especially in some of the more technical advanced ideas.

Identifying this allows the thesis to distinguish between if ideas are closely related to the market needs

or more focused on e.g. technology or processes within Novozymes. This enables the findings the ideas


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that are most likely to have a concrete customer to take the idea to market. The purpose of this

investigation is to discover if the crowd can take the market needs into their ideas and to see how this is

received by the rest of the crowd and the Screen Team.

In the second part of the analysis it will be tested if there is a significant evidence and correlation

between the ideas with high market articulation and if the crowd and screen team have the same

perception of the way they rate these.

Table 6; Distribution of ideas with market articulation

Distribution of market needs articulated: No. of ideas

Category 1 43

Category 2 15

Category 3 16

Total 74

The table above shows the outcome of the distribution of market needs articulated ideas, rated and

based on the presented criteria. These variables will be used in the regression model presented in the

second part of the analysis.

5.1.6 Screen team evaluation:

As presented in the Case description, the screen team was composed in a cross-functional matter with

experts within the detergent area. The screening criteria was based on both technical and market

aspects. The conducted documents from the screen team states which arguments and scores that lay

behind the final scoring of the ideas. The evaluation was done over a short period of 2 days, the

members were assigned to score the ideas individually and later the scores was summed up and

discussed at a final review meeting.

The screen team document reveals the specific scores and the individual ratings and comments on the

ideas from the members. The six scoring criteria were divided in two categories, a commercial score

and an enzyme score. Commercial score with refers to the market probability and the enzyme score


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refers to the technical probability. The reason this has been done is to investigate how the screen team

was viewing the online ideation and to what extent the technology and the market played a role in the

selection of the winning ideas. The two variables were distributed according to the ideas and reveals

that the relationship between the two scores in the evaluation process. (Appendix 1)

Figure 9; Statistical comparison of screen team scores given on commercial and technical criteria

The figure (9) above shows the distributed scores calculated based on the retrieved documents form the

screen team review meeting. The two scores were put into a t-Test to illustrate the difference in the

commercial and the technical scoring aspects.

When the screen team score is broken into these two series, it reveals that the customer score are rated

highest in the in the evaluation, this clearly indicates that the market is a heavy represented in the

screening process. This is also shown in the comments around these. The screen team is therefore not

only focused on the technology probabilities but mainly the market aspect of the ideas.

The two series states clear that the winning idea (idea 45) was very strong in both commercial and

technical probability, whereas a lot of the poorly rated ideas had a too strong focus on the market and

did not live up to the technical probabilities wanted to go into further assessment of the idea.

The evaluation process was not presented to the crowd, however in the following section of the

analysis this will be exposed more toughly through the regression models provided.

t-Test: Two-Sample Assuming Unequal Variances

Commercial Score Technical Score

Mean 29,405 23,338

Variance 322,546 231,925

Observations 74 74

Df 142

t Stat 2,217

P(T<=t) one-tail 0,014

t Critical one-tail 1,656

P(T<=t) two-tail 0,0282

t Critical two-tail 1,977


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5.2 Conclusion of analysis part 1:

In the first part of the analysis there was provided an overview of the performance and the distribution

of ideas generated in the idea campaign.

Firstly, the descriptive statistics of the output has demonstrated a good insight on where the ideas came

from and how they were constructed from the campaign. It was shown that 74 ideas were submitted by

53 participants, which is fairly okay with a submitting activity of 68%. Furthermore 38 were cross-

functional ideas and 27 with high novelty and 33 ideas with high market needs articulated in them. This

could indicate that the data sample is quit representative when looking at similar research within this

research area.

Secondly, the cross-functional distribution of ideas was examined, showing that the 50/50% spilt

resulted in 38 ideas with cross-functional involvement. It was examined further how these 38 ideas

originated, which showed that ideas created out of the Technical Service Department had the highest

likelihood of being cross-functional with 11 out 12 ideas. It was also revealed that R&D presence was

strong in these ideas; they were involved in 35 of the 38 cross-functional ideas.

Thirdly, the novelty of ideas was examined, with an overview of the distribution of new ideas. All the

27 ideas was rated high (a score of >10). Also here there where a strong presence from R&D who was

responsible for 18 of the 27 ideas generated, and furthermore it was that R&D was involved in almost

every new idea except for 2.

Fourthly, Figure (9) of the clustering of ideas was provided, in order to identify where the “winning

ideas” were lying and how they were scored The grouping of ideas showed that in order to score high

on a submitted idea it needed to be related within two different customer issues, and be within one

specific technology.

The market needs articulation was examined and explained by providing examples of ideas with

medium/high market needs from within the idea or comment. Here it was concluded by the results of


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the distribution provided, that out of the 74 ideas 15 where with medium market needs and 16 ideas

with high market needs articulated.

Lastly, the screen team evaluation documents revealed that, the screen team actually was rating the

market needs or customer issues relatively higher the technical ideas. This is a strong indicator of that

the expert team, goes into a far more specific background search to i.e. internal databases to see if the

ideas have been tested out before or if any of proposed customers actually want to pursue the ideas in

there respected markets. What is interesting here is that there seems to be an indifferent approach to

how the screen team evaluates the ideas, and how the crowd votes the ideas they think is the most

realistic to realize.

The second part of the analysis will focus on putting these findings into a regression model to see the

relations between the screen team and the crowd.


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5.3 Analysis part 2

The first part of the analysis provided a descriptive overview of the outcome of the idea campaign and

demonstrated how the crowd performed and how the screen team received and rated the ideas.

In this part of the analysis there will be provided a more in-depth investigation of implications on the

“market need” articulated in the ideas, and where the crowd and screen teams favorite ideas origin. The

hypotheses developed in the Problem statement will be tested in the regression analysis provided. The

goal is to see how these results match in terms of correlation between the crowd and the screen team.

The wanted test is created to see if the crowds and the screen teams are receptive to market needs

articulation, and if so, can this create an acceleration of the organization’s response to market needs,

faster and more accurate and deliver more customer-driven innovations.

5.3.1 Putting the numbers into relation

This section goes into investigation of how the different results from the previously analysis can be put

into use when answering the developed hypotheses. This will demonstrate how the relations are form

and is done though simple regression analysis. This will enable the thesis to identify if there is any

significant evidence or correlation, in how the perception was from the screen team results and how the

crowd voted. Secondly the investigated model will show if new ideas are significant to market needs

articulated or if they are cross-functional.

The method used for the regression analysis is described in the Chapter Method and research design.

Dependent Variables:

As dependent variables, as described earlier, the screen team score was used (mean = 1.92 and SD =

1.10) in the first model, the variable is generated into a linear string in order to use it in the regression

model. The second dependent variable is the votes from the crowd score (mean = 1.81 and SD = 1.12);

this variable is also generated into a linear string.

Independent Variables:

Market needs articulated in idea (i); is the variable that describes how many ideas had market needs

articulated within them, and the once that didn’t, (mean= 1.63 and SD= 0.82). Idea origin; (ii) As three

dummies: idea from Sales (mean = 0.22 and SD= 0.42), idea from R&D (mean = 0.48 and SD = 0.50),


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idea from Technical Service (mean = 0.14 and SD = 0.35) and idea from Marketing (mean = 0.12 and

SD = 0.34), (Marketing is sat as a base line). (iii) New idea to Novozymes is made from the evaluation

of all the ideas (mean = 0.58 and SD 0.49). (iv) the cross-functional variables (mean = 0.51 and SD =

0.50), is create from the descriptive analyses, where all the ideas was identified and examined for

cross-functional involvement.

Table 7; Regression model of the screen team Score and the crowd score:

Model 1 Model 2

VARIABLES Screen Team Score Crowd Score

Market needs articulated in idea -0.33183 0.64742**

(0.43062) (0.25057)

Idea from Sales Dept. 0.76443 -1.10436***

(0.74417) (0.39894)

Idea from R&D Dept. 0.61392 -0.21396

(0.77153) (0.35285)

New idea to NZ 0.39057 0.44262*

(0.44965) (0.26528)

Idea from Technical Service 0.60793 -0.10676

(0.72910) (0.40117)

Cross-Functional w. involvement 0.54272 -0.05331

(0.54876) (0.30033)

Constant 2.37272*** 1.68859***

(0.78560) (0.35666)

Observations 74 74

R-squared 0.05479 0.27269

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1


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6. Discussion

6.1. Revisiting the research hypotheses

The overall research question of this thesis was whether the crowd of ‘New Claims’ could be

considered a ‘wise crowd’. The nine hypotheses chosen to test ‘crowd wisdom’ were grouped in four

categories: idea quality, co-creation as expressed in cross-functional idea discussions, novelty and

responsiveness to ideas which contained market needs (Table 1).

Testing hypothesis 1 it was found that 82% of all ideas contained more than 420 characters, which

corresponded to three ‘tweet-length’ statements. Given the acceptance criterion of 75%, hypothesis 1

could be accepted indicating that the crowd articulated their ideas in test strings of sufficient length to

express relatively complex thoughts (Table 4, analysis 5.1.1.)

Likewise, the second hypothesis regarding idea quality was accepted as well since it turned out that

80% of all ideas contained at least two comments, with the acceptance criterion being 75% (Table 4,

analysis 5.1.1.). In this thesis ‘one idea plus two comments’ was viewed as the minimum requirement

to justify the term ‘idea discussion’.

Hypothesis 3 concerned the number of references stated in the prposed ideas, the threshold for

acceptance being that 25% of all ideas should contain at least one internal or external reference. Only

18% of the ideas lived up to this condition whereby hypothesis 3 is not accepted (Table 4, analysis

5.1.1.).

With regards to the crowd’s capability of posting novel ideas it was found that 37% of all ideas were

considered as novel by the screen team (figure 8, table 7, and analysis 5.1.3.) whereby hypothesis 4

could be accepted considering 25% as the threshold.

Regression analysis was performed to answer the question whether novel ideas were correlated with

high scores. This turned out to be the case for the crowd whose dot-vote score correlated with novelty

at a p-value smaller than 0.044 (significance level: p<0.05). Interestingly, this was not the case for the

screen team where p < 0.390 (Table 7, analysis 5.3.1).Apparently, the crowd gave high ratings to novel


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ideas but the screen team did not. Hence, hypothesis 5 is accepted but the discrepancy between crowd

and screen team perspective needs to be noted.

Evidence for co-development of ideas was investigated by analyzing the occurrence of cross-functional

involvement in the discussion of ideas. It was found that 51% of all ideas fulfilled this criterion

whereby hypothesis 6 could be accepted (threshold was 25%), (Table 5, analysis 5.1.2.).

Regarding the question whether cross-functional involvement also was a predictor of idea ranking it

was found that the correlation to the crowd’s dot-vote was p=0.053. Thus, hypothesis 7 is – in a strict

sense – not accepted for the crowd but still very close to the acceptance criterion of p<0.05. Even if the

criterion was formally not met it is safe to say that the crowd in ‘New Claims’ had a pronounced

tendency to rate ideas with cross-functional discussions as high. As with the novelty-test (hypothesis

5), cross-functional discussions were not a predictor for high scores as given by the screen team

(p=0.326) (Table 7, analysis 5.3.1.).

Finally, investigating the crowd’s responsiveness to ideas with articulated market needs it was observed

that 41% of all ideas contained elements and statements which reflected market needs (Table 7,

analysis 5.1.5.). Hence, hypothesis 8 was accepted. With regards to the correlation of ‘market need-

incidence’ with idea scores given by the crowd a p-value of 0.012 was found , which in sensu stricto

does not meet the acceptance criterion (p<0.05) but is still considered as substantial. Again, and in line

with the preceding observations, no correlation between ‘market need articulation’ and the screen team

score was found (p=0.444). Thus, hypothesis 9 was not accepted.


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Table 8; Summary of findings and hypotheses acceptance

Category Hypothesis

no.

Hypothesis results Acceptance? Method of

testing –

Idea quality 1) 82% of the ideas contained more than 420

characters.

Accepted Descriptive

statistics

2) 80%, thereby is the hypothesis accepted Accepted Descriptive

statistics

3) 18% therefore is this parameter not accepted. Not Accepted Descriptive

statistics

Idea Novelty

4) 37% of the ideas was rated highly novel

accepted


statistics

5) Crowd score p-value was p=0.044

Screen team score p-value was p=0.390.

Accepted Regression

analysis

Idea

co-

development

6) 51% of all the ideas lead to discussions with

cross-functional involvement.


statistics

7) Crowd score p-value was p=0.053,

Screen team score p-value was p=0.326

Not accepted in

sensu stricto

Regression

analysis

Responsivene

ss to

market needs

8) 41% thereby is the hypothesis accepted Accepted Descriptive

statistics

9) Crowd p-value was p=0.012,

Screen team p-value was p=0.444.

Not accepted

in sensu stricto

Regression

analysis

6.2 The crowd‘s excitement about the presented ideas was not shared by the screen team

When measured in relation to the nine formal criteria alone, the crowd in this thesis could indeed be

regarded as ‘wise’: they proposed many novel ideas which were of often of high quality; they

commented actively on each other’s ideas and many ensuing discussions took place across the involved

business functions, which is an indicator of co-development or co-maturation of the initial ideas.

It also appeared that many ideas brought forward by the crowd were stated in the context of - or in

relation to - market needs, which suggests a market-oriented, outward looking professional mindset

prevailing in the crowd.


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Furthermore, it was remarkable that the crowd through their collective dot-votes showed consistent

positive reactions to novel ideas, ideas developed through cross-functional discussions and – most

importantly – in relation to ideas containing market needs. The fact that hypotheses 7 and 9 were not

accepted in sensu stricto can be regarded as a mere formal detail which does not change the overall

picture of a by and large innovative, collaborative and ‘market-responsive’ crowd.

Again, this statement has to be viewed in a somewhat isolated and absolute perspective because as soon

as the comparative analysis of the screen team’s reactions comes into play one has to take a more

differentiated view on the crowd of ‘New Claims’. It is perhaps the most interesting finding of this

thesis that the screen team consistently did not share the crowd’s ‘excitement’ about novelty, co-

development and market needs. None of the three studied correlations was significant or came even

close to the significance level of p<0.05. In a sense one could say that the screen team was remarkably

irresponsive to key idea features which triggered strong reactions by the crowd.

6.2 The screen team’s perspective

The observation of consistent discrepancy between the crowd and the screen team’s perspectives called

for a closer investigation as presented in table 9. In comparison to the crowd the screen team’s

approach to the presented ideas was very different from a several point of views.

Firstly, instead of using freely distributable dot-votes, the screen team was confined to rate each and

every idea on six predetermined screening criteria using quantitative scores. Up to five points could be

given to each of the criteria. In this way a criteria-based ranking of ideas was established.

Secondly, the screen team also went to great lengths to record their reasons of their scorings in a

comprehensive spreadsheet (appendix 2). While scores were given by each of the ‘screeners’

individually, the comments to the scores were typed by the group during their screen team meetings.

These comments are the main source to explain why the screen team had significantly different

perspective on the presented ideas. It appears that the screen team checked ideas rigorously for the

presence, validity and consistency of supporting information. The quotes from the screen team

comments below are representative for their meticulous work. They recorded the absence of supporting


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evidence, they commented on inconsistencies between claims made by idea authors and the factual

source situation. (Appendix 2)

Idea no. 17, comments from the screen team: “Not clear what the substrate is - is mustard and turmeric

the same? We have looked at curry stains in the past. Not clear if oxidation or hydrolase is the answer.

Stains are only relevant in some regions. NA - 417 records in LUNA7 could have been read before

submitting as 'new' idea. oxidoreductase?”

Idea no. 62, comments from the screen team: “Stain act as mediator, Interesting for emerging markets,

if we have the enzyme. Tested before in an old Unilever project, Reckitt Benckiser is interested. Worth

revisiting.”

In summary, the impression prevails that the screen team’s approach to establish an idea ranking was

much more rational and critical than the crowd’s. Where the crowd seems to react almost impulsively

on trigger words indicating novelty and market relevance, the screen team assumes a calm stance and

takes deeper look to validate the provided information.

However, despite the evidence pointing to a sober and rational behavior there was one particular

observation which indicated that even the screen team did not act completely free of any bias. Out of

the six screening criteria three were of commercial nature and three of technical nature (appendix 1).

The statistical analysis of the scoring distribution showed that the screen team had a significant

tendency to award higher scores on the commercial criteria than on the technical ones (figure 9). This

could suggest that the screen team was more captivated by the potential market impact and commercial

relevance of the presented concepts (ideas) than by their technical quality. With some caution it may be

argued that this commercial bias reflects an outward-looking, market- and customer- oriented

perspective prevailing in the screen team.

7 LUNA is an internal database in Novozymes


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6.3 From singular ideas to ‘strategic idea landscapes’

A surprising and unintended but highly intriguing outcome of ‘New Claims’ was that the screen team

made an ad hoc decision to categorize and cluster all presented ideas. This was not a step foreseen in

the original procedure protocol of the ideation and by taking this step they pushed their ambition level.

Now it was not only about finding a few promising individual ideas but to look for idea patterns and for

new themes and trends. One of the screeners explained: “Reading all these ideas we realized that many

ideas were thematically related. They represented contextual clusters. Thus, we decided to take a closer

look and see if we could arrive at a systematic way of clustering”. They came up with a categorization

grid which consisted of ‘enzyme types’ as one dimension and ‘commercial claims’ as the other

(figure 8). As a next step they categorized each idea as belonging into one of the fields on the grid,

which allowed them to establish a synoptic overview.

During the research leading to this thesis it became obvious that one additional step could be taken. By

adding the screen team’s quantitative scores as a third dimension their idea-grid became an ‘idea-

landscape’. Now it was evident that the idea and score accumulation in certain fields gave rise to bona

fide ‘strategic peaks’: certain proposed technologies would allow certain new claims which were scored

as having different levels of attractiveness as viewed from the screen team’s perspective. Three-

dimensional maps like the one presented are highly intriguing because they provide a novel set of

perspectives which may complement or – even more interesting – conflict with the established strategic

paradigms prevalent in management circles of the organization.

Strategic maps derived from crowdsourcing, even in their present simple form, may helpful to

challenge conventions and to lead to discussions of new angles. In the case of ‘New Claims’ the

identification of the idea category ‘deglycosylation glycosyl transferases for the decolouring of fruit

and vegetable stains’ represented such a new angle for future development, which was hitherto not

explicitly articulated and discussed as a potential innovation vertical. A first step to explore that

innovation potential could be to run the next crowdsourcing exercise specifically on the identified new

theme or category.


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Finally, it also needs to be pointed out that the author of this winning idea was a relatively young

female scientist from Latin America who had moved recently to Copenhagen to join Novozymes. Her

significant innovation contribution was to apply scientific insights which she brought with her from

another part of the world in the context of her new company.

This finding was reminiscent of literature on combinatorial innovation and knowledge creation by

Keller, (2001) Cross-Functional Project Groups in Research and New Products Development:

Diversity. Thus, it could indicate that the theory presented by Surowiecki, 2004, is true in this case,

since the crowd was acting as a crowd with high exchange of ideas and collaboration (74 ideas with

over 200 comments). In Surowiecki, 2004, criteria regarding how to assemble the rational crowd state

that the crowd needs to have diversity of opinion, independence, be decentralized and aggregation in

there construction, in order to be a rational crowd and to gain Collective intelligence. However, since

the screen team was indifferent it can be argued that the incentives for creating ideas was not too clear

for the tasked crowd, which by Surowiecki 2004, is one of the reasons why a crowd can fail in an

attempt to be rational.

In the theory related to the amount of novelty generated in the ideas, where it was found that 37% was

in the category of a new idea and whereas R&D was responsible for over 60% of these, could indicate

that the notion of innovation still comes from R&D is still a key factor in developing new knowledge,

as argued by Bos et al. (2007), “Scientists generally work with ideas that are on the cutting edge of

what is understood. This knowledge often requires specialized expertise, is difficult to represent, may

be tacit, and changes rapidly”.

However, the regression analysis showed that there was no significant evidence that ideas coming from

R&D necessarily were better than from the other departments. In Poetz and Schreier (2012) it was

found that ideas coming from engineers had high technical feasibility, however, it was also found that

the customer benefits were represented fairly low in contrast to ideas coming from Sales or directly

from consumers. The study also showed that the ideas coming from the others than professional

engineers were much higher rated in terms of novelty and customer benefits. A similar trend was

found in the present research in relation to the distribution of cross-functional ideas, it was clear that


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ideas from Technical Service were widely discussed, with 11 out of 12 ideas. This indicated that the

combination of involvement from Sales or Marketing on one side and R&D on the other side created a

high flow of creative novel ideas. It is important to emphasize that in order to succeed as a new idea,

R&D needs to be involved in the idea, this goes good in hand with the study from Poetz and Schreier,

2012.

Nonaka (1994:15) “… although ideas are formed in the minds of individuals, interaction between

individuals typically plays a critical role in developing these ideas. That is to say, ‘communities of

interaction’ contribute to the amplification and development of new knowledge”.

This seems to be the case in this thesis of creating new knowledge. Poetz and Schreier (2012) stated

that professional engineers outperformed customers in the creation of feasible ideas, in contrast to the

ideas generated outside the company, in this thesis the screen team is acting in the same manner, which

is an indication that the screen team was clear in their criteria of the ratings, which is understandable

since they were to a higher degree responsible for further assessment of the ideas.

One of the benefits of internal crowdsourcing as argued by Simula et. al. 2012, is that, since it is an

internal crowd there are no issues in terms discussing IP or company secrets which is good when

developing new ideas, however Simula et.al. 2012 also argue that this could lead to a crowd not being

able to generate non-obvious ideas which in turn could lead to feasible market-relevant solutions. In the

present investigation of market needs as articulated in the presented ideas, it was found that the crowd

reacted positive to ideas containing such; however, there was no significant evidence that the screen

team follows suit. This could indicate that the screen team was more focused in the on technical

feasibility. The study of Poetz and Schreier (2012) showed the same indications, namely that the

screening of the ideas came out in favor of the commercial implementation rather than the technical

feasibility.


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7. Conclusions and Perspectives - Moving from idea-hunting to ‘crowd-strategizing’

The initial intention of ‘New Claims’ was essentially idea-hunting and identifying a few trophy ideas.

Seen in this light, the bona fide discovery of a cognitive step-sequence which leads to ‘crowd-

strategizing’ was completely serendipitous. However, it also needs to be pointed out that this

unintended ‘discovery’ was essentially enabled by a highly competent and ambitious screen team. It is

not known at present whether the step-sequence which unfolded in ‘New Claims’ can give rise to a

repeatable standard protocol, but a couple of conclusions and assumptions seem to be important:

(1.) An engaged and productive crowd of ‘right size’ - The basis for any success – be it idea-hunting

or crowd-strategizing - is a highly engaged crowd which produces a sufficiently large number

of ideas with acceptable quality, novelty, diversity and market relevance. Their ideas and

interactive comments represent a somewhat chaotic and unordered universe of biased notions,

perspectives, postulates and suggestions. However, this body of ideas and comments also

represents the indispensable raw-material for the ensuing cognitive process which leads to

ordered innovation insights. In this context it becomes clear that the size of the crowd is a

critical factor to consider: in the thriving innovation culture of Novozymes oversized crowds

are likely to generate oversized idea universes which would simply overwhelm the absorptive

capacity of the screen team. The size of the crowd and their engagement level must be gauged

in relation to the culture of the campaign-hosting company.

(2.) A cautious stance on pushing innovation burden onto the crowd - It is debatable whether active

attempts should be taken to ‘discipline’ the crowd during the chaotic idea posting and

discussion phase. For instance, screen teams may be tempted to ask the crowd to include more

supporting references. By doing so they would request the crowd to share some of their

innovation burden, the likely risk of posing this request is crowd demotivation. Thus, it

becomes a question of balance: does a screen team want to risk demotivating a crowd to gain

better referenced ideas?

(3.) Crowd creativity is more important than crowd wisdom. Dot-voting as a mechanism for

establishing an idea ranking through the crowd is certainly helpful because it provides a form of


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collective praise to popular ideas and their authors. However, the present thesis also clearly

showed that it cannot be trusted as a mechanism to identify ideas with significant innovation

impact. There was no correlation between the crowd’s perspective and the screen team’s

perspective. Thus, the main research question of this thesis can be answered such: the crowd of

‘New Claims’ can certainly not be regarded as wise when compared to the screen team.

However, it is also true that the screen team is very unlikely to have conceived the idea map

shown in Figure 9 by themselves alone - i.e. without the previous mass-input from the crowd.

(4.) Screen team wisdom and competency are essential. Clearly, a highly knowledgeable, critical

and open-minded screen team is needed to meticulously scrutinize crowd-input. Capable screen

teams need to recognize weakly supported ideas and rank them accordingly. On the other hand,

they also need to recognize the few truly new high-potential ideas. Furthermore, and even more

importantly, they must be able to elevate their perspective from the individual idea level to the

idea-universe as such in order to recognize the emerging clusters and themes. Ultimately, they

must strive to identify axioms of order which allow them to put idea clusters into a coherently

logical and systematic ‘strategic’ map.

(5.) A general protocol for cognitive performance for the screen team? The cognitive steps which

the screen team must take to transform ‘chaos into order’ and which are necessary to move from

‘notions to insights’ are undoubtedly very challenging and the right approach may differ

significantly from campaign to campaign. At this point it is unclear and speculative whether a

standard operation procedure could be developed here. However, looking at the rapid progress

and the spreading use of algorithms for dynamic tagging, visual clustering and semantic

searches it seems very likely that the next generation of idea management systems will support

such cognitive processes.

(6.) Building organizational innovation capabilities requires repeatability. Repeatability is a key

condition for successful learning cycles and continuous innovation in the organization. In the

present thesis a single campaign has been investigated in-depth but we need to remind ourselves

that Novozymes is running such internal crowdsourcing exercises on a regular basis and at a


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monthly to bi-monthly pace. To ensure long-term repeatability one must again consider crowd

motivation in the context of the prevailing company culture. Given the fact that the crowd

composition of many Novozymes R&D campaigns is fairly constant, how can one sustain a

high level of crowd engagement while running a high campaign frequency?

In order to avoid crowd-attrition and demotivation it is recommended to conduct a crow-

debriefing meeting after the online campaign has been finalized. Current praxis in Novozymes

is that the screen team announces the winning ideas on the intranet by e-mail and to award the

winners with tokens of recognition but to secure long-term crowd engagement. While such one-

way communication praxis ensures immediate gratification of the winners, many participants in

the crowd may be left behind with unanswered questions such as the underlying reasons for

picking certain ideas as winners but not others. Such questions should be addressed in an open

concluding dialogue with the screen team. Clearly, the screen team may not be in a position to

share all their knowledge with the crowd, e.g. due to confidentiality, but it is very likely that the

crowd will appreciate the screen team’s efforts to explain their decisions and resolve potential

misunderstandings.

Conclusively, it can be stated that the present internal crowdsourcing approach has a strong potential to

accelerate customer-centric innovation because it was found that a number of impactful novel ideas and

idea categories could be identified in a much shorter time period than with conventional processes. The

main reason for this acceleration effect was the cross-functional engagement of a collaborative crowd

and the subsequent idea filtering and clustering as performed by a highly competent screen team.

Clearly, the critical enabler was not the ‘wisdom’ of the crowd but that of the screen team. However, it

also seems quite evident that the structured, cognitive performance of the screen team would have not

been possible without the preceding ‘chaotic’ creativity of the crowd.


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7.1 Further Research

The research conducted and the findings of the thesis has led to some interesting results on how

Novozymes can use internal crowdsourcing in a sustainable way in the future, however, the thesis is

not able to make any generalizing conclusion in the broad perspective of crowdsourcing. In order to do

this, a larger scale of cases analysis is necessary to conduct, i.e. with more case companies and larger

crowds. This would require a large monitor tool and a new way of screen the ideas generated.

A future initiative could include crowdsourcing together with external participants, i.e. customers or

partners, to find how the ideas will be reviewed and in that sense easier to gauge their relevance

directly against market needs. This would follow the managerial recommendations, thus the knowledge

space and the innovation burden could be shared with other parties. This seems to be a logical next step

for Novozymes.

Another perspective would be to investigate an open-source development approach in the biotech

space, and how Novozymes can tap into this knowledge pool, i.e. through social media or via

prototypes experimentation. It could be investigated how external knowledge could be applied a way

that would enable Novozymes to continue development of their key applications while still benefiting

from open source input and engagement.


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

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