domain operations Commons: 3/5

Crowdsourcing Model

Also known as: Crowd-sourcing, Crowd Sourcing, Collective Intelligence, Community Sourcing

1. Overview

The Crowdsourcing Model is a business strategy that sources work, information, or opinions from a large, distributed group of people, often online. It leverages the crowd’s collective intelligence, skills, and labor to solve problems, generate ideas, or produce content. Coined by journalist Jeff Howe in a 2006 Wired article, it’s a form of outsourcing that taps into the power of a global community. [1] This approach provides access to a vast talent pool, leading to innovative, cost-effective, and efficient outcomes, democratizing innovation and production.

2. Core Principles

Open Call: Initiatives start with an open call to a large, undefined network, unlike traditional outsourcing’s targeted approach. This attracts diverse participants with varied skills and knowledge.
Self-Selection: Participants voluntarily choose tasks, ensuring motivation and interest, which often leads to higher quality contributions and a more engaged community.
Micro-tasking: Complex projects are broken into smaller micro-tasks, enabling parallel processing by many contributors and easy participation. This is a core principle of platforms like Amazon Mechanical Turk.
The Wisdom of the Crowd: Popularized by James Surowiecki, this principle states that a diverse group’s collective knowledge can surpass that of a single expert. [2] Crowdsourcing aggregates contributions for more robust and accurate outcomes.
Incentivization: Clear incentives, whether financial (payment) or non-financial (recognition, skill development, contributing to a cause), are crucial to attract and retain participants.
Quality Control: Essential mechanisms to ensure quality include peer review, reputation systems, and algorithmic checks.
Platform as Mediator: A technology platform acts as an intermediary, facilitating the entire process from task distribution to payment, providing the necessary infrastructure for the ecosystem.

3. Key Practices

Crowd Contests and Competitions: Organizations host competitions where the crowd submits solutions to a problem, with the best solution receiving a prize. This is a popular method for generating ideas and content, exemplified by the Netflix Prize. [3]
Macrotask Crowdsourcing: A large, complex project is outsourced to a crowd for collaborative work, often for creative or knowledge-based projects. Open-source software development, like Linux, is a prime example.
Microtask Crowdsourcing: A large, repetitive task is broken into small micro-tasks for many people to complete. This is effective for tasks like image tagging and data entry, with Amazon Mechanical Turk being a well-known platform. [4]
Crowdfunding: Raising funds by soliciting small contributions from many people online. It’s a popular way for entrepreneurs and artists to finance projects, with platforms like Kickstarter and Indiegogo enabling many successes. [5]
Citizen Science: The public is engaged in scientific research, often by collecting and analyzing data. These projects accelerate discovery and promote public engagement, with Zooniverse being a popular platform. [6]
Prediction Markets: The wisdom of the crowd is leveraged to forecast events. Participants trade shares in outcomes, with market prices reflecting collective predictions. Augur is a decentralized example. [7]
Gamified Crowdsourcing: Game mechanics are applied to tasks to make them more engaging. This motivates a wider audience and improves contribution quality. Foldit, a protein-folding game, is a successful example. [8]

4. Application Context

Best Used For:

Idea Generation and Innovation: Tapping into a diverse crowd to generate a wide range of ideas for new products, services, or business models.
Problem Solving: Sourcing solutions to complex technical, scientific, or social challenges from a global pool of experts and enthusiasts.
Content Creation: Generating a large volume of content, such as articles, reviews, images, or videos, in a cost-effective and scalable manner.
Data Collection and Analysis: Gathering and analyzing large datasets through the distributed efforts of a crowd, particularly for tasks that require human intelligence.
Market Research and Feedback: Quickly and inexpensively gathering feedback on products, services, or marketing campaigns from a large and diverse audience.

Not Suitable For:

Tasks Requiring Deep Contextual Knowledge: Projects that require a deep understanding of a specific organization’s culture, processes, or confidential information are not well-suited for crowdsourcing.
Highly Secure or Confidential Projects: The open nature of crowdsourcing makes it unsuitable for projects that involve sensitive data or intellectual property that needs to be protected.
Core Business Functions: Relying on the crowd for core business functions that are critical to a company’s competitive advantage can be risky and may lead to a loss of control.

Scale:

The Crowdsourcing Model can be applied at various scales, from individual projects to large-scale, ongoing initiatives. It can be used by:

Individuals: To fund creative projects or gather feedback on their work.
Teams: To solve specific problems or generate ideas for new projects.
Departments: To outsource non-core tasks or gather market intelligence.
Organizations: To drive innovation, improve efficiency, and engage with customers.
Multi-Organization/Ecosystem: To address large-scale societal challenges or create shared resources.

Domains:

Crowdsourcing is being used across a wide range of industries, including:

Technology: Open-source software development, bug testing, and data labeling for AI algorithms.
Marketing and Advertising: Idea generation for campaigns, content creation, and market research.
Media and Entertainment: User-generated content, funding for films and music, and script development.
Science and Research: Citizen science projects, data analysis, and problem-solving competitions.
Government and Non-profit: Civic engagement, public policy input, and fundraising for social causes.

5. Implementation

Prerequisites:

A Clearly Defined Problem or Task: The success of any crowdsourcing initiative hinges on having a well-defined problem or task that can be easily understood and acted upon by the crowd.
A Compelling Incentive Structure: A clear and appropriate incentive structure is crucial for attracting and motivating participants. This could be financial, social, or intrinsic, depending on the nature of the task and the target audience.
A Robust Technology Platform: A reliable and user-friendly technology platform is essential for managing the entire crowdsourcing process, from task distribution and submission to communication and payment.
A Community Management Strategy: Building and nurturing a community of contributors is vital for long-term success. This includes clear communication, feedback mechanisms, and recognition for valuable contributions.

Getting Started:

Define Your Goal: Clearly articulate what you want to achieve with crowdsourcing. Are you looking for innovative ideas, a solution to a specific problem, or help with a large-scale task?
Choose the Right Crowdsourcing Model: Select the crowdsourcing model that best fits your goal. This could be a contest, a collaborative project, a micro-tasking initiative, or a crowdfunding campaign.
Design Your Task and Incentives: Break down your project into manageable tasks and design an incentive structure that will motivate your target audience to participate.
Select a Platform: Choose a technology platform that meets the needs of your project. This could be an existing platform like HeroX or Amazon Mechanical Turk, or you could build your own.
Launch and Promote Your Initiative: Announce your crowdsourcing initiative to your target audience and promote it through various channels to attract a large and diverse crowd of participants.

Common Challenges:

Low Participation: If your initiative fails to attract enough participants, it is unlikely to succeed. To address this, ensure your task is compelling, your incentives are attractive, and your promotional efforts are effective.
Poor Quality Submissions: The quality of submissions can vary widely. To mitigate this, provide clear instructions, implement a quality control mechanism, and offer feedback to participants.
Intellectual Property Issues: The ownership of intellectual property can be a complex issue in crowdsourcing. It is important to have a clear and transparent policy in place to avoid disputes.
Participant Burnout: If participants feel that their contributions are not valued or that the process is unfair, they may become disengaged. To prevent this, foster a sense of community, recognize valuable contributions, and be transparent in your communication.

Success Factors:

A Clear and Compelling Vision: A strong vision that inspires and motivates the crowd is essential for success.
A Well-Designed Task: The task should be challenging enough to be interesting but not so difficult that it discourages participation.
A Fair and Transparent Process: The rules of engagement should be clear, and the evaluation process should be fair and transparent.
A Strong Sense of Community: A vibrant and engaged community is the lifeblood of any successful crowdsourcing initiative.
A Commitment to Openness and Collaboration: A willingness to share information and collaborate with the crowd is crucial for building trust and fostering a sense of shared ownership.

6. Evidence & Impact

Notable Adopters:

LEGO: Through its LEGO Ideas platform, the company allows fans to submit their own designs for new LEGO sets. If a design receives 10,000 votes from the community, it is reviewed by LEGO for potential production as an official product. This has led to the creation of many popular sets and has fostered a strong community of brand advocates. [9]
Starbucks: The coffee giant launched My Starbucks Idea, a platform where customers could submit their ideas for new products, store improvements, and social responsibility initiatives. The platform generated thousands of ideas and helped Starbucks to better understand and respond to the needs of its customers. [10]
PepsiCo: The multinational food and beverage company has used crowdsourcing for various initiatives, including its “Crash the Super Bowl” contest, where consumers were invited to create their own Super Bowl commercials for Doritos. The winning ads were aired during the game, generating significant buzz and engagement. [11]
Unilever: The consumer goods company has leveraged crowdsourcing to solve a variety of business challenges, from developing new product ideas to improving its supply chain. Its Open Innovation platform invites individuals and organizations to submit solutions to specific challenges, with the promise of a financial reward and the opportunity to partner with Unilever.
NASA: The U.S. space agency has a long history of using crowdsourcing to solve complex scientific and technical problems. Through its NASA Tournament Lab, the agency hosts competitions to develop new algorithms, software, and tools for its missions. [12]

Documented Outcomes:

Increased Innovation: Crowdsourcing has been shown to be a powerful tool for driving innovation. A study by the consulting firm McKinsey found that companies that use crowdsourcing are more likely to report successful innovation outcomes than those that do not. [13]
Cost Savings: By tapping into a global pool of talent, companies can often get work done at a lower cost than if they were to hire traditional employees or contractors. This is particularly true for micro-tasking platforms like Amazon Mechanical Turk, where workers are paid a small fee for each completed task.
Faster Time to Market: Crowdsourcing can help companies to accelerate the product development process by enabling them to quickly gather ideas, feedback, and solutions from a large and diverse group of people.
Improved Customer Engagement: By involving customers in the product development process, companies can build stronger relationships with them and create a sense of shared ownership. This can lead to increased brand loyalty and advocacy.

Research Support:

The Wisdom of Crowds: In his book of the same name, James Surowiecki provides a wealth of evidence to support the idea that large, diverse groups of people are often smarter than individual experts. His work provides a strong theoretical foundation for the crowdsourcing model. [2]
Crowdsourcing for a Better World: A report by the World Bank highlights the potential of crowdsourcing to address some of the world’s most pressing social and environmental challenges. The report provides numerous examples of how crowdsourcing is being used to improve everything from disaster response to public health. [14]
The Effectiveness of Crowdsourcing in Science: A growing body of research is demonstrating the effectiveness of crowdsourcing in a variety of scientific domains. For example, a study published in the journal Nature found that a gamified crowdsourcing project called Foldit was able to solve a long-standing scientific problem related to the structure of a protein. [15]

7. Cognitive Era Considerations

Cognitive Augmentation Potential:

The rise of artificial intelligence and automation presents significant opportunities to augment and enhance the Crowdsourcing Model. AI can be used to:

Automate Task Management: AI-powered platforms can automate the process of breaking down large projects into micro-tasks, matching tasks to the most suitable contributors based on their skills and past performance, and aggregating the results.
Improve Quality Control: AI algorithms can be used to automatically assess the quality of submissions, flag potential errors or inconsistencies, and even provide real-time feedback to contributors.
Enhance Idea Generation: AI can be used to analyze large volumes of ideas generated by the crowd, identify emerging themes and patterns, and even generate new ideas based on the initial input.
Personalize the Contributor Experience: AI can be used to create a more personalized and engaging experience for contributors by recommending tasks that match their interests, providing targeted feedback, and offering customized learning opportunities.

Human-Machine Balance:

While AI and automation can significantly enhance the efficiency and effectiveness of crowdsourcing, they are unlikely to replace the need for human intelligence and creativity entirely. The unique strengths of humans and machines are often complementary:

What Remains Uniquely Human: Tasks that require creativity, critical thinking, emotional intelligence, and complex problem-solving are likely to remain the domain of humans. The ability to understand context, nuance, and ambiguity is another area where humans currently outperform machines.
The Role of the Crowd in the Cognitive Era: In the cognitive era, the crowd will likely play a more strategic role, focusing on higher-value tasks that require human ingenuity. The crowd will also be essential for training and validating AI algorithms, providing the human feedback that is necessary for machine learning.

Evolution Outlook:

The Crowdsourcing Model is likely to evolve in several key ways in the cognitive era:

Hybrid Models: We will see the emergence of more hybrid models that combine the strengths of both humans and machines. For example, AI could be used to generate a first draft of a document, which is then refined and improved by a crowd of human editors.
Decentralized Platforms: The rise of blockchain technology could lead to the development of more decentralized crowdsourcing platforms that are owned and governed by their users. This could lead to a more equitable distribution of value and a greater sense of community ownership.
New Forms of Collaboration: We will see the development of new forms of collaboration between humans and machines, where AI acts as a partner and a tool to augment human intelligence. This could lead to new breakthroughs in science, technology, and the arts.

8. Commons Alignment Assessment (v2.0)

This assessment evaluates the pattern based on the Commons OS v2.0 framework, which focuses on the pattern’s ability to enable resilient collective value creation.

1. Stakeholder Architecture: The Crowdsourcing Model defines a multi-stakeholder ecosystem, including the initiating organization, the crowd of contributors, the platform provider, and end-users. However, the architecture of Rights and Responsibilities is often heavily skewed towards the organization and platform, which typically define the terms of engagement. The rights of contributors are often limited, with little say in governance or the disposition of the value they co-create, while the environment and future generations are rarely considered as active stakeholders.

2. Value Creation Capability: The pattern excels at enabling collective value creation, but this is often limited to economic and knowledge value for the sponsoring organization. While contributors may gain income, skills, or social recognition, the model can also lead to extractive practices, such as low pay and precarious work, which diminish the overall social value. The potential for creating ecological or resilience value is not inherent to the model and depends entirely on the specific application and the intent of the initiator.

3. Resilience & Adaptability: Crowdsourcing is highly adaptable, allowing systems to tap into a diverse, global talent pool to solve complex problems and innovate rapidly. This distributed nature can enhance resilience by providing a flexible workforce that can scale and pivot in response to changing needs. However, the resilience of the contributor community itself is often low, as it is subject to the whims of the platform and the initiator, with little long-term stability or coherence.

4. Ownership Architecture: Ownership in most crowdsourcing models is defined in traditional terms, with the intellectual property and resulting value being owned by the initiating organization. This represents a significant gap in its alignment with a commons-based approach, as it treats the crowd as a resource to be extracted from, rather than as co-owners of the value created. The Rights and Responsibilities associated with ownership are not distributed, but rather concentrated in the hands of the sponsor.

5. Design for Autonomy: The model is well-suited for a world of distributed systems, DAOs, and AI, as it is designed to coordinate the work of many autonomous agents through platforms and micro-tasking. The low coordination overhead makes it highly efficient for certain types of problems. AI can further enhance this by automating task distribution, quality control, and even aspects of the creative process, making it a highly compatible pattern for the cognitive era.

6. Composability & Interoperability: The Crowdsourcing Model is highly composable and interoperable, capable of being combined with a wide range of other patterns to build larger value-creation systems. It can be integrated with open innovation, crowdfunding, citizen science, and various governance models to create more complex and powerful socio-technical systems. This modularity is one of its key strengths, allowing it to be adapted to a wide variety of contexts.

7. Fractal Value Creation: The logic of crowdsourcing is fractal, meaning it can be applied at multiple scales, from small team projects to large-scale, global initiatives. The same basic principles of open calls, self-selection, and collective intelligence can be used to solve problems and create value at the individual, organizational, and ecosystem levels. However, this also means that the potential for extractive and inequitable value creation can be replicated at every scale if not consciously designed for a more commons-aligned approach.

Overall Score: 3 (Transitional)

Rationale: The Crowdsourcing Model is a powerful engine for collective action but in its common implementation, it functions more as a labor-sourcing mechanism than a true commons. It is transitional because while it leverages collective intelligence, it often falls short of creating a resilient, equitable system for value creation for all stakeholders. The ownership and governance structures are typically centralized and extractive, which limits its alignment with the core principles of the Commons OS v2.0 framework.

Opportunities for Improvement:

Implement more equitable value distribution models, such as revenue sharing, co-ownership of intellectual property, or contributor-owned cooperatives.
Redesign governance structures to give contributors a greater voice in the platform’s rules, policies, and decision-making processes.
Integrate mechanisms for building long-term community resilience, such as shared resources, mutual support systems, and pathways for professional development.

9. Resources & References

Essential Reading:

Howe, J. (2008). Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. Crown Business. This is the seminal book on crowdsourcing, written by the journalist who coined the term. It provides a comprehensive overview of the concept and its potential to transform business and society.
Surowiecki, J. (2004). The Wisdom of Crowds. Doubleday. This book provides the theoretical foundation for the crowdsourcing model, arguing that the collective intelligence of a diverse group of people can be smarter than that of a single expert.
Brabham, D. C. (2013). Crowdsourcing. The MIT Press. This book offers a more academic perspective on crowdsourcing, exploring its history, theory, and practical applications.

Organizations & Communities:

HeroX: An open innovation platform that connects organizations with a global community of problem solvers.
Zooniverse: A citizen science platform that hosts a wide range of research projects that rely on the contributions of volunteers.
Topcoder: A crowdsourcing platform for competitive programming, data science, and design.

Tools & Platforms:

Amazon Mechanical Turk: A micro-tasking platform that allows businesses to outsource small, repetitive tasks to a large workforce.
Kickstarter: A crowdfunding platform that helps creative projects come to life.
GitHub: A platform for open-source software development and collaboration.

References:

[1] Howe, J. (2006). The Rise of Crowdsourcing. Wired. [2] Surowiecki, J. (2004). The Wisdom of Crowds. Doubleday. [3] Netflix Prize. (n.d.). Retrieved from https://www.netflixprize.com/ [4] Amazon Mechanical Turk. (n.d.). Retrieved from https://www.mturk.com/ [5] Kickstarter. (n.d.). Retrieved from https://www.kickstarter.com/ [6] Zooniverse. (n.d.). Retrieved from https://www.zooniverse.org/ [7] Augur. (n.d.). Retrieved from https://www.augur.net/ [8] Foldit. (n.d.). Retrieved from https://fold.it/ [9] LEGO Ideas. (n.d.). Retrieved from https://ideas.lego.com/ [10] My Starbucks Idea. (n.d.). Retrieved from https://ideas.starbucks.com/ [11] PepsiCo. (n.d.). Crash the Super Bowl. Retrieved from https://www.pepsico.com/ [12] NASA Tournament Lab. (n.d.). Retrieved from https://www.nasa.gov/coeci/ntl/ [13] McKinsey & Company. (2018). The Business Value of Design. [14] World Bank. (2013). Crowdsourcing for Development: A Practical Guide. [15] Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., … & Popović, Z. (2010). Predicting protein structures with a multiplayer online game. Nature, 466(7307), 756-760.