Scaling crowdsourcing platforms: How to expand and sustain value creation and value capture with crowds

Table of Content

Acknowledgments

Abstract

List of Figures and Tables

List of Abbreviations

1. Introduction
1.1 Problem definition
1.2 Method
1.3 Contribution
1.4 Structure

2. Theoretical foundation on the value creation and value capture process of crowdsourcing platforms
2.1 Open innovation
2.2 Three core processes in open innovation
2.2.1 Outside-in process
2.2.2 Inside-out process
2.2.3 Coupled process
2.3 Crowdsourcing
2.3.1 Different types of crowdsourcing
2.3.2 User motivation
2.3.3 Crowdsourcing based business models
2.3.3 The value creation process
2.4 Scaling crowdsourcing platforms
2.4.1 Why scale matters
2.4.2 Challenges during creation
2.4.3 Ongoing challenges

3. Empirical analysis on scaling crowdsourcing platforms effectively
3.1 Research approach
3.2 Research findings
3.2.1 Understanding of scaling
3.2.2 Critical mass and chicken & egg problem
3.2.3 Lack of quality of core value unit
3.2.4 Lack of motivation and engagement
3.2.5 Paths to scale crowdsourcing platforms
3.2.6 Lessons learned
3.2.7 Limits of scaling

4. Conclusion
4.1 Theoretical implications
4.2 Managerial implications
4.2.1 Create platform awareness
4.2.2 Provide technological infrastructure
4.2.3 Create platform activity
4.2.4 Involve top creators
4.2.5 Refresh your community
4.2.6 Deploy community for quality control
4.2.7 Educate your crowd
4.2.8 Encourage desired crowd behavior
4.2.9 Award the crowd
4.2.10 Create a strong community culture
4.2.11 Main challenges for crowdsourcing platforms
4.3 Limitations and outlook

5. Bibliography

6. Appendix

List of Figures and Tables

Figure 1: The closed innovation model

Figure 2: The open innovation model

Figure 3: De-coupling the locus of innovation process

Figure 4: Three archetypes of open innovation processes

Figure 5: The three elements of crowdsourcing

Figure 6: Crowdsourcing business models

Figure 7: Business Model Canvas

Figure 8: Research findings divided by categories

Figure 9: Lessons learned

Figure 10: Main challenges for crowdsourcing platforms

Table 1: The key elements of a crowdsourcing initiative

Table 2: The collective intelligence genome for Wikipedia

Table 3: Interview partners – Managers

Table 4: Interview partners – Participants

Table 5: Interview partners – Experts

Table 6: Managerial implications

List of Abbreviations

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Acknowledgments

There are several people I would like to thank for supporting me during writing this thesis. First of all, I would like to thank my supervisor Dr. Thomas Kohler for arousing my interest for this interesting topic, for his support throughout the thesis and the valuable feedback.

I also want to say thank you to all of my interview partners, for taking the time to conduct the interviews and answering all of my questions. Without their shared insights and experiences about crowdsourcing platforms it would not have been possible to finish this work.

Finally, I want to thank all of my friends for supporting me throughout this intensive time. My proofreaders, Alex, Emma and Sonja, were also invaluable and I am extremely grateful for all the feedback and valuable input. Lastly, I want to thank my family and loved ones, especially my father, my mother and my sister, and her great family, Simon, Philip and Sophie. I am extremely thankful for the love and support I have received throughout my entire life. I am where I am today due to the support and help I have been given.

“However beautiful the strategy, you should occasionally look at the results.”

(Winston Churchill)

Abstract

Over the last years more and more companies have opened up their corporate boundaries and have built their business model upon the crowd. The creation of a crowdsourcing platform is a challenging endeavor, because on the one hand the crowd needs to be involved in the value creation process. On the other hand, to successfully keep them on the platform, the crowd needs also to be involved in the value capture process. To identify the main challenges a crowdsourcing platform needs to overcome in order to scale effectively, qualitative interviews with platform managers and founders, experts and platform participants were conducted. The results illustrate that the most important factors concerning successful scaling areplatform infrastructure, communication and community management.

1. Introduction

“Mass collaboration is becoming second nature. We are more connected now than at any other time in human history. Social media is the tip of the iceberg, and where we´re going next is collaboration – in business and organizations” (Nick Wright, 2014).

1.1 Problem definition

To be innovative is vital for every business, because it allows companies to grow and can be defined as a key to success. Therefore, companies strive to gain advantages by being innovative. In the past, new innovations were linked with large internal Research and Development (R&D) labs, operated by companies like IBM. Nevertheless, due to increasing development costs, lack of resources and shorter innovation cycles, this has changed and companies now rely on open innovation models to gain competitive advantage over their rivals. The open innovation model allows companies to include external ideas for their own innovation process (Chesbrough, 2003). A good example is the lead user approach, introduced by von Hippel (1986). This approach allows companies to successfully test new technologies or innovations and to establish collaborative teams, by integrating the users and customers actively into the innovation process.

One interesting method to benefit from external developed ideas is the crowdsourcing approach. In the literature, crowdsourcing is described as an umbrella term, because it consists of several different approaches including contests and competitions that are organized as an open call (Ren & Levina, 2010). The idea behind the crowdsourcing approach is to leverage tasks performed by the company internally to external crowd workers. The crowd can be appointed to develop new technologies, create new designs and logos, solve IT-problems or participants can help to analyze and categorize large amounts of data (Jahnke & Prilla, 2008).

On the one hand crowdsourcing offers companies the possibility to accelerate innovation. Furthermore, problems can be solved faster and fewer resources are needed. On the other hand, crowdsourcing offers innovative people, who lack resources to develop and implement their own ideas, the possibility to realize their ideas (Brabham, 2008; Vukovic, 2009).

Crowdsourcing offers the possibility to create and capture value by integrating the crowd in the innovation process. Therefore, if a company decides to create or change their current business model into an open business model, automatically the organization gets transformed into a platform. This means that every single product or service, whether it is a car or a social media network, will be transformed into a platform (Boudreau & Lakhani, 2009).

To create and operate a crowdsourcing platform effectively, both suppliers and users need to be included into the value creation process. This means that if the platform is not able to attract sufficient participants, it will not survive in the long run. Therefore, it is necessary to scale a crowdsourcing platform effectively. Before presenting one successful respectively one unsuccessful crowdsourcing platform, the term scaling will be introduced. According to Chesbrough and Appleyard (2007), scaling can be defined as the essence of a crowdsourcing based business model, because in contrast to organizational growth, increasing the number of internal resources, scaling up a crowdsourcing platform means benefiting from a larger crowd by not or only slightly increased resources deployed. A company who was able to scale successfully is the online T-shirt company Threadless. On this platform, members decide via a voting mechanism which T-shirt design will be produced next. Threadless is successful, because they were able to solve problems efficiently and used the right strategies to master challenges properly. On the other hand, the invention platform Quirky failed to scale up their business, because they were not able to provide sufficient product quality (Brabham, 2010).

Several different aspects, like how to provide adequate quality of the products or services, how to motivate the crowd to participate or how to create sufficient platform awareness, need to be taken into account to scale a crowdsourcing platform effectively. Therefore, the first research question aims to figure out what are the main challenges crowdsourcing platforms need to solve if they want to scale effectively.

Research question 1:What are the challenges to scale crowdsourcing platforms?

The second research question helps to identify cases where platforms successfully pivoted their way through in order to scale their business model effectively.

Research question 2:What are the paths to scale crowdsourcing platforms?

The third research question provides platform owners and founders with strategies of how to overcome problems respectively how to master challenges effectively in order to scale up their business.

Research question 3:What are the strategies to foster scaling of crowdsourcing platforms effectively?

1.2 Method

To answer the introduced research questions best, a qualitative research design is used, because crowdsourcing is in its infancy concerning theoretical foundations in literature and scaling still is a field with little insights about it. Therefore, semi-structured interviews with managers, CEO´s or community members of crowdsourcing platforms were conducted until theoretical saturation was reached. The interviews help to obtain an in-depth view about underlying assumptions (Bryman and Bell, 2011). During the research study eleven interviews with platform managers and three interviews with crowd participants were carried out. Furthermore, three interviews with crowdsourcing experts are included. Due to the fact that the sample size is relatively small, a combination of judgmental and convenience sampling was used (Marshall, 1996). The conducted interviews have been recorded with the software Audacity and have been transcribed for the data analysis, based on the principals of the grounded theory.

1.3 Contribution

Crowdsourcing still is a relative new phenomenon and comprises many practices. Due to steady improvements in the Information and Communication Technology (ICT), established companies are faced with ever new competition from competitors and startup companies. Companies like IBM have opened up their innovation process to profit from the innovativeness of online users by making money via licensing their intellectual property. Platforms like Airbnb, Amazons Mechanical Turk or Clickworker have revolutionized industries by creating value for both the supplier and the online users due to involving both sides into the value creation process. Despite the fact that these platforms were able to scale successfully, many other platforms failed, because they were not able to solve problems properly concerning the scaling process. Therefore, this research endeavor should add value to the work of Sangeet Paul Choudary in the field of scaling crowdsourcing platforms effectively. Furthermore, in addition to Carmelo Cennamo and Juan Santaló´s work about traps in the scaling process, this thesis delivers deeper insights in solving general problems during the scaling process.

Therefore, the aim of this research approach is to provide entrepreneurs and platform founders with further insights how to strengthen and develop their knowledge regarding crowdsourcing platforms. Additionally, findings and literature should support them to handle problems properly. This means that the insights of this thesis will help founders and managers to understand problems which specifically arise during the scaling phase of a platform respectively what ongoing problems in their daily business life will be.

1.4 Structure

This thesis is structured in four main blocks with respective subchapters. In the first part the problem, the research question and the contribution are presented. In the second chapter the theoretical concepts about open innovation and their three main concepts are shown. Furthermore, to present a proper definition of the term crowdsourcing, different literature will be considered. Before presenting four main challenges for crowdsourcing platforms, the topic scaling will be introduced. In the third chapter the empirical study will be presented including the research approach as well as the research findings. In the last chapter a conclusion will be drawn, including theoretical and managerial implications. Finally, the limitations are illustrated and an outlook for further research will be outlined.

2. Theoretical foundation on the value creation and value capture process of crowdsourcing platforms

2.1 Open innovation

“Open innovation is the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively. [This paradigm] assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology” (Henry Chesbrough, 2006).

In the past, companies like DuPont, IBM and AT&T were dominating the market for innovation due to the fact that only large corporations were able to provide the necessary resources to conduct internal R&D by creating their own labs. Internal R&D departments provided competitive advantage, were described as barriers of entry for rival companies and were clarified as crucial strategic assets. Furthermore, companies believed that successful innovation needs to be carried out within the firm’s boundaries. The so called closed innovation model (see Figure 1) argues that the innovation process needs to be fully controlled by the company. This means that a company has to create, develop, produce, commercialize, distribute and service ideas on their own (Chesbrough, 2003).

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Figure 1: The closed innovation model (Chesbrough, 2003, p. 36)

The closed innovation model can be defined as the dominating philosophy during the 20th century. The core strategy based on the closed innovation model was to conduct R&D only internally. This strategy required huge investments and resources to build internal research labs. Furthermore, companies tried to hire the best and smartest scientists to discover and develop the best innovations. Companies heavily defended their IP via patents, actively limiting the possibilities of competitors to exploit their knowledge. The model worked well through the 20th century but finally got eroded by a combined number of factors. Several reasons like the decreasing control over proprietary ideas due to the increase in mobility of experts and skilled workers, the leaking ideas from large research labs and the possibility to raise private venture capital to start new companies paved the way for a paradigm change (Chesbrough, 2003).

During the last decades the situation for companies has changed completely. Due to increasing global competition, higher development costs and shorter innovation cycles, companies are forced to find new strategies how to invent effectively (Gassmann & Enkel, 2004; Gassmann, 2006). The globalization of innovation led to an increase in R&D expenditures of European organizations. The Dutch company Philips and the Swedish company Ericsson can be defined as globally operating R&D companies, because both companies own more laboratories outside than inside their home countries. On average, organizations spend 30 to 50 per cent of their investment abroad (Gassmann & Zedtwitz, 1998, 2003). Furthermore, the improvements in information and communication technologies, like the Internet or mobile phone networks, are strongly influencing the strategic choices of companies (Khosrow-Pour, 2000). More and more established companies are faced with increased competition from startup companies despite the fact that these companies do not carry out research on their own respectively do little research, instead they receive their ideas through a different procedure. A good example of creating alternative innovations is Cisco System. The company was lacking internal R&D capabilities in comparison to their competitors but was able to stay ahead of their competitors. Lucent Technologies, which arose from the breakup of AT&T, invested huge resources in R&D whereas Cisco used an acquisition and partnering strategy to gain the newest technologies. This approach allowed Cisco to challenge their competitors successfully without doing as much research as their competitors did (Chesbrough, 2003).

According to Gassmann & Enkel (2004), companies who want to take advantage from ideas created through cooperative innovation processes with customers, partners and suppliers, organizations need to open up their solid boundaries to guarantee a valuable knowledge flow from the outside in and from the inside out. In order to gain competitive advantage over other companies, the exploitation of IP can be identified as a strategic asset too.

The new circumstances and the changing business environment led to a new approach. The open innovation model (see Figure 2) is characterized by commercializing internal as well as external ideas by generating value through the usage of inside and outside pathways to the market. Furthermore, one major difference to the closed model is that ideas that are generated outside the boundaries of the company can be brought in and this circumstance blurs the lines between companies and their environment and allows an open circulation of ideas (Chesbrough, 2003).

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Figure 2: The open innovation model (Chesbrough, 2003, p. 37)

A company who successfully made the transformation from a closed to an open approach is IBM. Due to growing market complexity, increasing dynamics and the steady increase of information and communication technologies, IBM needed to rethink its traditional strategy of internal idea creation. Therefore the company focused more on the market and customer demands which influenced the future strategic direction strongly. Nevertheless, the decision to license patents to outside partners took IBM around ten years. Nowadays, a big part of IBM´s profit is based on earnings from licensing. Furthermore, IBM uses also the lead user approach, introduced by von Hippel (1986) to successfully test new technologies and to establish collaborative teams. To integrated users and customers into to innovation process, IBM carries out around 350 workshops per year and also supports so called “innovation days”, where customers, suppliers, leading scientists as well as potential partners are formed in teams to work on specific topics of interest.

All in all several aspects lead to the success of IBM. The research strategy in combination with the open innovation process by integrating external knowledge can be identified as one major reason. In addition, through licensing unused ideas or ideas IBM alone cannot realize, the company is generating even more value which finally leads to a higher profit (Gassmann & Enkel, 2004).

2.2 Three core processes in open innovation

The main difference between the closed and the open innovation paradigm is that the open approach relies on the interaction with external units with the goal to increase the efficiency and the effectiveness of the innovation processes (Gassmann & Enkel, 2004).

The following figure (see Figure 3) illustrates how IBM de-coupled the idea transformation process (locus of innovation), with the invention (locus of knowledge creation) and the development and final exploitation (locus of commercialization) of a product or service (Gassmann & Enkel, 2004).

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Figure 3: De-coupling the locus of innovation process (Gassmann & Enkel, 2004, p. 6)

Additionally, new opportunities were created with the increase in the venture capital market as well as with the possibility to use unused internal ideas offering them to external partners. The above described case about IBM effectively shows how the company is reacting to market challenges with a flexible research strategy. One part of the success story is their well-executed patent strategy. Furthermore, IBM fosters the innovation process with the early integration of their customers, suppliers, and partner’s knowledge. Due to these strategies, IBM is able to commercialize ideas that cannot be realized internally (Gassmann & Enkel, 2004).

Based on the studies of Gassmann and Enkel (2004), three open innovation process archetypes can be identified (see Figure 4). The outside-in process is characterized by the active integration of external idea sources like customers and suppliers, because they can foster the innovation process of the company. The second archetype is named the inside-out process. This approach is based on the concept of transferring own ideas to the external environment through licensing strategies or selling IP. The third approach is called the coupled process. This process is defined by combining the outside-in and the inside-out by forming alliances with partners.

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Figure 4: Three archetypes of open innovation processes (Gassmann & Enkel, 2004, p. 7)

2.2.1 Outside-in process

By deciding to use the outside-in process, a company decides to open up its boundaries to external sources of innovation. This means that customers as well as suppliers get actively involved in the innovation process and enriches the process creation circle (Gassmann & Enkel, 2004).

- Outsourcing of R&D

On the one hand, the major reason for companies in Austria and Switzerland to outsource their IT-services is to reduce cost, due to the fact that labor costs are lower abroad (Schirmbrand, 2014). On the other hand, cooperative R&D for higher innovation rates has become more and more attractive for managers, because a company’s routine pathways of thinking are questioned by external partners, which allows organizations to foster out of the box thinking, which can lead to breakthrough ideas (Pisano, 1990; Quinn, 2000; Fritsch & Lukas, 2001). Furthermore, by involving external partners in the organizations innovation process, the so called not-invented-here (NIH) syndrome can be challenged effectively (Katz & Allen, 1982).

- Early supplier integration

Companies strive for ideas to receive a sustainable competitive advantage over their competitors. Therefore, Dyer and Singh (1998) recommend establishing early and differentiated relationships with suppliers, because the innovation process performance could be higher due to the specific knowledge and unique capabilities of every supplier (Hagedoorn, 1993). Furthermore, management scholars and practitioners endorse supplier involvement as a significant source of competitive advantage (Teece, 1986; Kaufman et al., 2000; Sobrero & Roberts, 2002). However, due to vast spill-over effects, less research attention is given to the role of non-suppliers in the high tech sector. A good example is the i-Drive system of BMW, which was developed with the California-based high-tech company Immersion in a cooperative way, despite the fact that beneficial spill-over effects only were possible at the beginning. Based on Takeishi (2001), establishing strong and long lasting relationships can be identified as a main goal for organizations to cope with the problem of vertical innovation cooperation’s.

- User innovation

According to von Hippel´s (1986) famous lead users approach, clients who develop improvised versions of products or services to satisfy their own needs have generally been accepted by companies as an important source of innovation (Olson and Bakke, 2001; Lilien et al., 2002; Bonner and Walker, 2004). Furthermore, also the degree to which users participate has changed tremendously. Nowadays, the number of users who participate actively in the innovation process of a company has increased gradually. Additionally, user are involved in the production process of a product or service they desire and they are willing to pay for (Drahan and Hauser, 2002; von Hippel and Katz, 2002; Gassmann et al., 2006). To structure and conduct the innovation process accurately, the integration of customers in the process is mainly defined through their roles (Brockhoff, 2003), risks (Enkel et al., 2005) and chances (von Hippel, 1998, 2005).

2.2.2 Inside-out process

According to Gassmann & Enkel (2004), the core principle of the inside-out process is the externalization of the companies’ knowledge and know-how. The aim is to bring ideas faster to the market by opening up the companies’ boundaries via licensing IP. This offers the possibility to increase revenues tremendously.

- External commercialization of technology

Patents to protect intellectual property have become more and more important for companies. From 1996 to 2004 the number of patents has risen by 25 per cent per year. By exploiting intellectual property more systematically, companies benefit through leverage effects by multiplying their internally generated patents and trademarks. This means that, patents can be clarified as strategic assets of high value and are even more crucial than for example buildings. Furthermore, many multinational companies started to install so called corporate incubators, these centers allow organizations to improve the external commercialization of technology. As an example, IBM generated around USD 1.5 billion in 2005 by know-how transfer and through licensing (Becker & Gassmann, 2006).

2.2.3 Coupled process

The combination of the outside-in and the inside-out process, results in the so called coupled process. The aim of this approach is to gain external knowledge and to bring internal ideas to the market. Cooperation and strategic networks with other companies is required. BMW´s car control system i-Drive is good example of successful cooperation. The i-Drive system, which is based on joystick technology, was developed in close cooperation with companies of different industries. For this approach, the give and take mentality can be acknowledged as the key source of success (Gassmann & Enkel, 2004). Furthermore, coupling can also be seen as a strategic option for companies, for example with the creation of alliances to share IP. In this case, collaboration is referred to the joint development of knowledge through an established relationship with key partners, such as an association of customers (von Hippel, 1988; Hakansson & Johansson, 1992), competitors (Hagedoorn, 1993; Chiesa & Manzini, 1998; Ingham & Mohte, 1998), suppliers and joint ventures and alliances (Kogut, 1988, Hamel, 1991; Mowery et al., 1996) as well as research institutes and universities (Bailett & Callahan, 1992; Conway, 1995; Cockburn & Henderson, 1998; Santoro & Chakrabarti, 2001). The core of the coupled process is based on the creation of a strategic alliance. Taking the mobile phone industry as an example, companies like Ericson, Nokia or Siemens need to work to together if they want to set an industry standard, because only a combined approach will convince the technology providers to adopt a new standard, which will finally lead to higher revenue streams for telecom companies (Gassmann & Enkel, 2004).

If companies are willing to open up their solid boundaries and allow a free circulation of knowledge, they will be able to benefit from the vast expertise and ideas of their customers and supplier of the company. Furthermore, the outflow of IP should not be blocked, because through licensing strategies companies are able to commercialize their unused ideas. However, there exist another approach which includes more than only customers and suppliers in the value creation process. Therefore, the crowdsourcing phenomena will be introduced in the next chapter, because it is based on the active integration of customers, users and other people in the value creation and value capture process.

2.3 Crowdsourcing

”Crowdsourcing is the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call” (Jeff Howe, 2006).

Crowdsourcing can be defined as an umbrella term, because it consists of several different approaches including contests that are organized as an open call, coordination in production, organizations who manage their own crowd base or companies who work as a middleman in the open innovation process (Ren & Levina, 2010). The idea behind the crowdsourcing concept is to use the innovativeness of online users to achieve company’s targets and realize ideas which could not solely be realized on an internal approach. The crowd can be used to for example develop new technologies, create new product designs, solve IT-problems or help to analyze and categorize vast amounts of data (Jahnke & Prilla, 2008). Furthermore, based on the theory ofthe wisdom of the crowd(Surowiecki, 2004), the innovativeness and the performance will be higher as the number of users who participate increases, because the expertise of this large group will be better than the knowledge of a few experts. A good example for this approach is the TV-show “Who wants to be a millionaire?”. In this game show, the candidate can ask the audience for help and can use the knowledge of the crowd to solve the questions. As already described above, crowdsourcing can therefore be defined as an approach that lies between the elements of the “crowd”, “outsourcing” and the improvements of the Internet and communication technology (Saxton, Oh & Kishore, 2013).

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Figure 5: The three elements of crowdsourcing (Saxton, Oh & Kishore, 2013, p. 3)

Despite the positive aspects of this approach also critics arose around crowdsourcing (Jahnke & Prilla, 2008). Uehlecke (2006) criticized that crowdsourcing is like outsourcing just an approach to safe costs for companies at the expense of the users. The author argues that only if the users get equally rewarded, this method will be a fair one too.

The crowdsourcing approach definitely offers companies many ways to accelerate innovation. Furthermore, for innovative users who lack the resources to develop and implement their ideas on their own, the crowdsourcing approach will help them to find the right partners via the internet. Nevertheless, it has to be stated that this approach can be harmful for users if companies do not reward them equally and this finally can have negative effects for the motivation of the users. The motivation and the reasons for participation will be discussed later on in this thesis.

2.3.1 Different types of crowdsourcing

Crowdsourcing still is a relative new phenomenon and comprises many practices. This led to a diversity of definitions of the term crowdsourcing and several types of crowdsourcing emerged (Howe, 2008). In this chapter different classification approaches for crowdsourcing platforms will be introduced and finally I will come up with a categorization for different types of crowdsourcing platforms.

Estellés-Arolas and Ladón-de-Guevara (2012) analyzed, categorized and defined the most crucial elements of crowdsourcing initiatives. The research was based and defined on eight common elements of every approach and it was possible to identify some clear cases of crowdsourcing. The following table shows the eight crucial elements of every crowdsourcing initiative.

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Table 1: The key elements of a crowdsourcing initiative (Estellés-Arolas & Ladrón-de-Guevara, 2012)

Based on the table shown above, the identification of some distinctive cases of crowdsourcing was possible, like Amazons Mechanical Turk, an online marketplace that requires human intelligence, InnoCentive, the open innovation marketplace for problem solvers, Threadless, the online T-shirt company and ModCloth. The company ModCloth, an Internet clothing shop where it is possible to vote for your favorite design, does fulfil all the requirements. The crowd can be identified clearly (customers from the whole world), a task (voting), reward (recognition received by the voting’s), the initiator (ModCloth), the recompense (cost saving and efficient use of resources), the process type (mindful participation of the crowd), the appeal to contribute (website of ModCloth) and the use of a medium (Internet). On the other hand, other cases failed of fulfilling all eight requirements like Delicious, a social book marking system. For the Delicious case it was not possible to identify four out of eight key elements. The missing parts are: A clear defined task, rewards for the crowd, the initiator, and the appeal to contribute. Therefore, Delicious cannot be defined as a crowdsourcing example (Estellés-Arolas & Ladrón-de-Guevara, 2012).

According to Malone, Laubacher & Dellarocas (2010), crowdsourcing platforms can be defined as a collective intelligence (CI) system. To be classified as such a system, a combination of several small but different blocks is needed. To be able to classify those blocks in a proper way, the following main questions need to be answered:

- What is being done?
- Who is doing it?
- Why are they doing it?
- How is it being done?

Furthermore, those blocks consist of so called “genes”. The genes represent the core elements of every CI system. To define the genes properly, the above mentioned questions (What, Who, Why or How) need to be answered accurately by associating them with single tasks in a CI system. The identification is crucial, because the genes can be defined as the heart of the collective intelligence system and a correct combination of genes with a particular example of CI can be illustrated as the “genome” of that whole system. To enable companies to use the genome approach systematically, it is necessary to classify the different types of genes, because without a classification it would not be possible to build a precise CI system. For that reason, 16 principal genes and sub genes will be presented. These 16 genes are classified in categories determined by the before introduced four central questions, What, Who, Why and How.

The first category (What) consists of two genes and refers to an organizations mission and goals respectively to its tasks. Therefore, the basic genes are:

- Create: The creation of a piece of software code, blog entry or a T-shirt.
- Decide: The contributors evaluate and select alternatives. The contributors decide which logo should be printed on a T-shirt.
The second category (Who) consists as well as the first category of two parts. The basic genes are:
- Hierarchy: A person in charge allocates a specific person or group to perform a task.
- Crowd: In contrast to the hierarchy gene, activities are undertaken by a large number of people, for example like voting’s in elections or submissions of designs and ideas.

The Crowd approach can provide organizations with several advantages. Like in the case of Wikipedia, they are able to save costs by delegating tasks to people who do them for free. Another example is InnoCentive, where companies can outsource problems that they were not able to solve internally. To use the skills of the crowd best, it is recommendable to split the tasks up into small pieces that can be performed then by different people based on their individual skills. If conditions for the Crowd approach are not given, it is possible to use the Hierarchy method. The circumstance that you already know which people have the skills and knowledge to perform a task successfully, allows you to directly sign the task to them.

The next category (Why) comprises three specific genes, which should reveal the motivation why people take part in such activities.

- Money: Financial rewards can be identified as an important motivation for people.
- Love: If there is no extrinsic motivation like money given, love plays a crucial role. People like to participate because they can on the one hand start social interaction with other people and they get the feeling that they contribute to something larger than themselves.
- Glory: The desire to be recognized by other people in the open innovation environment can also be a huge motivator.

The last category (How) is dealing with the question of how a task should be done within an organization. The hierarchy approach still plays a crucial role for most CI systems. Nevertheless, the importance of the Crowd has grown, for that reasons concentrates the last part on how the crowd performs the Create or Decide task. To answer this question properly, I will shed light on whether the different contributions and decisions made by the members of the crowd are independent or strongly dependent. Therefore, the genes Collection, Collaboration, Individual Decision and Group Decision will play a crucial role.

The first two important genes associated with the Create task are Collection and Collaboration:

- Collection: One of the most important characteristics of this gene is that the contributions of the members are done independently. A good example in this case is the video sharing website YouTube, because users independently load up videos and increase the collection of videos steadily. Another example is Flickr, an online collection of photographs. Due to the fact that the activities are divided up into small pieces, every single member can do their tasks independently.

Contests, like in the cases of Threadless or InnoCentive, where the best logos or ideas receive prize money, rewards or recognition has been identified as an important subtype of the Collection gene. The Contest gene is fully based on the Collection gene, but can be seen as favorable if only the best solutions are needed, like in the case of InnoCentive, where customers are only interested in the most valuable solutions. To increase the success rate of contests, the Why genes like Money need to be strong and motivating enough to attract sufficient users.

- Collaboration: This gene can be defined by its dependencies between its members during the creation process of something. Furthermore, also Wikipedia can be defined as a collaboration approach, because every editorial change made by its contributors is highly interdependent. This approach is recommendable if it is not possible to split up the activity in small, independent pieces. Furthermore, if there are strong dependencies between the work of individuals, Collaboration should be used.

To be able to manage the dependencies among the pieces properly, usually a kind of combination of Decide genes is necessary. The two important genes for the Decide task are Group Decisions and Individual Decisions.

- Group Decision: The core of that gene are the collected ideas and the finally generated decision of the whole group. Furthermore, Voting and Consensus as well as Averaging and Prediction Markets are central variants of the Group Decision gene.

The Voting approach, like on Threadless, the T-shirt logo with the most voting does win the competition. For Consensus, Wikipedia can be defined as a good example, because all group members need to agree to a final decision, which means that articles that stay unchanged are based on common agreement of the users. For the third sub gene Averaging, users can use a rating system to evaluate their latest book or CD buying’s, like on the e-commerce platform Amazon. Afterwards these ratings are averaged and an overall score will be drawn. Another interesting way of involving the crowd in decision making are Prediction Markets. Here the crowd tries to make prediction about future events. If the prediction is right, the user receives prize money, points for his online portfolio, badges or other kinds of rewards. To use the collective intelligence of their employee’s best, companies like Google and Microsoft have adopted this approach.

- Individual Decisions: For this category YouTube is a good example, because despite the fact that every user can see the ranking and the recommendations of others, finally every single user decides itself which video he wants to watch. Furthermore, also the sub genes Markets and Social Networks play a crucial role. Markets are based on decisions, sell or buy and therefore do take place an exchange, for example in the form of money. Due to improvements in technology, nowadays also electronic market places are available, like the e-commerce company eBay, where sellers can place their goods they want to sell and interested buyers can bid for them. In Social Networks, that is based on forming relationships around similarities like opinions, hobbies or other common characteristics. Here member of the network give different weights to the input of others and afterwards they make their individual decision about it. Nevertheless, it has to be stated, that in cases where money is used to motivate people, Markets are especially useful. On the other hand Social Networks are highly effective if no payment is required and people find feedback or opinions of other users useful to make their choices.

The following overview will give a better understanding about the introduced concept of the collective intelligence genome by taking the free encyclopedia Wikipedia as an example.

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Table 2: The collective intelligence genome for Wikipedia (Malone, Laubacher & Dellarocas, 2012)

Due to the high amount of different crowdsourcing concepts, Lakhani (2013) determined the crowdsourcing approach and came up with the four core types of crowdsourcing:

- Crowd Contest

Inviting the crowd to participate in a contest is not a new approach to solve tough technical and highly scientific problems. Already in the past it was common to use contests to solve these challenges. One of the most famous examples was the so called “The Longitude Prize” of the British Parliament established in 1714. The winner of the contest, a carpenter named John Harrison, was rewarded with 15,000 pounds because he was able to deliver the most accurate chronometer in comparison to famous scientist of that time like Isaac Newton or Giovanni Domenico Cassini. Contests work best when it is not possible to define in advance which skills and technical approaches will work best. Furthermore, experimentation will deliver a broad amount of possible solutions. Modern examples of crowd contests are the online platforms InnoCentive and Kaggle, a platform for analyzing and modelling scientific data. One of the major advantages of this approach is that the company receives several different solutions in comparison to limited and cost intensive internal R&D information. Contests are also very useful if a company wants to solve design problems, because through the contest, the organization receives a vast amount of creative ideas. A good example in that case is Tongal. The crowd based advertising company frequently develops campaigns for consumer goods companies.

To successfully conduct such crowd based contest, the management needs to tackle several challenges. First of all, it is important to identify an important problem which is worth solving it. Secondly, the problem needs to be extracted from the company. Thirdly, a generalization of the problem is needed to generate immediate understanding. Fourthly, to protect sensitive information about the company, the problem needs to be abstracted. Finally, the contest should be promoted well to get the engagement of motivated and skilled people who will be able to deliver useful solutions.

- Crowd Collaborative Communities

In the late 1990´s IBM recognized that it was losing the game on software development. For that reason, the company announced collaboration with Apache, an online community of webmasters and technologists, in 1998. This was the first time that a technology company like IBM abandoned its internal development programs of web server infrastructure and created cooperation with an online community. IBM acknowledged that collaboration brings two crucial advantages. First of all, members of the Apache community are able to figure out failures of the software and have the knowledge to fix them on their own. Secondly, after solving a problem their solutions were integrated in the software. Therefore, IBM changed its strategy and focused more on hardware and services.

One of the major advantages of working with communities is the richness of diverse participants. On the other hand, due to different members from all over the world, cohesiveness will usually be very low. Therefore, to successfully work with online communities, companies need to create clear structures and systems to control, guide and organize the contributors. Furthermore, a free circulation of information is needed to boost the idea creation process, which means that the protection of IP becomes impossible. This means that companies should focus on complementary business to generate profit. A good example in that case is Google. The company is generating money from the monetization of mobile search algorithms and not directly from the operating system Android which it offers for free.

- Crowd Complementors

The next type of crowdsourcing is based on the concept of transforming a product into a platform which finally creates complementary innovations. The third type can be described best with the example of Apple´s online shop iTunes. The service is organized around Apple´s core products, like the iPhone, iPad and iPod. Through generating iTunes around the three core products, developers all over the world create new complementary innovations like apps and podcasts for Apple. In comparison to the previously described crowd communities and contests, is it with complementors possible to solve more than one specific problem. This is based on the possibility to deliver a vast amount of solutions. For Apple this is very profitable, because via iTunes the company receives profits through licensing or transactions fees from complementors and the complementors benefit from selling their products to customers of the core product, like the iPhone. Additionally, due to the diversity of complementary offers, also the demand for the core product can increase, because its usage increases with ever new and better applications for it. This means that the company is benefiting from network effects. One of the major challenges of using crowd complementors successfully is to permit access to sensitive information about the functions of the core products. A good way to provide this information is by using technological interfaces, because they will help external users by developing their complementary innovations.

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