Crowdsourcing: Aggregation and selection mechanisms and the impact of peer contributions on contests

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universitt

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Reihe B:

Wirtschafts- und

Sozialwissenschaften

Thomas Gegenhuber

CrowdsourcingAggregation and selection mechanisms andthe impact of peer contributions on contests


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Reihe B Wirtschafts- und Sozialwissenschaften

Impressum

Thomas Gegenhuber

CrowdsourcingAggregation and selection mechanisms and the impact ofpeer contributions on contests

2013Johannes Kepler Universitt Linz

Approbiert am 9. Mrz 2012

Begutachter:a. Univ.-Prof. Dr. Robert Bauer

Herstellung:Kern:Johannes Kepler Universitt Linz,Altenberger Strae 69, 4040 Linz,sterreich/Austria

Umschlag:TRAUNER DRUCK GmbH & Co KG,4020 Linz, Kglstrae 14,

sterreich/Austria

ISBN 978-3-99033-139-2www.trauner.at


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III

ForewordImagine the following situation: You have to write a paper. At some

point, you do not see any mistakes. So what can you do to mitigate this

effect? You put away your manuscript for a day or two. After two daysyou have a fresh perspective on your work, you spot mistakes easily

and you improve the clarity of the arguments.

I want to draw an analogy between the example above and the brain-

storming literature. Findings from the brainstorming literature indicate

that separation from idea generation leads to selection of better ideas.

The separation reduces your involvement in your work, which would

otherwise cloud your judgment. One topic that I explore in my thesisare hybrids in crowdsourcing, which attempt to achieve separation in

several ways. In short, hybrids are the application of different actors,

group structures as well as aggregation and selection mechanisms in a

single-round, multi-round or iterative crowdsourcing process. To give

you an example for such a hybrid: An organisation makes an open call

to harness ideas from the crowd via a web-based platform. In the first

step, the crowd individually creates the ideas. Next, members of thecrowd vote and comment on each others ideas. After this step, the

R&D department of the organisation has a look at the 20 highest

ranked ideas (i.e. ideas, which have received the most votes). Out of

this pool of ideas, the R&D department selects three ideas that the or-

ganisation implements. In this example, idea generation is separated

from idea selection. First, the members of the crowd judge the ideas,

which they have not created themselves. Second, the employees of the

R&D department, who are not involved in the idea generation process

at all, make the final selection. While the literature indicates that the

crowd does pretty well in creating original ideas, the employees of the

R&D department have a better perspective on the feasibility of the

ideas for the organisation. Furthermore, the ideas of the crowd must

be integrated into the organisation. In this stage the R&D department

plays a crucial role. Giving them the last call might mitigate the not-


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IV

invented-here syndrome, because management signals to its R&D em-

ployees that it still relies on their professional expertise.

This book is written for two audiences. First, for scholars who are in-

terested in the topic of crowdsourcing and innovation: I think you

might be interested in the in-depth discussion about crowdsourcing

definitions, the aggregation and selection mechanisms, effects of the

different types of peer contributions on contests as well as hybrids.

Second, the thesis is useful for practitioners who plan to create their

own crowdsourcing platform or look for an appropriate intermediary.

My thesis will give you a detailed understanding of the processes in-

volved in crowdsourcing, especially if you rely on the contest model.

But lets go back to the example at the beginning of this foreword and

how it relates to this book. In December 2012 I started with the prepa-

rations to publish my thesis as a book. I submitted this thesis in March

2012 and I have not had a detailed look at it since then. To my discon-

tent, I spotted some mistakes, which needed to be fixed. I also realized

that changes would be useful to ease the flow of reading. The result is

an edited version of my thesis in this book. Since I submitted in MarchI have read a lot of the literature available and therefore I considered

adding a substantial body of new material. But doing so would consti-

tute a major adaption of the original work. I thought this would not

serve the goal of this publication, namely to document my in-depth in-

volvement with the topic of crowdsourcing at the time of my gradu-

ation. This thesis is the starting point of my academic journey and sets

the path for future exploration.

Thomas Gegenhuber

January 2013, Linz


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Acknowledgements

I deeply thank my advisor, Prof. Robert Bauer, for his time, supervi-

sion and advice. The numerous discussions with him provided me with

essential insights for my thesis.

I want to thank the team of the Institute for Organisation and Global

Management Education (Prof. Guiseppe Delmestri, a. Prof. Johannes

Lehner, Ass. Prof. Ccilia Innreiter-Moser and Dr. Claudia Schnugg)

for taking time to discuss ideas or providing feedback to my presenta-

tions.

I want to thank following colleagues and friends for literature sugges-tions, discussions or providing feedback to my work: Alexander Bliem,

Stacy Kirpichova, Susi Aichinger, Karina Lichtenberger, Marko Hrelja,

Erica Chen, Bernhard Prokop, Sean Wise, Jeff DeChambeau, Naumi

Haque and Dave Valliere. I also want to thank Skye M. Hughes for

proofreading the edited version of my thesis.

Special thanks to Melanie Wurzer for always being there for me.

Finally I want to express gratitude to my parents, Ilse and Wolfgang

Gegenhuber, for their patience and support through the duration of

my studies.


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VI

Abstract

Crowdsourcing can be understood as an agent (organisation or indi-

vidual) using web-based platforms, which are often managed by inter-

mediaries, to call for ex anteunidentified, ideally large and diverse setsof individuals (the crowd) to solve problems identified and defined by

the agent. A crowdsourcing platform provides the means to aggregate

and select the contributions of the crowd. This analysis suggests that

the three aggregation mechanisms, collection, contest, and collabor-

ation, not only exist in their pure form, but may also overlap. Drawing

upon transaction cost theory, selection instruments can be categorized

into four governance mechanisms: hierarchy, standardization, meritoc-racy and market. The second part of the thesis places an emphasis on

the contest model in crowdsourcing and discusses how the access to

peer contributions in contests influences the quality of ideas, the co-

operative orientation, the motivation of the crowd, and the strategic

and marketing considerations of the agent. Moreover, this thesis lays

the groundwork for future research on hybrid models in crowdsour-

cing. A hybrid model uses a combination of different actors, aggrega-tion and selection mechanisms, and group structures in a single, multi-

round or an iterative process.


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VII

Table of Contents

1 Introduction ......................................................................................... 1

1.1 Structure of the Thesis ................................................................... 21.2 Method ............................................................................................3

2 Crowdsourcing ..................................................................................... 4

2.1 Defining Crowdsourcing................................................................. 4

2.1.1 The Agent ....................................................................................................9

2.1.2 The Crowd.................................................................................................11

2.2 Related Concepts ...........................................................................192.2.1 Open Source Development.....................................................................20

2.2.2 Open Innovation.......................................................................................22

2.2.3 Co-creation ................................................................................................25

2.3 Crowdsourcing Classifications...................................................... 27

2.3.1 Functional Perspective .............................................................................27

2.3.1.1 Howe (2008).........................................................................................272.3.1.2 Brabham (2012) ...................................................................................29

2.3.1.3 Papsdorf (2009)...................................................................................31

2.3.1.4 Gassmann et al. (2010) .......................................................................34

2.3.1.5 Doan et al. (2011)................................................................................37

2.3.2 Task Perspective ........................................................................................40

2.3.2.1 Schenk and Guittard (2010)...............................................................402.3.2.2 Rouse (2010).........................................................................................41

2.3.2.3 Corney et al. (2009).............................................................................43

2.3.3 Process Perspective...................................................................................45

2.3.3.1 Geiger et al. (2011)..............................................................................45

2.3.4 Conclusion .................................................................................................47

2.4 Aggregation and Selection ............................................................ 48


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2.4.1 Aggregation ...............................................................................................51

2.4.1.1 Integrative and Selective Crowdsourcing ........................................51

2.4.1.2 Collective Intelligence Genome Framework...................................54

2.4.1.3 Overlapping Dimensions in Aggregation........................................56

2.4.2 Selection .....................................................................................................59

2.4.2.1 Decide Gene ........................................................................................60

2.4.2.2 Evaluate gene.......................................................................................63

2.4.2.3 Selection Instruments and Governance Mechanisms ...................65

2.4.3 Aggregation and Selection Framework .................................................72

3 Contests in Crowdsourcing ................................................................74

3.1 Contests in General........................................................................74

3.1.1 Winner-take-all Model and Distributive Justice ...................................74

3.1.2 Generic Contest Process .........................................................................77

3.1.3 Contest Design Elements ........................................................................79

3.1.4 Form Follows Function ...........................................................................81

3.2 Impact of Accessibility of Peer Contributions on Contests .........833.2.1 Classification of Accessibility of Peer Contributions.........................84

3.2.2 The Relation between Access to Peer Contributions and Marketing

and Strategic Considerations of the Agent......................................................88

3.2.3 Accessibility of Peer Contributions and Motivation of the Crowd .91

3.2.4 Accessibility of Peer Contributions and Quality of the Best Idea ...95

3.2.5 Access to Peer Contributions and the Cooperative Orientation of the

Crowd...................................................................................................................101

3.3 Discussion.................................................................................... 108

3.3.1 Reward Structure ....................................................................................111

3.3.2 Hybrids.....................................................................................................113

4 Final conclusion ............................................................................... 120

4.1 Critical Reflection ........................................................................ 120

4.2 Practical Implications.................................................................. 121

4.3 Questions for Further Research .................................................. 122


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4.4 Contributions ...............................................................................124

References...............................................................................................126

Appendix 1: List of Platforms.................................................................139


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List of Abbreviations

CI Collective Intelligence

CIO Chief Information Officer

CS Crowdsourcing

EBS Electronic Brainstorming

FLOSS Free/Libre and Open Software

GNU GNU's Not Unix

HIT Human Intelligence Task

ICC Intra-Corporate Crowdsourcing

IP Intellectual Property

MNC Multinational Corporation

NGT Nominal Group Technique

OI Open Innovation

OS Open Source

R&D Research & Development

TCT Transaction Cost Theory


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XI

List of Figures

Figure 1: Onion-structure in FLOSS (Crowston and Howison, 2005).................17!

Figure 2: Open Innovation Paradigm (Chesbrough, 2006)......................................23!

Figure 3: Connection between Crowdsourcing and Open Innovation..................24!

Figure 4: Crowdsourcing Applications and Corresponding Industries (Pabsdorf,

2009)..........................................................................................................................33 !

Figure 5: Categorization of Crowdsourcing Initiatives (Gassmann et al., 2010)..36!

Figure 6: Crowdsourcing Taxonomy (Rouse, 2010)..................................................43!

Figure 7: Selective Crowdsourcing...............................................................................52!

Figure 8: Integrative Crowdsourcing ...........................................................................53!

Figure 9: Collection Gene .............................................................................................55!

Figure 10: Collaboration gene.......................................................................................56!

Figure 11: Overlapping Dimensions in Aggregation................................................59!

Figure 12: Voting Process..............................................................................................60!

Figure 13: Prediction Market ........................................................................................62!

Figure 14: Aggregation and Selection Framework ....................................................73!

Figure 15: Generic Contest Process.............................................................................78!

Figure 16: Threshold Accessibility of Peer Contributions.......................................86!

Figure 17: Connection between Accessibility of Peer Contributions and

Aggregation/Selection Mechanisms ....................................................................87!

Figure 18: Screenshot Threadless.com ........................................................................89!

Figure 19: Correlation between Innovativeness and Cooperative Orientation

(Bullinger et al., 2011)...........................................................................................105!

Figure 20: Relationship between User Types and Successful Innovation

Outcomes (based on Hutter et al., 2011) ..........................................................105!

Figure 21: Recursive Incentive Structure (Tang et al., 2011)..................................112!


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XII

List of Tables

Table 1: User Types, Community Impact, Traits and Approximate Percentage ofCommunity (Moffitt and Dover, 2011)...............................................................18!

Table 2: Crowdsourcing Typology (Brabham, 2012) ................................................ 30!

Table 3: Sample of CS Systems on the WWW (Doan et al., 2011) ........................ 39!

Table 4: Characteristics of Crowdsourcing Applications (Schenk and Guittard,

2010) .........................................................................................................................41!

Table 5: Classification of Crowdsourcing Activities (Corney et al., 2009) ............44!

Table 6: Taxonomy of Crowdsourcing Processes (Geiger et al., 2011) .................46!

Table 7: InnoCentive Genome (Malone et al., 2009)................................................ 54!

Table 8: Overview of Individual and Group Decisions...........................................60!

Table 9: Evaluation Gene (Wise et al., 2010) .............................................................64!

Table 10: Tagging and Flagging Gene.........................................................................65!

Table 11: Morphological Analysis of Selection Instruments and Governance

Mechanisms ............................................................................................................. 72!

Table 12: Contest Design Elements (Bullinger and Moeslein, 2010) ..................... 79!

Table 13: Overview of Contest Types (Hallerstede and Bullinger, 2010) .............82!

Table 14: Comparison of Design Elements of Studies on Cooperative

Orientation of the Crowd ...................................................................................108!

Table 15: Overview of Propositions re: Impact of Accessibility of Peer

Contributions.........................................................................................................109!

Table 16: Genome Deloittes Innovation Quest (Gegenhuber and Hrelja, 2012)

.................................................................................................................................118 !


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1IntroductionThe future of the web: Intellectual mercenaries will meet on elec-tronic marketplaces. An organisation is able to easily gather thousands

of those intellectual mercenaries to solve one of its problems. Within a

few hours and days, the task is completed and the problem solved. The

mercenaries receive their reward and seek the next assignment. This

web is made for individuals who prefer flexible work arrangements to

the mundane routine of a nine-to-five job. Malone and Rockart envi-

sioned this scenario in an article that was published in 1991. Today, ithas become reality.

MechanicalTurk is a marketplace for individuals who are willing to

complete well-defined micro-tasks for a micro-payment.

Threadless, a successful venture and well-cited example in business re-

search, solved the problem of how to continuously offer a wide range

of cool T-shirts. To do this, Threadless makes an open call to the

crowd and asks the crowd to generate T-shirt designs. The submitted

T-shirts are aggregated in a collection. The crowd then assesses and se-

lects the best designs by rating and commenting. Submitted designs

with the highest score will be considered for production by Threadless.

Still, the Threadless management has the final decision whether or not

to produce the design. If a new design is implemented for production,

the individual who posted the design is awarded a prize.

Another well-known example is InnoCentive, which was founded in

2001 (one year later than Threadless). InnoCentive makes it possible

for an organisation to broadcast a scientific problem in the form of a

contest to a network of potential solvers who will individually try to

find a solution. The contestants do not have access to the solutions of

their peers. The organisation awards the winning solution with a prize

and has to pay a fee to the intermediary InnoCentive.Threadless and InnoCentive were founded in the early 2000s. Six years


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later, in an article for Wired magazine, Jeff Howe and Mark Robinson

(2006a) introduced the term crowdsourcing to describe platforms such as

Mechanical Turk, Threadless and InnoCentive. The term crowdsour-

cing is derived from the words crowd and outsourcing. Crowdsourcing

is discussed intensively in the trade press and industry blogs likecrowdsourcing.org. Crowdsourcing also sparked the interest of the sci-

entific community. A related research stream has emerged that analyses

how innovation contests work (Leimeister et al., 2009, Bullinger et al.,

2010, Hutter et al., 2011), others investigate innovation contests under

the umbrella of distributed/open innovation(Jeppesen and Lakhani, 2010).

Innovation contests basically use the broadcast search model of Inno-

Centive, where the sponsor of a challenge can choose from a varietyof solutions provided by the crowd. Threadless is also based on the

contest logic, but in contrast to InnoCentive, the crowd can assess the

contributions of their peers. Even though the final decision of which

T-shirt to produce is in the hands of Threadless, the crowd plays a ma-

jor role in peer-reviewing the designs. At InnoCentive the crowd pro-

duces ideas, while the organisation selects the best one. In this model,

members of the crowd do not interact with each other and offer no in-

put on the organisations final decision.

The thesis attempts to answer following questions: How should crowd-

sourcing be defined and how does it overlap with related concepts?

What platforms should be considered as crowdsourcing and what

classifications do exist? How does the aggregation and selection pro-

cess in crowdsourcing work? And what role does the access to peercontributions play in contests such as InnoCentive or Threadless?

1.1Structure of the ThesisThe structure of the thesis is as follows: First, I shall define crowd-

sourcing and review how crowdsourcing differs from related concepts.

Next, I shall examine literature that attempts to classify different typesof crowdsourcing and discuss different modes of gathering and select-


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ing the inputs of the crowd. In the second part of the thesis I shall

place an emphasis on contests in crowdsourcing. After briefly explain-

ing the foundation of contests and their externalities, I will elaborate

on how the accessibility of peer contributions affects the outcome of a

contest. In the conclusion I will integrate the findings of the first andsecond part of the thesis and investigate hybrids, a combination of dif-

ferent actors, aggregation and selection mechanisms and group struc-

tures. Additionally I am going to outline a new reward system. Finally I

will summarise the practical implications and contributions of this the-

sis and outline avenues for further research.

1.2MethodThis thesis attempts to make a conceptual contribution to the crowd-

sourcing literature. The methodological approach has two strands: an

extensive literature review and analysis as well as an examination of

crowdsourcing platforms on the web.

The literature review encompassed a search on Google Scholar, whichis linked to the databases of the Ryerson University Library (Toronto).

The Ryerson library has access to a wide range of databases, including

Academic Search Premier, Proquest Research Library, Ebsco, ACM

Digital Library and Science Direct. The initial keyword search used the

term crowdsourcing. It led to the identification of approximately

11,000 articles. I carried out a preliminary scan of papers (abstract +

keywords) that were often cited and which discussed crowdsourcing ingeneral. I read all papers in detail that were, according to Google

Scholar, quoted more often than 50 times. In a second step I devel-

oped a categorization for each research question. The categories are

Crowdsourcing in General, Taxonomies,Aggregation and Selectionand Contests.

I added literature to the categories using a two-step approach. First, I

used the reference lists of the most-cited papers to identify further lit-

erature. Second, I made another keyword search using numerous com-


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binations of a + b. a referred to crowdsourcing, while b was

changed in each search. For b I used the following terms: classifica-

tion, taxonomy, typology, open source, innovation contests, con-

tests, nominal group, innovation tournaments, co-creation, open

innovation. I only added literature to the categories that provided in-formational value for the research. Articles that had no connection to

the research focus were discarded (e.g. papers focusing on the econom-

ics of MechanicalTurk).

To examine crowdsourcing platforms I signed up to the following

websites: Threadless, 99designs, DesignCrowd, CrowdSpring, Innova-

tionExchange, Spreadshirt, MechanicalTurk, Vencorps and Dells

Ideastorm. I also visited the website of each platform that is men-

tioned in this thesis.1 This step helped me to gain a deeper understand-

ing of the aggregation and selection mechanisms of crowdsourcing. It

was also beneficial to reflect the literature with the actual crowdsour-

cing applications on the web.

2Crowdsourcing2.1Defining CrowdsourcingHowe (2006b) describes crowdsourcing as:

() the act of a company or institution taking a function once per-

formed by employees and outsourcing it to an undefined (and generally)

large network of people in the form of an open call. This can take the

form or peer-production (when the job is performed collaboratively) but is

also often undertaken by sole individuals. The crucial prerequisite is the

use of the open call format and the large network of potential laborers.

The term outsourcing is an important attribute in Howes definition.

1 All platforms and their corresponding URLs are listed in Appendix 1.


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He acknowledges that crowdsourcing is a disruptive business model

and reduces the price of intellectual labour (Howe 2008). Crowdsour-

cing makes good use of the fact that the digital world is even flatter

than the real world; as long as the crowd has access to a computer

and the Internet, activities can be performed from anywhere in theworld. Howe (2008) admits that crowdsourcing, like outsourcing, takes

advantage of disparities between developed and developing countries.

Due to income disparities, winning a $300 (USD) bounty for a logo de-

sign challenge is worth much more for a designer from Pakistan than

for a designer who lives in the US. At Mechanical Turk it is hardly pos-

sible to earn a sum that is equivalent to the minimum wage in one

hour.2 Howe (2008) is aware of the disruptive effects of crowdsour-cing and calls for a firewall against exploitation, otherwise crowdsour-

cing might be the digital home sweatshop of the future. Von Ahn

(2010) also argues that if crowdsourcing is the future of work, regula-

tion should be considered. Crowdsourcing generally shifts the risk of

failure from an organisation to the crowd (Schenk and Guittard, 2010)

and is perceived as a strategy to reduce costs (Howe, 2008). Whereas

outsourcing typically has a one-to-one relationship between an organi-

sation and a third party that needs to deliver specific results (Rouse,

2010; Schenk and Guittard, 2010), crowdsourcing has a one-to-many

relationship between the organisation and the crowd.

In addition Howes definition (2006b) highlights the open call nature

of crowdsourcing, which gives an organisation access to a large net-

work of individuals outside of the organisation. The open call resultsin a self-selection of individuals who participate due to numerous mo-

tivations (c.f. Brabham, 2010; 2012). Although some crowdsourcing

platforms use fixed or success-based-payment or restrict who can par-

ticipate (Geiger et al., 2011), no platform can ex ante identify exactly

who will participate in a crowdsourcing effort. In organisations, a

2 To get a sense of why MechanicalTurk is also called the digital assembly line (Stross, 2010), I advise the reader to self-experiment and try to earn as much money on MechanicalTurk within two hours.


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supervisor may instruct an employee into performing a certain task. If

the employee refuses to perform the task, he/she faces the risk of be-

ing expelled. In crowdsourcing, individuals independently decide for

themselves whether or not to accept the task and can refuse to accom-

plish the task without (hardly any) consequences.

Building on Howe (2006b), Brabham (2008) defines crowdsourcing as

an online, distributed problem-solving and production model (2008:

75). Brabham (2008) explains that crowdsourcing is a business model

that benefits from the input of the crowd.

Gassmann et al. (2010) provide following definition of crowdsourcing:

Crowdsourcing is a strategy to outsource idea generation and solving prob-lems to external actors in the form of an open call. Additionally to

solving problems and idea generation, crowdsourcing may be used for

solving micro-tasks. In general, the open call is realized through an online

platform.3 (2010: 14)

Gassmann et al. (2010) examined a large variety of crowdsourcing plat-

forms, including the platform InnoCentive@Work. InnoCen-

tive@Work makes an open call to a large group within the

organisation. This collaborative software is based on the InnoCentive

model that claims to build an innovation community within the organi-

sation. If one applies the definition above to InnoCentive@Work , it

cannot, strictly speaking, be classified as crowdsourcing. Thus, applica-

tion of crowdsourcing within an organisation cannot be labelled as

outsourcing.Villarroel and Reis (2010) call the application of crowdsourcing within

the firm Intra-Corporate Crowdsourcing (ICC) and provide follow-

ing definition:

ICC refers to the distributed organizational model used by the firm to ex-

3 Translated from German: Crowdsourcing ist eine Strategie des Auslagerns von Wissensgenerierung und Problemlsung an

externe Akteure durch einen ffentlichen Aufruf an eine groe Gruppe. Typischerweise stehen Problemlsung und

Ideengenerierung im Zentrum, aber es sind auch repetitive Aufgaben mglich. In der Regel wird dieser Aufruf durch eine

Website realisiert (Gassmann et al., 2010: 14).


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tend problem-solving to a large and diverse pool of self-selected contribu-

tors beyond the formal internal boundaries of a multi-business firm:

across business divisions, bridging geographic locations, leveling hierarchi-

cal structures. (2010: 2)

Other scholars do not differentiate whether crowdsourcing takes place

inside or outside an organisation. Doan et al. (2011) offer a very broad

definition of crowdsourcing and view it as a "general-purpose prob-

lem-solving method" (2011: 87). Consequently they consider a plat-

form (e.g. Threadless) as a crowdsourcing system "if it enlists a crowd

of humans to help solve a problem defined by the systems owners"

(2011: 87).

It appears that there is no clear consensus about what constitutes

crowdsourcing. Howe (2006b) stresses the notion of outsourcing and

the open call format. Brabham (2008) and Doan et al. (2011) place an

emphasis on distributed problem-solving. Gassmann et al. (2010) pro-

vide a definition that does not consider crowdsourcing within a firm,

although they describe examples of the latter in their book. The defini-

tion of Villarroel and Reis (2010) highlights the problem-solving capa-bilities of crowdsourcing within organisations.

I conclude that due to the emergence of crowdsourcing within organi-

sations, outsourcing is not the key element of a crowdsourcing defini-

tion. Based on this literature review, I propose the following definition

of crowdsourcing:

Crowdsourcing is the act of an agent who uses a platform (ofan intermediary) to call for an ex ante unidentified, self-

selected, ideally large and diverse set of individuals (crowd)

who explicitly or implicitly solve a problem (defined by the

agent).

The key element of this definition is the distributed problem solving

capacity of crowdsourcing. The agent (either an organisation or an in-

dividual) calls for a self-selected crowd by using a platform. A platform


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should be a web-based system. The web deserves credit for the rise of

crowdsourcing with features like speed, reach, anonymity [and the]

opportunity for asynchronous engagement (Brabham, 2012: 3). Doan

et al. (2011) add that the web () can help recruit a larger number of

users, enable a high degree of automation, and provide a large set ofsocial software (for example, email, wiki, discussion group blogging,

and tagging) that CS systems can use to manage their users" (2011: 88).

However, the term web-based does not necessarily entail that the plat-

form must be located on the Web. An agent may prefer an internal

web-based solution for crowdsourcing within the organisation due to

security concerns.

The agent can also use the platform of a third party (intermediary),

which is an optional element of my crowdsourcing definition. Doan et

al. (2011) suggest that the system owner defines the problem. But if an

agent uses the system of an intermediary to solve a problem (e.g. In-

noCentive), the problem is defined by the agent and not by the system

owner. However, by using an intermediary, the agent is often limited to

the pre-defined problem-solving processes of the intermediary system.My definition also captures the open call prerequisite defined by Howe

(2006b). Note that self-selection means the same as open call. The

crowd responds to an open call by an agent, therefore the individuals

of the crowd are self-selected. Crowdsourcing within an organisation

must follow the open call principle. The organisation cannot build its

own crowd (Schenk and Guittard, 2010: 3). Although a firm can con-

trol the context (e.g. only employees participate)4 it cannot determineexactly who will respond to the open call within this context (i.e. it is

not possible to identify ex antewho is part of the crowd). Thus, pre-

selection constrains but does not violate the open call principle (c.f.

Geiger et al., 2011).

Finally this definition tries to capture whether the crowd participates in

4 A firm could ask employees as well as selected partners to participate. The selection of partners would violate the open call

principle.


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a crowdsourcing effort explicitly or implicitly (Doan et al., 2011). The

crowd can participate explicitly on platforms such as InnoCentive or

Threadless, while an example of implicit crowdsourcing is Re-

CAPTCHA. ReCAPTCHA piggybacks on other websites and is a tool

that prevents bots from signing in on a website. The individual usersolves a reCAPTCHA by typing the letters that he/she sees on a pic-

ture. By recognizing the letters of the picture the user is allowed to sign

in. The act of typing in the letters into the form creates a useful side

effect: the crowd helps to digitalise books by recognising words that

could not be read by software (c.f. Google, 2012). I shall discuss the

explicit/implicit dichotomy in more detail later.

2.1.1 The AgentThe starting point in crowdsourcing is the agent.5 The agent defines

the nature of the problem that the crowd should solve. The agent can

be either an organisation, or an individual. I consider the agent (organi-

sation or individual) as a client, when the agent uses the services of an

intermediary.

Organisation: An organisation may use crowdsourcing eitherwithin or outside itself to solve a problem. Crowdsourcing may

be the key element for an organisations business model. For in-

stance, Threadless, harnesses the creative input of the crowd to

minimize the cost of designing T-shirts (Howe, 2008; Brabham,

2008). Others use crowdsourcing as an instrument to get ideas

from outside of the organisation for future innovation (e.g.

OSRAM LED Design Contest). Deloitte stages an innovation

contest within the organisation to identify future opportunities

(Terwisch and Ullrich, 2009).

5 From the perspective of agency theory (Jensen & Meckling, 1976) it would be have been evident to use the term principal.

As I do not apply agency theory in this thesis, I use the term agent to describe an actor, who can be either an organisa-tion or an individual.


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Individual: An individual may start a crowdsourcing efforthim/herself and in most cases uses an intermediary platform. By

using an intermediary, the individual does not necessarily become

a client. For instance, ChallengePost allows individuals to chal-

lenge the crowd; for basic contests the individual does not payany fees. If the individual has to pay, he or she becomes a client.

Another way for an individual to be the agent in crowdsourcing

is crowdsourcing within crowdsourcing. Sakamoto et al. (2011)

introduced the concept of recursive crowdsourcing. In recursive

crowdsourcing, the individuals of the crowd assign a sub-task to

a sub-crowd. Normally crowdsourcing has two levels of hier-

archy: the agent and the crowd. Recursive crowdsourcing adds

another level. Sakamoto et al. (2011) refer to Scratch where

young participants are using open-ended discussion forums to

initiate their own contests, ask their own peers to enter, and

provide their own prizes" (2011: 351). Sakamoto et al. (2011) be-

lieve that recursive crowdsourcing will improve the capability of

the crowd to solve open-ended problems. To make this modelwork, they suggest that a change of incentives is needed to bal-

ance the risk between the individual of the crowd and his/her

sub-crowd.

Client: If an organisation or an individual makes a monetarytransaction to use the services of an intermediary, the individ-

ual/organisation becomes a client.

As an intermediary InnoCentive connects agents (seekers) who

look for the solution of a problem and the crowd (solvers) who

attempt to solve the problem. By protecting the interests of both

actors, InnoCentive solves the information paradox problem.

Morgan and Wang (2010) briefly explain the information paradox

put forward by Kenneth Arrow: "(...) [T]he questioner does not

know the true value of the idea ex anteunless the answerer re-

veals the idea. However, once the idea is revealed, the questioner

could behave opportunistically and pay little, if any at all, to the


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answerer" (2010: 82). To post a challenge at InnoCentive, the cli-

ent has to pay a fee. The client receives access to a vast network

of potential solvers who only see a summary of the problem

when browsing challenges. InnoCentive grants a solver full ac-

cess to a challenge if the solver signs an agreement that includesconfidentiality clauses; the intellectual property (IP) for accepted

solutions must be transferred to the client (dependent on the

challenge type). After the contest ends, all entries are blind re-

viewed by the client. The client chooses the winning solution (or

not) and InnoCentive transfers the money to the solver, if the in-

tellectual rights have been successfully transferred to the client.

The client is not allowed to use information from submissionsthat are not accepted. InnoCentive enforces this by having the

right to audit the laboratories of the client (c.f. Lakhani et al.,

2007; Jeppesen and Lakhani, 2010).

Another example for an intermediary are design contest plat-

forms, such as 99designs, where a client can harness the creativity

of the crowd by staging a design contest. In design contests, theclient awards the winner with a bounty. Additionally the client has

to pay a fee to the intermediary.

2.1.2 The CrowdSimply put, without a crowd, there is no crowdsourcing. My definition

of crowdsourcing states that an ex ante unidentified, self-selected,ideally large and diverse set of individuals forms a crowd. Although

the crowd is self-selected, there are several ways to create entry barri-

ers. First an agent may pre-select the crowd. Another entry barrier is

whether the crowd has to participate individually, as a team or can

choose between both to form one entity of participant (Bullinger und

Moeslein, 2010). However, an agent cannot control if a member of the

crowd is an individual or team that chooses to use one account.


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Due to the broad reach of the web it is fairly easy to gather a large

crowd. Crowdsourcing thereby exploits the spare cycles(Howe, 2008) or

cognitive surplus(Shirky, 2010) of the crowd. Howe (2008) explains that

spare cycles are the "downtime not claimed by work or family obliga-

tions - that quantity is now in surplus" (2008: XIV). The idea of sparecycles is related to Clay Shirkys idea of cognitive surplus: the ability of

users around the world to contribute and collaborate voluntarily on

large projects. This is possible due to the untapped free time and tal-

ents of users as well as the technological advancement that not only al-

lows users to consume, but also to follow their desire to create and

share (Shirky, 2010).

Still the open question is why crowds should be ideally large and di-

verse. Therefore I will briefly review two theoretical concepts: dispersed

knowledgeand collective intelligence.

1)Dispersed Knowledge: Hayeks (1945) concept of dispersedknowledge, which emphasises that knowledge is asymmetrically

distributed in a society, is one theoretical foundation for crowd-

sourcing. Crowdsourcing is a matchmaking process betweenabundant time and talent and those who need it to solve a prob-

lem or perform a task (Howe, 2008). In other words crowdsour-

cing provides a means to harness the dispersed knowledge of the

crowd and aggregates it by using web technology. The import-

ance of dispersed knowledge can be explained by looking at In-

noCentive. InnoCentive enables an agent to broadcast a problem

to a large network of potential solvers. The logic is to tear downthe walls of the organisation and to realize that there is someone

outside of the organisation who will have the knowledge to find

a solution that the research & development (R&D) department

within the organisation cannot find (c.f. Tapscott and Williams,

2006). Knowledge is so widely dispersed that even for large

multinational firms it is impossible to have a R&D department


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with enough resources to find a solution for any problem.6 Lak-

hani et al. (2007) found in their study of 166 broadcasted prob-

lems posted on InnoCentive that the crowd solved 30% of the

problems that could not be solved within the traditional boun-

daries of organisations. The broadcast search occurs if a firm in-itiates a "problem-solving process by disclosing the details of the

problem at hand and inviting the participation of anyone who

deems themselves qualified to solve the problems" (Jeppesen and

Lakhani, 2010: 1016). Broadcast search is an alternative to local

search, which is constrained by the resources, the heuristics, and

the perspectives of the organisations employees. A broadcast

search helps to find unexpected and useful ignorant individualswho are able to solve the problem. Each problem needs a diverse

solver base. Specialists, who are distant to the field of the prob-

lem, are more likely to be successful (Lakhani et al., 2007). Lak-

hani et al. (2007) put forward the concept ofmarginalityand argue

that marginality leads to a successful transfer of knowledge be-

tween the fields. Jeppesen and Lakhani (2010) view marginality as

an asset, because solvers are not burdened with prior assump-

tions of the field. People use prior knowledge and experience to

solve problems. "Once a perspective on a problem is set, the

problem solver then employs heuristics to actually find the solu-

tion" (Jeppesen and Lakhani, 2010: 1090). But problems are not

fixed and cannot be exclusively defined by the field. Marginal

solvers bring in new perspectives and heuristics in their attempt

to solve a problem. Jeppesen and Lakhani (2010) identified two

types of marginality: technicaland social. Technical marginality, re-

fers to those coming from a different field than the problem

while social marginality means individuals who are distant from

their own professional community (e.g. women who are not in-

6 Additionally a large R&D department may not result in numerous innovations. Terwisch and Ullrich (2009) dem-onstrate that there is no correlation between R&D spending and growth of the firm.


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14

cluded in the scientific community). Villarroel and Reis (2010)

add rank and site marginality in their article about intra-corporate

crowdsourcing (ICC). Their study, about an intro-corporate pre-

diction market (stock market of innovation), finds that the lower

the position of an employee (rank marginality) and the greaterthe distance of an employee to the headquarters (site marginality)

the more beneficial for the innovation performance of ICC (Vil-

larroel and Reis, 2010). The work of Villarroel and Reis (2010)

shows that the concept of dispersed knowledge also applies

within the organisation. Employees typically have more skills

than they are hired for (c.f. Baker and Nelson, 2005) or they

might have knowledge that is not used by the organisation. Forexample salespeople have tacit knowledge about their customers

that a top-tier manager does not have (Howe, 2008). Crowdsour-

cing instruments, such as a prediction market ease the transfer of

sticky knowledge (Von Hippel, 1994) into aggregated data within

the organisation (Howe, 2008; Villarroel and Reis, 2010).

2)Collective Intelligence: Howe (2008) draws upon the DiversityTrumps Ability Theorem that postulates that a randomly selected

collection of problem solvers outperforms a collection of the

best individual problem solvers (Howe 2008: 131). Malone et al.

(2010) briefly define collective intelligence(CI) broadly as groups of

individuals doing things collectively that seem intelligent (2010:

2). Collective intelligence can mitigate biases in the decision mak-

ing process, but the effect of diversity is limited (Bonabeau,

2009). For example a group of nuclear engineers would beat a

random crowd in designing a new element for a nuclear plant. In

this case, concentrated knowledge of the nuclear engineers is bet-

ter suited to solve the problem. Collective intelligence is a key

element for crowdsourcing. While CI can happen in the real

world, the web makes it easier to facilitate. Primary forms of CIin crowdsourcing are prediction markets, broadcasting a problem


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and idea jams (online brainstorming) (Howe, 2008). However a

higher level of interaction between individual members of the

crowd can cause group thinking (Bonabeau, 2009; Howe, 2008).

Quinn and Bederson (2011) state that CI only applies "when the

process depends on a group of participants" (2011:4). If a singleuser does a translation on demand the work "would not be con-

sidered collective intelligence because there is no group, and thus

no group behavior at work" (Quinn and Bederson, 2011: 4).

Contrasting Howe (2008) with Quinn and Bederson (2011) leads

to the following question which I will briefly discuss using the

example of InnoCentive. At InnoCentive each individual submits

his or her solutions to a contest independently. The questionthen is if the crowd posts solutions independently and the agent

chooses one solution, should this be considered collective intelli-

gence?

In the spirit of Quinn and Bederson (2011) the argument is that

the numerous individual inputs are independent from each other.

The agent selects one input and the rest is discarded. Therefore

there is no group behaviour and inputs are not used in a collec-

tive manner.

Steiner (1972) offers a different perspective on this question.

Consider the example of pulling a rope. Members of the group

are asked to pull as hard as possible and the rule is that only one

person at a time is allowed to pull the rope. In this case, the

group performance depends on the groups ability to select the

strongest member. Steiner (1972) calls this a disjunctive task be-

cause

it requires an "either-or" decision; the group can accept only one of

the available individual contributions as its own, and must reject

all others. One member receives total "weight" in determining the

group product and others are accorded no weight at all. (1972:

17)While the tasks at InnoCentive could be considered disjunctive


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tasks, a difference is that at InnoCentive the agent and not the

crowd decides what should be the best solution. But is who se-

lects the winning solution the key element to determining dis-

junctive tasks? Steiner (1972) refers to studies where the groups

had to perform a disjunctive task but did not select the ideathemselves. One conclusion would be that it does not matter

who selects the best input, what counts is whether the collection

of ideas created by the crowd contains a solution to the problem.

InnoCentives success relies on a group of participants (who do

not interact with other) to submit solutions. The performance of

the crowd is dependent on the one member of the crowd who is

able to submit a successful solution, therefore it could be con-sidered as collective intelligence. But to provide a satisfactory an-

swer lies beyond the scope of this thesis and would require more

research.

The concepts of dispersed knowledge and collective intelligence pro-

vide an answer as to why the crowd in crowdsourcing should be largeand diverse. But the term diversity has another meaning from the per-

spective of social structure and activity levels within a crowd. Social

structure and activity levels explain that within the crowd, each individ-

ual has different roles and a varying degree of participation. Dispersed

knowledge leads to dispersed interests, which results in a division of

labour. For instance, some members of the crowd at Threadless focus

on submitting designs while others just rate those designs.Crowston & Howison (2005) analyse social structure in free/libre and

open software (FLOSS) projects and propose that the teams in FLOSS

have an onion-like structure. Figure 1 below is an extract taken from

Crowston and Howison (2005), which shows the onion-structure of

development teams; this implies a hierarchical relationship between the

actors:


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Figure 1: Onion-structure in FLOSS (Crowston and Howison, 2005)

Crowston and Howison (2005) refer to Mockus et al. (2002) who re-port that 15 developers created 80% of the code in an Apache project.

In successful open source projects it is the vital few that create the ma-

jority of the code, while the useful many will help fix the bugs (Mockus

et al., 2002).

Crowdsourcing literature also suggests that individual users can be di-

vided into different user types based on their propensity to act and

interact. Crowdsourcing platforms, such as Threadless, often usecommunities. Von Hippel (2005) refers to Wellman et al. (2002) who

define communities as networks of interpersonal ties that provide

sociability, support, information, a sense of belonging, and social iden-

tity (2002: 4). Moffitt and Dover (2011) distinguish between four dif-

ferent types of users in communities: lurkers, contributors, creators

and evangelists. Table 1, an extract taken from Moffitt and Dover

(2011), shows the different types of users along with their accompany-ing activity level and an estimation about the percentage of each user

type within the population of a platform:


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Table 1: User Types, Community Impact, Traits and Approximate Percentage of Community (Moffitt and

Dover, 2011)

Moffitt and Dover (2011) point in the same direction as the FLOSS lit-

erature. The trivial many, or to put differently, the useful many, con-

tribute by voting, commenting or, in the case of lurkers, simply

consume the content. This suggests that there is a link to the 80:20

rule. At its heart, the basic idea behind the 80:20 rule is the concept of

the vital few and trivial many. Juran (1954, 1975) popularized the 80:20

rule in quality management and believes that the 80:20 rule is a univer-sal power law of distribution. Shirky (2003) stated that 20% of the

blogs attract 80% of the readers, eBay found the shopping behaviour

of the vital few of their users accounts for more than the half of all

transactions (Magretta, 2002). Also the figure of Moffitt and Dover

(2011) suggests that 18% of the crowd (i.e. the vital few) create most

of the content. However, it would be premature to conclude that all

crowdsourcing platforms follow the 80:20 rule. Howe (2008) refers tothe 1:10:89 rule, which states that one user will create something, 10


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will vote and 89 will consume. Arthur (2006) writes that 70% of the

Wikipedia articles are written by 1.8% of the users. Nevertheless, the

literature seems to agree on following point. There is a definite division

of labour within the crowd. The vital few are responsible for the ma-

jority of core activities (e.g. idea creation) of a crowdsourcing plat-form. The useful many help with selection of ideas (e.g. Threadless),

help to spot bugs (e.g. FLOSS) or are simply an instrument to attract

active users.

The basic social structure is a key element for designing a crowdsour-

cing platform. Doan et al. (2011) distinguish between guests, regulars,

editors, administrators and dictators and suggest that one needs to

think about low-profile tasks for low-ranking users such as guests (e.g.

flagging inappropriate contents), and high-profile tasks for high-

ranking users (e.g. become admin at Wikipedia). Howe (2008) also ar-

gues that a crowdsourcing platform needs to consider the different

time resources of each user. Some users want to do tasks that do not

last longer than ten minutes, others, who are very enthusiastic, are will-

ing to spend more time completing a task.

7

2.2Related ConceptsSchenk and Guittard (2010) acknowledge that crowdsourcing is an em-

erging phenomenon without clear borders and is often confused with

related concepts. Indeed, crowdsourcing is connected with concepts

such as open source development (Raymond, 1999), open innovation (Ches-

brough, 2003) and (virtual) co-creation(Prahalad & Ramaswamy, 2004).

7 Note that in some forms of implicit crowdsourcing, such as ReCaptcha, it is not possible to identify different user types.


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2.2.1 Open Source DevelopmentRaymond (1999) distinguishes between cathedral and bazaar ap-

proaches for developing software. The cathedral approach believes that

programs, which require a certain complexity, must be developed

within a firm in a centralized approach. The design of the architecture,

features of the program and elimination of bugs should occur prior to

the release.

In contrast the bazaar approach follows Linux Torvalds style of de-

velopment - release early and often, delegate everything you can, be

open to the point of promiscuity"(Raymond, 1999: 26), a community

that takes submissions from anyone. This approach looks chaotic onthe surface but through self-regulation a stable system emerges.

A brief summary of Raymonds aphorisms for an efficacious open

source development:

Rewrite and Reuse Plan to throw away See users as co-developers who enable rapid improvement The more co-developers you have, the better the capability to

spot and fix bugs. Raymond calls this Linuss Law: Given

enough eyeballs, all bugs are shallow (Raymond, 1999:31)

Recognize good ideas from users Realize your concept was wrong Perfection means that there is nothing more to take away Open Source Development need social skills and leadership

that acts on the basis of common understanding

Open source is the blueprint for crowdsourcing. Unix was developed

decentralized because the developers were able to break down the la-

bour into small pieces (Howe, 2008). The division of labour is an es-sential element to tame the complexity of developing software


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(Raymond, 1999). Crowdsourcing also uses the idea of dividing a task

into numerous subtasks. An extreme form is MechanicalTurk. On this

platform, tasks are divided into small and independent pieces, so-called

HITs (e.g. categorizing websites). Each HIT provides an instruction

to the user. For successful completion, the user is rewarded with a mi-cro-payment (Rogstadius et al., 2011).

Open source development reduces the costs for creating software.

Raymond (1999) argues it is often cheaper and more effective to re-

cruit self-selected volunteers from the Internet than it is to manage

buildings full of people who would rather be doing something else

(1999: 25). The self-organized volunteers of Linux outperform the in-

dustry (Raymond, 1999; Howe, 2008). Extrinsic rewards play a minor

role in open source development.8 Developers are rewarded with

solving interesting technical problems (Raymond, 1999), get experience

and are credited by others within the community (Brabham, 2008).

Similarly, Threadless relies on community-driven motivated users

(Brabham, 2010) and uses the eyeballs of many as a filter for good de-

signs. Community is a key element of open-source development. Acommunity in crowdsourcing is not a necessary pre-requisite (e.g.

MechanicalTurk) but it may be used (e.g. Threadless). Despite the fact

that crowdsourcing is seen as an instrument for many to reduce costs,

many scholars argue one should exercise caution when using crowd-

sourcing as a simple cost reducing strategy. A crowd that feels ex-

ploited might not participate in the crowdsourcing effort, or even

worse, may turn against the crowdsourcer. (Howe, 2008, Rouse, 2010).Franke and Klausberger (2010) explored the motivations of the crowd

to participate in crowdsourcing and showed that the size of the organi-

sation (start-up vs. MNC) has an impact on the expected remuneration.

Rouse (2010) draws upon trade press blogs about crowdsourcing and

argues that open source is about developing for a common good and

8 Except of developers who paid by a firm to participate in open source development, like IBM does in the case of Linux.


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has many contributors (the crowd) and many possible beneficiaries. In

contrast to open source development, crowdsourcing has many contri-

butors (the crowd) but mostly only a few beneficiaries (the agent, the

intermediary and a few users) (Rouse, 2010). Most crowdsourcing ap-

plications demand that the IP is transferred from the crowd to the or-ganisation (Schenk and Guittard, 2010).

Conclusion

Open source and crowdsourcing have a different approach towards in-

tellectual property (IP). But if IP was to be the key distinguishing as-

pect, one would need to distinguish between private-benefit-crowdsourcing (e.g. Threadless), commons-crowdsourcing (e.g.

Wikipedia) or citizensourcing9 (c.f. Taewoo, 2011). Although IP has an

impact on the motivation of the crowd (c.f. Franke and Klausberger,

2010), it does not fundamentally change the mechanisms of crowd-

sourcing.

Open source development is the blueprint for crowdsourcing; at the

same time open source development can be considered as crowdsour-

cing. Open source uses the distributed problem-solving capabilities of

developers who respond to the open call of an agent. For instance, Li-

nus Torvalds made an open call to the crowd for Linux. As Malone et

al. (2009, 2010) demonstrate, the crowd collaboratively creates new

software modules, but Torvalds and his lieutenants make the final deci-

sion about which modules are used for the next release.

2.2.2 Open InnovationChesbrough (2003) coined the term Open Innovation (OI), which

is a paradigm that assumes that firms can and should use external

ideas as well as internal ideas, and internal and external paths to mar-

9 Citizensourcing is the application of crowdsourcing in the public sector.


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ket, as they look to advance their technology (Chesbrough, 2006: 1).

Open Innovation acknowledges the importance of transferring exter-

nal knowledge into the organisation. Furthermore Open Innovation

introduces a new perspective on managing IP. Many firms use only a

small amount of their available IP. This IP might not be useful for thecurrent business model of firm A, but for firm B. Conversely firm

C might have IP that could be useful for firm A. Chesbrough (2006)

proposes that IP can flow inbound from outside of the organisation

(license in, spin in, acquire) or outbound in the form of licensing, spin

outs or divesting. Figure 2, an extract taken from Chesbrough (2006)

visualizes the Open Innovation paradigm:

Figure 2: Open Innovation Paradigm (Chesbrough, 2006)

Summing up, Chesbrough (2003, 2006) created a new paradigm that is

derived from following elements:

The equal importance of external/internal knowledge Harvesting abundant knowledge landscape in- and outside of

the firm

New business models for exploiting R&D. Evaluation of pro-jects to identify possible opportunities for false negative

projects (e.g. licensing)

Proactive IP management


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The OI paradigm is based on qualitative evidence of high technology

industries, consequently one needs to exercise caution when applying it

to other industries.

Conclusion

In contrast to crowdsourcing, open innovation provides a strategic in-

novation framework for firms (Wise et al., 2010). Schenk and Guittard

(2011) underline the fact that OI shares with crowdsourcing the as-

sumption that an organisation should tap into the distributed know-

ledge outside of the firm. Whereas OI focuses on knowledge flows

between firms, crowdsourcing links a firm with a crowd. FurthermoreOI focuses on innovation, which is not a necessary requirement of

crowdsourcing. But from the perspective of OI, crowdsourcing would

be another instrument to create an inbound knowledge flow to the or-

ganisation (Schenk and Guittard, 2011). Figure 3 visualises the relation-

ship between OI and Crowdsourcing:

Figure 3: Connection between Crowdsourcing and Open Innovation

Phillips (2010) suggests that IBM uses the IBM Innovation jam as an

open innovation instrument to brainstorm ideas within the organisa-


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tion. From this perspective OI can also be conceived as a means to

overcome traditional approaches of in-house innovations by enlarging

the knowledge base through harvesting the abundant knowledge land-

scape within the organisation. In other words, the organisation in this

perspective acknowledges that employees have more knowledge thanthey are hired for or have knowledge due to their position, which is not

yet used. This opens up the innovation process to a broader set of

people.

2.2.3 Co-creationPrahalad & Ramaswamy (2004) argue that traditional perspectives on

value creation have a firm- and product centric perspective. A market

in this perspective is an aggregation of customers. This perspective

suggests that customers solely interact with the firm by buying the

products or services or not. The customer can be targeted (e.g. specific

segments) but has no role in value creation. The firm decides what

value or experiences a customer can consume. The shift to a co-creation of value is required due to the empowered, networked and in-

formed customer (Prahalad & Ramaswamy, 2004). "(...)[C]ompanies

must escape the firm-centric view of the past and seek to co-create

value with customers through obsessive focus on personalized interac-

tions between the consumer and the company" (Prahalad and

Ramaswamy, 2004: 7). Co-creation places an emphasis on the joint cre-

ation of value of firms with their customers. The two principles of co-creation are dialogue (the market is a set of conversations) and trans-

parency (less information asymmetries between firm and customers,

customers extract value from the firm). Examples for successful co-

creation are some video games (co-creation of content by players),

eBay, Expedia or Amazon (Prahalad & Ramaswamy, 2004).

Zwass (2011) defines co-creation as the participation of consumers

along with the producers in the creation of value in the market place


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(Zwass, 2011: 13); it encompasses all activities of independent con-

sumers as well as those initiated by the firm in the production domain.

Fller (2010) uses the term virtual co-creationand underlines the idea that

the unique feature of virtual co-creation is not only that consumers are

asked about their preferences but that the consumers also contributetheir creativity and problem-solving skills.

Co-creation can satisfy the need of individual customers in a cost-

effective manner. Most co-created goods have the characteristics of

being digital and non-rival and may be perceived as more valuable

through network effects (more users attract more users) (Zwass, 2011).

Zwass (2011) distinguishes between sponsored co-creation and au-

tonomous co-creation. The agent initiates sponsored co-creation and

makes an open call to individuals to participate in the value creation

process (e.g. the contest). Autonomous co-creation signifies that indi-

viduals or consumer communities produce marketable value in volun-

tary activities conducted independently of any established organisation,

although they may be using platforms provided by such organisations,

which benefit economically (Zwass, 2011: 11). Examples of autono-mous co-creation are open source software and Wikipedia.

Conclusion

Co-creation is a transdisciplinary field. Co-creation happens within the

intellectual space of virtual communities, the commons-like open

source, collective intelligence and open innovation (Zwass, 2010). It isdifficult to disentangle co-creation from crowdsourcing. Both draw al-

most from the same intellectual space and at first sight both terms are

often used interchangeably. Sponsored co-creation is analogous to

crowdsourcing. But some cases of autonomous co-creation may also

be considered as crowdsourcing. The key difference is that virtual co-

creation emphasises a value perspective and is a marketing paradigm

whereas crowdsourcing has a task/distributed problem-solving per-spective. Similar to OI, co-creation sees crowdsourcing as an instru-


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ment to engage customers in the value creation process.

2.3Crowdsourcing ClassificationsSeveral scholars proposed classifications/typologies/taxonomies of

crowdsourcing to categorize the numerous crowdsourcing applications.

What all three terms have in common is that the authors separate like

with like from the unalike. Obviously there is a huge difference be-

tween the micro-jobs of MechanicalTurk and a community-driven con-

test. Creating categories creates avenues to examine the effects of

similar applications. Additionally, the classifications show what kind of

platforms should be considered as a crowdsourcing application.

The focus lies on the review of the existing literature on different tax-

onomies and I will highlight the most comprehensive one for the pur-

pose of this thesis. To ease the flow of reading, I shall use the term

classification synonymously with the terms typology and taxonomy. In

my review, I identified three broad categories of classifications:

Functional perspective Task structure perspective Process perspective

2.3.1 Functional Perspective2.3.1.1

Howe (2008)

Howe (2008) classifies crowdsourcing initiatives into four categories:

Collective Intelligence/Crowd Wisdom, Crowd Creation, Crowd Judging and

Crowdfunding.

Collective Intelligence/Crowd Wisdom includes prediction markets,

idea jams and crowd casting. Crowd casting is another word for broad-

cast search and encompasses platforms, such as InnoCentive, who

broadcast a problem to the crowd.


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Crowd Creation platforms rely on user-generated content to produce

TV (e.g. Current TV) or advertising. An example of the latter is Dori-

tos Crash the Super Bowl contest. The best advertisement was aired

during a commercial break of the Super Bowl.

Crowd Judging encompasses platforms that use the judgement of the

crowd or asks the crowd to organize information. For instance, Digg

ranks articles based on popularity.

Crowdfunding platforms connect a borrower with a crowd of potential

backers. Instead of asking a bank or other wealthy entity to finance a

project, crowdfundinguses a web platform to gather numerous contribu-

tions from individuals. One contribution alone may be small, but dueto the large number in the crowd, the borrower may be able to raise a

considerable sum. The crowd receives, in most cases, voting rights for

the future development of the project or a final product. The higher

the donation amount, the more valuable the reward. Examples for

crowdfunding are platforms such as Kickstarter or Kiva or Barack

Obamas first presidential campaign.

Among all scholars, Howe (2008) is the only one who classifies crowd-funding as a subcategory of crowdsourcing. Lambert and Schwien-

bacher (2010) define crowdfunding as follows:

Crowdfunding involves an open call, essentially through the Internet, for

the provision of financial resources either in the form of donation or in

exchange for some form of reward and/or voting rights in order to sup-

port initiatives for specific purposes. (2010: 6)Solely in crowdfunding there is a monetary transaction from the crowd

to the agent. In other applications, such as MechanicalTurk, InnoCen-

tive or Threadless, the crowd (or one member of the crowd) receives

money from the agent to successfully solve a problem. Instead of cre-

ating a digital artifact, the primary task of the crowd is to donate

money. The question is whether donating money, as a response to an

open call, constitutes the act of solving a problem defined by theagent. Because the definition of crowdsourcing is rather broad, one


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could argue that crowdfunding is a subcategory of crowdsourcing. But

this discussion requires future research and is not the focus of this the-

sis.10

2.3.1.2 Brabham (2012)Brabham (2012) distinguishes between four types of crowdsourcing:

The Knowledge Discovery and Management Approach, the Broadcast Search Ap-

proachand Distributed Human Intelligence Tasking.

The Knowledge Discovery and Management Approach helps to find

existing knowledge or discover new knowledge. The agent defines

which information the crowd should search or organize. For instances,SeeClickFix asks citizens to report infrastructure problems (e.g. pot-

holes) in a city.

The Broadcast Search Approach is oriented towards finding the single

specialist with time on his or her hands, probably outside the direct

field of expertise of the problem, who is capable of adapting previous

work to produce a solution" (Brabham, 2012: 8). Broadcast searchproblems are difficult but well-defined challenges.

The Peer-vetted Creative Production approach uses the creative abili-

ties of the crowd to produce digital artifacts. The crowd produces a

flood of ideas, but only a few ideas are useful for the agent. The crowd

assumes the role of gatekeeper and selects the best ideas.

The Distributed Human Intelligence Tasking is an appropriate ap-

proach for crowdsourcing when a corpus of data is known and the

problem is not to produce designs, find information, or develop solu-

tions. Rather, it is appropriate when the problem itself involves pro-

cessing data" (Brabham, 2012: 11). The large task is decomposed into

small tasks. Users get specific instructions for the tasks and get a mi-

10 It seems that several scholars attempt to establish crowdfunding as a unique research stream. Kappel (2009) discusses the

legal issues of crowdsourcing, other scholars, such as Bellaflamme et al. (2010) discuss under which conditions crowdfund-ing is preferred and give examples of how it is used to finance ventures.


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cro-payment per piece.

Table 2, an extract taken from Brabham (2012) provides an overview

of all four types:

Table 2: Crowdsourcing Typology (Brabham, 2012)

The Broadcast Search and Peer-vetted Production are both based on

the contest logic. The description indicates that the role of the crowd

in the Peer-vetted Production, unlike the Broadcast Search, is to assess

and select the contributions of their peers. The categorisation ofBrabham (2012) reflects the premise that contests apply different

mechanisms and thus serve different purposes. Wikipedia would be-

long in the category of Knowledge Discovery and Management Ap-

proach. But Brabham (2012) considers Wikipedia a commons-based peer

production (Benkler, 2002; Benkler, 2006) and, in contrast to Wikipedia,

a platform such as Peer-to-Patent determines in a top-down and man-

aged process which type of information is sought.


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2.3.1.3 Papsdorf (2009)Pabsdorf distinguishes between Open Idea Competitions11 (offener Ideen-

wettbewerb), Virtual Microjobs (ergebnisorientierterte virtuelle Micro-

job), User-designed Mass Production (userdesignbasierte Massenfertigung),

Collaborative Platforms for Ideas (Userkollaboration basierende Ideen-

plattform) and Implicit Exploitation of User-content(indirekte Vernutzung

von Usercontent).

Open Idea Competition covers initiatives such as Dells Ideastorm or

MyStarbucksIdea. Some allow assessment and selection through the

crowd, some not. Agents use Open Idea Competitions to get new pro-

duct ideas, or to seek feedback for current products. Additionally theOpen Idea Competition is used for marketing purposes.

Virtual Microjobs describe the practice where agents call for users who

should perform a well-defined task. The user gets paid for successful

completion of the task. Pabsdorf (2009) includes in this category in-

itiatives such as InnoCentive, Wilogo (crowd creates logos) or

MechanicalTurk.

User-designed Mass Production platforms enable the crowd to use on-

line editors or their own graphic programs to produce designs. The

crowd receives a success-based commission for their services. For ex-

ample, a user receives commission per sold item that used his/her de-

sign. Examples are the LEGO-Factory and Spreadshirt who combine

mass production with mass-customization.

Collaborative Platforms for Ideas are mostly intermediary platformsthat sell crowdsourcing services to agents. The purposes of these plat-

forms are manifold; tasks on Crowdspirit range from new ideas to

business models.12 Some platforms award best ideas with money, some,

such as Incuby, offer access to useful networks (e.g. investors).13 Other

11 Translated from German.

12 The business model of Crowdspirit was apparently not sustainable. The website is offline (Last Visit: February 17, 2012)

13 Also Incuby is offline and the website is for sale (Last Visit: February 17, 2012)


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platforms share the success of an idea with users by paying a premium

depending on the value of the comment for the realisation of the idea

or give points for useful contributions because payment would conflict

with the community spirit.

Implicit Exploitation of User-content means that the contributions of

the crowd are the means to an end. Agents use the contributions of

the crowd to increase popularity and traffic of a platform with the in-

tention of attracting more advertising. For instance Suite101 asks users

to publish articles that are linked with advertising. BILD newspaper

asks users to send in worthwhile pictures while CNN developed the

user-generated citizen journalism platform iReport.

Figure 4, an extract taken from Pabsdorf (2009) shows all five catego-

ries ordered according to their function and application in their corres-

ponding industries:


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Figure 4: Crowdsourcing Applications and Corresponding Industries (Pabsdorf, 2009)


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The category Implicit Exploitation of User-content would also apply

to Facebook and YouTube. On the latter platform other individuals

consume the content created by the crowd. The more viewers You-

Tube has, the easier it is to attract advertisements. The crowd explicitly

creates items on the website, shares videos with friends as well as ac-tively rating and commenting on the videos of others. The crowd im-

plicitly engages in determining the popularity of videos by viewing

them. Facebook implicitly exploits the data of users to attract advertis-

ers. Haque (2010) posits that the users are the product and not the cus-

tomers of Facebook.

Papsdorf (2009) mentions that his classification is not representative

and his key interest is the structure of crowdsourcing. Yet, his classifi-

cation does not consider the underlying structure and mechanisms of

crowdsourcing. Pabsdorf (2009) is the only author who puts InnoCen-

tive and MechanicalTurk into the same category. The tasks of Inno-

Centive are well defined, but are at the same time rather sophisticated

ones that require a high degree of formal education; users are compen-

sated with a large reward. In contrast, on MechanicalTurk, tasks arewell defined and each user receives a payment based on a successful

completion of each micro-task.

2.3.1.4 Gassmann et al. (2010)Gassmann et al. (2010) combine the functional view with an actors

perspective and identify five major crowdsourcing categories: Intermedi-

aries (Intermedire), Common-based Production (Gemeinsam eine freie

Lsung), Company Platforms (Unternehmenseigene Plattformen), Idea

Marketplaces (Markplatz fr eigene Ideen) and Citizen Sourcing (ffent-

liche Initiativen).

Intermediaries connect agents with the crowd. Gassmann et al. (2010)

created four subcategories of intermediaries: R&D Platforms (e.g. In-

noCentive), Marketing & Design (e.g. 99designs), Freelancer (e.g. Hu-manGrid) and Idea platforms (e.g. Atizo).

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Crowdsourcing: Aggregation and selection mechanisms and the impact of peer contributions on contests

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